DOI: 10.1038/s41467-024-50027-3

Resource: None

Curator: @Apiekniewska

SciCrunch record: RRID:WB-STRAIN:WBStrain00028994

What is this?

DOI: 10.1038/s41467-024-50027-3

Resource: (WB Cat# WBStrain00028974,RRID:WB-STRAIN:WBStrain00028974)

Curator: @Apiekniewska

SciCrunch record: RRID:WB-STRAIN:WBStrain00028974

What is this?

DOI: 10.1007/s13205-024-04017-3

Resource: Caenorhabditis Genetics Center (RRID:SCR_007341)

Curator: @Apiekniewska

SciCrunch record: RRID:SCR_007341

What is this?

DOI: 10.1038/s41467-025-65016-3

Resource: UK Data Archive (RRID:SCR_014708)

Curator: @scibot

SciCrunch record: RRID:SCR_014708

What is this?

Reviewer #3 (Public review):

Summary:

The authors present a variant of a previously described fluorescence lifetime sensor for calcium. Much of the manuscript describes the process of developing appropriate assays for screening sensor variants, and thorough characterization of those variants (inherent fluorescence characteristics, response to calcium and pH, comparisons to other calcium sensors). The final two figures show how the sensor performs in cultured cells and in vivo drosophila brains.

Strengths:

The work is presented clearly and the conclusion (this is a new calcium sensor that could be useful in some circumstances) is supported by the data.

Weaknesses:

There are probably few circumstances where this sensor would facilitate experiments (calcium measurements) that other sensors would prove insufficient.

Comment on revised version:

I think the manuscript has been significantly improved and I concur with the eLife Assessment statement.

[Editors' note: There are no further requests by the reviewers. All of them expressed their approval of the new version of the manuscript.]

Author response:

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

Reviewer #1 (Public review):

Summary:

van der Linden et al. report on the development of a new green-fluorescent sensor for calcium, following a novel rational design strategy based on the modification of the cyan-emissive sensor mTq2-CaFLITS. Through a mutational strategy similar to the one used to convert EGFP into EYFP, coupled with optimization of strategic amino acids located in proximity of the chromophore, they identify a novel sensor, GCaFLITS. Through a careful characterization of the photophysical properties in vitro and the expression level in cell cultures, the authors demonstrate that G-CaFLITS combines a large lifetime response with a good brightness in both the bound and unbound states. This relative independence of the brightness on calcium binding, compared with existing sensors that often feature at least one very dim form, is an interesting feature of this new type of sensors, which allows for a more robust usage in fluorescence lifetime imaging. Furthermore, the authors evaluate the performance of G-CaFLITS in different subcellular compartments and under two-photon excitation in Drosophila. While the data appears robust and the characterization thorough, the interpretation of the results in some cases appears less solid, and alternative explanations cannot be excluded.

Strengths:

The approach is innovative and extends the excellent photophysical properties of the mTq2-based to more red-shifted variants. While the spectral shift might appear relatively minor, as the authors correctly point out, it has interesting practical implications, such as the possibility to perform FLIM imaging of calcium using widely available laser wavelengths, or to reduce background autofluorescence, which can be a significant problem in FLIM.

The screening was simple and rationally guided, demonstrating that, at least for this class of sensors, a careful choice of screening positions is an excellent strategy to obtain variants with large FLIM responses without the need of high-throughput screening.

The description of the methodologies is very complete and accurate, greatly facilitating the reproduction of the results by others, or the adoption of similar methods. This is particularly true for the description of the experimental conditions for optimal screening of sensor variants in lysed bacterial cultures.

The photophysical characterization is very thorough and complete, and the vast amount of data reported in the supporting information is a valuable reference for other researchers willing to attempt a similar sensor development strategy. Particularly well done is the characterization of the brightness in cells, and the comparison on multiple parameters with existing sensors.

Overall, G-CaFLITS displays excellent properties for a FLIM sensor: very large lifetime change, bright emission in both forms and independence from pH in the physiological range.

Weaknesses:

The paper demonstrates the application of G-CaFLITS in various cellular subcompartments without providing direct evidence that the sensor's response is not affected by the targeting. Showing at least that the lifetime values in the saturated state are similar in all compartments would improve the robustness of the claims.

In some cases, the interpretation of the results is not fully convincing, leaving alternative hypotheses as a possibility. This is particularly the case for the claim of the origin of the strongly reduced brightness of G-CaFLITS in Drosophila. The explanation of the intensity changes of G-CaFLITS also shows some inconsistency with the basic photophysical characterization.

While the claims generally appear robust, in some cases they are conveyed with a lack of precision. Several sentences in the introduction and discussion could be improved in this regard. Furthermore, the use of the signal-to-noise ratio as a means of comparison between sensors appears to be imprecise, since it is dependent on experimental conditions.

We thank the reviewer for a thorough evaluation and for suggestions to improve our manuscript. We are happy with the recognition of the strengths of this work. The list with weaknesses has several valid points which will be addressed in a point-by-point reply and a revision.

Reviewer #2 (Public review):

Summary:

Van der Linden et al. describe the addition of the T203Y mutation to their previously described fluorescence lifetime calcium sensor Tq-Ca-FLITS to shift the fluorescence to green emission. This mutation was previously described to similarly red-shift the emission of green and cyan FPs. Tq-Ca-FLITS_T203Y behaves as a green calcium sensor with opposite polarity compared with the original (lifetime goes down upon calcium binding instead of up). They then screen a library of variants at

two linker positions and identify a variant with slightly improved lifetime contrast (TqCa-FLITS_T203Y_V27A_N271D, named G-Ca-FLITS). The authors then characterize the performance of G-Ca-FLITS relative to Tq-Ca-FLITS in purified protein samples, in cultured cells, and in the brains of fruit flies.

Strengths:

This work is interesting as it extends their prior work generating a calcium indicator scaffold for fluorescent protein-based lifetime sensors with large contrast at a single wavelength, which is already being adopted by the community for production of other FLIM biosensors. This work effectively extends that from cyan to green fluorescence. While the cyan and green sensors are not spectrally distinct enough (~20-30nm shift) to easily multiplex together, it at least shifts the spectra to wavelengths that are more commonly available on commercial microscopes.

The observations of organellar calcium concentrations were interesting and could potentially lead to new biological insight if followed up.

Weaknesses:

(1) The new G-Ca-FLITS sensor doesn't appear to be significantly improved in performance over the original Tq-Ca-FLITS, no specific benefits are demonstrated.

(2) Although it was admirable to attempt in vivo demonstration in Drosophila with these sensors, depolarizing the whole brain with high potassium is not a terribly interesting or physiological stimulus and doesn't really highlight any advantages of their sensors; G-Ca-FLITS appears to be quite dim in the flies.

We thank the reviewer for a thorough evaluation and for suggestions to improve our manuscript. Although the spectral shift of the green variant is modest, we have added new data (figure 7) to the manuscript that demonstrates multiplex imaging of G-Ca-FLITS and Tq-Ca-FLITS.

As for the listed weaknesses we respond here:

(1) Although we agree that the performance in terms of dynamic range is not improved, the advantage of the green sensor over the cyan version is that the brightness is high in both states.

(2) We agree that the performance of G-Ca-FLITS is disappointing in Drosophila. We feel that this is important data to report, and it makes it clear that Tq-Ca-FLITS is a better choice for this system. Depolarization of the entire brain was done to measure the maximal lifetime contrast.

Reviewer #3 (Public review):

Summary:

The authours present a variant of a previously described fluorescence lifetime sensor for calcium. Much of the manuscript describes the process of developing appropriate assays for screening sensor variants, and thorough characterization of those variants (inherent fluorescence characteristics, response to calcium and pH, comparisons to other calcium sensors). The final two figures show how the sensor performs in cultured cells and in vivo drosophila brains.

Strengths:

The work is presented clearly and the conclusion (this is a new calcium sensor that could be useful in some circumstances) is supported by the data.

Weaknesses:

There are probably few circumstances where this sensor would facilitate experiments (calcium measurements) that other sensors would prove insufficient.

We thank the reviewer for the evaluation of our manuscript. As for the indicated weakness, we agree that the main application of genetically encoded calcium biosensors is to measure qualitative changes in calcium. However, it can be argued that due to a lack of tools the absolute quantification has been very challenging. Now, thanks to large contrast lifetime biosensors the quantitative measurements are simplified, there are new opportunities, and the probe reported here is an improvement over existing probes as it remains bright in both states, further improving quantitative calcium measurements.

Reviewer #1 (Recommendations for the authors):

While the science in the paper appears solid, the methods well grounded and excellently documented, the manuscript would benefit from a revision to improve the clarity of the exposition. In particular:

Part of the introduction appears like a patchwork of information with poor logical consequentiality. The authors rapidly pass from the impact of brightness on FLIM accuracy, to mitochondrial calcium in pathology, to the importance of the sensor's affinity, to a sentence on sensor's kinetics, to fluorescent dyes and bioluminescence, to conclude that sensors should be stable at mitochondrial pH. I highly recommend rewriting this part.

We thank the referee for the comment and we have adjusted to introduction to better connect the parts and increase the logic. The updated introduction addresses all the feedback by the reviewers on different aspects of the introductory text, and we have removed the section on dyes and bioluminescence. We feel that the introduction is better structured now.

The reference to particular amino acid positions would greatly benefit from including images of the protein structure in which the positions are highlighted, similar to what the same authors do in their fluorescent protein development papers. While in the case of sensors a crystal structure might be lacking, highlighting the positions with respect to an AlphaFold-generated structure or the structure of mTq2 might still be helpful.

We appreciate this remark and we have added a sequence alignment of the FLITS probes to supplemental Figure S4. This shows the residues with number, and we have also highlighted the different domains, linkers and mutations. We think that this linear representation works better than a 3D structure (one issue is that alphafold fails to display the chromophore and it has usually poor confidence for linker residues).

The use of SNR, as defined by the authors (mean of the lifetime divided by standard deviation) appears a poorly suited parameter to compare sensors, as it depends on the total number of collected photons and on the strength of the algorithms used to retrieve the lifetime value. In an extreme example, if one would collect uniform images with millions of photons per pixel, most likely SNR would be extremely good for all sensors in all states, irrespective of the fact that some states are dimmer (within reasonable limits). On the other hand, if the same comparison would be performed at a level of thousands or hundreds of photons per pixel, the effect of different brightness on the SNR would be much more dramatic. While in general I fully agree with the core concept of the paper, i.e. that avoiding low-brightness forms leads more easily to experiments with higher SNR, I would suggest to stick to comparing the sensors in terms of brightness and refer to SNR (if needed) only when describing the consequences on measurements.

The reviewer is right that in absolute terms the SNR is not meaningful. In addition to acquisition time, it depends on expression levels. Yet, it is possible to compare the change in SNR between the apo- and saturated states, and that is what is shown in figure 5. We have added text to better explain that the change in SNR is relevant here:

“The absolute SNR is not relevant here, as it will depend on the expression level and acquisition time. But since we have measured the two extremes in the same cells, we can evaluate how the SNR changes between these states for each separate probe”

Some statements from the authors or aspects of the paper appear problematic:

(1) "Additionally, the fluorescence of most sensors is a non-linear function of calcium concentration, usually with Hill coefficients between 2 and 3. This is ideal when the probe is used as a binary detector for increases in Ca2+ concentrations, but it makes robust quantification of low, or even intermediate, calcium concentrations extremely challenging."

To the best of my knowledge, for all sensors the fluorescence response is a nonlinear function of calcium concentrations. If the authors have specific examples in mind in which this is not true, they should cite them specifically. Furthermore, the Hill coefficient defines the range of concentrations in which the sensor operates, while the fact that "low concentrations" might be hard to detect depends only on the dim fluorescence of some sensors in the unbound form.

We agree with the reviewer that this part is not clearly written and confusing, as the sentence “Additionally, the fluorescence of most sensors is a non-linear function of calcium concentration, usually with Hill coefficients between 2 and 3” was not relevant in this section and so we removed it. Now it reads:

“Many GECIs harboring a single fluorescent protein (FP), like GCaMPs, are optimized for a large intensity change, and have a (very) dim state when calcium levels are below the KD of the probe (Akerboom et al., 2013; Dana et al., 2019; Shen et al., 2018; Zhang et al., 2023; Zhao et al., 2011). This is ideal when the probe is used as a binary detector for increases in Ca2+ concentrations, but it makes robust quantification of low, or even intermediate, calcium concentrations extremely challenging”

(2) "The affinity of a sensor is of major importance: a low KD can underestimate high concentrations and vice versa."

It is not clear to me why the concentrations would be underestimated, rather than just being less precise. Also, if a calibration curve is plotted in linear scale rather than logarithmic scale, it appears that the precision problem is much more severe near saturation (where low lifetime changes result in large concentration changes) than near zero (where low concentration changes produce large lifetime changes).

We agree that this could be better explained, what we meant to say that concentrations that are ~10x lower or higher than the KD cannot be precisely measured. See also our reply to the next comment.

(3) "Differences can also arise due to the method of calibration, i.e. when the absolute minimum and maximum signal are not reached in the calibration procedure (Fernandez-Sanz et al., 2019)."

Unless better explained, this appears obvious and not worth mentioning.

What may be obvious to the reviewer (and to us) may not be obvious to the reader, and that’s why this is included. To make it clearer we rephrased this part as a list of four items:

“Accurate determination of the affinity of a sensor is important and there are several issues that need to be considered during the calibration and the measurements: (i) the concentrations can only be measured with sufficient precision when it is in the range between 10x K_D and 1/10x K_D, (ii) the calibration is only valid when the two extremes are reached during the calibration procedure (Fernandez-Sanz et al., 2019), (iii) the sensor’s kinetics should be sufficiently fast enough to be able to track the calcium changes, and (iv) the biosensor should be compatible with the high mitochondrial pH of 8 (Cano Abad et al., 2004; Llopis et al., 1998).”

(4) In the experiments depicted in Figure 6C the underlying assumption is that the sensor behaves in the same way independently of the compartment to which it is targeted. This is not necessarily the case. It would be valuable to see the plots of Figure 6C and D discussed in terms of lifetime. Is the saturating lifetime value the same in all compartments?

This is a valid point and we have now included a plot with the actual lifetime data for each of the organelles (figure S15). 

We have also added text to discuss this point: “We note that the underlying assumption of the quantification of organellar calcium concentrations is that the lifetime contrast is the same. This is broadly true for most of the measurements (Figure S15). Yet, there are also differences. It is currently unclear whether the discrepancies are due to differences in the physicochemical properties of the compartments, or whether there is a technical reason (the efficiency of ionomycin for saturating the biosensor in the different compartments is unknown, as far as we know). This is something that is worth revisiting. A related issue that deserves attention is the level of agreement between in vitro and in vivo calibrations.”

(5) A similar problem arises for the observation of different calcium levels in peripheral mitochondria. In figure S11b, the values of the two lifetime components of a biexponential fit are displayed. Both the long and short components seem to be different. This is an interesting observation, as in an ideal sensor (in which the "long lifetime conformation" is the same whether the sensor is bound to the analyte or not, and similarly for the short lifetime one) those values should be identical. While it is entirely possible that this is not the case for G-CaFLITS, since the authors have conducted a calibration experiment using time-domain FLIM, could they show the behavior of the lifetimes and preamplitudes? Are the trends consistent with their interpretation of a different calcium level in the two mitochondrial populations?

We have analyzed the calibration data from TCSPC experiments done with the Leica Stellaris. From these data (acquired at high photon counts as it is purified protein in solution), we infer that both the short and long lifetime do change as a function of calcium concentration. In particular the long lifetime shows a substantial change, which we cannot explain at this moment. We agree that this is interesting and may potentially give insight in the conformation changes that give rise to the lifetime change.

The lifetime data of the mitochondria has been acquired with a different FLIM setup, but the trend is consistent, both the long and short lifetime decrease in the peripheral mitochondria that have a higher calcium concentration.

Author response image 1

(6) "The lifetime response of Tq-Ca-FLITS and the ΔF/F response of jGCaMP7f resembled each other, with both signals gradually increasing over the span of 3-4 minutes after we increased external [K+]; the two signals then hit a plateau for ~1 min, followed by a return to baseline and often additional plateaus (Figure 8B-C). By comparison, G-Ca-FLITS responses were more variable, typically exhibiting a smaller ramping phase and seconds-long spikes of activity rather than minutes-long plateaus (Figure 8C)."

This statement does not appear fully consistent with the data in Figure 8. While in figure 8B it looks like GCaMP and mTq-CaFLITS have very similar profiles, these curves come from one single experiment out of a very variable dataset (see Figure 8C). If one would for example choose the second curve of GCaMP in Figure 8C, it would look very similar to the response of G-CaFLITS in figure 8B, and the argument would be reversed. How do the averages look like?

Indeed, the dynamics of the responses are very variable and we do not want to draw attention to these differences in the dynamics, so we have removed the comparison. Instead, the difference in intensity change and lifetime contrast are of importance here. To answer the question of the reviewer, we have added a new panel (D) which shows the average responses for each of the GECIs.  

(7) "Although the calibration is equipment independent under ideal conditions, and only needs to be performed once, we prefer to repeat the calibration for different setups to account for differences in temperature or pulse frequency."

While I generally agree with the statement, it is imprecise. A change in temperature is generally expected to affect the Kd, so rather than "preferring to repeat", it is a requirement for accurate quantification at different concentrations. I am not sure I understand what the pulse frequency is in this context, and how it affects the Kd.

We thank the referee for pointing out that our text is imprecise and confusing. What we meant to say is that we see differences between different set-ups and we have clarified this by changing the text. We have also added that it is “necessary” to repeat the calibration:

“Although the calibration is equipment independent under ideal conditions, and only needs to be performed once, we do see differences between different set-ups. Therefore, it is necessary to repeat the calibration for different set-ups.”

(8) "A recent effort to generate a green emitting lifetime biosensor used a GFP variant as a template (Koveal et al., 2022), and the resulting biosensor was pH sensitive in the physiological range. On the other hand, biosensors with a CFP-like chromophore are largely pH insensitive (van der Linden et al., 2021; Zhong et al., 2024)."

The dismissal of the use of T-Sapphire as a pH independent template is inaccurate. The same group has previously reported other sensors (SweetieTS for glucose and Peredox for redox ratio) that are not pH sensitive. Furthermore, in Koveal et al. also many of the mTq2-based variants showed a pH response, suggesting that the pHdependence for the Lilac sensor might be more complex. Still, G-CaFLITS present advantages in terms of the possibility to excite at longer wavelengths, which could be mentioned instead.

We only want to make the point that adding the T203Y mutation to Turquoise-based lifetime biosensors may be a good approach for generating pH insensitive green biosensors. There is no point in dismissing other green biosensors and we have changed the text to: “Since biosensors with a CFP-like chromophore are largely pH insensitive (van der Linden et al., 2021; Zhong et al., 2024), and we show here that the pH independence is retained for the Green Ca-FLITS, we expect that adding the T203Y mutation to a cyan sensor is a good approach for generating pH-insensitive green lifetime-based sensors.”

(9) "Usually, a higher QY results in a higher intensity; however, in G-Ca-FLITS the open state has a differential shaped excitation spectrum which leads to a decreased intensity. These effects combined have resulted in a sensor where the two different states have a similar intensity despite displaying a large QY and lifetime contrast."

This statement does not seem to reflect the excitation spectra of Figure 1. If this explanation would be true, wouldn't there be an isoemissive point in the excitation spectrum (i.e. an excitation wavelength at which emission intensity would not change)?

The excitation spectra in figure 1 are not ideal for the interpretation as these are not normalized. The normalized spectra are shown in figure S10, but for clarity we show the normalized spectra here below as well. For the FD-FLIM experiments we used a 446 nm LED that excites the calcium bound state more efficiently. Therefore, the lower brightness due to a lower QY of the calcium bound state is compensated by increased excitation. So the limited change in intensity is excitation wavelength dependent. We have added a sentence to the discussion to stress this:

“The smallest intensity change is obtained when the calcium-bound state is preferably excited (i.e. near 450 nm) and the effect is less pronounced when the probe is excited near its peak at 474 nm”   

(10) "We evaluated the use of Tq-Ca-FLITS and G-Ca-FLITS for 2P-FLIM and observed a surprisingly low brightness of the green variant in an intact fly brain. This result is consistent with a study finding that red-shifted fluorescent-protein variants that are much brighter under one-photon excitation are, surprisingly, dimmer than their blue cousins in multi-photon microscopy (Molina et al., 2017). The responses of both probes were in line with their properties in single photon FLIM, but given the low brightness of G-Ca-FLITS under 2-photon excitation, the Tq-Ca-FLITS may be a better choice for 2P-FLIM experiments."

The differences appear strikingly high, and it seems improbable that a reduction in two-photon absorption coefficient might be the sole cause. How can the authors rule out a problem in expression (possibly organism-specific)?

The reviewers are correct that the changes in brightness between G-Ca-FLITS and Tq-Ca-FLITS may arise from changes in expression levels. It is difficult to calibrate for these changes explicitly without a stable reference fluorophore. However, both the G-Ca-FLITS and Tq-Ca-FLITS transgenic flies produced used the same plasmid backbone (the Janelia 20x-UAS-IVS plasmid), landed in the same insertion site (VK00005) of the same genetic background and were crossed to the same Janelia driver line (R60D05-Gal4), so at the level of the transcriptional machinery or genetic regulatory landscape the two lines are probably identical except for the few base pair differences between the G-Ca-FLITS and Tq-Ca-FLITS sequence. But the same level of transcription may not correspond to the same amount of stable protein in the ellipsoid body. So, we cannot rule out any organism-specific problems in expression. To examine the 2P excitation efficiency relative to 1P excitation efficiency, we have measured the fluorescence intensity of purified G-Ca-FLITS and Tq-Ca-FLITS on beads. See also response to reviewer 3 and supplemental figure S14

Suggestions

(1) The underlying assumption of any experiment using a biosensor is that the concentration of the biosensor should be roughly 2 orders of magnitude lower than the concentration of the analyte, otherwise the calibration equations do not hold. When measuring nM concentrations of calcium, this problem can be in principle very significant, as the concentration of the sensor in cells is likely in the low micromolar range. Calcium regulation by the cell should compensate for the problem, and the equations should hold. However, this might not hold true during experimental conditions that would disrupt this tight regulation. It might be a good thing to add a sentence to inform users about the limitations in interpreting calcium concentration data under such conditions.

Good point. We have added this to the discussion: “All calcium indicators also act as buffers, and this limits the accuracy of the absolute measurements, especially for the lower calcium concentrations (Rose et al., 2014), as the expression of the biosensor is usually in the low micromolar range.”

(2) Different methods of lifetime "averaging", such as intensity or amplitude-weighted lifetime in time domain FLIM or phase and modulation in frequency domain might lead to different Kd in the same calibration experiment. This is an underappreciated factor that might lead to errors by users. Since the authors conducted calibrations using both frequency and time-domain, it would be useful to mention this fact and maybe add a table in the Supporting Information with the minima, maxima and Kds calculated using different lifetime averaging methods.

To avoid biases due to fitting we prefer to use the phasor plot, this can be used for both frequency and time-domain methods and we added a sentence to the discussion to highlight this: “We prefer to use the phasor analysis (which can be used for both frequency- and time-domain FLIM), as it makes no assumptions about the underlying decay kinetics.”

(3) The origin of the redshift observed in G-CaFLITS is likely pi-stacking, similar to the EGFP-to-EYFP case. While previous studies suggest that for mTq2 based sensors a change in rigidity would lead to a change in the non-radiative rate, which would result in similar changes in quantum yield and (amplitude-weighted average) lifetime. If pi-stacking plays a role, there could be an additional change in the radiative rate (as suggested also by the change in absorption spectra). Could this play a role in the relation between brightness and lifetime in G-CaFLITS? Given the extensive data collected by the authors, it should be possible to comment on these mechanistical aspects, which would be useful to guide future design.

We do appreciate this suggestion, but we currently do not have the data to answer this question. The inverted response that we observe, solely due to the introduction of the tyrosine is puzzling. Perhaps introduction of the mutation that causes the redshift in other cyan probes will provide more insight.

Reviewer #2 (Recommendations for the authors):

Specific points:

The first section of Results is basically a description of how they chose the lysis conditions for screening in bacteria. I didn't see anything particularly novel or interesting about this, anyone working with protein expression in bacteria likely needs to optimize growth, lysis, purification, etc. This section should be moved to the Methods.

As reviewer 1 lists the thorough documentation of this approach as one of the strengths, we prefer to keep it like this. We see this section as method development, rather than purely a method. When this section would be moved to methods, it remains largely invisible and we think that’s a shame. Readers that are not interested can easily skip this section.

In the Results section Characterization of G-Ca-FLITS, the authors state "Here, the calcium affinity was KD = 339 nM, higher compared to the calibration at 37{degree sign}C. This is in line with the notion that binding strength generally increases with decreasing temperature." However, the opposite appears to be true - at 37C they measured a KD of 209 nM which would represent higher binding strength at higher temperature.

Thanks for catching this, we’ve made a mistake. We rephrase this to “higher compared to the calibration at 37 ˚C. This is unexpected as it not in line with the notion that binding strength generally increases with decreasing temperature.”

In Figure 8c, there should be a visual indicator showing the onset of application of high potassium, as there is in 8b.

This is a good suggestion; a grey box is added to indicates time when high K+ saline was perfused.

Reviewer #3 (Recommendations for the authors):

I think the science of the manuscript is sound and the presentation is logical and clear. I have some stylistic recommendations.

Supp Fig 1: The figure requires a bit of "eyeballing" to decide which conditions are best, and figuring out which spectra matched the final conditions took a little effort. Is there a way to quantify the fluorescence yield to better show why the one set of conditions was chosen? If it was subjective, then at least highlight the final conditions with a box around the spectra, making it a different colour, or something to make it stand out.

Thanks for the comment; we added a green box.

Supp Fig 3: Similar suggestion. Highlight the final variant that was carried forward (T203Y). The subtle differences in spectra are hard to discern when they are presented separately. How would it look if they were plotted all on one graph? Or if each mutant were presented as a point on a graph of Peak Em vs Peak Ex? Would T203Y be in the top right?

We have added a light blue box for reference to make the differences clearer.

Supp Fig 4 & Fig 1: Too much of the graph show the uninteresting tails of the spectra and condenses the interesting part. Plotting from 400 nm to 600 nm would be more informative.

We appreciate the suggestion but disagree. We prefer to show the spectra in its entirety, including the tails. The data will be available so other plots can be made by anyone.

Fig 3a: People who are not experts in lifetime analysis are probably not very familiar with the phase/modulation polar plot. There should be an additional sentence or two in the main text that _briefly_ describes the basis for making the polar plot and the transformation to the fractional saturation plot in 3B. I can't think of a good way to transform Eq 3 from Supp Info into a sentence, but that's what I think is needed to make this transformation clearer.

We appreciate the suggestion and feel that it is well explained here:

"The two extreme values (zero calcium and 39 μM free calcium) are located on different coordinates in the polar plot and all intermediate concentrations are located on a straight line between these two extremes. Based on the position in the polar plot, we determined the fraction of sensor in the calcium-bound state, while considering the intensity contribution of both states"  

Fig 4: The figure is great, and I love the comparison of different calcium sensors. But where is Tq-Ca-FLITS? I get that this is a figure of green calcium sensors, but it would be nice to see Tq-Ca-FLITS in there as well. The G-Ca-FLITS is compared to Tq-Ca-FLITS in Fig 5. Maybe I'm just missing why the bottom panel of Fig 5 cannot be replotted and included in Fig 4.

The point is that we compare all the data with identical filter sets, i.e. for green FPs.using these ex/em settings, the Tq probe would seriously underperform. Note that the data in fig. 5 is not normalized to a reference RFP and can therefore not be compared with data presented in figure 4.

Fig 6: The BOEC data could easily be moved to Supp Figs. It doesn't contribute much relevant info.

We are not keen of moving data to supplemental, as too often the supplemental data is ignored. Moreover, we think that the BOEC data is valuable (as BOEC are primary cells and therefore a good model of a healthy human cell) and deserves a place in the main manuscript.

2P FLIM / Fig 8 / Fig S4: The lack of brightness of G-Ca-FLITS in the 2P FLIM of fruit fly brain could have been predicted with a 2P cross section of the purified protein. If the equipment to perform such measurements is available, it could be incorporated into Fig S4.

Unfortunately, we do not have access to equipment that measures the 2P cross section. As an alternative, we compared the 2P excitation efficiency with 1P excitation efficiency. To this end, we have used beads that were loaded with purified G-Ca-FLITS or Tq-Ca-FLITS. We have evaluated the fluorescence intensity of the beads using 1P (460 nm) and 2P (920 nm) excitation. Although the absolute intensity cannot be compared (the G-Ca-FLITS beads have a lower protein concentration), we can compare the relative intensities when changing from 1P to 2P. The 2P excitation efficiency of G-Ca-FLITS is comparable (if not better) to that of Tq-Ca-FLITS. This excludes the option that the G-Ca-FLITS has poor 2P excitability. We will include this data as figure S12.

We also have added text to the results: “We evaluated the relative brightness of purified Tq-Ca-FLITS and G-Ca-FLITS on beads by either 1-Photon Excitation (1PE) (at 460 nm) or 2-Photon Excitation (2PE) (at 920 nm) and observed a similar brightness between the two modes of excitations (figure S14). This shows that the two probes have similar efficiencies in 2PE and suggest that the low brightness of GCa-FLITS in Drosophila is due to lower expression or poor folding.” and discussion: “The responses of both probes were in line with their properties in single photon FLIM, but given the low brightness of G-Ca-FLITS under 2-photon excitation in Drosphila, the Tq-Ca-FLITS is a better choice in this system. Yet, the brightness of G-Ca-FLITS with 2PE at 920 nm is comparable to Tq-Ca-FLITS, so we expect that 2P-FLIM with G-Ca-FLITS is possible in tissues that express it well.”

imagine a waiter (The Event Loop) and a table of customers (The Tasks).

Synchronous (Blocking): The waiter takes an order from Table 1. He walks to the kitchen and stands there waiting for the chef to cook the food. He ignores Table 2, Table 3, and Table 4. Nothing else happens in the restaurant until Table 1 eats.

Asynchronous (await): The waiter takes an order from Table 1 (await food). He gives the ticket to the kitchen. instead of waiting, he immediately turns around and goes to serve Table 2. When the kitchen rings the bell (Task done), he goes back to Table 1.

a. The complementary strand of DNA is: 3'--AATTACCCTGTTCGAACACATCTC--5'

b. The mRNA sequence transcribed from the complementary DNA strand is: 5'--AAU UAC CCU GUC GAA CAC AUC UC--3'

c. Using the genetic code table, the amino acid sequence is: I. Start codon: Met II. Stop codon: Stop

The given DNA sequence is 3' CGTCCACGT 5'.

The complementary strand will be built by pairing the bases: C with G, G with C, T with A, and A with T.

So, the complementary strand is 5' GCAGGTGC 3 I'd use the DNA template strand (3' CGTCCACGT 5'). In RNA, uracil (U) replaces thymine (T).

So, the mRNA sequence will be 5' GCAGGUGCA 3'.

1. The mRNA molecule is 5' GCAGGUGCA 3'.

Answer: 5' GCAGGUGCA 3'

. Assembling the Original DNA: You start with a double-stranded DNA molecule. One strand has the sequence 5'-GCAT-3', and it's paired with its complementary strand, which is 3'-CGTA-5'. Remember, A always pairs with T, and G always pairs with C.

Separating the Strands (Helicase): This is like the job of the enzyme DNA helicase. It unwinds and separates the double-stranded DNA into two single strands.

Building Daughter Strands : Each of the original strands now serves as a template for building a new, complementary strand. This is what DNA polymerase does. It adds nucleotides to the 3' end of the new strand, following the base-pairing rules. So, for the template 5'-GCAT-3', the new strand will be 3'-CGTA-5'. And for the template 3'-CGTA-5', the new strand will be 5'-GCAT-3'. Disassembling the Model: This just refers to taking apart the physical model you built to represent the DNA. It's not a step that happens in actual DNA replication in a cell.

Final Answer: DNA replication steps: assembling original DNA, separating strands (helicase), building daughter strands (DNA polymerase), and disassembling the model.

DNA replication is how a DNA molecule makes an exact copy of itself. This is crucial for cell division, ensuring each new cell gets a complete set of genetic instructions.

The process is semi-conservative, meaning each new DNA molecule has one original strand and one newly synthesized strand. This helps reduce errors during copying.

Key steps include: 1. The DNA double helix unwinds. 2. New DNA bases (A, T, G, C) are added to each original strand. 3. Two new DNA molecules are created, each with one old and one new strand.

Several enzymes are involved, including RNA primase, DNA helicase, DNA polymerase, and DNA ligase, each playing a specific role in the process.

the paper aims to studly how stander English effects schooling

Author Response:

Reviewer #1 (Public Review):

The work by Wang et al. examined how task-irrelevant, high-order rhythmic context could rescue the attentional blink effect via reorganizing items into different temporal chunks, as well as the neural correlates. In a series of behavioral experiments with several controls, they demonstrated that the detection performance of T2 was higher when occurring in different chunks from T1, compared to when T1 and T2 were in the same chunk. In EEG recordings, they further revealed that the chunk-related entrainment was significantly correlated with the behavioral effect, and the alpha-band power for T2 and its coupling to the low-frequency oscillation were also related to behavioral effect. They propose that the rhythmic context implements a second-order temporal structure to the first-order regularities posited in dynamic attention theory.

Overall, I find the results interesting and convincing, particularly the behavioral part. The manuscript is clearly written and the methods are sound. My major concerns are about the neural part, i.e., whether the work provides new scientific insights to our understanding of dynamic attention and its neural underpinnings.

1) A general concern is whether the observed behavioral related neural index, e.g., alpha-band power, cross-frequency coupling, could be simply explained in terms of ERP response for T2. For example, when the ERP response for T2 is larger for between-chunk condition compared to within-chunk condition, the alpha-power for T2 would be also larger for between-chunk condition. Likewise, this might also explain the cross-frequency coupling results. The authors should do more control analyses to address the possibility, e.g., plotting the ERP response for the two conditions and regressing them out from the oscillatory index.

Many thanks for the comment. In short, the enhancement in alpha power and cross-frequency coupling results in the between-cycle condition compared with those in the within-cycle condition cannot be accounted for by the ERP responses for T2.

In general, the rhythmic stimulation in the AB paradigm prevents EEG signals from returning to the baseline. Therefore, we cannot observe typical ERP components purely related to individual items, except for the P1 and N1 components related to the stream onset, which reveals no difference between the two conditions and are trailed by steady-state responses (SSRs) resonating at the stimulus rate (Fig. R1).

Fig. R1. ERPs aligned to stream onset. EEG signals were filtered between 1–30 Hz, baseline-corrected (-200 to 0 ms before stream onset) and averaged across the electrodes in left parieto-occipital area where 10-Hz alpha power showed attentional modulation effect.

To further inspect the potential differences in the target-related ERP signals between the within- and between-cycle conditions, we plotted the target-aligned waveforms for these experimental conditions. As shown in Fig. R2, a drop of ERP amplitude occurred for both conditions around T2 onset, and the difference between these two conditions was not significant (paired t-test estimated on mean amplitude every 20 ms from 0 to 700 ms relative to T1 onset, p > .05, FDR-corrected).

Fig. R2. ERPs aligned to T1 onset. EEG signals were filtered between 1–30 Hz, and baseline-corrected using signals -100 to 0 ms before T1 onset. The two dash lines indicate the onset of T1 and T2, respectively.

Since there is a trend of enhanced ERP response for the between-cycle relative to the within-cycle condition during the period of 0 to 100 ms after T2 onset (paired t-test on mean amplitude, p =.065, uncorrected), we then directly examined whether such post-T2 responses contribute to the behavioral attentional modulation effect and behavior-related neural indices. Crucially, we did not find any significant correlation of such T2-related ERP enhancement with the behavioral modulation index (BMI), or with the reported effects of alpha power and cross-frequency coupling (PAC). Furthermore, after controlling for the T2-related ERP responses, there still remains a significant correlation between the delta-alpha PAC and the BMI (rpartial = .596, p = .019), which is not surprising given that the PAC is calculated based on an 800-ms time window covering more pre-T2 than post-T2 periods (see the response to point #4 for details) rather than around the T2 onset. Taken together, these results clearly suggest that the T2-related ERP responses cannot explain the attentional modulation effect and the observed behavior-related neural indices.

2) The alpha-band increase for T2 is indeed contradictory to the well known inhibitory function of alpha-band in attention. How could a target that is better discriminated elicit stronger inhibitory response? Related to the above point, the observed enhancement in alpha-band power and its coupling to low-frequency oscillation might derive from an enhanced ERP response for T2 target.

Many thanks for the comment. We have briefly discussed this point in the revised manuscript (page 18, line 477).

A widely accepted function of alpha activity in attention is that alpha oscillations suppress irrelevant visual information during spatial selection (Kelly et al., 2006; Thut et al., 2006; Worden et al., 2000). However, it becomes a controversial issue when there exists rhythmic sensory stimulation at alpha-band, just like the situation in the current study where both the visual stream and the contextual auditory rhythm were emitted at 10 Hz. In such a case, alpha-band neural responses at the stimulation frequency can be interpreted as either passively evoked steady-state responses (SSR) or actively synchronized intrinsic brain rhythms. From the former perspective (i.e., the SSR view), an increase in the amplitude or power at the stimulus frequency may indicate an enhanced attentional allocation to the stimulus stream that may result in better target detection (Janson et al., 2014; Keil et al., 2006; Müller & Hübner, 2002). Conversely, the latter view of the inhibitory function of intrinsic alpha oscillations would produce the opposite prediction. In a previous AB study, Janson and colleagues (2014) investigated this issue by separating the stimulus-evoked activity at 12 Hz (using the same power analysis method as ours) from the endogenous alpha oscillations ranging from 10.35 to 11.25 Hz (as indexed by individual alpha frequency, IAF). Interestingly, they found a dissociation between these two alpha-band neural responses, showing that the RSVP frequency power was higher in non-AB trials (T2 detected) than in AB trials (T2 undetected) while the IAF power exhibited the opposite pattern. According to these findings, the currently observed increase in alpha power for the between-cycle condition may reflect more of the stimulus-driven processes related to attentional enhancement. However, we don’t negate the effect of intrinsic alpha oscillations in our study, as the current design is not sufficient to distinguish between these two processes. We have discussed this point in the revised manuscript (page 18, line 477). Also, we have to admit that “alpha power” may not be the most precise term to describe our findings of the stimulus-related results. Thus, we have specified it as “neural responses to first-order rhythms at 10 Hz” and “10-Hz alpha power” in the revised manuscript (see page 12 in the Results section and page 18 in the Discussion section).

As for the contribution of T2-related ERP response to the observed effect of 10 Hz power and cross-frequency coupling, please refer to our response to point #1.

References:

Janson, J., De Vos, M., Thorne, J. D., & Kranczioch, C. (2014). Endogenous and Rapid Serial Visual Presentation-induced Alpha Band Oscillations in the Attentional Blink. Journal of Cognitive Neuroscience, 26(7), 1454–1468. https://doi.org/10.1162/jocn_a_00551

Keil, A., Ihssen, N., & Heim, S. (2006). Early cortical facilitation for emotionally arousing targets during the attentional blink. BMC Biology, 4(1), 23. https://doi.org/10.1186/1741-7007-4-23

Kelly, S. P., Lalor, E. C., Reilly, R. B., & Foxe, J. J. (2006). Increases in Alpha Oscillatory Power Reflect an Active Retinotopic Mechanism for Distracter Suppression During Sustained Visuospatial Attention. Journal of Neurophysiology, 95(6), 3844–3851. https://doi.org/10.1152/jn.01234.2005

Müller, M. M., & Hübner, R. (2002). Can the Spotlight of Attention Be Shaped Like a Doughnut? Evidence From Steady-State Visual Evoked Potentials. Psychological Science, 13(2), 119–124. https://doi.org/10.1111/1467-9280.00422

Thut, G., Nietzel, A., Brandt, S., & Pascual-Leone, A. (2006). Alpha-band electroencephalographic activity over occipital cortex indexes visuospatial attention bias and predicts visual target detection. The Journal of Neuroscience : The Official Journal of the Society for Neuroscience, 26(37), 9494–9502. https://doi.org/10.1523/JNEUROSCI.0875-06.2006

Worden, M. S., Foxe, J. J., Wang, N., & Simpson, G. V. (2000). Anticipatory Biasing of Visuospatial Attention Indexed by Retinotopically Specific α-Bank Electroencephalography Increases over Occipital Cortex. Journal of Neuroscience, 20(6), RC63–RC63. https://doi.org/10.1523/JNEUROSCI.20-06-j0002.2000

3) To support that it is the context-induced entrainment that leads to the modulation in AB effect, the authors could examine pre-T2 response, e.g., alpha-power, and cross-frequency coupling, as well as its relationship to behavioral performance. I think the pre-stimulus response might be more convincing to support the authors' claim.

Many thanks for the insightful suggestion. We have conducted additional analyses.

Following this suggestion, we have examined the 10-Hz alpha power within the time window of -100–0 ms before T2 onset and found stronger activity for the between-cycle condition than for the within-cycle condition. This pre-T2 response is similar to the post-T2 response except that it is more restricted to the left parieto-occipital cluster (CP3, CP5, P3, P5, PO3, PO5, POZ, O1, OZ, t(15) = 2.774, p = .007), which partially overlaps with the cluster that exhibits a delta-alpha coupling effect significantly correlated with the BMI. We have incorporated these findings into the main text (page 12, line 315) and the Fig. 5A of the revised manuscript.

As for the coupling results reported in our manuscript, the coupling index (PAC) was calculated based on the activity during the second and third cycles (i.e., 400 to 1200 ms from stream onset) of the contextual rhythm, most of which covers the pre-T2 period as T2 always appeared in the third cycle for both conditions. Together, these results on pre-T2 10-Hz alpha power and cross-frequency coupling, as well as its relationship to behavioral performance, jointly suggest that the observed modulation effect is caused by the context-induced entrainment rather than being a by-product of post-T2 processing.

4) About the entrainment to rhythmic context and its relation to behavioral modulation index. Previous studies (e.g., Ding et al) have demonstrated the hierarchical temporal structure in speech signals, e.g., emergence of word-level entrainment introduced by language experience. Therefore, it is well expected that imposing a second-order structure on a visual stream would elicit the corresponding steady-state response. I understand that the new part and main focus here are the AB effects. The authors should add more texts explaining how their findings contribute new understandings to the neural mechanism for the intriguing phenomena.

Many thanks for the suggestion. We have provided more discussion in the revised manuscript (page 17, line 447).

We have provided more discussion on this important issue in the revised manuscript (page 17, line 447). In brief, our study demonstrates how cortical tracking of feature-based hierarchical structure reframes the deployment of attentional resources over visual streams. This effect, distinct from the hierarchical entrainment to speech signals (Ding et al., 2016; Gross et al., 2013), does not rely on previously acquired knowledge about the structured information and can be established automatically even when the higher-order structure comes from a task-irrelevant and cross-modal contextual rhythm. On the other hand, our finding sheds fresh light on the adaptive value of the structure-based entrainment effect by expanding its role from rhythmic information (e.g., speech) perception to temporal attention deployment. To our knowledge, few studies have tackled this issue in visual or speech processing.

References:

Ding, N., Melloni, L., Zhang, H., Tian, X., & Poeppel, D. (2016). Cortical tracking of hierarchical linguistic structures in connected speech. Nature Neuroscience, 19(1), 158–164. https://doi.org/10.1038/nn.4186

Gross, J., Hoogenboom, N., Thut, G., Schyns, P., Panzeri, S., Belin, P., & Garrod, S. (2013). Speech Rhythms and Multiplexed Oscillatory Sensory Coding in the Human Brain. PLoS Biol, 11(12). https://doi.org/10.1371/journal.pbio.1001752

Reviewer #2 (Public Review):

In cognitive neuroscience, a large number of studies proposed that neural entrainment, i.e., synchronization of neural activity and low-frequency external rhythms, is a key mechanism for temporal attention. In psychology and especially in vision, attentional blink is the most established paradigm to study temporal attention. Nevertheless, as far as I know, few studies try to link neural entrainment in the cognitive neuroscience literature with attentional blink in the psychology literature. The current study, however, bridges this gap.

The study provides new evidence for the dynamic attending theory using the attentional blink paradigm. Furthermore, it is shown that neural entrainment to the sensory rhythm, measured by EEG, is related to the attentional blink effect. The authors also show that event/chunk boundaries are not enough to modulate the attentional blink effect, and suggest that strict rhythmicity is required to modulate attention in time.

In general, I enjoyed reading the manuscript and only have a few relatively minor concerns.

1) Details about EEG analysis.

. First, each epoch is from -600 ms before the stimulus onset to 1600 ms after the stimulus onset. Therefore, the epoch is 2200 s in duration. However, zero-padding is needed to make the epoch duration 2000 s (for 0.5-Hz resolution). This is confusing. Furthermore, for a more conservative analysis, I recommend to also analyze the response between 400 ms and 1600 ms, to avoid the onset response, and show the results in a supplementary figure. The short duration reduces the frequency resolution but still allows seeing a 2.5-Hz response.

Thanks for the comments. Each epoch was indeed segmented from -600 to 1600 ms relative to the stimulus onset, but in the spectrum analysis, we only used EEG signals from stream onset (i.e., time point 0) to 1600 ms (see the Materials and Methods section) to investigate the oscillatory characteristics of the neural responses purely elicited by rhythmic stimuli. The 1.6-s signals were zero-padded into a 2-s duration to achieve a frequency resolution of 0.5 Hz.

According to the reviewer’s suggestion, we analyzed the EEG signals from 400 ms to 1600 ms relative to stream onset to avoid potential influence of the onset response, and showed the results in Figure 4. Basically, we can still observe spectral peaks at the stimulus frequencies of 2.5, 5 (the harmonic of 2.5 Hz), and 10 Hz for both power and ITPC spectrum. However, the peak magnitudes were much weaker than those of 1.6-s signals especially for 2.5 Hz, and the 2.5-Hz power did not survive the multiple comparisons correction across frequencies (FDR threshold of p < .05), which might be due to the relatively low signal-to-noise ratio for the analysis based on the 1.2-s epochs (only three cycles to estimate the activity at 2.5 Hz). Importantly, we did identify a significant cluster for 2.5 Hz ITPC in the left parieto-occipital region showing a positive correlation with the individuals’ BMI (Fig. R3; CP5, TP7, P5, P7, PO5, PO7, O1; r = .538, p = .016), which is consistent with the findings based on the longer epochs.

Fig. R3. Neural entrainment to contextual rhythms during the period of 400–1600 ms from stream onset. (A) The spectrum for inter-trial phase coherence (ITPC) of EEG signals from 400 to 1600 ms after the stimulus onset. Shaded areas indicate standard errors of the mean. (B) The 2.5-Hz ITPC was significantly correlated with the behavioral modulation index (BMI) in a parieto-occipital cluster, as indicated by orange stars in the scalp topographic map.

Second, "The preprocessed EEG signals were first corrected by subtracting the average activity of the entire stream for each epoch, and then averaged across trials for each condition, each participant, and each electrode." I have several concerns about this procedure.

(A) What is the entire stream? It's the average over time?

Yes, as for the power spectrum analysis, EEG signals were first demeaned by subtracting the average signals of the entire stream over time from onset to offset (i.e., from 0 to 1600 ms) before further analysis. We performed this procedure following previous studies on the entrainment to visual rhythms (Spaak et al., 2014). We have clarified this point in the “Power analysis” part of the Materials and Methods section (page 25, line 677).

References:

Spaak, E., Lange, F. P. de, & Jensen, O. (2014). Local Entrainment of Alpha Oscillations by Visual Stimuli Causes Cyclic Modulation of Perception. The Journal of Neuroscience, 34(10), 3536–3544. https://doi.org/10.1523/JNEUROSCI.4385-13.2014

(B) I suggest to do the Fourier transform first and average the spectrum over participants and electrodes. Averaging the EEG waveforms require the assumption that all electrodes/participants have the same response phase, which is not necessarily true.

Thanks for the suggestion. In an AB paradigm, the evoked neural responses are sufficiently time-locked to the periodic stimulation, so it is reasonable to quantify power estimate with spectral decomposition performed on trial-averaged EEG signals (i.e., evoked power). Moreover, our results of inter-trial phase coherence (ITPC), which estimated the phase-locking value across trials based on single-trial decomposed phase values, also provided supporting evidence that the EEG waveforms were temporally locked across trials to the 2.5-Hz temporal structure in the context session.

Nevertheless, we also took the reviewer’s suggestion seriously and analyzed the power spectrum on the average of single-trial spectral transforms, i.e., the induced power, which puts emphasis on the intrinsic non-phase-locked activities. In line with the results of evoked power and ITPC, the induced power spectrum in context session also peaked at 2.5 Hz and was significantly stronger than that in baseline session at 2.5 Hz (t(15) = 4.186, p < .001, FDR-corrected with a p value threshold < .001). Importantly, Person correlation analysis also revealed a positive cluster in the left parieto-occipital region, indicating the induced power at 2.5 Hz also had strong relevance with the attentional modulation effect (P7, PO7, PO5, PO3; r = .606, p = .006). We have added these additional findings to the revised manuscript (page 11, line 288; see also Figure 4—figure supplement 1).

2) The sequences are short, only containing 16 items and 4 cycles. Furthermore, the targets are presented in the 2nd or 3rd cycle. I suspect that a stronger effect may be observed if the sequence are longer, since attention may not well entrain to the external stimulus until a few cycles. In the first trial of the experiment, they participant may not have a chance to realize that the task-irrelevant auditory/visual stimulus has a cyclic nature and it is not likely that their attention will entrain to such cycles. As the experiment precedes, they learns that the stimulus is cyclic and may allocate their attention rhythmically. Therefore, I feel that the participants do not just rely on the rhythmic information within a trial but also rely on the stimulus history. Please discuss why short sequences are used and whether it is possible to see buildup of the effect over trials or over cycles within a trial.

Thanks for the comments. Typically, to induce a classic pattern of AB effect, the RSVP stream should contain 3–7 distractors before the first target (T1), with varying lengths of distractors (0–7) between two targets and at least 2 items after the second target (T2). In our study, we created the RSVP streams following these rules, which allowed us to observe the typical AB effect that T2 performance was deteriorated at Lag 2 relative to that at Lag 8. Nevertheless, we agree with the reviewer that longer streams would be better for building up the attentional entrainment effect, as we did observe the attentional modulation effect ramped up as the stream proceeded over cycles, consistent with the reviewer’s speculation. In Experiments 1a (using auditory context) and 2a (using color-defined visual context), we adopted two sets of target positions—an early one where T2 appeared at the 6th or 8th position (in the 2nd cycle) of the visual stream, and a late one where T2 appeared at the 10th or 12th position (in the 3rd cycle) of the visual stream. In the manuscript, we reported T2 performance with all the target positions combined, as no significant interaction was found between the target positions and the experimental conditions (ps. > .1). However, additional analysis demonstrated a trend toward an increase of the attentional modulation effect over cycles, from the early to the late positions. As shown in Fig. R4, the modulation effect went stronger and reached significance for the late positions (for Experiment 1a, t(15) = 2.83, p = .013, Cohen’s d = 0.707; for Experiment 2a, t(15) = 3.656, p = .002, Cohen’s d = 0.914) but showed a weaker trend for the early positions (for Experiment 1a, t(15) = 1.049, p = .311, Cohen’s d = 0.262; for Experiment 2a, t(15) = .606, p = .553, Cohen’s d = 0.152).

Fig. R4. Attentional modulation effect built up over cycles in Experiments 1a & 2a. Error bars represent 1 SEM; * p<0.05, ** p<0.01.

However, we did not observe an obvious buildup effect across trials in our study. The modulation effect of contextual rhythms seems to be a quick process that the effect is evident in the first quarter of trials in Experiment 1a (for, t(15) = 2.703, p = .016, Cohen’s d = 0.676) and in the second quarter of trials in Experiment 2a (for, t(15) = 2.478, p = .026, Cohen’s d = 0.620.

3) The term "cycle" is used without definition in Results. Please define and mention that it's an abstract term and does not require the stimulus to have "cycles".

Thanks for the suggestion. By its definition, the term “cycle” refers to “an interval of time during which a sequence of a recurring succession of events or phenomena is completed” or “a course or series of events or operations that recur regularly and usually lead back to the starting point” (Merriam-Webster dictionary). In the current study, we stuck to the recurrent and regular nature of “cycle” in general while defined the specific meaning of “cycle” by feature-based periodic changes of the contextual stimuli in each experiment (page 5, line 101; also refer to Procedures in the Materials and Methods section for details). For example, in Experiment 1a, the background tone sequence changed its pitch value from high to low or vice versa isochronously at a rate of 2.5 Hz, thus forming a rhythmic context with structure-based cycles of 400 ms. Note that we did not use the more general term “chunk”, because arbitrary chunks without the regularity of cycles are insufficient to trigger the attentional modulation effect in the current study. Indeed, the effect was eliminated when we replaced the rhythmic cycles with irregular chunks (Experiments 1d & 1e).

4) Entrainment of attention is not necessarily related to neural entrainment to sensory stimulus, and there is considerable debate about whether neural entrainment to sensory stimulus should be called entrainment. Too much emphasis on terminology is of course counterproductive but a short discussion on these issues is probably necessary.

Thanks for the comments. As commonly accepted, entrainment is defined as the alignment of intrinsic neuronal activity to the temporal structure of external rhythmic inputs (Lakatos et al., 2019; Obleser & Kayser, 2019). Here, we are interested in the functional roles of cortical entrainment to the higher-order temporal structure imposed on first-order sensory stimulation, and used the term entrainment to describe the phase-locking neural responses to such hierarchical structure following literature on auditory and visual perception (Brookshire et al., 2017; Doelling & Poeppel, 2015). In our study, the consistent results of power and ITPC have provided strong evidence that neural entrainment at the structure level (2.5 Hz) is significantly correlated with the observed attentional modulation effect. However, this does not mean that the entrainment of attention is necessarily associated with neural entrainment to sensory stimulus in a broader context, as attention may also be guided by predictions based on non-isochronous temporal regularity without requiring stimulus-based oscillatory entrainment (Breska & Deouell, 2017; Morillon et al._2016).

On the other hand, there has been a debate about whether the neural alignment to rhythmic stimulation reflects active entrainment of endogenous oscillatory processes (i.e., induced activity) or a series of passively evoked steady-state responses (Keitel et al., 2019; Notbohm et al., 2016; Zoefel et al., 2018). The latter process is also referred to as “entrainment in a broad sense” by Obleser & Kayser (2019). Given that a presented rhythm always evokes event-related potentials, a better question might be whether the observed alignment reflects the entrainment of endogenous oscillations in addition to evoked steady-state responses. Here we attempted to tackle this issue by measuring the induced power, which emphasizes the intrinsic non-phase-locked activity, in addition to the phase-locked evoked power. Specifically, we quantified these two kinds of activities with the average of single-trial EEG power spectra and the power spectra of trial-averaged EEG signals, respectively, according to Keitel et al. (2019). In addition to the observation of evoked responses to the contextual structure, we also demonstrated an attention-related neural tracking of the higher-order temporal structure based on the induced power at 2.5 Hz (see Figure 4—figure supplement 1), suggesting that the observed attentional modulation effect is at least partially derived from the entrainment of intrinsic oscillatory brain activity. We have briefly discussed this point in the revised manuscript (page 17, line 460).

References:

Breska, A., & Deouell, L. Y. (2017). Neural mechanisms of rhythm-based temporal prediction: Delta phase-locking reflects temporal predictability but not rhythmic entrainment. PLOS Biology, 15(2), e2001665. https://doi.org/10.1371/journal.pbio.2001665

Brookshire, G., Lu, J., Nusbaum, H. C., Goldin-Meadow, S., & Casasanto, D. (2017). Visual cortex entrains to sign language. Proceedings of the National Academy of Sciences, 114(24), 6352–6357. https://doi.org/10.1073/pnas.1620350114

Doelling, K. B., & Poeppel, D. (2015). Cortical entrainment to music and its modulation by expertise. Proceedings of the National Academy of Sciences, 112(45), E6233–E6242. https://doi.org/10.1073/pnas.1508431112

Henry, M. J., Herrmann, B., & Obleser, J. (2014). Entrained neural oscillations in multiple frequency bands comodulate behavior. Proceedings of the National Academy of Sciences, 111(41), 14935–14940. https://doi.org/10.1073/pnas.1408741111

Keitel, C., Keitel, A., Benwell, C. S. Y., Daube, C., Thut, G., & Gross, J. (2019). Stimulus-Driven Brain Rhythms within the Alpha Band: The Attentional-Modulation Conundrum. The Journal of Neuroscience, 39(16), 3119–3129. https://doi.org/10.1523/JNEUROSCI.1633-18.2019

Lakatos, P., Gross, J., & Thut, G. (2019). A New Unifying Account of the Roles of Neuronal Entrainment. Current Biology, 29(18), R890–R905. https://doi.org/10.1016/j.cub.2019.07.075

Morillon, B., Schroeder, C. E., Wyart, V., & Arnal, L. H. (2016). Temporal Prediction in lieu of Periodic Stimulation. Journal of Neuroscience, 36(8), 2342–2347. https://doi.org/10.1523/JNEUROSCI.0836-15.2016

Notbohm, A., Kurths, J., & Herrmann, C. S. (2016). Modification of Brain Oscillations via Rhythmic Light Stimulation Provides Evidence for Entrainment but Not for Superposition of Event-Related Responses. Frontiers in Human Neuroscience, 10. https://doi.org/10.3389/fnhum.2016.00010

Obleser, J., & Kayser, C. (2019). Neural Entrainment and Attentional Selection in the Listening Brain. Trends in Cognitive Sciences, 23(11), 913–926. https://doi.org/10.1016/j.tics.2019.08.004

Zoefel, B., ten Oever, S., & Sack, A. T. (2018). The Involvement of Endogenous Neural Oscillations in the Processing of Rhythmic Input: More Than a Regular Repetition of Evoked Neural Responses. Frontiers in Neuroscience, 12. https://doi.org/10.3389/fnins.2018.00095

Reviewer #3 (Public Review):

The current experiment tests whether the attentional blink is affected by higher-order regularity based on rhythmic organization of contextual features (pitch, color, or motion). The results show that this is indeed the case: the AB effect is smaller when two targets appeared in two adjacent cycles (between-cycle condition) than within the same cycle defined by the background sounds. Experiment 2 shows that this also holds for temporal regularities in the visual domain and Experiment 3 for motion. Additional EEG analysis indicated that the findings obtained can be explained by cortical entrainment to the higher-order contextual structure. Critically feature-based structure of contextual rhythms at 2.5 Hz was correlated with the strength of the attentional modulation effect.

This is an intriguing and exciting finding. It is a clever and innovative approach to reduce the attention blink by presenting a rhythmic higher-order regularity. It is convincing that this pulling out of the AB is driven by cortical entrainment. Overall, the paper is clear, well written and provides adequate control conditions. There is a lot to like about this paper. Yet, there are particular concerns that need to be addressed. Below I outline these concerns:

1) The most pressing concern is the behavioral data. We have to ensure that we are dealing here with a attentional blink. The way the data is presented is not the typical way this is done. Typically in AB designs one see the T2 performance when T1 is ignored relative to when T1 has to be detected. This data is not provided. I am not sure whether this data is collected but if so the reader should see this.

Many thanks for the suggestion. We appreciate the reviewer for his/her thoughtful comments. To demonstrate the AB effect, we did include two T2 lag conditions in our study (Experiments 1a, 1b, 2a, and 2b)—a short-SOA condition where T2 was located at the second lag of T1 (i.e., SOA = 200 ms), and a long-SOA condition where T2 appeared at the 8th lag of T1 (i.e., SOA = 800 ms). In a typical AB effect, T2 performance at short lags is remarkably impaired compared with that at long lags. In our study, we consistently replicated this effect across the experiments, as reported in the Results section of Experiment 1 (page 5, line 106). Overall, the T2 detection accuracy conditioned on correct T1 response was significantly impaired in the short-SOA condition relative to that in the long-SOA condition (mean accuracy > 0.9 for all experiments), during both the context session and the baseline session. More crucially, when looking into the magnitude of the AB effect as measured by (ACClong-SOA - ACCshort-SOA)/ACClong-SOA, we still obtained a significant attentional modulation effect (for Experiment 1a, t(15) = -2.729, p = .016, Cohen’s d = 0.682; for Experiment 2a, t(15) = -4.143, p <.001, Cohen’s d = 1.036) similar to that reflected by the short-SOA condition alone, further confirming that cortical entrainment effectively influences the AB effect.

Although we included both the long- and short-SOA conditions in the current study, we focused on T2 performance in the short-SOA condition rather than along the whole AB curve for the following reasons. Firstly, for the long-SOA conditions, the T2 performance is at ceiling level, making it an inappropriate baseline to probe the attentional modulation effect. We focused on Lag 2 because previous research has identified a robust AB effect around the second lag (Raymond et al., 1992), which provides a reasonable and sensitive baseline to probe the potential modulation effect of the contextual auditory and visual rhythms. Note that instead of using multiple lags, we varied the length of the rhythmic cycles (i.e., a cycle of 300 ms, 400 ms, and 500 ms corresponding to a rhythm frequency of 3.3 Hz, 2.5 Hz, and 2 Hz, respectively, all within the delta band), and showed that the attentional modulation effect could be generalized to these different delta-band rhythmic contexts, regardless of the absolute positions of the targets within the rhythmic cycles.

As to the T1 performance, the overall accuracy was very high, ranging from 0.907 to 0.972, in all of our experiments. The corresponding results have been added to the Results section of the revised manuscript (page 5, line 103). Notably, we did not find T1-T2 trade-offs in most of our experiments, except in Experiment 2a where T1 performance showed a moderate decrease in the between-cycle condition relative to that in the within-cycle condition (mean ± SE: 0.888 ± 0.026 vs. 0.933 ± 0.016, respectively; t(15) = -2.217, p = .043). However, by examining the relationship between the modulation effects (i.e., the difference between the two experimental conditions) on T1 and T2, we did not find any significant correlation (p = .403), suggesting that the better performance for T2 was not simply due to the worse performance in detecting T1.

Finally, previous studies have shown that ignoring T1 would lead to ceiling-level T2 performance (Raymond et al., 1992). Therefore, we did not include such manipulation in the current study, as in that case, it would be almost impossible for us to detect any contextual modulation effect.

References:

Raymond, J. E., Shapiro, K. L., & Arnell, K. M. (1992). Temporary suppression of visual processing in an RSVP task: An attentional blink? Journal of Experimental Psychology: Human Perception and Performance, 18(3), 849–860. https://doi.org/10.1037/0096-1523.18.3.849

2) Also, there is only one lag tested. The ensure that we are dealing here with a true AB I would like to see that more than one lag is tested. In the ideal situation a full AB curve should be presented that includes several lags. This should be done for at least for one of the experiments. It would be informative as we can see how cortical entrainment affects the whole AB curve.

Many thanks for the suggestion. Please refer to our response to the point #1 for “Reviewer #3 (Public Review)”. In short, we did include two T2 lag conditions in our study (Experiments 1a, 1b, 2a and 2b), and the results replicated the typical AB effect. We have clarified this point in the revised manuscript (page 5, line 106).

3) Also, there is no data regarding T1 performance. It is important to show that this the better performance for T2 is not due to worse performance in detecting T1. So also please provide this data.

Many thanks for the suggestion. Please refer to our response to the point #1 or “Reviewer #3 (Public Review)”. We have reported the T1 performance in the revised manuscript (page 5, line 103), and the results didn’t show obvious T1-T2 trade-offs.

4) The authors identify the oscillatory characteristics of EEG signals in response to stimulus rhythms, by examined the FFT spectral peaks by subtracting the mean power of two nearest neighboring frequencies from the power at the stimulus frequency. I am not familiar with this procedure and would like to see some justification for using this technique.

According to previous studies (Nozaradan, 2011; Lenc e al., 2018), the procedure to subtract the average amplitude of neighboring frequency bins can remove unrelated background noise, like muscle activity or eye movement. If there were no EEG oscillatory responses characteristic of stimulus rhythms, the amplitude at a given frequency bin should be similar to the average of its neighbors, and thus no significant peaks could be observed in the subtracted spectrum.

References:

Lenc, T., Keller, P. E., Varlet, M., & Nozaradan, S. (2018). Neural tracking of the musical beat is enhanced by low-frequency sounds. Proceedings of the National Academy of Sciences, 115(32), 8221–8226. https://doi.org/10.1073/pnas.1801421115

Nozaradan, S., Peretz, I., Missal, M., & Mouraux, A. (2011). Tagging the Neuronal Entrainment to Beat and Meter. The Journal of Neuroscience, 31(28), 10234–10240. https://doi.org/10.1523/JNEUROSCI.0411-11.2011

Author Response:

Reviewer #1 (Public Review):

The manuscript by Chakraborty focuses on methods to direct dsDNA to specific cell types within an intact multicellular organism, with the ultimate goal of targeting DNA-based nanodevices, often as biosensors within endosomes and lysosomes. Taking advantage of the endogenous SID-2 dsRNA receptor expressed in C. elegans intestinal cells, the authors show that dsDNA conjugated to dsRNA can be taken into the intestinal endosomal system via feeding and apical endocytosis, while dsDNA alone is not an efficient endocytic cargo from the gut lumen. Since most cells do not express a dsRNA receptor, the authors sought to develop a more generalizable approach. Via phage display screening they identified a novel camelid antibody 9E that recognizes a short specific DNA sequence that can be included at the 3' end of synthesized dsDNAs. The authors then showed that this antibody can direct binding, and in some cases endocytosis, of such DNAs when 9E was expressed as a fusion with transmembrane protein SNB-1. This approach was successful in targeting microinjected dsDNA pan-neuronally when expressed via the snb-1 promoter, and to specific neuronal subsets when expressed via other promoters. Endocytosed dsDNA appeared in puncta moving in neuronal processes, suggesting entry into endosomes. Plasma membrane targeting appeared feasible using 9E fusion to ODR-2.

The major strength of the paper is in the identification and testing of the 9E camelid antibody as part of a generalizable dsDNA targeting system. This aspect of the paper will likely be of wide interest and potentially high impact, since it could be applied in any intact animal system subject to transgene expression. A weakness of the paper is the choice of "nanodevice". It was not clear what utility was present in the DNAs used, such as D38, that made them "devices", aside from their fluorescent tag that allowed tracking their localization.

We used a DNA nanodevice, denoted pHlava-9E, that uses pHrodo as a pH-sensitive dye. pHlava-9E is designed to provide a digital output of compartmentalization i.e., its pH profile is such that even if it is internalized into a mildly acidic vesicle, the pH readout is as high as one would observe with a lysosome. This gives an unambiguous readout of surface-immobilized probe to endocytosed probe.

Another potential weakness is that the delivered DNA is limited to the cell surface or the lumen of endomembrane compartments without access to the cytoplasm or nucleus. In general the data appeared to be of high quality and was well controlled, supporting the authors conclusions.

We completely agree that we cannot target DNA nanodevices to sub-cellular locations such as the cytoplasm or the nucleus with this strategy. However, we do not see this as a “weakness”, but rather, as a limitation of the current capabilities of DNA nanotechnology. It must be mentioned that though fluorescent proteins were first described in 1962, it was 30 years before others targeted them to the endoplasmic reticulum (1992) or the nucleus (1993)(Brini et al., 1993; Kendall et al., 1992). Probe technologies undergo stage-wise improvements/expansions. We have therefore added a small section in the conclusions section outlining the future challenges in sub-cellular targeting of DNA-nanodevices.

Reviewer #2 (Public Review):

The authors demonstrate the tissue-specific and cell-specific targeting of double-stranded DNA (dsDNA) using C. elegans as a model host animal. The authors focused on two distinct tissues and delivery routes: feeding dsDNA to target a class of organelles within intestinal cells, and injecting dsDNA to target presynaptic endocytic structures in neurons. To achieve efficient intestinal targeting, the authors leveraged dsRNA uptake via endogenous intestinal SID-2 receptors by fusing dsRNA to a fluorophore-labeled dsDNA probe. In contrast, neuronal endosome/synaptic vesicle (SV) targeting was achieved by designing a nanobody that specifically binds a short dsDNA motif fused to the fluorophore-labeled dsDNA probe. Combining dsDNA probe injection with nanobody neuronal expression (fused to a neuronal vSNARE to achieve synaptic targeting), the authors demonstrated that the injected dsDNA could be taken up by a variety of distinct neuronal subtypes.

Strengths:

While nanodevices built on dsDNA platforms have been shown to be taken up by scavenger receptors in C. elegans (including previous work from several of these authors), this strategy will not work in many tissue types lacking these receptors. The authors successfully circumvented this limitation using distinct strategies for two cell types in the worm, thereby providing a more general approach for future efforts. The approaches are creative, and the nanobody development in particular allows for endocytic delivery in any cell type. The authors exploited quantitative imaging approaches to examine the subcellular targeting of dsDNA probes in living animals and manipulated endogenous receptors to demonstrate the mechanism of dsRNA-based dsDNA uptake in intestinal cells.

Weaknesses:

To validate successful delivery of a functional nanodevice, one would ideally demonstrate the function of a particular nanodevice in at least one of the examples provided in this work. The authors have successfully used a variety of custom-designed dsDNA probes in living worms in numerous past studies, so this would not be a technical hurdle. In the current study, the reader has no means of assessing whether the dsDNA is intact and functional within its intracellular compartment.

We now demonstrate the use of a functional nanodevice to detect pH profiles of a given microenvironment. This functional nanodevice contains two fluorescent reporter dyes, each attached to one of the strands of a DNA duplex. In order to obtain pH readouts, the device integrity is essential for ratiometric sensing.

Coelomocytes are cells known for their scavenging and degradative lysosomal machinery. Previous studies of the stability of variously structured DNA nanodevices in coelomocytes, have shown that DNA devices based on 38 bp DNA duplexes have a half life of >8 hours in actively scavenging cells such as coelomocytes (Chakraborty et al., 2017; Surana et al., 2013) Given that our sensing in the gut as well as in the neuron are performed in <1 hour post feeding or injection, pHlava-9E is >97% intact.

Another minor weakness is the lack of a quantitative assessment of colocalization in intestinal cells or neurons in an otherwise nicely quantitative study. Since characterization of the targeting described here is an essential part of evaluating the method, a stronger demonstration of colocalization would significantly buttress the authors' claims.

We have now quantified colocalization in each cellular system. Please see Figure R1 below (Figure 1 Supplementary figure 1 and Figure 4 Supplementary figure 2 of the revised manuscript).

Figure R1: a) Pearson’s correlation coefficient (PCC) calculated for the colocalization between R50D38 (red) and lysosomal markers LMP-1 or GLO-1 (green) in the indicated transgenic worms. b) & d) Representative images of nanodevice nD647 uptake (red) in transgenics expressing both prab-3::gfp::rab-3 (green) and psnb-1:snb-1::9E c - e) Normalized line intensity profiles across the indicated lines in b and d; f) Percentage colocalization of nD647 (red) with RAB3:GFP (green). Error bar represents the standard deviation between two data sets.

While somewhat incomplete, this study represents a step forward in the development of a general targeting approach amenable to nanodevice delivery in animal models.

Author Response

Summary:

This work is of interest because it increases our understanding of the molecular mechanisms that distinguish subtypes of VIP interneurons in the cerebral cortex and because of the multiple ways in which the authors address the role of Prox1 in regulating synaptic function in these cells.

The authors would like to thank the reviewers for their constructive comments. In response, we would like to clarify a number of issues, as well as outline how we plan to resolve major concerns.

Reviewer #1:

Stachiak and colleagues examine the physiological effects of removing the homeobox TF Prox1 from two subtypes of VIP neurons, defined on the basis of their bipolar vs. multipolar morphology.

The results will be of interest to those in the field, since it is known from prior work that VIP interneurons are not a uniform class and that Prox1 is important for their development.

The authors first show that selective removal of a conditional Prox1 allele using a VIP cre driver line results in a change in paired pulse ratio of presumptive excitatory synaptic responses in multipolar but not bipolar VIP interneurons. The authors then use RNA-seq to identify differentially expressed genes that might contribute and highlight a roughly two-fold reduction in the expression of a transcript encoding a trans-synaptic protein Elfn1 known to contribute to reduced glutamate release in Sst+ interneurons. They then test the potential contribution of Elfn1 to the phenotype by examining whether loss of one allele of Elfn1 globally alters facilitation. They find that facilitation is reduced both by this genetic manipulation and by a pharmacological blockade of presynaptic mGluRs known to interact with Elfn1.

Although the results are interesting, and the authors have worked hard to make their case, the results are not definitive for several reasons:

1) The global reduction of Elfn1 may act cell autonomously, or may have other actions in other cell types. The pharmacological manipulation is less subject to this interpretation, but these results are not as convincing as they could be because the multipolar Prox1 KO cells (Fig. 3 J) still show substantial facilitation comparable, for example to the multipolar control cells in the Elfn1 Het experiment (controls in Fig. 3E). This raises a concern about control for multiple comparisons. Instead of comparing the 6 conditions in Fig 3 with individual t-tests, it may be more appropriate to use ANOVA with posthoc tests controlled for multiple comparisons.

The reviewer’s concerns regarding non-cell-autonomous actions of global Elfn1 KO are well founded. Significant phenotypic alterations have previously been reported, both in the physiology of SST neurons as well in the animals’ behavior (Stachniak, Sylwestrak, Scheiffele, Hall, & Ghosh, 2019; Tomioka et al., 2014). The homozygous Elfn1 KO mouse displays a hyperactive phenotype and epileptic activity after 3 months of age, suggesting generalcortical activity differences exist (Dolan & Mitchell, 2013; Tomioka et al., 2014). Nevertheless, we have not observed such changes in P17-21 Elfn1 heterozygous (Het) animals.

Comparing across different experimental animal lines, for example the multipolar Prox1 KO cells (Fig. 3 J) to the multipolar control cells in the Elfn1 Het experiment (controls in Fig. 3E), is in our view not advisable. There is a plethora of examples in the literature on the effect of mouse strain on even the most basic cellular functions and hence it is always expected that researchers use the correct control animals for their experiments, which in the best case scenario are littermate controls. For these reasons, we would argue that statistical comparisons across mouse lines is not ideal for our study. Elfn1 Het and MSOP data are presented side by side to illustrate that Elfn1 Hets (3C,E) phenocopy the effects of Prox1 deletion (3G,H,I,J). (See also point 3) MSOP effect sizes, however, do show significant differences by ANOVA with Bonferroni post-hoc (normalized change in EPSC amplitude; multipolar prox1 control: +12.1 ± 3.8%, KO: -8.4 ± 4.3%, bipolar prox1 control: -5.2 ± 4.3%, KO: -3.4 ± 4.7%, cell type x genotype interaction, p= 0.02, two way ANOVA).

2) The isolation of glutamatergic currents is not described. Were GABA antagonists present to block GABAergic currents? Especially with the Cs-based internal solutions used, chloride reversal potentials can be somewhat depolarized relative to the -65 mV holding potential. If IPSCs were included it would complicate the analysis.

No, in fact GABA antagonists were not present in these experiments. The holding voltage in our evoked synaptic experiments is -70 mV, which combined with low internal [Cl-] makes it highly unlikely that the excitatory synaptic responses we study are contaminated by GABA-mediated ones, even with a Cs MeSO4-based solution. Nevertheless, we have now performed additional experiments where glutamate receptor blockers were applied in bath and we observe a complete blockade of the synaptic events at -70mV proving that they are AMPA/NMDA receptor mediated. When holding the cell at 0mV with these blockers present, outward currents were clearly visible, suggesting intact GABA-mediated events.

3) The assumption that protein levels of Elfn1 are reduced to half in the het is untested. Synaptic proteins can be controlled at the level of translation and trafficking and WT may not have twice the level of this protein.

We thank reviewer for pointing this out. Our rationale for using the Elfn1 heterozygous animals is rather that transcript levels are reduced by half in heterozygous animals, to match the reduction we found in the mRNA levels of VIP Prox1 KO cells (Fig 2). The principle purpose of the Elfn1 KO experiment was to determine whether the change in Elfn1 transcript levels could be sufficient to explain the synaptic deficit observed in VIP Prox1 KO cells. As the reviewer notes, translational regulation and protein trafficking could ultimately result in even larger changes than 0.5x protein levels at the synapse. This may ultimately explain the observed multipolar/bipolar disparity, which cannot be explained by transcriptional regulation alone (Fig 4).

4) The authors are to be commended for checking whether Elfn1 is regulated by Prox1 only in the multipolar neurons, but unfortunately it is not. The authors speculate that the selective effects reflect a selective distribution of MgluR7, but without additional evidence it is hard to know how likely this explanation is.

Additional experiments are underway to better understand this mechanism.

Reviewer #2:

Stachniak et al., provide an interesting manuscript on the postnatal role of the critical transcription factor, Prox1, which has been shown to be important for many developmental aspects of CGE-derived interneurons. Using a combination of genetic mouse lines, electrophysiology, FACS + RNAseq and molecular imaging, the authors provide evidence that Prox1 is genetically upstream of Elfn1. Moreover, they go on to show that loss of Prox1 in VIP+ cells preferentially impacts those that are multipolar but not the bipolar subgroup characterized by the expression of calretinin. This latter finding is very interesting, as the field is still uncovering how these distinct subgroups emerge but are at a loss of good molecular tools to fully uncover these questions. Overall, this is a great combination of data that uses several different approaches to come to the conclusions presented. I have suggestions that I think would strengthen the manuscript:

1) Can the authors add a supplemental table showing the top 20-30 genes up and down regulated in their Prox1 KOS? This would make these, and additional, data more tenable to readers.

We would be happy to provide supplementary tables with candidate genes at both P8 and P12.

2) It is interesting that loss of Prox1 or Elfn1 leads to phenotypes in multipolar but are not present or mild in bipolar VIP+ cells. The authors test different hypotheses, which they are able to refute and discuss some ideas for how multipolar cells may be more affected by loss of Elfn1, even when the transcript is lost in both multipolar and bipolar after Prox1 deletion. If there is any way to expand upon these ideas experimentally, I believe it would greatly strengthen the manuscript. I understand there is no perfect experiment due to a lack of tools and reagents but if there is a way to develop one of the following ideas or something similar, it would be beneficial:

We thank the reviewer for the note.

a) Would it be possible to co-fill VIPCre labeled cells with biocytin and a retroviral tracer? Then, after the retroviral tracer had time to label a presynaptic cell, assess whether these were preferentially different between bipolar and multipolar cell types, the latter morphology determined by the biocytin fill? This would test whether each VIP+ subtype is differentially targeted.

Although this is a very elegant experiment and we would be excited to do it, we do feel that single-cell rabies virus tracing is technically very challenging and will take many months to troubleshoot before being able to acquire good data. Hence, we think it is beyond the scope of this study.

b) Another biocytin possibility would be to trace filled VIP+ cells and assess whether the dendrites of multipolar and bipolar cells differentially targeted distinct cortical lamina and whether these lamina, in the same section or parallel, were enriched for mGluR7+ afferents.

We thank the reviewer for their suggestion and we are planning on doing these kinds of experiments.

Reviewer #3:

In this work Stachiak and colleagues investigate the role of Prox1 on the development of VIP cells. Prox1 is expressed by the majority of GABAergic derived from the caudal ganglionic eminence (CGE), and as mentioned by the authors, Prox1 has been shown to be necessary for the differentiation, circuit integration, and maintenance of CGE-derived GABAergic cells. Here, Stachiak and colleagues show that removal of Prox1 in VIP cells leads to suppression of synaptic release probability onto cortical multipolar VIP cells in a mechanism dependent on Elfn1. This work is of interest for the field because it increases our understanding of differential synaptic maturation of VIP cells. The results are noteworthy, however the relevance of this manuscript would potentially be increased by addressing the following suggestions:

1) Include histology to show when exactly Prox1 is removed from multipolar and bipolar VIP-expressing cells by using the VIP-Cre mouse driver.

We can address this by performing an in-situ hybridization against Prox1 from P3 onwards (when Cre becomes active).

2) Clarify if the statistical analysis is done using n (number of cells) or N (number of animals). The analysis between control and mutants (both Prox1 and Elfn1) need to be done across animals and not cells.

Statistics for physiology were done across n (number of cells) while statistics for ISH are done across number of slices. We will clarify this point in the text and update the methods.

Regarding the statistics for the ISH, these have been done across n (number of slices) for control versus KO tissue (N = 3 and N = 2 animals, respectively). We will add more animals to this analysis to compare by animal instead, although we do not expect any change in the results.

Regarding the physiology, we would provide a two-pronged answer. We first of all feel that averaging synaptic responses for each animal would hide a good deal of the biological variability in PPR present in different cells (response Fig 1), the characterization of which is integral to the central findings of the paper. Secondly, to perform such analysis asked by the reviewer one would need to obtain recordings from ~10 animals or so per condition for each condition, which, to our knowledge, is something that is not standard when utilizing in vitro electrophysiological recordings from single cells. For example, in these very recent studies that have performed in vitro electrophysiological recordings all the statistics are performed using “n” number of cells and not the average of all the cells recorded per animal collapsed into a single data point. (Udakis, Pedrosa, Chamberlain, Clopath, & Mellor, 2020) https://www.nature.com/articles/s41467-020-18074-8

(Horvath, Piazza, Monteggia, & Kavalali, 2020) https://elifesciences.org/articles/52852

(Haas et al., 2018) https://elifesciences.org/articles/31755

Nevertheless, we have now re-run the analysis grouping the cells and averaging the values we get per animal, since we have obtained our data from many animals. The results are more or less indistinguishable from the ones presented in the original submission, except for on p value that rose to 0.07 from 0.03 due to the lack of the required number of animals. We hope that the new plots and statistics presented herein address the concern put forward by the reviewer.

Response Fig 1: A comparison of cell wise versus animal-wise analysis of synaptic physiology. Some cell to cell variability is hidden, and the reduction in numbers impacts the P values.

(A) PPR of multipolar Prox1 Control for 14 cells from 9 animals (n/N=14/9) under baseline conditions and with MSOP, cell-wise comparison p = 0.02 , t = 2.74 and (B) animal-wise comparisons (p = 0.04, t stat = 2.45). Statistics: paired t-test.

(C) PPR of multipolar Prox1 KO cells (n/N=9/8) under baseline conditions and with MSOP, cell-wise comparison p = 0.2, t = 1.33 and (D) animal-wise comparisons (p = 0.2, t stat = 1.56). Statistics: paired t-test. Comparisons for PPR of bipolar Prox1 Control (n/N=8/8) and KO cells (n/N=9/9) did not change.

(E) PPR for Prox1 control (n/N=18/11) and KO (n/N=13/11) bipolar VIP cells, cell-wise comparison p = 0.3, t = 1.1 and (F) animal-wise comparisons (p = 0.4, t stat = 0.93). Statistics: t-test.

(G) PPR of Elfn1 Control (n/N=12/4) and Het (n/N=12/4) bipolar VIP cells, cell-wise comparison p = 0.3, t = 1.06 and (H) animal-wise comparisons (p = 0.4, t stat = 0.93)

(I) PPR of Prox1 control (n/N=33/18) and KO (n/N=19/14) multipolar VIP cells, cell-wise comparison p = 0.03, t = 2.17. and (J) animal-wise comparisons (p = 0.07, t stat = 1.99).

(K) PPR of Elfn1 Control (n/N=14/6) and Het (n/N=20/8) multipolar VIP cells, cell-wise comparison p = 0.008, t = 2.84 and (L) animal-wise comparisons (p = 0.007, t stat = 3.23).

3) Clarify what are the parameters used to identify bipolar vs multipolar VIP cells. VIP cells comprise a wide variety of transcriptomic subtypes, and in the absence of using specific genetic markers for the different VIP subtypes, the authors should either include the reconstructions of all recorded cells or clarify if other methods were used.

We thank the reviewer for this comment. The cell parameter criteria will be amended in the methods: “Cell type was classified as bipolar vs. multipolar based on cell body morphology (ovoid vs. round) and number and orientation of dendritic processes emanating from it (2 or 3 dendrites perpendicular to pia (for bipolar) vs. 3 or more processes in diverse orientations (for multipolar). In addition, the laminar localization of the two populations differs, with multipolar cells found primarily in the upper layer 2, while bipolar cells are found throughout layers 2 and 3. Initial determination of cell classification was made prior to patching fluorescent-labelled cells, but whenever possible this initial assessment was confirmed with post-hoc verification of biocytin filled cells.”

Reference:

Dolan, J., & Mitchell, K. J. (2013). Mutation of Elfn1 in Mice Causes Seizures and Hyperactivity. PLOS ONE, 8(11), e80491. Retrieved from https://doi.org/10.1371/journal.pone.0080491

Haas, K. T., Compans, B., Letellier, M., Bartol, T. M., Grillo-Bosch, D., Sejnowski, T. J., … Hosy, E. (2018). Pre-post synaptic alignment through neuroligin-1 tunes synaptic transmission efficiency. ELife, 7, e31755. https://doi.org/10.7554/eLife.31755

Horvath, P. M., Piazza, M. K., Monteggia, L. M., & Kavalali, E. T. (2020). Spontaneous and evoked neurotransmission are partially segregated at inhibitory synapses. ELife, 9, e52852. https://doi.org/10.7554/eLife.52852

Stachniak, T. J., Sylwestrak, E. L., Scheiffele, P., Hall, B. J., & Ghosh, A. (2019). Elfn1-Induced Constitutive Activation of mGluR7 Determines Frequency-Dependent Recruitment of Somatostatin Interneurons. The Journal of Neuroscience, 39(23), 4461 LP – 4474. https://doi.org/10.1523/JNEUROSCI.2276-18.2019

Tomioka, N. H., Yasuda, H., Miyamoto, H., Hatayama, M., Morimura, N., Matsumoto, Y., … Aruga, J. (2014). Elfn1 recruits presynaptic mGluR7 in trans and its loss results in seizures. Nature Communications. https://doi.org/10.1038/ncomms5501

Udakis, M., Pedrosa, V., Chamberlain, S. E. L., Clopath, C., & Mellor, J. R. (2020). Interneuron-specific plasticity at parvalbumin and somatostatin inhibitory synapses onto CA1 pyramidal neurons shapes hippocampal output. Nature Communications, 11(1), 4395. https://doi.org/10.1038/s41467-020-18074-8

Author Response:

Evaluation Summary:

This manuscript will be of interest to a broad audience of immunologists especially those studying host-pathogen interactions, mucosal immunology, innate immunity and interferons. The study reveals a novel role for neutrophils in the regulation of pathological inflammation during viral infection of the genital mucosa. The main conclusions are well supported by a combination of precise technical approaches including neutrophil-specific gene targeting and antibody-mediated inhibition of selected pathways.

We would like to thank the reviewers for taking the time to review our manuscript, would also like to thank the editors for handling our manuscript. We are grateful for the positive response to our work and the thoughtful suggestions.

Reviewer #1 (Public Review):

Overall this is a well-done study, but some additional controls and experiments are required, as discussed below. The authors have done a considerable amount of work, resulting in quite a lot of negative data, and so should be commended for persistence to eventually identify the link between neutrophils with IL-18, though type I IFN signaling.

Thank you! We appreciate the feedback and suggestions for strengthening the study.

Major Comments:

-A major conclusion of this manuscript is prolonged type I IFN production following vaginal HSV-2 infection, but the data presented herein did not actually demonstrate this. At 2 days post infection, IFN beta was higher (although not significantly) in HSV-2 infection, but much higher in HSV-1 infection compared to uninfected controls. At 5 days post infection the authors show mRNA data, but not protein data. If the authors are relying on prolonged type I IFN production, then they should demonstrate increased IFN beta during HSV-2 infection at multiple days after infection including 5dpi and 7dpi.

We apologize for not including the IFN protein data and have now have provided this information in new Figure 3 and Figure 3 - Supplement 3. This new addition shows measurement of secreted IFNb in vaginal lavages at 4, 5 and 7 d.p.i., as well as total IFNb levels in vaginal tissue at 7 d.p.i..

-Does the CNS viral load or kinetics of viral entry into the CNS differ in mice depleted of neutrophils, IFNAR cKO mice, or mice treated with anti- IL-18? Do neutrophils and/or IL-18 participate at all in neuronal protection from infection?

To maintain the focus of our study on the host factors that contribute specifically to genital disease, we have not included discussion on viral dissemination into the PNS or CNS, especially as viral invasion of

the CNS seems to be an infrequent occurrence during genital herpes in humans. However, we have performed some preliminary exploration of this interesting question, and find that viral invasion of the nervous system is unaltered in the absence of neutrophils. This is in accordance with the lack of antiviral neutrophil activity we have described in the vagina after HSV-2 infection. These preliminary data are provided below as a Reviewer Figure 1. We have not yet begun to investigate whether IL-18 modulates neuroprotection, but agree this is an important question to address in future studies.

RFigure 1. Viral burden in the nervous system is similar in the presence or absence of neutrophils. Graphs show viral genomes measured by qPCR from the DRG, lower half of of the spinal cord and the brainstem at the indicated days post- infection.

-In Figure 3 the authors show that neutrophil "infection" clusters 2 and 5 express high levels of ISGs. Only 4 of these ISGs are shown in the accompanying figures. Please list which ISGs were increased in neutrophils after both HSV-2 and HSV-1 infection, perhaps in a table. Were there any ISGs specifically higher after HSV-2 infection alone, any after HSV-1 infection alone?

These tables listing differentially-expressed neutrophils ISGs during HSV-1 and HSV-2 have now been provided in new Figure 3 - Supplement 1, with complete lists of DEGs provided as Source Files for the same figure.

-The authors claim that HSV-1 infection recruits non-pathogenic neutrophils compared to the pathogenic neutrophils recruited during HSV-2 infection. Can the authors please discuss if these differences in inflammation or transcriptional differences between the neutrophils in these two different infections could be due to differences in host response to these two viruses rather than differences in inflammation? Please elaborate on why HSV-1 used as opposed to a less inflammatory strain of HSV-2. Furthermore, does HSV-1 infection induce vaginal IL-18 production in a neutrophil-dependent fashion as well?

These are excellent questions, and we have emphasized that differences in host responses against HSV-1 and HSV-2 likely lead to distinct inflammatory milieus that differentially affect neutrophil responses in lines 374-375 and 409-419. We completely agree that differences in neutrophil responses are likely due to distinct host responses against HSV-1 and HSV-2 and apologize for not making that clear. We have previously described some of the other differences in the immunological response against these two viruses (Lee et al, JCI Insight 2020). We would suggest that differences in the host response against these two viruses would naturally result in differences in the local inflammatory milieu, which then modulates neutrophil responses. Whether the transcriptomes of neutrophils beyond the immediate site of infection (outside the vagina) are different between HSV-1 and HSV-2 is currently an open question.

As for why we used HSV-1 instead of a less inflammatory strain of HSV-2, we had originally been interested in trying to model the distinct disease outcomes that have previously been described during HSV-1 vs HSV-2 genital herpes in humans and thought this would be a relevant comparison. We have not yet examined infection with less inflammatory HSV-2 strains, but agree that this is a great idea. We have also not yet examined neutrophil-dependent IL-18 production in the context of HSV-1.

Reviewer #2 (Public Review):

This manuscript will be of interest to a broad audience of immunologists especially those studying host-pathogen interactions, mucosal immunology, innate immunity and interferons. The study reveals a novel role for neutrophils in the regulation of pathological inflammation during viral infection of the genital mucosa. The main conclusions are well supported by a combination of precise technical approaches including neutrophil-specific gene targeting and antibody-mediated inhibition of selected pathways.

In this study by Lebratti, et al the authors examined the impact of neutrophil depletion on disease progression, inflammation and viral control during a genital infection with HSV-2. They find that removal of neutrophils prior to HSV-2 infection resulted in ameliorated disease as assessed by inflammatory score measurements. Importantly, they show that neutrophil depletion had no significant impact on viral burden nor did it affect the recruitment of other immune cells thus suggesting that the observed improvement on inflammation was a direct effect of neutrophils. The role of neutrophils in promoting inflammation appears to be specific to HSV-2 since the authors show that HSV-1 infection resulted in comparable numbers of neutrophils being recruited to the vagina yet HSV-1 infection was less inflammatory. This observation thus suggests that there might be functional differences in neutrophils in the context of HSV-2 versus HSV-1 infection that could underlie the distinct inflammatory outcomes observed in each infection. In ordered to uncover potential mechanisms by which neutrophils affect inflammation the authors examined the contributions of classical neutrophil effector functions such as NETosis (by studying neutrophil-specific PAD4 deficient mice), reactive oxygen species (using mice global defect in NADH oxidase function) and cytokine/phagocytosis (by studying neutrophil-specific STIM-1/STIM-2 deficient mice). The data shown convincingly ruled out a contribution by the neutrophil factors examined. The authors thus performed an unbiased single cell transcriptomic analysis of vaginal tissue during HSV-1 and HSV-2 infection in search for potentially novel factors that differentially regulate inflammation in these two infections. tSNE analysis of the data revealed the presence of three distinct clusters of neutrophils in vaginal tissue in mock infected mice, the same three clusters remained after HSV-1 infection but in response to HSV-2 only two of the clusters remained and showed a sustained interferon signature primarily driven by type I interferons (IFNs). In order to directly interrogate the impact of type I IFN on the regulation of inflammation the authors blocked type I IFN signaling (using anti IFNAR antibodies) at early or late times after infection and showed that late (day 4) IFN signaling was promoting inflammation while early (before infection) IFN was required for antiviral defense as expected. Importantly, the authors examined the impact of neutrophil-intrinsic IFN signaling on HSV-2 infection using neutrophil-specific IFNAR1 knockout mice (IFNAR1 CKO). The genetic ablation of IFNAR1 on neutrophils resulted in reduced inflammation in response to HSV-2 infection but no impact on viral titers; findings that are consistent with observations shown for neutrophil-depleted mice. The use of IFNAR1 CKO mice strongly support the importance of type I IFN signaling on neutrophils as direct regulators of neutrophil inflammatory activity in this model. Since type I IFNs induce the expression of multiple genes that could affect neutrophils and inflammation in various ways the authors set out to identify specific downstream effectors responsible for the observed inflammatory phenotype. This search lead them to IL-18 as possible mediator. They showed that IL-18 levels in the vagina during HSV-2 infection were reduced in neutrophil-depleted mice, in mice with "late" IFNAR blockade and in IFNAR1 CKO mice. Furthermore, they showed that antibody-mediated neutralization of IL-18 ameliorated the inflammatory response of HSV-2 infected mice albeit to a lesser extent that what was seen in IFNAR1 CKO. Altogether, the study presents intriguing data to support a new role for neutrophils as regulators of inflammation during viral infection via an IFN-IL-18 axis.

In aggregate, the data shown support the author's main conclusions, but some of the technical approaches need clarification and in some cases further validation that they are working as intended.

Thank you! We appreciate the enthusiasm for our work as well as the suggestions for improving our study.

1) The use of anti-Ly6G antibodies (clone 1A8) to target neutrophil depletion in mice has been shown to be more specific than anti-Gr1 antibodies (which targets both monocytes and neutrophils) thus anti-Ly6G antibodies are a good technical choice for the study. Neutrophils are notoriously difficult to deplete efficiently in vivo due at least in part to their rapid regeneration in the bone marrow. In order to sustain depletion, previous reports indicate the need for daily injection of antibodies. In the current study the authors report the use of only one, intra-peritoneal injection (500 mg) of 1A8 antibodies and that this single treatment resulted in diminished neutrophil numbers in the vagina at day 5 after viral infection (Fig 1A). Data shown in figure 2B suggests that there are neutrophils present in the vagina of uninfected mice, that there is a significant increase in their numbers at day 2 and that their numbers remain fairly steady from days 2 to 5 after infection. In order to better understand the impact antibody-mediated depletion in this model the authors should have examined the kinetics of depletion from day 0 through 5 in the vaginal tissue after 1A8 injection as compared to the effect of antibodies in the periphery. These additional data sets would allow for a deeper understanding of neutrophil responses in the vagina as compared to what has been published in other models of infection at other mucosal sites.

We agree and apologize for not providing this information in the original submission. Neutrophil depletion kinetics from the vagina have been shown in new Figure 1A, while depletion from the blood is shown in new Figure 1 - Supplement 1.

2) The authors used antibody-mediated blockade as a means to interrogate the impact of type I IFNs and IL-18 in their model. The kinetics of IFNAR blockade were nicely explained and supported by data shown in supplementary figure 4. IFNAR blockade was done by intra-peritoneal delivery of antibodies at one day before infection or at day 4 after infection. When testing the role of IL-18 the authors delivered the blocking antibody intra-vaginally at 3 days post infection. The authors do not provide a rationale for changing delivery method and timing of antibody administration to target IL-18 relative to IFNAR signaling. Since the model presented argues for an upstream role for IFNAR as inducer of IL-18 it is unclear why the time point used to target IL-18 is before the time used for IFNAR.

We thank Reviewer #2 for raising this point and apologize for not providing an explanation for the differences in antibody treatment regimens for modulating IFNAR and IL-18. As the anti-IL-18 mAb is a cytokine neutralizing antibody, we hypothesized that administering the antibody vaginally would help to concentrate the antibody at the relevant site of cytokine production and increase the potency of neutralization. This is in contrast to systemic administration of the anti-IFNAR1 mAb that acts to block signaling in the 'receiving' cell. We expect the anti-IFNAR1 mAb (given in much higher doses) to bind both circulating cells that are recruited to the site of infection as well as cells that are already at the site of infection. Similarly, we started the anti-IL-18 antibody treatment one day earlier to allow a presumably sufficient amount antibody to accumulate in the vagina. Our rationale has been included in the revised manuscript (lines 351-353). We are pleased to report, however, that we have conducted preliminary studies in which mice were treated beginning at 4 d.p.i. rather than 3 d.p.i., and observe similar trends. This data is provided below as Reviewer Figure 3.

RFigure 3. Mice treated with anti-IL-18 mAb starting at 4 d.p.i. exhibit reduced disease severity. Mice were infected with HSV-2 and treated ivag with 100ug of anti-IL-18 on 4, 5 and 6 d.p.i.. Mice were monitored for disease until 7 d.p.i.. Data was analyzed by repeated measured two-way ANOVA with Geisser-Greenhouse correction and Bonferroni's multiple comparisons test.

3) An open question that remains is the potential mechanism by which IL-18 is acting as effector cytokine of epithelial damage. As acknowledged by the authors the rescue seen in IFNAR1 CKO mice (Fig 5C) is more dramatic that targeting IL-18 (Fig 6D). It is thus very likely that IFNAR signaling on neutrophils is affecting other pathways. It would have been greatly insightful to perform a single cell RNA seq experiment with IFNAR CKO mice as done for WT mice in Fig 3. Such an analysis might would have provided a more thorough understanding of neutrophil-mediated inflammatory pathways that operate outside of classical neutrophil functions.

We agree that the proposed scRNA-seq experiment comparing vaginal cells from IFNAR CKO and WT mice would be very interesting and insightful. Although a bit beyond the scope of the current manuscript, we are currently planning on performing these types of studies to better understand IFN-mediated regulation of inflammatory neutrophil functions.

4) The inflammatory score scale used is nicely described in the methods and it took into consideration external signs of vaginal inflammation by visual observation. It would have been helpful to mention whether the inflammation scoring was done by individuals blinded to the experimental groups.

This is an important point and we apologize for not making this clear. We have now provided this information in the methods section of the revised manuscript (lines 778).

5) The presence of distinct clusters of neutrophils in the scRNA-seq data analysis is a fascinating observation that might suggest more diversity in neutrophils than what is currently appreciated. In this study, the authors do not provide a list of the genes expressed in each cluster within the data shown in the paper. Although the entire data set is deposited and publicly available, having the gene lists within the paper would have been helpful to provide a deeper understanding of the current study.

The heterogeneity of the vaginal neutrophil population after HSV infection is indeed an unexpected finding. To provide a deeper understanding of these transcriptionally distinct clusters, we have now included complete lists of DEGs between the different clusters as Source Files for Figure 3.

Reviewer #3 (Public Review):

This paper examines the role of neutrophils, inflammatory immune cells, in disease caused by genital herpes virus infection. The experiments describe a role for type I interferon stimulation of neutrophils later in the infection that drives inflammation. Blockade of interferon, and to a lesser degree, IL-18 ameliorated disease. This study should be of interest to immunologists and virologists.

This study sought to examine the role of neutrophils in pathology during mucosal HSV-2 infection in a mouse model. The data presented in this manuscript suggest that late or sustained IFN-I signals act on neutrophils to drive inflammation and pathology in genital herpes infection. The authors show that while depletion of neutrophils from mice does not impact viral clearance or recruitment of other immune cells to the infected tissue, it did reduce inflammation in the mucosa and genital skin. Single cell sequencing of immune cells from the infected mucosa revealed increased expression of interferon stimulated genes (ISGs) in neutrophils and myeloid cells in HSV-2 infected mice. Treatment of anti-IFNAR antibodies or neutrophil-specific IFNAR1 conditional knockout mice decreased disease and IL-18 levels. Blocking IL-18 also reduced disease, although these data show that other signals are likely to also be involved. It is interesting that viral titers and anti-viral immune responses were unaffected by IFNAR or IL-18 blockade when this treatment was started 3-4 days after infection, because data shown here (for IFN-I) and by others in published studies (for IFN-I or IL-18) have shown that loss of IFN-I or IL-18 prior to infection is detrimental.

These data are interesting and show pathways (namely IFN-I and IL-18) that could be blocked to limit disease. While this suggests that IL-18 blockade might be an effective treatment for genital inflammation caused by HSV-2 infection, the utility of IL-18 blockade is still unclear, because the magnitude of the effect in this mouse model was less than IFNAR blockade. Additionally, further experiments, such as conditional loss of IL-18 in neutrophils, would be required to better define the role and source(s) of IL-18 that drive disease in this model.

We thank the reviewer for the positive response and agree that additional studies would likely be necessary to fully understand the role of IL-18 during HSV-2 infection.

Author Response

Reviewer #1:

The Lambowitz group has developed thermostable group II intron reverse transcriptases (TGIRTs) that strand switch and also have trans-lesion activity to provide a much wider view of RNA species analyzed by massively parallel RNA sequencing. In this manuscript they use several improvements to their methodology to identify RNA biotypes in human plasma pooled from several healthy individuals. Additionally, they implicate binding by proteins (RBPs) and nuclease-resistant structures to explain a fraction of the RNAs observed in plasma. Generally I find the study fascinating and argue that the collection of plasma RNAs described is an important tool for those interested in extracellular RNAs. I think the possibility that RNPs are protecting RNA fragments in circulation is exciting and fits with elegant studies of insects and plants where RNAs are protected by this mechanism and are transmitted between species.

I have one major comment for the authors to consider. In my view the use of pooled plasma samples prevented the important opportunity to provide a glimpse on human variation in plasma RNA biotypes. This significantly limits the use of this information to begin addressing RNA biotypes as biomarkers. While I realize that data from multiple individuals represents a significant undertaking and may be beyond the scope of this manuscript, I urge the authors to do two things: (1) downplay the significance of the current study on the development of biomarkers in the current manuscript (e.g., in the abstract and discussion - e.g., "The ability of TGIRT-seq to simultaneously profile a wide variety of RNA biotypes in human plasma, including structured RNAs that are intractable to retroviral RTs, may be advantageous for identifying optimal combinations of coding and non-coding RNA biomarkers for human diseases."). (2) Carry out an analysis in multiple individuals - including racially diverse individuals - very important information will come of this - similar to C. Burge's important study in Nature ~2008 where it was clear that there is important individual variation in alternative splicing decisions - very likely genetically determined. This second suggestion could be added here or constitute a future manuscript.

The identification of biomarkers in human plasma is an important application of this study, as was noted by reviewer 3 -- "Overall, this study provided a robust dataset and expanded picture of RNA biotypes one can detect in human plasma. This is valuable because the findings may have implications in biomarker identification in disease contexts." The present manuscript lays the foundation for such applications, which we have been carrying out in parallel. In one such study in collaboration with Dr. Naoto Ueno (MD Anderson), we used TGIRT-seq to identify combinations of mRNA and non-coding RNA biomarkers in FFPE-tumor slices, PBMCs and plasma from inflammatory breast cancer patients compared to non-IBC breast cancer patients and healthy controls (manuscript in preparation; data presented publicly in seminars), and in another, we explored the potential of using full-length excised intron (FLEXI) RNAs as biomarkers. In the latter study, we identified >8,000 FLEXI RNAs in different human cell lines and tissues and found that they are expressed in a cell-type specific manner, including hundreds of differences between matched tumor and healthy tissues from breast cancer patients and cell lines. A manuscript describing the latter findings was submitted for publication after this one and has been uploaded as a pertinent related manuscript. This new manuscript follows directly from the last sentence of the present manuscript and fully references the BioRxiv preprint currently under review for eLife.

Reviewer #2:

Yao et al used thermostable group II intron reverse transcriptase sequencing (TGIRT-seq) to study apheresis plasma samples. The first interesting discovery is that they had identified a number of mRNA reads with putative binding sites of RNA-binding proteins. A second interesting discovery from this work is the detection of full-length excised intron RNAs.

I have the following comments:

1) One doubt that I have is how representative is apheresis plasma when compared with plasma that one obtains through routine centrifugation of blood. The authors have reported the comparison of apheresis plasma versus a single male plasma in a previous publication. I think that to address this important question, a much increased number of samples would be necessary.

Detailed comparison of plasma prepared by apheresis to that prepared by centrifugation would require a separate large-scale study, preferably by multiple laboratories using different methods to prepare plasma. However, our impression both from our findings and from the literature (Valbonesi et al. 2001, cited in the manuscript) is that apheresis-prepared plasma has very low levels of cellular contamination (required to meet clinical standards) compared to plasma prepared by centrifugation, even with protocols designed to minimize contamination from intact 4 or broken cell (e.g., preparing plasma from freshly drawn blood, centrifugation into a Ficoll cushion to minimize cell breakage, and carefully avoiding contamination from sedimented cells).

We do have additional information about the degree of variation in protein-coding gene transcripts detected by TGIRT-seq in plasma samples prepared by centrifugation from five healthy females controls in our collaborative study with Dr. Naoto Ueno (M.D. Anderson; see above), and we have added it to the manuscript citing a manuscript in preparation with permission from Dr. Ueno (p. 10, beginning line 6 from bottom) as follows:

“The identities and relative abundances of different protein-coding gene transcripts in the apheresis-prepared plasma were broadly similar to those in the previous TGIRT analysis of plasma prepared by Ficoll-cushion sedimentation of blood from a healthy male individual (Qin et al., 2016) (r = 0.62-0.80; Figure 3C) and between high quality plasma samples similarly prepared from five healthy females in a collaborative study with Dr. Naoto Ueno, M.D. Anderson (r = 0.53-0.67; manuscript in preparation).” See Author Response Image below.

2) For the important conclusion of the presence of binding sites of RNA-binding proteins in a proportion of apheresis plasma mRNA molecules, the authors need to explore whether there is any systemic difference in terms of mapping quality (i.e. mapping quality scores in alignment results) between RBP binding sites and non-RBP binding sites, so that any artifacts of peaks caused by the alignment issues occurring in RNA-seq analysis could be revealed and solved subsequently. Furthermore, it would be prudent to perform immunoprecipitation experiments to confirm this conclusion in at least a proportion of the mRNA.

We have added a figure panel comparing MAPQ scores for reads from peaks containing RBP-binding site to other long RNA reads (Figure 4–figure supplement 2A) and have added further details about the methods used to obtain peaks with high quality reads, including the following (p. 13, beginning line 3 from the bottom).

“After further filtering to remove read alignments with MAPQ <30 (a cutoff that eliminates reads mapping equally well at more than one locus) or ≥5 mismatches from the mapped locus, we were left with 950 high confidence peaks ranging in size from 59 to 1,207 nt with ≥5 high quality read alignments at the peak maximum (Supplementary File).”

3) In Fig. 2D, one can observe that there are clearly more RNA reads in TGIRT-seq located in the 1st exon of ACTB, compared with SMART-seq. Is there any explanation? Will this signal be called as a peak (a potential RBP binding site) in the peak calling analysis (MACS2)? Is ACTB supposed to be bound by a certain RBP?

The higher coverage of the ACTB 5'-exon in the TGIRT-seq datasets reflects in part the more uniform 5' to 3' coverage of mRNA sequences by TGIRT-seq compared to SMART-seq, which is biased for 3'-mRNA sequences that have poly(A) tails (current Figure 3F). The signal in the first exon of ACTB was in fact called as a peak by MACS2 (peak ID#893, Supplementary file), which overlapped an annotated binding site for SERBP1 (see Supplementary File).

4) For Fig 2A, it would be informative for the comparison of RNA yield and RNA size profile among different protocols if the author also added the results of TGIRT-seq.

Figure 3D (previously Figure 2A) shows a bioanalyzer trace of PCR amplified cDNAs obtained by SMART-Seq. These cDNAs correspond to 3' mRNA sequences that have poly(A) tails and are not comparable to the bioanalyzer profiles of plasma RNA (Figure 1–figure supplement 1) or read span distributions in the TGIRT-seq datasets (Figure 1B), which are dominated by sncRNAs. The coverage plots for protein-coding gene transcripts show that TGIRT-seq captures mRNA fragments irrespective of length that span the entire mRNA sequence, whereas SMART-seq is biased for 3' sequences linked to poly(A) (Figure 3F). We also note that coverage plots and mRNAs detected by TGIRT-seq remain similar, even if the plasma RNA is chemically fragmented prior to TGIRT-seq library construction (Figure 3F and Figure 3–figure supplement 2).

5) As shown in Figure 4 C (the track of RBP binding sites), it seems quite pervasive in some gene regions. How many RBP binding sites from public eCLIP-seq results are used for overlapping peaks present in TGIRT-seq of plasma RNA? What percentage of plasma RNA reads have fallen within RBP binding sites? Are those peaks present in TGRIT-seq significantly enriched in RBPs binding regions?

Some of these points are addressed under Reviewer 1-comment #4. Additionally, we noted that 109 RBP-binding sites were searched in the original analysis, and we have now added further analyses for 150 RBPs currently available in ENCODE eCLIP datasets with and without irreproducible discovery rate (IDR) analysis (Figure 6 and Figure 6–figure supplement 1). We have also added a tab to the Supplementary File identifying the 109 and 150 RBPs whose binding sites were searched. The requested statistical analysis has been added in Figure 4–figure supplement 2C. The analysis shows that enrichment of RBP-binding site sequences in the 467 called peaks was statistically significant (p<0.001) (p. 14, para. 3, last sentence).

6) Since there is a considerable portion of TGIRT-seq reads related to simple repeat, one possible reason is likely the high abundance of endogenous repeat-related RNA species in plasma. Nonetheless, have authors studied whether the ligation steps in TGIRT-seq have any biases (e.g. GC content) when analyzing human reference RNAs and spike ins (page 4, paragraph 2)?

We have added a note to the manuscript indicating that although repeat RNAs constitute a high proportion of the called peaks, they do not constitute a similarly high proportion of the total RNA reads (Figure 1C; p. 18, para. 2, first sentence). The TGIRT-seq analysis of human reference RNAs and spike-ins showed that TGIRT-seq recapitulates the relative abundance of human transcripts and spike-in comparably to non-strand-specific TruSeq v2 and better than strand-specific TruSeq v3 (Nottingham et al. RNA 2016). Subsequently, we used miRNA reference sets for detailed analysis of TGIRT-seq biases, including developing a computer algorithm for bias correction based on a random forest regression model that provides insight into different factors that contribute to these biases (Xu et al. Sci. Report. 2019). Overall GC content does not make a significant contribution to TGIRT-seq biases (Figure 9 of Xu et al. Sci. Report, 2017). Instead, biases in TGIRT-seq are largely confined to the first three nucleotides at the 5'-end (due to bias of the thermostable 5' App DNA ligase used for 5' RNA-seq adapter addition) and the 3' nucleotide (due to TGIRT-template switching). These end biases are not expected to significantly impact the quantitation of repeat RNAs.

7) As described in Figure 2 legend, there are 0.25 million deduplicated reads for TGIRT-seq reads assigned to protein-coding genes transcripts which are far less than 2.18 million reads for SMART-seq. The authors need to discuss whether the current protocol of TGIRT-seq would cause potential dropouts in mRNA analysis, compared with SMART-seq?

We have added the following to the manuscript (p. 11, para. 1, line 15).

“The larger number of mRNA reads compared to TGIRT-seq (0.28 million) largely reflects that SMART-seq selectively profiles polyadenylated mRNAs, while TGIRT-seq profiles mRNAs together with other more abundant RNA biotypes. In addition, ultra low input SMART-Seq is not strand-specific, resulting in redundant sense and antisense strand reads (Figure 3–figure supplement 1).”

The manuscript contains the following statement regarding potential drop outs (p. 11, para. 2, line 1).

“A scatter plot comparing the relative abundance of transcripts originating from different genes showed that most of the polyadenylated mRNAs detected in DNase I-treated plasma RNA by ultra low input SMART-Seq were also detected by TGIRT-seq at similar TPM values when normalized for protein-coding gene reads (r=0.61), but with some, mostly lower abundance mRNAs undetected either by TGIRT-seq or SMART-Seq, and with SMART-seq unable to detect non-polyadenylated histone mRNAs, which are relatively abundant in plasma (Figure 3E and Figure 3–figure supplement 1).”

8) While scientific thought-provoking, the practical implication of the current work is still unclear. The authors have suggested that their work might have applications for biomarker development. Is it possible to provide one experimental example in the manuscript?

We addressed the relevance of the manuscript to biomarker identification and noted parallel studies that supports this application in the response to reviewer 1--comment 1. We have also modified the final paragraph of the Discussion (p. 30, para. 2).

“The ability of TGIRT-seq to simultaneously profile a wide variety of RNA biotypes in human plasma, including structured RNAs that are intractable to retroviral RTs, may be advantageous for identifying optimal combinations of coding and non-coding RNA biomarkers that could then be incorporated in target RNA panels for diagnosis and routine monitoring of disease progression and response to treatment. The finding that some mRNAs fragments persist in discrete called peaks suggests a strategy for identifying relatively stable mRNA regions that may be more reliably detected than other more labile regions in targeted liquid biopsies. Finally, we note that in addition to their biological and evolutionary interest, short full-length excised intron RNAs and intron RNA fragments, such as those identified here, may be uniquely well suited to serve as stable RNA biomarkers, whose expression is linked to that of numerous protein-coding genes."

Reviewer #3:

In this work, Yao and colleagues described transcriptome profiling of human plasma from healthy individuals by TGIRT-seq. TGIRT is a thermostable group II intron reverse transcriptase that offers improved fidelity, processivity and strand-displacement activity, as compared to standard retroviral RT, so that it can read through highly structured regions. Similar analysis was performed previously (ref. 20), but this study incorporated several improvements in library preparation including optimization of template switching condition and modified adapters to reduce primer dimer and introduce UMI. In their analysis, the authors detected a variety of structural RNA biotypes, as well as reads from protein-coding mRNAs, although the latter is in low abundance. Compared to SMART-Seq, TGIRT-seq also achieved more uniform read coverage across gene bodies. One novel aspect of this study is the peak analysis of TGIRT-seq reads, which revealed ~900 peaks over background. The authors found that these peaks frequently overlap with RBP binding sites, while others tend to have stable predicted secondary structures, which explains why these regions are protected from degradation in plasma. Overall, this study provided a robust dataset and expanded picture of RNA biotypes one can detect in human plasma. This is valuable because the findings may have implications in biomarker identification in disease contexts. On the other hand, the manuscript, in the current form, is relatively descriptive, and can be improved with a clearer message of specific knowledge that can be extracted from the data.

Specific points:

1) Several aspects of bioinformatics analysis can be clarified in more detail. For example, it is unclear how sequencing errors in UMI affect their de-duplication procedure. This is important for their peak analysis, so it should be explained clearly.

We have added details of the procedure used for de-duplication to the following paragraph in Materials and methods (p. 35, para. 2).

“Deduplication of mapped reads was done by UMI, CIGAR string, and genome coordinates (Quinlan, 2014). To accommodate base-calling and PCR errors and non-templated nucleotides that may have been added to the 3' ends of cDNAs during TGIRT-seq library preparation, one mismatch in the UMI was allowed during deduplication, and fragments with the same CIGAR string, genomic coordinates (chromosome start and end positions), and UMI or UMIs that differed by one nucleotide were collapsed into a single fragment. The counts for each read were readjusted to overcome potential UMI saturation for highly-expressed genes by implementing the algorithm described in (Fu et al., 2011), using sequencing tools (https://github.com/wckdouglas/sequencing_tools ).”

Also, it is not described how exon junction reads (when mapped to the genome) are handled in peak calling, although the authors did perform complementary analysis by mapping reads to the reference transcriptome.

We have added this to first sentence of the paragraph describing peak calling against the transcriptome reference (p. 16, line 4), which now reads as follows:

"Peak calling against the human genome reference sequence might miss RBP-binding sites that are close to or overlap exon junctions, as such reads were treated by MACS2 as long reads that span the intervening intron."

2) Overall, the authors provided convincing data that TGIRT-seq has advantages in detecting a wide range of RNA biotypes, especially structured RNAs, compared to other protocols, but these data are more confirmatory, rather than completely new findings (e.g., compared to ref. 20).

As indicated in the response to Reviewer 1, comment 2, we modified the first paragraph of the Discussion to explicitly describe what is added by the present manuscript compared to Qin et al. RNA 2016 (p. 24, para. 2). Additionally, further analysis in response to the reviewers' comments resulted in the interesting finding that stress granule proteins comprised a high proportion of the RBPs whose binding sites were enriched in plasma RNAs (to our knowledge a completely new finding), consistent with a previously suggested link between RNP granules, EV packing, and RNA export (p. 16, last sentence; data shown in Figure 6 and Figure 6–figure supplement 1). Also highlighted in the Discussion p. 26, last sentence, continuing on p. 27).

3) The peak analysis is more novel. The authors observed that 50% of peaks in long RNAs overlap with eCLIP peaks. However, there is no statistical analysis to show whether this overlap is significant or simply due to the pervasive distribution of eCLIP peaks. In fact, it was reported by the original authors that eCLIP peaks cover 20% of the transcriptome.

We have added statistical analysis, which shows that the enrichment of RBP-binding sites in the 467 called peaks is statistically significant at p<0.001 (p. 14, para. 3, last sentence; Figure 4–Figure supplement 2C), as well as scatter plots identifying proteins whose binding sites were more highly represented in plasma than cellular RNAs or vice versa (p. 16, last two sentences; Figure 6 and Figure 6-figure supplement 1).

Similarly, the authors found that a high proportion of remaining peaks can fold into stable secondary structures, but this claim is not backed up by statistics either.

First, near the beginning of the paragraph describing these findings, we added the following to provide a guide as to what can and can't be concluded by RNAfold (p. 17, line 6 from the bottom).

"To evaluate whether these peaks contained RNAs that could potentially fold into stable secondary structures, we used RNAfold, a tool that is widely used for this purpose with the understanding that the predicted structures remain to be validated and could differ under physiological conditions or due to interactions with proteins."

Second, at the end of the same paragraph, we have added the requested statistics (p. 18, para. 1, last sentence).

"Subject to the caveats above regarding conclusions drawn from RNAfold, simulations using peaks randomly generated from long RNA gene sequences indicated that enrichment of RNAs with more stable secondary structures (lower MFEs) in the called RNA peaks was statistically significant (p≤0.019; Figure 4–figure supplement 2D)."

4) Ranking of RBPs depends on the total number of RBP binding sites detected by eCLIP, which is determined by CLIP library complexity and sequencing depth. This issue should be at least discussed.

We have added scatter plots in Figure 6 and Figure 6–figure supplement 1, which show that the relative abundance of different RBP-binding sites detected in plasma differs markedly from that for cellular RNAs in the eCLIP datasets (both for the 109 RBPs searched initially and for 150 RBPs with or without irreproducible discovery rate (IDR) analysis from the ENCODE web site,) As mentioned in comments above, this analysis identified a number of RBP-binding sites that were substantially enriched in plasma RNAs compared to cellular RNAs or vice versa and led to what we think is the important new finding that plasma RNAs are enriched binding sites for a number of stress granule proteins (Figure 6 and Figure 6–figures supplement 1). We thank the reviewers for this and related comments that led to this additional analysis.

5) Enrichment of RBP binding sites and structured RNA in TGIRT-seq data is certainly consistent with one's expectation. However, the paper can be greatly improved if the authors can make a clearer case of what is new that can be learned, as compared to eCLIP data or other related techniques that purify and sequence RNA fragments crosslinked to proteins. What is the additional, independent evidence to show the predicted secondary structures are real?

Compared to CLIP and related methods, peak calling enables more facile identification of candidate RBPs and putatively structured RNAs for further analysis and may be particularly useful for the vanishingly small amounts of RNA present in plasma and other bodily fluids. New findings resulting from peak calling in the present manuscript include that plasma RNAs are enriched in binding sites for stress granule proteins (see above) and the discovery of a variety of novel RNAs, including the full-length excised intron RNAs first identified here and subsequently studied in cellular RNAs in the Yao et al. pertinent submitted manuscript. We also note that peak calling enables the identification of protein-protected and structured mRNA regions that are relatively stable in plasma and may be more reliably detected in targeted liquid biopsy assays than are more labile mRNA regions (p. 17, para. 1, last sentence; and p. 30, para. 2, beginning on line 5).

6) The authors should probably discuss how alignment errors can potentially affect detection of repetitive regions.

In the Empirical Bayes method that we used for the analysis of repeats, repeat sequences were quantified by aggregate counts irrespective of the genomic locus to which they mapped (Materials and methods, p. 38, para. 2, line 5), which should not be affected by alignment errors.

7) Many figures are IGV screenshots, which can be difficult to follow. Some of them can probably be summarized to deliver the message better.

Some IGV-based figures are crucial for showing key features of the RNAs that are called as peaks (e.g., the predicted secondary structures of the full-length excised intron RNAs and intron RNA fragments). However, in the process of reformatting, we have switched in and added non-IGV main text figures including Figure 2 (microbiome analysis), Figure 3 (TGIRT-seq versus SMART-Seq), Figure 4 (repeats), and Figure 6 (new figure comparing relative abundance of RBP-binding sites in plasma versus cells).

Author Response:

Reviewer #1 (Public Review):

Strengths:

1) The model structure is appropriate for the scientific question.

2) The paper addresses a critical feature of SARS-CoV-2 epidemiology which is its much higher prevalence in Hispanic or Latino and Black populations. In this sense, the paper has the potential to serve as a tool to enhance social justice.

3) Generally speaking, the analysis supports the conclusions.

Other considerations:

1) The clean distinction between susceptibility and exposure models described in the paper is conceptually useful but is unlikely to capture reality. Rather, susceptibility to infection is likely to vary more by age whereas exposure is more likely to vary by ethnic group / race. While age cohort are not explicitly distinguished in the model, the authors would do well to at least vary susceptibility across ethnic groups according to different age cohort structure within these groups. This would allow a more precise estimate of the true effect of variability in exposures. Alternatively, this could be mentioned as a limitation of the the current model.

We agree that this would be an important extension for future work and have indicated this in the Discussion, along with the types of data necessary to fit such models:

“Fourth, due to data availability, we have only considered variability in exposure due to one demographic characteristic; models should ideally strive to also account for the effects of age on susceptibility and exposure within strata of race and ethnicity and other relevant demographics, such as socioeconomic status and occupation \cite{Mulberry2021-tc}. These models could be fit using representative serological studies with detailed cross-tabulated seropositivity estimates.”

2) I appreciated that the authors maintained an agnostic stance on the actual value of HIT (across the population & within ethnic groups) based on the results of their model. If there was available data, then it might be possible to arrive at a slightly more precise estimate by fitting the model to serial incidence data (particularly sorted by ethnic group) over time in NYC & Long Island. First, this would give some sense of R_effective. Second, if successive waves were modeled, then the shift in relative incidence & CI among these groups that is predicted in Figure 3 & Sup fig 8 may be observed in the actual data (this fits anecdotally with what I have seen in several states). Third, it may (or may not) be possible to estimate values of critical model parameters such as epsilon. It would be helpful to mention this as possible future work with the model.

Caveats about the impossibility of truly measuring HIT would still apply (due to new variants, shifting use & effective of NPIs, etc….). However, as is, the estimates of possible values for HIT are so wide as to make the underlying data used to train the model almost irrelevant. This makes the potential to leverage the model for policy decisions more limited.

We have highlighted this important limitation in the Discussion:

“Finally, we have estimated model parameters using a single cross-sectional serosurvey. To improve estimates and the ability to distinguish between model structures, future studies should use longitudinal serosurveys or case data stratified by race and ethnicity and corrected for underreporting; the challenge will be ensuring that such data are systematically collected and made publicly available, which has been a persistent barrier to research efforts \cite{Krieger2020-ss}. Addressing these data barriers will also be key for translating these and similar models into actionable policy proposals on vaccine distribution and non-pharmaceutical interventions.”

3) I think the range of R0 in the figures should be extended to go as as low as 1. Much of the pandemic in the US has been defined by local Re that varies between 0.8 & 1.2 (likely based on shifts in the degree of social distancing). I therefore think lower HIT thresholds should be considered and it would be nice to know how the extent of assortative mixing effects estimates at these lower R_e values.

We agree this would be of interest and have extended the range of R0 values. Figure 1 has been updated accordingly (see below); we also updated the text with new findings: “After fitting the models across a range of $\epsilon$ values, we observed that as $\epsilon$ increases, HITs and epidemic final sizes shifted higher back towards the homogeneous case (Figure \ref{fig:model2}, Figure 1-figure supplement 4); this effect was less pronounced for $R_0$ values close to 1.”

Figure 1: Incorporating assortativity in variable exposure models results in increased HITs across a range of $R_0$ values. Variable exposure models were fitted to NYC and Long Island serosurvey data.

4) line 274: I feel like this point needs to be considered in much more detail, either with a thoughtful discussion or with even with some simple additions to the model. How should these results make policy makers consider race and ethnicity when thinking about the key issues in the field right now such as vaccine allocation, masking, and new variants. I think to achieve the maximal impact, the authors should be very specific about how model results could impact policy making, and how we might lower the tragic discrepancies associated with COVID. If the model / data is insufficient for this purpose at this stage, then what type of data could be gathered that would allow more precise and targeted policy interventions?

We have conducted additional analyses exploring the important suggestion by the reviewers that social distancing could affect these conclusions. The text and figures have been updated accordingly:

“Finally, we assessed how robust these findings were to the impact of social distancing and other non- pharmaceutical interventions (NPIs). We modeled these mitigation measures by scaling the transmission

rate by a factor $\alpha$ beginning when 5\% cumulative incidence in the population was reached. Setting the duration of distancing to be 50 days and allowing $\alpha$ to be either 0.3 or 0.6 (i.e. a 70\% or 40\% reduction in transmission rates, respectively), we assessed how the $R_0$ versus HIT and final epidemic size relationships changed. We found that the $R_0$ versus HIT relationship was similar to in the unmitigated epidemic (Figure 1-figure supplement 5). In contrast, final epidemic sizes depended on the intensity of mitigation measures, though qualitative trends across models (e.g. increased assortativity leads to greater final sizes) remained true (Figure 1-figure supplement 6). To explore this further, we systematically varied $\alpha$ and the duration of NPIs while holding $R_0$ constant at 3. We found again that the HIT was consistent, whereas final epidemic sizes were substantially affected by the choice of mitigation parameters (Figure 1-figure supplement 7); the distribution of cumulative incidence at the point of HIT was also comparable with and without mitigation measures (Figure 2-figure supplement 8). The most stringent NPI intensities did not necessarily lead to the smallest epidemic final sizes, an idea which has been explored in studies analyzing optimal control measures \cite{Neuwirth2020- nb,Handel2007-ee}. Longitudinal changes in incidence rate ratios also were affected by NPIs, but qualitative trends in the ordering of racial and ethnic groups over time remained consistent (Figure 3- figure supplement 3).

Figure 1-figure supplement 6: Final epidemic sizes versus $R_0$ in variable exposure models with mitigation measures for $\alpha = 0.3$ (top) and $\alpha = 0.6$ (bottom). NPIs were initiated when cumulative incidence reached 5\% in all models and continued for 50 days. Models were fitted to NYC and Long Island serosurvey data.

Figure 1-figure supplement 7: Sensitivity analysis on the impact of intensity and duration of NPIs on final epidemic sizes. HIT values for the same mitigation parameters were 46.4 $\pm$ 0.5\% (range). The smallest final size, corresponding to $\alpha = 0.6$ and duration = 100, was 51\%. Census-informed assortativity models were fit to Long Island seroprevalence data. NPIs were initiated when cumulative incidence reached 5\% in all models.

See points 1 and 2 above for examples of additional data required.

Minor issues:

-This is subjective but I found the words "active" and "high activity" to describe increases in contacts per day to be confusing. I would just say more contacts per day. It might help to change "contacts" to "exposure contacts" to emphasize that not all contacts are high risk.

To clarify this, we have replaced instances of “activity level” (and similar) with “total contact rate”, indicating the total number of contacts per unit time per individual; e.g. “The estimated total contact rate ratios indicate higher contacts for minority groups such as Hispanics or Latinos and non-Hispanic Black people, which is in line with studies using cell phone mobility data \cite{Chang2020-in}; however, the magnitudes of the ratios are substantially higher than we expected given the findings from those studies.”

We have also clarified our definition of contacts: “We define contacts to be interactions between individuals that allow for transmission of SARS-CoV-2 with some non-zero probability.”

-The abstract has too much jargon for a generalist journal. I would avoid words like "proportionate mixing" & "assortative" which are very unique to modeling of infectious diseases unless they are first defined in very basic language.

We have revised the abstract to convey these same concepts in a more accessible manner: “A simple model where interactions occur proportionally to contact rates reduced the HIT, but more realistic models of preferential mixing within groups increased the threshold toward the value observed in homogeneous populations.”

-I would cite some of the STD models which have used similar matrices to capture assortative mixing.

We have added a reference in the assortative mixing section to a review of heterogeneous STD models: “Finally, under the \textit{assortative mixing} assumption, we extended this model by partitioning a fraction $\epsilon$ of contacts to be exclusively within-group and distributed the rest of the contacts according to proportionate mixing (with $\delta_{i,j}$ being an indicator variable that is 1 when $i=j$ and 0 otherwise) \cite{Hethcote1996-bf}:”

-Lines 164-5: very good point but I would add that members of ethnic / racial groups are more likely to be essential workers and also to live in multigenerational houses

We have added these helpful examples into the text: “Variable susceptibility to infection across racial and ethnic groups has been less well characterized, and observed disparities in infection rates can already be largely explained by differences in mobility and exposure \cite{Chang2020-in,Zelner2020- mb,Kissler2020-nh}, likely attributable to social factors such as structural racism that have put racial and ethnic minorities in disadvantaged positions (e.g., employment as frontline workers and residence in overcrowded, multigenerational homes) \cite{Henry_Akintobi2020-ld,Thakur2020-tw,Tai2020- ok,Khazanchi2020-xu}.”

-Line 193: "Higher than expected" -> expected by who?

We have clarified this phrase: “The estimated total contact rate ratios indicate higher exposure contacts for minority groups such as Hispanics or Latinos and non-Hispanic Black people, which is in line with studies using cell phone mobility data \cite{Chang2020-in}; however, the magnitudes of the ratios are substantially higher than we expected given the findings from those studies.”

-A limitation that needs further mention is that fact that race & ethnic group, while important, could be sub classified into strata that inform risk even more (such as SES, job type etc….)

We agree and have added this to the Discussion: “Fourth, due to data availability, we have only considered variability in exposure due to one demographic characteristic; models should ideally strive to also account for the effects of age on susceptibility and exposure within strata of race and ethnicity and other relevant demographics, such as socioeconomic status and occupation \cite{Mulberry2021-tc}. These models could be fit using representative serological studies with detailed cross-tabulated seropositivity estimates.”

Reviewer #2 (Public Review):

Overall I think this is a solid and interesting piece that is an important contribution to the literature on COVID-19 disparities, even if it does have some limitations. To this point, most models of SARS-CoV-2 have not included the impact of residential and occupational segregation on differential group-specific covid outcomes. So, the authors are to commended on their rigorous and useful contribution on this valuable topic. I have a few specific questions and concerns, outlined below:

We thank the reviewer for the supportive comments.

1) Does the reliance on serosurvey data collected in public places imply a potential issue with left-censoring, i.e. by not capturing individuals who had died? Can the authors address how survival bias might impact their results? I imagine this could bring the seroprevalence among older people down in a way that could bias their transmission rate estimates.

We have included this important point in the limitations section on potential serosurvey biases: “First, biases in the serosurvey sampling process can substantially affect downstream results; any conclusions drawn depend heavily on the degree to which serosurvey design and post-survey adjustments yield representative samples \cite{Clapham2020-rt}. For instance, because the serosurvey we relied on primarily sampled people at grocery stores, there is both survival bias (cumulative incidence estimates do not account for people who have died) and ascertainment bias (undersampling of at-risk populations that are more likely to self-isolate, such as the elderly) \cite{Rosenberg2020-qw,Accorsi2021-hx}. These biases could affect model estimates if, for instance, the capacity to self-isolate varies by race or ethnicity -- as suggested by associations of neighborhood-level mobility versus demographics \cite{Kishore2020- sy,Kissler2020-nh} -- leading to an overestimate of cumulative incidence and contact rates in whites.”

2) It might be helpful to think in terms of disparities in HITs as well as disparities in contact rates, since the HIT of whites is necessarily dependent on that of Blacks. I'm not really disagreeing with the thrust of what their analysis suggests or even the factual interpretation of it. But I do think it is important to phrase some of the conclusions of the model in ways that are more directly relevant to health equity, i.e. how much infection/vaccination coverage does each group need for members of that group to benefit from indirect protection?

We agree with this important point and indeed this was the goal, in part, of the analyses in Figure 2. We have added additional text to the Discussion highlighting this: “Projecting the epidemic forward indicated that the overall HIT was reached after cumulative incidence had increased disproportionately in minority groups, highlighting the fundamentally inequitable outcome of achieving herd immunity through infection. All of these factors underscore the fact that incorporating heterogeneity in models in a mechanism-free manner can conceal the disparities that underlie changes in epidemic final sizes and HITs. In particular, overall lower HIT and final sizes occur because certain groups suffer not only more infection than average, but more infection than under a homogeneous mixing model; incorporating heterogeneity lowers the HIT but increases it for the highest-risk groups (Figure \ref{fig:hitcomp}).”

For vaccination, see our response to Reviewer #1 point 4.

3) The authors rely on a modified interaction index parameterized directly from their data. It would be helpful if they could explain why they did not rely on any sources of mobility data. Are these just not broken down along the type of race/ethnicity categories that would be necessary to complete this analysis? Integrating some sort of external information on mobility would definitely strengthen the analysis.

This is a great suggestion, but this type of data has generally not been available due to privacy concerns from disaggregating mobility data by race and ethnicity (Kishore et al., 2020). Instead, we modeled NPIs as mentioned in Reviewer #1 point 4, with the caveat that reduction in mobility was assumed to be identical across groups. We added this into the text explicitly as a limitation: “Third, we have assumed the impact of non-pharmaceutical interventions such as stay-at-home policies, closures, and the like to equally affect racial and ethnic groups. Empirical evidence suggests that during periods of lockdown, certain neighborhoods that are disproportionately wealthy and white tend to show greater declines in mobility than others \cite{Kishore2020-sy,Kissler2020-nh}. These simplifying assumptions were made to aid in illustrating the key findings of this model, but for more detailed predictive models, the extent to which activity level differences change could be evaluated using longitudinal contact survey data \cite{Feehan2020-ta}, since granular mobility data are typically not stratified by race and ethnicity due to privacy concerns \cite{Kishore2020-mg}.”

Reviewer #3 (Public Review):

Ma et al investigate the effect of racial and ethnic differences in SARS-CoV-2 infection risk on the herd immunity threshold of each group. Using New York City and Long Island as model settings, they construct a race/ethnicity-structured SEIR model. Differential risk between racial and ethnic groups was parameterized by fitting each model to local seroprevalence data stratified demographically. The authors find that when herd immunity is reached, cumulative incidence varies by more than two fold between ethnic groups, at approximately 75% of Hispanics or Latinos and only 30% of non-Hispanic Whites.

This result was robust to changing assumptions about the source of racial and ethnic disparities. The authors considered differences in disease susceptibility, exposure levels, as well as a census-driven model of assortative mixing. These results show the fundamentally inequitable outcome of achieving herd immunity in an unmitigated epidemic.

The authors have only considered an unmitigated epidemic, without any social distancing, quarantine, masking, or vaccination. If herd immunity is achieved via one of these methods, particularly vaccination, the disparities may be mitigated somewhat but still exist. This will be an important question for epidemiologists and public health officials to consider throughout the vaccine rollout.

We thank the reviewer for the detailed and helpful summary and suggestions.

Author Response

Summary: A major tenet of plant pathogen effector biology has been that effectors from very different pathogens converge on a small number of host targets with central roles in plant immunity. The current work reports that effectors from two very different pathogens, an insect and an oomycete, interact with the same plant protein, SIZ1, previously shown to have a role in plant immunity. Unfortunately, apart from some technical concerns regarding the strength of the data that the effectors and SIZ1 interact in plants, a major limitation of the work is that it is not demonstrated that the effectors alter SIZ1 activity in a meaningful way, nor that SIZ1 is specifically required for action of the effects.

We thank the editor and reviewers for their time to review our manuscript and their helpful and constructive comments. The reviews have helped us focus our attention on additional experiments to test the hypothesis that effectors Mp64 (from an aphid) and CRN83-152 (from an oomycete) indeed alter SIZ1 activity or function. We have revised our manuscript and added the following data:

1) Mp64, but not CRN83-152, stabilizes SIZ1 in planta. (Figure 1 in the revised manuscript).

2) AtSIZ1 ectopic expression in Nicotiana benthamiana triggers cell death from 3-4 days after agroinfiltration. Interestingly CRN83-152_6D10 (a mutant of CRN83-152 that has no cell death activity), but not Mp64, enhances the cell death triggered by AtSIZ1 (Figure 2 in the revised manuscript).

For 1) we have added the following panel to Figure 1 as well as three biological replicates of the stabilisation assays in the Supplementary data (Fig S3):

Figure 1 panel C. Stabilisation of SIZ1 by Mp64. Western blot analyses of protein extracts from agroinfiltrated leaves expressing combinations of GFP-GUS, GFP Mp64 and GFP-CRN83_152_6D10 with AtSIZ1-myc or NbSIZ1-myc. Protein size markers are indicated in kD, and equal protein amounts upon transfer is shown upon ponceau staining (PS) of membranes. Blot is representative of three biological replicates , which are all shown in supplementary Fig. S3. The selected panels shown here are cropped from Rep 1 in supplementary Fig. S3.

For 2) we have added the folllowing new figure (Fig. 2 in the revised manuscript):

Fig. 2. SIZ1-triggered cell death in N. benthamiana is enhanced by CRN83_152_6D10 but not Mp64. (A) Scoring overview of infiltration sites for SIZ1 triggered cell death. Infiltration site were scored for no symptoms (score 0), chlorosis with localized cell death (score 1), less than 50% of the site showing visible cell death (score 2), more than 50% of the site showing cell death (score 3). (B) Bar graph showing the proportions of infiltration sites showing different levels of cell death upon expression of AtSIZ1, NbSIZ1 (both with a C-terminal RFP tag) and an RFP control. Graph represents data from a combination of 3 biological replicates of 11-12 infiltration sites per experiment (n=35). (C) Bar graph showing the proportions of infiltration sites showing different levels of cell death upon expression of SIZ1 (with C-terminal RFP tag) either alone or in combination with aphid effector Mp64 or Phytophthora capsica effector CRN83_152_6D10 (both effectors with GFP tag), or a GFP control. Graph represent data from a combination of 3 biological replicates of 11-12 infiltration sites per experiment (n=35).

Our new data provide further evidence that SIZ1 function is affected by effectors Mp64 (aphid) and CRN83-152 (oomycete), and that SIZ1 likely is a vital virulence target. Our latest results also provide further support for distinct effector activities towards SIZ1 and its variants in other species. SIZ1 is a key immune regulator to biotic stresses (aphids, oomycetes, bacteria and nematodes), on which distinct virulence strategies seem to converge. The mechanism(s) underlying the stabilisation of SIZ1 by Mp64 is yet unclear. However, we hypothesize that increased stability of SIZ1, which functions as an E3 SUMO ligase, leads to increased SUMOylation activity towards its substrates. We surmise that SIZ1 complex formation with other key regulators of plant immunity may underpin these changes. Whether the cell death, triggered by AtSIZ1 upon transient expression in Nicotiana benthamiana, is linked to E3 SUMO ligase activity remains to be investigated. Expression of AtSIZ1 in a plant species other than Arabidopsis may lead to mistargeting of substrates, and subsequent activation of cell death. Dissecting the mechanistic basis of SIZ1 targeting by distinct pathogens and pests will be an important next step in addressing these hypotheses towards understanding plant immunity.

Reviewer #1:

In this manuscript, the authors suggest that SIZ1, an E3 SUMO ligase, is the target of both an aphid effector (Mp64 form M. persicae) and an oomycete effector (CRN83_152 from Phytophthora capsica), based on interaction between SIZ1 and the two effectors in yeast, co-IP from plant cells and colocalization in the nucleus of plant cells. To support their proposal, the authors investigate the effects of SIZ1 inactivation on resistance to aphids and oomycetes in Arabidopsis and N. benthamiana. Surprisingly, resistance is enhanced, which would suggest that the two effectors increase SIZ1 activity.

Unfortunately, not only do we not learn how the effectors might alter SIZ1 activity, there is also no formal demonstration that the effects of the effectors are mediated by SIZ1, such as investigating the effects of Mp64 overexpression in a siz1 mutant. We note, however, that even this experiment might not be entirely conclusive, since SIZ1 is known to regulate many processes, including immunity. Specifically, siz1 mutants present autoimmune phenotype, and general activation of immunity might be sufficient to attenuate the enhanced aphid susceptibility seen in Mp64 overexpressers.

To demonstrate unambiguously that SIZ1 is a bona fide target of Mp64 and CRN83_152 would require assays that demonstrate either enhanced SIZ1 accumulation or altered SIZ1 activity in the presence of Mp64 and CRN83_152.

The enhanced resistance upon knock-down/out of SIZ1 suggests pathogen and pest susceptibility requires SIZ1. We hypothesize that the effectors either enhance SIZ1 activity or that the effectors alter SIZ1 specificity towards substrates rather than enzyme activity itself. To investigate how effectors coopt SIZ1 function would require a comprehensive set of approaches and will be part of our future work. While we agree that this aspect requires further investigation, we think the proposed experiments go beyond the scope of this study.

After receiving reviewer comments, including on the quality of Figure 1, which shows western blots of co-immunoprecipitation experiments, we re-analyzed independent replicates of effector-SIZ1 coexpression/ co-immunoprecipitation experiments. The reviewer rightly pointed out that in the presence of Mp64, SIZ1 protein levels increase when compared to samples in which either the vector control or CRN83-152_6D10 are co-infiltrated. Through carefully designed experiments, we can now affirm that Mp64 co-expression leads to increased SIZ1 protein levels (Figure 1C and Supplementary Figure S3, revised manuscript). Our results offer both an explanation of different SIZ1 levels in the input samples (original submission, Figure 1A/B) as well as tantalizing new clues to the nature of distinct effector activities.

Besides, we were able to confirm a previous preliminary finding not included in the original submission that ectopic expression of AtSIZ1 in Nicotiana benthamiana triggers cell death (3/4 days after infiltration) and that CRN83-152_6D10 (which itself does not trigger cell death) enhances this phenotype.

We have considered overexpression of Mp64 in the siz1 mutant, but share the view that the outcome of such experiments will be far from conclusive.

In summary, we have added new data that further support that SIZ1 is a bonafide target of Mp64 and CRN83-152 (i.e. increased accumulation of SIZ1 in the presence of Mp64, and enhanced SIZ cell death activation in the presence of CRN83-152_6D10).

Reviewer #2:

The study provides evidence that an aphid effector Mp64 and a Phytophthora capsici effector CRN83_152 can both interact with the SIZ1 E3 SUMO-ligase. The authors further show that overexpression of Mp64 in Arabidopsis can enhance susceptibility to aphids and that a loss-of-function mutation in Arabidopsis SIZ1 or silencing of SIZ1 in N. benthamiana plants lead to increased resistance to aphids and P. capsici. On siz1 plants the aphids show altered feeding patterns on phloem, suggestive of increased phloem resistance. While the finding is potentially interesting, the experiments are preliminary and the main conclusions are not supported by the data.

Specific comments:

The suggestion that SIZ1 is a virulence target is an overstatement. Preferable would be knockouts of effector genes in the aphid or oomycete, but even with transgenic overexpression approaches, there are no direct data that the biological function of the effectors requires SIZ1. For example, is SIZ1 required for the enhanced susceptibility to aphid infestation seen when Mp64 is overexpressed? Or does overexpression of SIZ1 enhance Mp64-mediated susceptibility?

What do the effectors do to SIZ1? Do they alter SUMO-ligase activity? Or are perhaps the effectors SUMOylated by SIZ1, changing effector activity?

We agree that having effector gene knock-outs in aphids and oomycetes would be ideal for dissecting effector mediated targeting of SIZ1. Unfortunately, there is no gene knock-out system established in Myzus persicae (our aphid of interest), and CAS9 mediated knock-out of genes in Phytophthora capsici has not been successful in our lab as yet, despite published reports. Moreover, repeated attempts to silence Mp64, other effector and non-effector coding genes, in aphids (both in planta and in vitro) have not been successful thus far, in our hands. As detailed in our response to Reviewer 1, we considered the use of transgenic approaches not appropriate as data interpretation would become muddied by the strong immunity phenotype seen in the siz1-2 mutant.

As stated before, we hypothesize that the effectors either enhance SIZ1 activity or alter SIZ1 substrate specificity. Mp64-induced accumulation of SIZ1 could form the basis of an increase in overall SIZ1 activity. This hypothesis, however, requires testing. The same applies to the enhanced SIZ1 cell death activation in the presence of CRN83-152_6D10.

Whilst our new data support our hypothesis that effectors Mp64 and CRN83-152 affect SIZ1 function, how exactly these effectors trigger susceptibility, requires significant work. Given the substantial effort needed and the research questions involved, we argue that findings emanating from such experiments warrant standalone publication.

While stable transgenic Mp64 overexpressing lines in Arabidopsis showed increased susceptibility to aphids, transient overexpression of Mp64 in N. benthamiana plants did not affect P. capsici susceptibility. The authors conclude that while the aphid and P. capsici effectors both target SIZ1, their activities are distinct. However, not only is it difficult to compare transient expression experiments in N. benthamiana with stable transgenic Arabidopsis plants, but without knowing whether Mp64 has the same effects on SIZ1 in both systems, to claim a difference in activities remains speculative.

We agree that we cannot compare effector activities between different plant species. We carefully considered every statement regarding results obtained on SIZ1 in Arabidopsis and Nicotiana benthamiana. We can, however, compare activities of the two effectors when expressed side by side in the same plant species. In our original submission, we show that expression of CRN83 152 but not Mp64 in Nicotiana benthamiana enhances susceptibility to Phytophthora capsici. In our revised manuscript, we present new data showing distinct effector activities towards SIZ1 with regards to 1) enhanced SIZ1 stability and 2) enhanced SIZ1 triggered cell death. These findings raise questions as to how enhanced SIZ1 stability and cell death activation is relevant to immunity. We aim to address these critical questions by addressing the mechanistic basis of effector-SIZ1 interactions.

The authors emphasize that the increased resistance to aphids and P. capsici in siz1 mutants or SIZ1 silenced plants are independent of SA. This seems to contradict the evidence from the NahG experiments. In Fig. 5B, the effects of siz1 are suppressed by NahG, indicating that the resistance seen in siz1 plants is completely dependent on SA. In Fig 5A, the effects of siz1 are not completely suppressed by NahG, but greatly attenuated. It has been shown before that SIZ1 acts only partly through SNC1, and the results from the double mutant analyses might simply indicate redundancy, also for the combinations with eds1 and pad4 mutants.

We emphasized that siz1-2 increased resistance to aphids is independent of SA, which is supported by our data (Figure 5A). Still, we did not conclude that the same applies to increased resistance to Phytophthora capsici (Figure 5B). In contrast, the siz1-2 enhanced resistance to P. capsici appears entirely dependent on SA levels, with the level of infection on the siz1-2/NahG mutants even slightly higher than on the NahG line and Col-0 plants. We exercise caution in the interpretation of this data given the significant impact SA signalling appears to have on Phytophthora capsici infection.

The reviewer commented on the potential for functional redundancy in the siz1-2 double mutants. Unfortunately, we are unsure what redundancy s/he is referring to. SNC1, EDS1, and PAD4 all are components required for immunity, and their removal from the immune signalling network (using the mutations in the lines we used here) impairs immunity to various plant pathogens. The siz1-2 snc1-11, siz1-2 eds1-2, and siz1-2 pad4-1 double mutants have similar levels of susceptibility to the bacterial pathogen Pseudomonas syringae when compared to the corresponding snc1-11, eds1-2 and pad4-1 controls (at 22oC). These previous observations indicate that siz1 enhanced resistance is dependent on these signalling components (Hammoudi et al., 2018, Plos Genetics).

In contrast to this, we observed a strong siz1 enhanced resistance phenotype in the absence of snc1- 11, eds1 2 and pad4-1. Notably, the siz1-2 snc1-11 mutant does not appear immuno-compromised when compared to siz1-2 in fecundity assays, indicating that the siz1-2 phenotype is independent of SNC1. In our view, these data suggest that signalling components/pathways other than those mediated by SNC1, EDS1, and PAD4 are involved. We consider this to be an exciting finding as our data points to an as of yet unknown SIZ1-dependent signalling pathway that governs immunity to aphids.

How do NahG or Mp64 overexpression affect aphid phloem ingestion? Is it the opposite of the behavior on siz1 mutants?

We have not performed further EPG experiments on additional transgenic lines used in the aphid assay. These experiments are quite challenging and time consuming. Moreover, accommodating an experimental set-up that allows us to compare multiple lines at the same time is not straightforward. Considering that NahG did not affect aphid performance (Figure 5A), we do not expect to see an effect on phloem ingestion.

Author Response

1) Please comment on why many of the June samples failed to provide sufficient sequence information, especially since not all of them had low yields (supp table 2 and supp figure 5).

An extended paragraph about experimental intricacies of our study has been added to the Discussion. It has also been also slightly restructured to give a better and wider overview of how future freshwater monitoring studies using nanopore sequencing can be improved (page 18, lines 343-359).

We wish to highlight that all three MinION sequencing runs here analysed feature substantially higher data throughput than that of any other recent environmental 16S rRNA sequencing study with nanopore technology, as recently reviewed by Latorre-Pérez et al. (Biology Methods and Protocols 2020, doi:10.1093/biomethods/bpaa016). One of this work's sequencing runs has resulted in lower read numbers for water samples collected in June 2018 (~0.7 Million), in comparison to the ones collected in April and August 2018 (~2.1 and ~5.5 Million, respectively). While log-scale variabilities between MinION flow cell throughput have been widely reported for both 16S and shotgun metagenomics approaches (e.g. see Latorre-Pérez et al.), the count of barcode-specific 16S reads is nevertheless expected to be correlated with the barcode-specific amount of input DNA within a given sequencing run. As displayed in Supplementary Figure 7b, we see a positive, possibly logarithmic trend between the DNA concentration after 16S rDNA amplification and number of reads obtained. With few exceptions (April-6, April-9.1 and Apri-9.2), we find that sample pooling with original 16S rDNA concentrations of ≳4 ng/µl also results in the surpassing of the here-set (conservative) minimum read threshold of 37,000 for further analyses. Conversely, all June samples that failed to reach 37,000 reads did not pass the input concentration of 4 ng/µl, despite our attempt to balance their quantity during multiplexing.

We reason that such skews in the final barcode-specific read distribution would mainly arise from small concentration measurement errors, which undergo subsequent amplification during the upscaling with comparably large sample volume pipetting. While this can be compensated for by high overall flow cell throughput (e.g. see August-2, August-9.1, August-9.2), we think that future studies with much higher barcode numbers can circumvent this challenge by leveraging an exciting software solution: real-time selective sequencing via “Read Until”, as developed by Loose et al. (Nature Methods 2016, doi:10.1038/nmeth.3930). In the envisaged framework, incoming 16S read signals would be in situ screened for the sample-barcode which in our workflow is PCR-added to both the 5' and 3' end of each amplicon. Overrepresented barcodes would then be counterbalanced by targeted voltage inversion and pore "rejection" of such reads, until an even balance is reached. Lately, such methods have been computationally optimised, both through the usage of GPUs (Payne et al., bioRxiv 2020, https://doi.org/10.1101/2020.02.03.926956) and raw electrical signals (Kovaka et al., bioRxiv 2020, https://doi.org/10.1101/2020.02.03.931923).

2) It would be helpful if the authors could mention the amount (or proportion) of their sequenced 16S amplicons that provided species-level identification, since this is one of the advantages of nanopore sequencing.

We wish to emphasize that we intentionally refrained from reporting the proportion of 16S rRNA reads that could be classified at species level, since we are wary of any automated species level assignments even if the full-length 16S rRNA gene is being sequenced. While we list the reasons for this below, we appreciate the interest in the theoretical proportion of reads at species level assignment. We therefore re-analyzed our dataset, and now also provide the ratio of reads that could be classified at species level using Minimap2 (pages 16-17, lines 308-314).

To this end, we classified reads at species level if the species entry of the respective SILVA v.132 taxonomic ID was either not empty, or neither uncultured bacterium nor metagenome. Therefore, many unspecified classifications such as uncultured species of some bacterial genus are counted as species-level classifications, rendering our approach lenient towards a higher ratio of species level classifications. Still, the species level classification ratios remain low, on average at 16.2 % across all included river samples (genus-level: 65.6 %, family level: 76.6 %). The mock community, on the other hand, had a much higher species classification rate (>80 % in all three replicates), which is expected for a well-defined, well-referenced and divergent composition of only eight bacterial taxa, and thus re-validates our overall classification workflow.

On a theoretical level, we mainly refrain from automated across-the-board species level assignments because: (1) many species might differ by very few nucleotide differences within the 16S amplicon; distinguishing these from nanopore sequencing errors (here ~8 %) remains challenging (2) reference databases are incomplete and biased with respect to species level resolution, especially regarding certain environmental contexts; it is likely that species assignments would be guided by references available from more thoroughly studied niches than freshwater

Other recent studies have also shown that across-the-board species-level classification is not yet feasible with 16S nanopore sequencing, for example in comparison with Illumina data (Acharya et al., Scientific Reports 2019, doi:10.25405/data.ncl.9693533) which showed that “more reliable information can be obtained at genus and family level”, or in comparison with longer 16S-ITS-23S amplicons (Cusco et al., F1000Research 2019, doi: 10.12688/f1000research.16817.2), which “remarkably improved the taxonomy assignment at the species level”.

3) It is not entirely clear how the authors define their core microbiome. Are they reporting mainly the most abundant taxa (dominant core microbiome), and would this change if you look at a taxonomic rank below the family level? How does the core compare, for example, with other studies of this same river?

The here-presented core microbiome indeed represents the most abundant taxa, with relatively consistent profiles between samples. We used hierarchical clustering (Figure 4a, C2 and C4) on the bacterial family level, together with relative abundance to identify candidate taxa. Filtering these for median abundance > 0.1% across all samples resulted in 27 core microbiome families. To clarify this for the reader, we have added a new paragraph to the Material and Methods (section 2.7; page 29, lines 653-658).

We have also performed the same analysis on the bacterial genus level and now display the top 27 most abundant genera (median abundance > 0.2%), together with their corresponding families and hierarchical clustering analysis in a new Supplementary Figure 4. Overall, high robustness is observed with respect to the families of the core microbiome: out of the top 16 core families (Figure 4b), only the NS11-12 marine group family is not represented by the top 27 most abundant genera (Supplementary Figure 4b). We reason that this is likely because its corresponding genera are composed of relatively poorly resolved references of uncultured bacteria, which could thus not be further classified.

To the best of our knowledge, there are only two other reports that feature metagenomic data of the River Cam and its wastewater influx sources (Rowe et al., Water Science & Technology 2016, doi:10.2166/wst.2015.634; Rowe et al., Journal of Antimicrobial Chemotherapy 2017, doi:10.1093/jac/dkx017). While both of these primarily focus on the diversity and abundance of antimicrobial resistance genes using Illumina shotgun sequencing, they only provide limited taxonomic resolution on the river's core microbiome. Nonetheless, Rowe et al. (2016) specifically highlighted Sphingobium as the most abundant genus in a source location of the river (Ashwell, Hertfordshire). This genus belongs to the family of Sphingomonadaceae, which is also among the five most dominant families identified in our dataset. It thus forms part of what we define as the core microbiome of the River Cam (Figure 4b), and we have therefore highlighted this consistency in our manuscript's Discussion (page 17, lines 316-319).

4) Please consider revising the amount of information in some of the figures (such as figure 2 and figure 3). The resulting images are tiny, the legends become lengthy and the overall impact is reduced. Consider splitting these or moving some information to the supplements.

To follow this advice, we have split Figure 2 into two less compact figures. We have moved more detailed analyses of our classification tool benchmark to the supplement (now Supplementary Figure 1). Supplementary Figure 1 notably also contains a new summary of the systematic computational performance measurements of each classification tool (see minor suggestions).

Moreover, we here suggest that the original Figure 3 may be divided into two figures: one to visualise the sequencing output, data downsampling and distribution of the most abundant families (now Figure 3), and the other featuring the clustering of bacterial families and associated core microbiome (now Figure 4). We think that both the data summary and clustering/core microbiome analyses are of particular interest to the reader, and that they should be kept as part of the main analyses rather than the supplement – however, we are certainly happy to discuss alternative ideas with the reviewers and editors.

5) Given that the authors claim to provide a simple, fast and optimized workflow it would be good to mention how this workflow differs or provides faster and better analysis than previous work using amplicon sequencing with a MinION sequencer.

Data throughput, sequencing error rates and flow cell stability have seen rapid improvements since the commercial release of MinION in 2015. In consequence, bioinformatics community standards regarding raw data processing and integration steps are still lacking, as illustrated by a thorough recent benchmark of fast5 to fastq format "basecalling" methods (Wick et al., Genome Biology 2019, doi: 10.1186/s13059-019-1727-y).

Early on during our analyses, we noticed that a plethora of bespoke pipelines have been reported in recent 16S environmental surveys using MinION (e.g. Kerkhof et al., Microbiome 2017, 10.1186/s40168-017-0336-9; Cusco et al., F1000 Research 2018, 10.12688/f1000research.16817.2; Acharya et al., Scientific Reports 2019, 10.1038/s41598-019-51997-x; Nygaard et al., Scientific Reports 2020, doi: 10.1038/s41598-020-59771-0). This underlines a need for more unified bioinformatics standards of (full-length) 16S amplicon data treatment, while similar benchmarks exist for short-read 16S metagenomics approaches, as well as for nanopore shotgun sequencing (e.g. Ye et al., Cell 2019, doi: 10.1016/j.cell.2019.07.010; Latorre-Pérez et al., Scientific Reports 2020, doi:10.1038/s41598-020-70491-3).

By adding a thorough speed and memory usage summary (new Supplementary Figure 1b), in addition to our (mis)classification performance tests based on both mock and complex microbial community analyses, we provide the reader with a broad overview of existing options. While the widely used Kraken 2 and Centrifuge methods provide exceptional speed, we find that this comes with a noticeable tradeoff in taxonomic assignment accuracy. We reason that Minimap2 alignments provide a solid compromise between speed and classification performance, with the MAPseq software offering a viable alternative should memory usage limitation apply to users.

We intend to extend this benchmarking process to future tools, and to update it on our GitHub page (https://github.com/d-j-k/puntseq). This page notably also hosts a range of easy-to-use scripts for employing downstream 16S analysis and visualization approaches, including ordination, clustering and alignment tests.

The revised Discussion now emphasises the specific advancements of our study with respect to freshwater analysis and more general standardisation of nanopore 16S sequencing, also in contrast to previous amplicon nanopore sequencing approaches in which only one or two bioinformatics workflows were tested (page 16, lines 297-306).

They also mention that nanopore sequencing is an "inexpensive, easily adaptable and scalable framework" The term "inexpensive" doesn't seem appropriate since it is relative. In addition, they should also discuss that although it is technically convenient in some aspects compared to other sequencers, there are still protocol steps that need certain reagents and equipment that is similar or the same to those needed for other sequencing platforms. Common bottlenecks such as DNA extraction methods, sample preservation and the presence of inhibitory compounds should be mentioned.

We agree with the reviewers that “inexpensive” is indeed a relative term, which needs further clarification. We therefore now state that this approach is “cost-effective” and discuss future developments such as the 96-sample barcoding kits and Flongle flow cells for small-scale water diagnostics applications, which will arguably render lower per-sample analysis costs in the future (page 18, lines 361-365).

Other investigators (e.g. Boykin et al., Genes 2019, doi:10.3390/genes10090632; Acharya et al., Water Technology 2020, doi:10.1016/j.watres.2020.116112) have recently shown that the full application of DNA extraction and in-field nanopore sequencing can be achieved at comparably low expense: Boykin et al. studied cassava plant pathogens using barcoded nanopore shotgun sequencing, and estimated costs of ~45 USD per sample, while we calculate ~100 USD per sample in this study. Acharya et al. undertook in situ water monitoring between Birtley, UK and Addis Ababa, Ethiopia, estimated ~75-150 USD per sample and purchased all necessary equipment for ~10,000 GBP – again, we think that this lies roughly within a similar range as our (local) study's total cost of ~3,670 GBP (Supplementary Table 6).

The revised manuscript now mentions the possibility of increasing sequencing yield by improving DNA extraction methods, by taking sample storage and potential inhibitory compounds into account in the planning phase (page 18, lines 348-352).

Minor points:

-Please include a reference to the statement saying that the river Cam is notorious for the "infections such as leptospirosis".

There are indeed several media reports that link leptospirosis risk to the local River Cam (e.g. https://www.cambridge-news.co.uk/news/cambridge-news/weils-disease-river-cam-leptosirosis-14919008 or https://www.bbc.com/news/uk-england-cambridgeshire-29060018). As we, however, did not find a scientific source for this information, we have slightly adjusted the statement in our manuscript from referring to Cambridge to instead referring to the entire United Kingdom. Accordingly, we now cite two reports from Public Health England (PHE) about serial leptospirosis prevalence in the United Kingdom (page 13, lines 226-227).

-Please check figure 7 for consistency across panels, such as shading in violet and labels on the figures that do not seem to correspond with what is stated in the legend. Please mention what the numbers correspond to in outer ring. Check legend, where it says genes is probably genus.

Thank you for pointing this out. We have revised (now labelled) Figure 8 and removed all inconsistencies between the panels. The legend has also been updated, which now includes a description of the number labelling of the tree, and a clearer differentiation between the colour coding of the tree nodes and the background highlighting of individual nanopore reads.

-Page 6. There is a "data not shown" comment in the text: "Benchmarking of the classification tools on one aquatic sample further confirmed Minimap2's reliable performance in a complex bacterial community, although other tools such as SPINGO (Allard, Ryan, Jeffery, & Claesson, 2015), MAPseq (Matias Rodrigues, Schmidt, Tackmann, & von Mering, 2017), or IDTAXA (Murali et al., 2018) also produced highly concordant results despite variations in speed and memory usage (data not shown)." There appears to be no good reason that this data is not shown. In case the speed and memory usage was not recorded, is advisable to rerun the analysis and quantify these variables, rather than mentioning them and not reporting them. Otherwise, provide an explanation for not showing the data please.

This is a valid point, and we agree with the reviewers that it is worth properly following up on this initial observation. To this end, our revised manuscript now entails a systematic characterisation of the twelve tools' runtime and memory usage performance. This has been added as Supplementary Figure 1b and under the new Materials and Methods section 2.2.4 (page 26, lines 556-562), while the corresponding results and their implications are discussed on page 16, lines 301-306. Particularly with respect to the runtime measurements, it is worth noting that these can differ by several orders of magnitude between the classifiers, thus providing an additional clarification on our choice of the - relatively fast - Minimap2 alignments.

-In Figure 4, it would be important to calculate if the family PCA component contribution differences in time are differentially significant. In Panel B, depicted is the most evident variance difference but what about other taxa which might not be very abundant but differ in time? One can use the fitFeatureModel function from the metagenomeSeq R library and a P-adjusted threshold value of 0.05, to validate abundance differences in addition to your analysis.

To assess if the PC component contribution of Figure 5 (previously Figure 4) significantly differed between the three time points, we have applied non-parametric tests to all season-grouped samples except for the mock community controls. We first applied Kruskal-Wallis H-test for independent samples, followed by post-hoc comparisons using two-sided Mann-Whitney U rank tests.

The Kruskal-Wallis test established a significant difference in PC component contributions between the three time points (p = 0.0049), with most of the difference stemming from divergence between April and August samples according to the post-hoc tests (p = 0.0022). The June sampled seemed to be more similar to the August ones (p = 0.66) than to the ones from April (p = 0.11), recapitulating the results of our hierarchical clustering analysis (Figure 4a).

We have followed the reviewers' advice and applied a complementary approach, using the fitFeatureModel of metagenomeSeq to fit a zero-inflated log-normal mixture model of each bacterial taxon against the time points. As only three independent variables can be accounted for by the model (including the intercept), we have chosen to investigate the difference between the spring (April) and summer (June, August) months to capture the previously identified difference between these months. At a nominal P-value threshold of 0.05, this analysis identifies seven families to significantly differ in their relative composition between spring and summer, namely Cyanobiaceae, Armatimonadaceae, Listeriaceae, Carnobacteriaceae, Azospirillaceae, Cryomorphaceae, and Microbacteriaceae. Three out of these seven families were also detected by the PCA component analysis (Carnobacteriacaea, Azospirillaceae, Microbacteriaceae) and two more (Listeriacaea, Armatimonadaceae) occured in the top 15 % of that analysis (out of 357 families).

This approach represents a useful validation of our principal component analysis' capture of likely seasonal divergence, but moreover allows for a direct assessment of differential bacterial composition across time points. We have therefore integrated the analysis into our manuscript (page 10, lines 184-186; Materials and Methods section 2.6, page 29, lines 641-647) – thank you again for this suggestion.

-Page 12-13. In the paragraph: "Using multiple sequence alignments between nanopore reads and pathogenic species references, we further resolved the phylogenies of three common potentially pathogenic genera occurring in our river samples, Legionella, Salmonella and Pseudomonas (Figure 7a-c; Material and Methods). While Legionella and Salmonella diversities presented negligible levels of known harmful species, a cluster of reads in downstream sections indicated a low abundance of the opportunistic, environmental pathogen Pseudomonas aeruginosa (Figure 7c). We also found significant variations in relative abundances of the Leptospira genus, which was recently described to be enriched in wastewater effluents in Germany (Numberger et al., 2019) (Figure 7d)."

Here it is important to mention the relative abundance in the sample. While no further experiments are needed, the authors should mention and discuss that the presence of DNA from pathogens in the sample has to be confirmed by other microbiology methodologies, to validate if there are viable organisms. Definitively, it is a big warning finding pathogen's DNA but also, since it is characterized only at genus level, further investigation using whole metagenome shotgun sequencing or isolation, would be important.

We agree that further microbiological assays, particularly target-specific species isolation and culturing, would be essential to validate the presence of living pathogenic bacteria. Accordingly, our revised Discussion now contains a paragraph that encourages such experiments as part of the design of future studies (with a fully-equipped laboratory infrastructure); page 17, 338-341.

-Page 15: "This might help to establish this family as an indicator for bacterial community shifts along with water temperature fluctuations."

Temperature might not be the main factor for the shift. There could be other factors that were not measured that could contribute to this shift. There are several parameters that are not measured and are related to water quality (COD, organic matter, PO4, etc).

We agree that this was a simplified statement, given our currently limited number of samples, and have therefore slightly expanded on this point (page 17, lines 323-325). It is indeed possible that differential Carnobacteriaceae abundances between the time point measurements may have arisen not as a consequence of temperature fluctuations (alone), but instead as a consequence of the observed hydrochemical changes like e.g. Ca2+, Mg2+, HCO3- (Figure 6b-c) or possible even water flow speed reductions (Supplementary Figure 6d).

-"A number of experimental intricacies should be addressed towards future nanopore freshwater sequencing studies with our approach, mostly by scrutinising water DNA extraction yields, PCR biases and molar imbalances in barcode multiplexing (Figure 3a; Supplementary Figure 5)."

Here you could elaborate more on the challenges, as mentioned previously.

We realise that we had not discussed the challenges in enough detail, and the Discussion now contains a substantially more detailed description of these intricacies (page 18, lines 343-359).

Author Response

Reviewer #1:

Summary:

In this paper, the authors utilize CRISPR-Cas9 to generate two different DMD cell lines. The first is a DMD human myoblast cell line that lacks exon 52 within the dystrophin gene. The second is a DMD patient cell line that is missing miRNA binding sites within the regulatory regions of the utrophin gene, resulting in increased utrophin expression. Then, the authors proceeded to test antisense oligonucleotides and utrophin up-regulators in these cell lines.

Overall opinion (expanded in more detail below).

The paper suffers from the following weaknesses:

1) The protocol used to generate the myoblast cell lines is rather inefficient and is not new.

2) Many of the data figures are of low quality and are missing proper controls (detailed in points 5,7,10, 12, 13,14)

Detailed critiques:

1) The title needs to be changed. The method used by the authors is inefficient. The title should instead focus on the two cell lines generated.

We appreciate the reviewer’s comments: thanks to them, we have realized the focus of the manuscript should be in the new models we described and less in the methodology used to create them.

Originally, we wanted to share the problems we faced when applying new CRISPR/Cas9 edition techniques to myoblasts: our conversations with other researchers in the field confirmed that many were having similar problems. However, the reviewer is right in the fact that there are many ways around this problem. We do describe ours and we are working in a new version of the manuscript with additional data to characterize our new models further and where the method used to create them, although included, is not the main focus of the manuscript. In this new version we will change the title accordingly.

2) Line 104: The authors declare that the efficiency of CRISPR/Cas9 is currently too low to provide therapeutic benefit for DMD in vivo. There are lots of papers that show efficient recovery of dystrophin in small and large animals following CRISPR/Cas9 therapy. The authors should cite them properly.

Thank you for your appreciation. We have reviewed the literature again to include new evidences of efficient dystrophin recovery as well as other studies with lower efficiency.

3) Figures 1, 2,3, and 4 can be merged into one figure.

4) Figure 2A and 2B can be moved to supplementary.

5) Figure 2C and 2D are not clear. Are the duplicates the same? Please invert the black and white colors of the blots.

Thank you for your comments. We have inverted the colors of the blots and changed the marks used in figure 2C and 2D to clarify that duplicates are indeed the same sample, assayed in duplicates. We have also merged figures 1 and 4 and moved figures 2 and 3 to supplementary in this new version.

6) Figure 3: In order to optimize the efficiency of myoblast transfection, the plasmids containing the Cas9 and the sgRNA should have different fluorophores (GFP and mCherry). This approach would increase the percentage of positive edited clones among the clones sorted.

We think the reviewer may have misunderstood our methodology: we are not using a plasmid with the Cas9 and another with the sgRNA, we are using two plasmids, both containing Cas9 and each a different sgRNA. We did try to use two different plasmids, one expressing GFP and one expressing puromycin resistance, but we found out that single GFP positive cell selection plus puromycin selection was too inefficient. We could have tried with two different fluorophores, but we tested the tools we had in our hands first and were successful at obtaining enough clones to continue with their characterization, so we did so instead of a further optimization to our editing protocol.

7) Figure 4A: In the text, the authors state that only 1 clone had the correct genomic edit, but from the PCR genotyping in this figure shows at least 2 positive clones (number 4 and 7).

Thank you for your appreciation. As you said, we got two positive clones (as we also indicate in figure 3B) but we completed the full characterization of one of them (clone number 7= DMD-UTRN-Model). In the new version of the manuscript we explain this further.

8) Figure 4C: The authors should address whether one or both copies of the UTRN gene was edited in their clones.

Thank you for your comment. Both copies of the UTRN gene were edited in our clones. We have included this information both in the text and in the figure 4 legend.

9) Figure 4 B and D: The authors should report the sequence below the electropherograms.

Thank you for this correction, we have included the sequence under the electropherograms.

10) Figure 5B: This western blot is of poor quality. Also, the authors should specify that the samples are differentiated myoblasts. Lastly, a standard protein should be included as a loading control.

Thank you for your comment. Poor quality of dystrophin and utrophin western blots was the main reason to validate a new method in our laboratory to measure these proteins directly in cell culture (1) like an alternative to western blotting. Since then, the myoblot method has been routinely used by us and in collaboration with other groups and companies. We included the western blot as it is sometimes easier for those used to this technique to be able to assess a blot in which there is no dystrophin expression. As you pointed out, our samples were all differentiated myotubes, not myoblasts, and we have modified this accordingly. Thank you very much for pointing out this mistake

On the other hand, as described in the methods, Revert TM 700 Total Protein Stain (Li-Cor) and alpha-actinin were included as standards in dystrophin and utrophin western blots, respectively.

11) Figure 5E: We would like to see triplicates for the level of Utrophin expression.

We thank the reviewer for his/her recommendation, but we do not consider western blotting a good quantitative technique, we have included western blots to show the expression/absence of protein at the same level. We have included many more replicates than needed to show at the level of utrophin by myoblots. We acknowledge that western blotting is the preferred method for some reviewers, so in the new version of our manuscript we clearly indicate the value we give to each technique, being myoblots our choice for quantification.

12) Figure 6: A dystrophin western blot should be included to demonstrate protein recovery following antisense oligonucleotide treatment. Also, the RT-PCR data could be biased as you can have preferential amplification of shorter fragments.

Thank you for your recommendation but as we have explained before, myoblots have been validated in our laboratory to replace western blot for accurate dystrophin quantification in cell culture.

13) Figure 6A: Invert the black and white colors. The authors should also report the control sequences and sequences of the clones under the electropherograms.

Thank you for your suggestion, we have inverted the colors and added the sequences under the electropherograms.

14) Figure 6B: Control myoblasts should be included in figure 5C.

Thank you for this correction, we will include control myoblasts in the new manuscript version.

15) Figure S2A: Invert the black and white colors.

Thank you for your suggestion, we have inverted the colors.

Reviewer #2:

The work from Soblechero-Martín et al reports the generation of a human DMD line deleted for exon 52 using CRISPR technology. In addition, the authors introduced a second mutation that leads to upregulation of utrophin, a protein similar to dystrophin, which has been considered as a therapeutic surrogate. The authors provide a careful description of the methodology used to generate the new cell line and have conducted meticulous evaluations to test the validity of the reagents.

However, if the main purpose of this cell line is to perform drug or small molecule compound screenings, a single line might not be sufficient to draw robust conclusions. The generation of additional DMD lines in different genetic backgrounds using the reagents developed in this study will strengthen the work and will be of interest to the DMD field.

Thank you for your appreciation. We think that a well characterized immortalized culture, like the one we describe is sufficient for compound screening, as described in other recently published studies (2), (3). About the other suggestion, we have indeed used our method to generate other cultures for collaborators, but they will be reported in their own publications, as they are interested in them as tools in their own research projects.

Further, the future use of the edited DMD line with upregulated utrophin is unclear. The utrophin upregulation adds a complexity to this line that might complicate the assessment of screened compounds. In contrast, this line could be used to test if overexpression of utrophin generates myotubes that produce increased force compared to the control DMD line.

We think we may have not explained our screening platform well enough. Our suggestion is to offer our newly generated culture ALONGSIDE the original unedited culture: the original is treated with potential drug candidates, while the new one may or may not be treated, if these drug candidates are thought to act by activating the edited region (see an example in the figure below). In this case, the new culture will be a reliable positive control to the effects that may be reported in the unedited cultures by the drug candidates. We will make this clear in the new version of the manuscript.

Created with BioRender.com

In summary, while there is support and enthusiasm for the techniques and methodological approach of the study, the future use of this single line might be dubious and could be strengthened if additional lines are generated.

We share the reviewer’s enthusiasm for this approach, and we have included in the new version of the manuscript further characterization of this new cell culture that we think would demonstrate its usefulness better.

Author Response

Author Response refers to a revised version of the manuscript, Version 3, which was posted October 23, 2020.

Summary:

Serra-Marques, Martin et al. investigate the individual and cooperative roles of specific kinesins in transporting Rab6 secretory vesicles in HeLa cells using CRISPR and live-cell imaging. They find that both KIF5B and KIF13B cooperate in transporting Rab6 vesicles, but Eg5 and other kinesin-3s (KIF1B and KIF1C) are dispensable for Rab6 vesicle transport. They show that both KIF5B and KIF13B localize to these vesicles and coordinate their activities such that KIF5B is the main driver of the cargos on older, MAP7-decorated microtubules, and KIF13B takes over as the main transporter on freshly-polymerized microtubule ends that are largely devoid of MAP7. Interestingly, their data also indicate that KIF5B is important for controlling Rab6 vesicle size, which KIF13B cannot rescue. By analyzing subpixel localization of the motors, they find that the motors localize to the front of the vesicle when driving transport, but upon directional cargo switching, KIF5B localizes to the back of the vesicle when opposing dynein. Overall, this paper provides substantial insight into motor cooperation of cargo transport and clarifies the contribution of these distinct classes of motors during Rab6 vesicle transport.

We thank the reviewers for their thoughtful and constructive suggestions, and for the positive feedback.

Reviewer #1:

In their manuscript, Serra-Marques, Martin, et al. investigate the individual and cooperative roles of specific kinesins in transporting Rab6 vesicles in HeLa cells using CRISPR and live-cell imaging. They find that both KIF5B and KIF13B cooperate in transporting Rab6 vesicles, but KIF5B is the main driver of transport. In these cells, Eg5 and other kinesin-3s (KIF1B and KIF1C) are dispensable for Rab6 vesicle transport. They find that both KIF5B and KIF13B are present on these vesicles and coordinate their activities such that KIF5B is the main driver of the cargos on older, MAP7-decorated MTs, and KIF13B takes over as the main transporter on freshly-polymerized MT ends that are largely devoid of MAP7. Interestingly, their data also indicate that KIF5B is important for controlling Rab6 vesicle size, which KIF13B cannot rescue. Upon cargo switching from anterograde to retrograde transport, KIF5B, but not KIF13B, engages in mechanical competition with dynein. Overall, this paper provides substantial insight into motor cooperation of cargo transport and clarifies the contribution of these distinct classes of motors during Rab6 vesicle transport. The experiments are well-performed and the data are of very high quality.

Major Comments:

1) In Figure 5, it is very interesting that only KIF5B opposes dynein. It would be informative to determine which kinesin was engaged on the Rab6 vesicle before the switch to the retrograde direction. Can the authors analyze the velocity of the run right before the switch to the retrograde direction? If the velocity corresponds with KIF5B (the one example provided seems to show a slow run prior to the switch), this could indicate that KIF5B opposes dynein more actively because KIF5B was the motor that was engaged at the time of the switch. Or if the velocity corresponds with KIF13B, this could indicate that KIF5B becomes specifically engaged upon a direction reversal. In any case, an analysis of the speed distributions before the switch would provide insight into vesicle movement and motor engagement before the change in direction.

Directional switching was only analyzed in rescue experiments, where the vesicles were driven by either KIF5B alone or by KIF13B alone, and the speeds of vesicles were representative of these motors (please see panels on the right). The number of vesicle runs where two motors were detected simultaneously (KIF5B vs KIF13B in Figure 5G,H,J) were significantly lower, and therefore, unfortunately we could not perform the analysis of their directional switching with sufficient statistical power.

2) One of the most interesting aspects of this paper is the different lattice preferences for KIF5B, which shows runs predominantly on "older" polymerized MTs decorated by MAP7, and for KIF13B, whose runs are predominantly restricted to newly polymerized MTs that lack MAP7. The results in Figure 8 suggest a potential switch from KIF5B to KIF13B motor engagement upon a change in lattice/MAP7 distribution. In general, do the authors observe the fastest runs at the cell periphery, where there should be a larger population of freshly polymerized MTs? For Figure 4E, are example 1 and example 2 in different regions of the cell?

This is indeed a very interesting point and we have considered it carefully. As can be seen in Figure 8B (grey curve), vesicle speed remains relatively constant along the cell radius in control HeLa cells. We note, however, that our previous work has shown that in these cells microtubules are quite stable even at the cell periphery, due to the high activity of the CLASP-containing cortical microtubule stabilization complex (Mimori-Kiyosue et al., 2005, Journal of Cell Biology, PMID: 15631994; van der Vaart et al., 2013, Developmental Cell, PMID: 24120883). We therefore hypothesized that changes in vesicle speed distribution along the cell radius would be more obvious in cells with highly dynamic microtubule networks and performed a preliminary experiment in MRC5 human lung fibroblasts, which have a very sparse and dynamic microtubule cytoskeleton (Splinter et al., 2012, Molecular Biology of the Cell, PMID: 22956769). As shown in the figure below, we indeed found that vesicles move faster at the cell periphery. Even though these data are suggestive, characterization of this additional cell model goes beyond the scope of the current study, and we prefer not to include them in the manuscript.

In Figure 4E, the two examples are from different cells, and were both recorded at the cell periphery. The difference in vesicle speeds reflects general speed variability.

Do the authors think the intermediate speeds are a result of the motors switching roles? Additional discussion would help the reader interpret the results.

Presence of intermediate speeds of cargos driven by multiple motors of two types is most clear in Figure 3F-H, where multiple and different ratios of KIF5B and KIF13B motors are recruited to peroxisomes. As can be seen in Fig. 3G, the kymographs in these conditions are “smooth” and no evidence of motor switching can be detected at this spatiotemporal resolution. On the other hand, it has been previously beautifully shown by the Verhey lab that when artificial cargos are driven by just two motor molecules of different nature, switching does occur (Norris et al., 2014, Journal of Cell Biology, PMID: 25365993). This point is emphasized on page 12 of the revised manuscript. These data suggest that motors working in teams show different properties, and more detailed biophysical analysis will be needed to understand them.

Reviewer #2:

The manuscript by Serra-Marques, Martin, et al provides a tour de force in the analysis of vesicle transport by different kinesin motor proteins. The authors generate cell lines lacking a specific kinesin or combination of kinesins. They analyze the distribution and transport of Rab6 as a marker of most, if not all, secretory vesicles and show that both KIF5B and KIF13B localize to these vesicles and describe the contribution of each motor to vesicle transport. They show that the motors localize to the front of the vesicle when driving transport whereas KIF5B localizes to the back of the vesicle when opposing dynein. They find that KIF5B is the major motor and its action on "old" microtubules is facilitated by MAP7 whereas KIF13B facilitates transport on "new" microtubules to bring vesicles to the cell periphery. The manuscript is well-written, the data are properly controlled and analyzed, and the results are nicely presented. There are a few things the authors could do to tie up loose ends but these would not change the conclusions or impact of the work and I only have a couple of clarifying questions.

In Figure 2E, it seems like about half of the KIF5B events start at or near the Golgi whereas most of the KIF13B events are away from the Golgi? Did the authors find this to be generally true or just apparent in these example images?

We sincerely apologize for the misunderstanding here. To automatically track the vesicles, we had to manually exclude the Golgi area. Moreover, only processive and not complete tracks are shown. Therefore, no conclusions can be made from these data on the vesicle exit from the Golgi. We have indicated this clearly in the Results (page 8) and Discussion (page 21) of the revised manuscript and included more representative images in the revised Figure 2E.

In Figure 8G, the tracks for KIF13B-380 motility are difficult to see, which is surprising as KIF13B has been shown to be a superprocessive motor. Is this construct a dimer? If not, do the authors interpret the data as a high binding affinity of the monomer for new microtubules and if so, do they have any speculation on what could be the molecular mechanism? It appears as if KIF13B-380 and EB3 colocalize at the plus ends for a period of time before both are lost but then quickly replenished. Is this common?

KIF13B-380 construct used here contains a leucine zipper from GCN4 and is therefore dimeric. In the revised version of the paper, we have indicated this more clearly in the Results section on page 17 of the revised manuscript. KIF13B-380 does show processive motility, although this is difficult to see close to the outermost microtubule tip as the motor tends to accumulate there. This does not necessarily correlate with a strong accumulation of EB3, likely because EB3 signal is more sensitive to the dynamic state of the microtubule (it diminishes when microtubule growth rate decreases). We now provide a kymograph in Fig. 8G where the processive motility of KIF13B-380 is clearer.

Reviewer #3:

Serra-Marques and co-authors use CRISPR/Cas9 gene editing and live-cell imaging to dissect the roles of kinesin-1 (KIF5) and kinesin-3 (KIF13) in the transport of Rab6-positive vesicles. They find that both kinesins contribute to the movement of Rab6 vesicles. In the context of recent studies on the effect of MAP7 and doublecortin on kinesin motility, the authors show that MAP7 is enriched on central microtubules corresponding to the preferred localization of constitutively-active KIF5B-560-GFP. In contrast, KIF13 is enriched on dynamic, peripheral microtubules marked by EB3.

The manuscript provides needed insight into how multiple types of kinesin motors coordinate their function to transport vesicles. However, I outline several concerns about the analysis of vesicle and kinesin motility and its interpretation below.

Major concerns:

1) The metrics used to quantify motility are sensitive to tracking errors and uncertainty. The authors quantify the number of runs (Fig. 2D,F; 7C) and the average speed (Fig. 3A,B,D,E,H). The number of runs is sensitive to linking errors in tracking. A single, long trajectory is often misrepresented as multiple shorter trajectories. These linking errors are sensitive to small differences in the signal-to-noise ratio between experiments and conditions, and the set of tracking parameters used. The average speed is reported only for the long, processive runs (tracks>20 frames, segments<6 frames with velocity vector correlation >0.6). For many vesicular cargoes, these long runs represent <10% of the total motility. In the 4X-KO cells, it is expected there is very little processive motility, yet the average speed is higher than in control cells. Frame-to-frame velocities are often over-estimated due to the tracking uncertainty. Metrics like mean-squared displacement are less sensitive to tracking errors, and the velocity of the processive segments can be determined from the mean-squared displacement (see for example Chugh et al., 2018, Biophys. J.). The authors should also report either the average velocity of the entire run (including pauses), or the fraction of time represented by the processive segments to aid in interpreting the velocity data.

Two stages of the described tracking and data processing are responsible for the extraction of processive runs: the “linking” method used during the tracking, and the “trajectory segmentation” method, applied to the obtained tracks. The detection and linking of vesicles have been performed using our previously published tracking method (Chenouard et al., 2014, Nature Methods, PMID: 24441936). Our linking method uses multi-frame data association, taking into account detections from four subsequent image frames in order to extend and create a trajectory at any given time. This allows for dealing with temporal disappearance of particles (missing detections) for 1-2 frames and avoiding creation of breaks in longer trajectories. The method is robust to noise, spurious and missing detections and had been fully evaluated in the aforementioned paper (Chenouard et al., 2014) showing excellent performance compared to other tracking methods.

Having the trajectories describing the behavior of each particle, the track segmentation method had been applied to split each trajectory into a sequence of smaller parts (tracklets) describing processive runs and pieces of undirected (diffusive) motion. The algorithm that we used was validated earlier on an artificial dataset (please see Fig.S2e in Katrukha et al., Nat Commun 2017, PMID: 28322225). The chosen parameters were in the range where the algorithm provided less than 10% of false positives. Since the quantified and reported changes in the number of runs are six-fold (Fig.2D,F), we are quite certain that this estimated error (inherent to all automatic image analysis methods) does not affect our conclusions. Moreover, it is consistent with visual observations and manual analysis of representative movies.

Further, we agree that frame-to-frame velocities are often somewhat over-estimated due to the tracking uncertainty. We are aware of such overestimation which is very difficult to avoid. In our case, we estimated (using a Monte Carlo simulation) that such overestimation will positively bias the average not more than 3-6%. Since we focus not on the absolute values of velocities, but rather on the comparison between different conditions, such biasing will be present in all estimates of average velocity and will not affect the presented conclusions.

The usage of mean square displacement (MSD) to analyze trajectories containing both periods of processive runs and diffusive motion is confusing, since it represents average value over whole trajectories, resulting in the MSD slope which is in the range of 1.5 (i.e. between 1, diffusive and 2, processive; please see Fig.2c in Katrukha et al., 2017, Nature Communications, PMID: 28322225). Therefore, initial segmentation of trajectories is necessary, as it was performed in the paper by Chugh et al (Chugh et al., 2018, Biophysical Journal, PMID: 30021112; please see Fig.2e in that paper), suggested by the reviewer. In this paper the authors used an SCI algorithm, which is very similar to our analysis, relying on temporal correlations of velocities. Indeed, MSD analysis of only processive segments is less sensitive to tracking errors, but it reports an average velocity of the whole population of runs. This method is well suited if one would expect monodisperse velocity distribution (the case in Chugh et al, where single motor trajectories are analyzed). If there are subpopulations with different speeds (as we observed for Rab6 by manual kymograph analysis), this information will be averaged out. Therefore, we used histogram/distribution representations for our speed data, which in our opinion represents these data better.

Finally, we fully agree with the reviewers that the fractions of processive/diffusive motion should be reported. In the revised version, we have added new plots to the revised manuscript (Figure 2G-I, Figure 2 - figure supplement 2G) illustrating these data for different conditions. Our data fully support the reviewer’s statement that processive runs represent less than 10% of total vesicle motility (new Figure 2G). As could be expected, the total time vesicles spent in processive motion and the percentage of trajectories containing processive runs strongly depended on the presence of the motors (new Figure 2H,I). However, within trajectories that did have processive segments, the percentage of processive movement was similar (new Figure 2I).

We note that while our analysis is geared towards identification and characterization of processive runs (which was verified manually), analysis of diffusive movements poses additional challenges and is even more sensitive to linking errors. Therefore, we do not make any strong quantitative conclusions about the exact percentage and the properties of diffusive vesicle movements, and their detailed studies will require additional analytic efforts.

2) The authors show that transient expression of either KIF13B or KIF5B partially rescues Rab6 motility in 4X-KO cells and that knock-out of KIF13B and KIF5B have an additive effect. They also analyze two vesicles where KIF13B and KIF5B co-localize on the same vesicle. The authors conclude that KIF13B and KIF5B cooperate to transport Rab6 vesicles. However, the nature of this cooperation is unclear. Are the motors recruited sequentially to the vesicles, or at the same time? Is there a subset of vesicles enriched for KIF13B and a subset enriched for KIF5B? Is motor recruitment dependent on localization in the cell? These open questions should be addressed in the discussion.

Unfortunately, only fluorescent motors and not the endogenous ones can be detected on vesicles, so we cannot make any strong statements on this issue. Since KIF13B can compensate for the absence of KIF5B, it can be recruited to the vesicle when it emerges from the Golgi apparatus. However, in normal cells, KIF5B likely plays a more prominent role in pulling the vesicles from the Golgi, as Rab6 vesicles generated in the presence of KIF5B are larger (Figure 5I). We show in Figure 1G,H that KIF13B does not exchange on the vesicle and stays on the vesicle until it fuses with the plasma membrane. These data suggest that once recruited, KIF13B stays bound to the vesicle. Obtaining such data for KIF5B is more problematic because fewer copies of this motor are typically recruited to the vesicle (Figure 4B) and its signal is therefore weaker. Further research with endogenously tagged motors and highly sensitive imaging approaches will be needed to address the important open questions raised by the reviewer. We have added these points to the Discussion on pages 19 and 21 of the revised manuscript.

3) The authors suggest that KIF5B transports Rab6 vesicles along centrally-located microtubules while KIF13B drives transport on peripheral microtubules. Is the velocity of Rab6 vesicles different on central and peripheral microtubules in control cells?

As indicated in our answer to Major Comment 2 of Reviewer 1, we show in Figure 8B (grey curve) that vesicle speed remains relatively constant along the cell radius in control HeLa cells. We note, however, that our previous work has shown that in these cells microtubules are quite stable even at the cell periphery, due to the high activity of the CLASP-containing cortical microtubule stabilization complex (Mimori-Kiyosue et al., 2005, Journal of Cell Biology, PMID: 15631994; van der Vaart et al., 2013, Developmental Cell, PMID: 24120883). We therefore hypothesized that changes in vesicle speed distribution along the cell radius would be more obvious in cells with highly dynamic microtubule networks and performed a preliminary experiment in MRC5 human lung fibroblasts, which have a very sparse and dynamic microtubule cytoskeleton (Splinter et al., 2012, Molecular Biology of the Cell, PMID: 22956769). As shown in the figure above, we indeed found that vesicles move faster at the cell periphery.

4) The imaging and tracking of fluorescently-labeled kinesins in cells as shown in Fig. 4 is impressive. This is often challenging as kinesin-3 forms bright accumulations at the cell periphery and there is a large soluble pool of motors, making it difficult to image individual vesicles. The authors should provide additional details on how they addressed these challenges. Control experiments to assess crosstalk between fluorescence images would increase confidence in the colocalization results.

Imaging of vesicle motility was performed using TIRF microscopy focusing on regions where no strong motor accumulation was observed. We have little cross-talk between red and green channels, but channel cross talk in the three-color images shown in Figure 4E was indeed a potential concern. To address this potential issue, we performed the appropriate controls and added a new figure to the revised manuscript (Figure 4 – figure supplement 1). We conclude that we can reliably simultaneously detect blue, green and red channels without significant cross-talk on our microscope setup.

Author Response

Summary

This manuscript examines how N-linked glycosylation regulates the binding of polysaccharide hyaluronan (HA) to cell surface receptor CD44, to conclude that multiple sites exist but are controlled by the nature of the glycosylation. The reviewers appreciated many aspects of the work, but they have raised serious concerns about the experimental and simulation design. The reviewers suggested that the proposed alternative binding site may not be biologically relevant, as the relevant CD44-HA interactions are multivalent and cannot be supported by that site. They also suggested that the findings are not well supported by the NMR experiments, which could have been extended to allow comparisons of the glycosylation patterns hypothesised. Moreover, the MD simulations, despite being considerable in size, were limited in sampling different possibilities without bias from the initial HA placement, and there is not enough data to convince the readers of thorough sampling and reproducibility.

We understand the concerns raised in the review process. However, these concerns can be readily explained and fixed, as we explain below and are briefly introduced here.

• Our data are compatible with the currently accepted multivalent interaction of hyaluronan with several CD44 receptors. The argument that our data goes against it stems from an unfortunate figure provided in the first version of the manuscript. This figure suggested that a bound hyaluronan would not be able to span the length the protein in the upright binding mode. That is not true. We now show another, and more relevant snapshot where the bound hyaluronan indeed spans the length of HABD. Hence, we show that multivalent interaction is not precluded by the upright binding mode.

• We also clarify how our extensive simulation data were designed to avoid any bias. We admit that this was not obvious in the phrasing of our previous version.

• Many of the raised issues stem from the lack of certain critical simulations. We have now added these simulations into the revision.

Below we summarize the main issues raised by the reviewers, accompanied by our responses on how we have fixed them in the revised version of the manuscript.

Reviewer #1

The authors use MD simulations and NMR to study the cell surface adhesion receptor CD44 with the purpose of understanding the binding of carbohydrate polymer, hyaluronan (HA). In particular, this study focuses on the effects of N-glycosylation of the CD44 glycoprotein on potential HA binding. The authors previously proposed two lower affinity HA binding modes as alternatives to the primary mode seen in the crystal structure of the HA binding domain of CD44, driven by different arginine interactions, but overlapping with glycosylation sites that will affect HA binding. This study suggests that, because the canonical site appears blocked by glycans attached to the surface, HA would instead likely bind to an alternate parallel site with lower affinity, thus changing receptor affinity. The authors do not study HA binding to the glycosylated form directly, but undertake simulations of bound glycans to draw their conclusion. They do, however, place HA near the non-glycosylated CD44 in simulations, although it is not clear that MD sampling has been designed to provide unbiased observations of HA binding, or how the simulations help explain the NMR experiments.

To better highlight the message, we left out a significant portion of our total simulation data from the initial version of the manuscript. We have now added e.g. simulations of HA binding to the glycosylated form into our revised manuscript. Furthermore, we are confident that our design of the simulation systems allows unbiased sampling of the binding surface. That is, the hyaluronan hexamers were initially placed several nanometres away from the protein surface. After this, they were allowed to spontaneously sample the space and find their respective binding sites during the course of the simulations. They were not placed into the binding sites manually. However, there was a one system with two HA hexamers from which the other was placed into the canonical binding groove. This was done to test where the freely floating hexamer would bind when the primary binding site is taken. These points are illustrated more clearly in the new version of the manuscript. Finally, all our simulation data is publicly available (see the DOIs provided in the paper).

The data rely on libraries of MD simulation, which are substantial, with several replicas of a microsecond each. But what have these simulations really proved with reliability? Figure 2a shows that, while glycans stay roughly where they started, they are dynamic and cover much of the canonical HA binding site, which may be the case. From this the authors imply that the crystallographic site is significantly obstructed, the lower-affinity upright mode remains most accessible, and that the level of occlusion of the main site depends on the degree of glycosylation and size of the oligosaccharides. However, a full simulation of HA binding to this glycosylated surface was not attempted. It would have been good to see the glycans actually block unbiased simulation of canonical binding to the crystallographic site on long timescales (not being dislodged), but allow alternative binding to the parallel site, without initial placement there.

Commenting both points 1.1 and 1.2, we cropped a large portion of our simulation data from the initial version of the manuscript in order to better highlight the current message. However, we do have extensive simulation data of hyaluronan binding spontaneously to CD44 with different glycosylation patterns. For example, see Figure A below where HA is bound to glycosylated CD44-HABD. These data have been carefully analysed and incorporated into the revised manuscript.

Figure A. A representative binding pose between HA oligomer (dark red) and glycosylated (light blue, yellow, green, pink and purple) CD44-HABD (pale surface) extracted from our simulations.

HA was, however, added to the non-glycosylated CD44-HABD surface in simulations, but no clear data is shown to illustrate the extent of sampling, convergence and reproducibility, beyond some statistical analysis of contacts. It seems a total of 30 microseconds of the non-glycosylated protein with 2 or 3 nearby HA placed was run, leading to contacts. But how well did these 30 simulations sample HA movement and relative binding to sites, if at all? Figure 4 suggests that the HA stay where they have been put. As the MD is the dominant source of data for the paper, the extent of sampling and how the outcomes depend on the initial placement of molecules requires proof. Was any sampling of HA movement, such as between canonical and alternative parallel conformations seen in MD?

It is important to note that, in the non-glycosylated systems, the hyaluronan hexamers were initially placed several nanometres away from the protein surface. After this, they were allowed to spontaneously sample the space and find their respective binding sites during the course of the simulations. That is, they were not manually placed into the binding sites. We have changed the manuscript to better illustrate this key point.

We have also made the simulation data publicly available (see the DOIs provided in the paper). After inspection of the simulations, we are confident that the reviewers will agree that the results are reliable and do not suffer from convergence problems that could compromise the message we provide.

Moreover, we have even more simulation replicas ready with slightly different initial conditions that provide the same qualitative picture, see Figure B below (compare with Figure 4c in the original submission where one of the hyaluronan hexamers was initially placed in the crystallographic binding site). In these simulations, the hexamers have enhanced contacts with the crystallographic and upright mode residues despite being initially placed far from these binding sites. These simulations were already part of the manuscript.

Figure B. Hyaluronate-perturbed residues in the simulations. The colored surface displays the probability of a given residue to be in contact with HA6 in our additional simulations, where three hyaluronan hexamers were placed in solution far from the binding site.

The NMR is suggested to show that a short HA hexamer can bind to non-glycosylated CD44-HABD simultaneously in several modes at distinct binding sites, and that MD "correlates" with this. But is this MD biased by initial choices of where and how many HAs are placed, given HA movement is likely not well sampled?

The hyaluronan hexamers were initially placed several nanometers away from the binding sites. They were not placed into these binding sites manually. During the simulations the hexamers displayed several binding and unbinding events as they were spontaneously sampling the space and finding their respective binding sites during the course of the simulations.

While we saw multiple binding events to the proposed binding sites, the short size of the hyaluronan fragments was likely not enough for stable binding as the fragments often dissociated within few hundreds of nanoseconds. These points are now more clearly presented in the revised manuscript.

No MD seems to have been used to examine the blocking or lack thereof by antibody MEM-85 in glycosylated or non-glycosylated CD44.

This is not feasible using MD simulations, since the structure of the antibody is not available. Fortunately, there is no need for it, as we have direct and reliable experimental evidence using NMR as provided in the manuscript and in our previous work (Skerlova et.al. 2015; doi: 10.1016/j.jsb.2015.06.005). We therefore know where the antibody binds in CD44.

Reviewer #2

This manuscript is focused on understanding how N-linked glycosylation regulates the binding of the (very large) polysaccharide hyaluronan (HA) to its major cell surface receptor CD44, a question relevant, for example to the role of CD44 in mediating leukocyte migration in inflammation. The paper concludes that multiple binding sites for HA exist and that their occupancy is determined by the nature of the glycosylation, a suggestion first made by Teriete et al. (2004). The work is based on atomistic simulations with different glycan compositions and NMR spectroscopy on a non-glycosylated CD44 HA-binding domain (HABD) expressed in E. coli. While the question being researched is interesting and of biological relevance, there are flaws in the work.

The relevance also stems from the increasing applicability of HA in many biomedical devices and treatment strategies, such as tissue scaffolds and HA-coated nanoparticles for targeted drug delivery. However, we respectfully disagree with the proposed flaws. We address these suggested issues point-by-point in sections 2.2–2.5.

The paper describes how the well-established HA-binding site on CD44 (determined by a co-crystal structure; Banerji et al., 2007) is blocked by N-linked glycosylation (principally at N25 with a contribution from glycans at N100 and N110) and how certain glycans favour binding at a completely distinct binding site that lies perpendicular to the canonical 'crystallographic' binding site. This alternative 'upright' binding site, which has been proposed previously by the authors (Vuorio et al., 2017), needs further supporting experimental data.

Indeed, a characterization of the upright mode can be found from (Vuorio et al., 2017. PloS CB. 13:7). This characterization is based on mircoseconds of unbiased MD simulation data as well as extensive free energy calculations. We for example analysed the most important interactions, orientations of the sugar rings, and binding affinities. These data indicate that while the upright binding mode is weaker than the canonical binding mode (Banerji et al., 2007), it has good shape complementarity between the protein, with e.g. most of the sugar rings lying flat on the surface of the protein, indicating that it might have biological relevance.

The supporting experimental data is presented in the current publication. It has been improved and clarified for the revised version of the manuscript.

Firstly, unlike the 'crystallographic' binding site that forms an open-ended shallow groove on the surface of the protein allowing polymeric HA to bind (and multivalent interactions to take place), the 'upright' binding site is closed at one end and can thus only accommodate the reducing end of the polysaccharide (as apparent from Appendix 1 Figure 1). Its configuration means that it would be impossible for this mode of binding to allow multivalent interactions with polymeric HA. This is a major problem since biologically relevant CD44-HA interactions are multivalent where a single HA polymer interacts with a large number of CD44 molecules (e.g. see Wolny et al., 2010 J. Biol. Chem. 285, 30170-30180). So even if this binding site existed, an interaction between a single CD44 molecule on the cell surface with the reducing terminus of an HA polymer would be exceptionally weak.

We have data to show that our proposed secondary binding mode does not preclude multivalent CD44-hyaluronan interactions. This multivalent interaction, where a long hyaluronan binds simultaneously to several CD44 moieties, is important, and our secondary mode is compatible with it, see the new Figure C below. We acknowledge that our Figure 1 in the Appendix 1 was not sufficiently clear on this matter. That figure illustrated a structure of one possible CD44-hyaluronan complex obtained from just one of our simulations. However, we have a number of related CD44-hyaluronan complexes from other simulations where the bound ligand spans the full length of the protein, showing that the binding site can accommodate more than just the reducing end of the polysaccharide, and this is highlighted in the attached Figure C. Therefore, multivalent binding is not precluded by the upright binding mode. Unfortunately, the figure depicted in the SI of the original manuscript was misleading. To avoid this issue, it has been replaced in the revised manuscript.

Figure C. The secondary CD44-hyaluronan binding mode.

Secondly the NMR experiments performed in this study, purporting to provide evidence for multiple modes of binding, are problematic. Why weren't differentially glycosylated proteins used, i.e. where individual sites were mutated (e.g. +/- N25); this would have allowed comparisons of the glycosylation patterns hypothesised (based on the computer simulations) to favour the 'crystallographic' versus 'upright' modes.

Indeed, NMR experiments with glycosylated material would be ideal, but obtaining the required quantities of isotopically labelled protein with a homogeneous glycosylation pattern is not possible even using the state-of-the-art technology. In addition, the substantially increased molecular weight of the glycosylated protein would be out of the experimental window accessible by NMR spectroscopy. We strongly believe that the message of the paper is already sustained by a combination of our observations based on NMR experiments and MD simulation techniques together with the available literature data as detailed in Appendix A (see below).

While being aware of the difficulties of dealing with glycosylated CD44 using NMR, we designed a way to bypass this issue by combining multiple data from different experimental and simulation setups. All the data support the claims and conclusions made in our paper, see appendix A of this rebuttal. The existence of a weaker binding mode promoted upon glycosylation due to the primary binding site being covered is compatible with all available experimental and simulation data.

Furthermore, previous NMR studies have shown that the binding of HA to CD44 causes a considerable number of chemical shift changes due to the induction of a large conformational change in the protein (Teriete et al., 2004; Banerji et al., 2007), making it very difficult to identify amino acids directly involved in HA binding based on the NMR data. Moreover, this conformational change has been fully characterised for mouse CD44 with structures available in the absence and presence of HA (Banerji et al., 2007); this information should have been used to inform the interpretation of the shift mapping. In fact, the way in which the shift mapping data are interpreted is simplistic and doesn't fully take account of the reasons that NMR spectra can exhibit different exchange regimes.

We interpreted the NMR data very carefully. We are aware of the extent of conformational changes induced by HA binding in CD44-HABD, in fact, we identified them as a molecular mechanism underlying the mode of action for the MEM-85 antibody (Skerlova et.al. 2015; doi: 10.1016/j.jsb.2015.06.005). Therefore, we focused on the differential changes in the NMR signal positions of surface exposed residues upon titration with HA and MEM-85. We also observed different exchange regimes that allowed us to discriminate between different HA binding sites. We emphasized these points in the revised manuscript.

Reviewer #3

Vuorio and colleagues combine atomic resolution molecular dynamics simulations and NMR experiments to probe how glycosylation can bias binding of hyaluronan to one of several binding sites/modes on the CD44 hyaluronan binding domain. The results are of interest specifically to the field of CD44 biophysics and more generally to the broad field of glycosylation-dependent protein-ligand binding. The manuscript is clearly written, and the combination of data from computational and experimental methodologies is convincing. I especially commend the authors on the thorough molecular dynamics work, wherein they ran multiple simulations at microsecond timescale and tried different force fields to minimize the likelihood of their findings being an artifact of a particular force field.

The use of multiple force fields was indeed meant to alleviate potential force field specific issues. Likewise, the use of multiple simulation repeats with different starting positions and randomized atom velocities were meant to provide comprehensive statistics, minimizing the chances of over-interpreting any isolated phenomena.

Appendix A: Summary of the logic of the research procedure together with the experimental, simulation and literature results supporting each step.

1) Non-glycosylated CD44 binds HA *(NMR experiments) *

2) Non-glycosylated CD44 also binds HA in the presence of MEM-85 (NMR experiments)

3) Glycosylated CD44s that bind HA do not bind HA in the presence of MEM-85 (from literature [J. Bajorath, B. Greenfield, S. B. Munro, A. J. Day, A. Aruffo, Journal of Biological Chemistry 273, 338 (1998).]).

4) We show the MEM-85 binding site in non-glycosylated CD44 to be far from the canonical crystallographic binding region (NMR experiments). This MEM-85 binding site region is mostly inaccessible to typical N-glycans found in CD44 (MD simulation). Therefore, we expect that MEM-85 binds glycosylated CD44 in the same region. *(Our working hypothesis) *

5) Taken together, the above points indicate that MEM-85 covers at least partially the relevant HA binding mode in glycosylated CD44, which has zero overlap with the crystallographic mode. This supports the idea of an alternative binding mode to the crystallographic mode which must be readily available for glycosylated CD44. (Our finding)

6) Furthermore, heavily glycosylated CD44 variants cover a significant fraction of the crystallographic mode binding region (MD simulation), potentially making it unavailable for HA binding. This explains why non-glycosylated CD44 binds HA in the presence of MEM-85 (i.e., crystallographic mode is free), while glycosylated CD44 does not (i.e., crystallographic mode is covered with N-glycans). The upright region, on the other hand, experiences only minor coverage by the N-glycans in the glycosylated CD44 and is thus free to bind the ligand (MD simulations).

7) Non-glycosylated CD44 binds HA simultaneously with the crystallographic mode and the upright mode when exposed to high concentrations of small hyaluronan hexamers *(NMR titration and MD simulations). *

8) Pinpointing the position of the residues that experience the largest chemical shift during the titration experiments using non-glycosylated CD44 clearly shows the fingerprint of the canonical crystallographic mode but also a region compatible with our proposed upright mode (NMR titration experiments). These results are compatible with our simulations of several hyaluronan hexamers (MD simulation).

9) Upright binding mode is accessible to hyaluronan binding in the glycosylated CD44 (MD simulations shown in this letter that could be included to the paper if deemed necessary).

Glycosylation, and glycoscience in general, is one of the most challenging topics to understand in life sciences. We believe that our paper makes a very significant contribution to this area of research in the context of a central research problem and is exceptionally able to provide an atomic-level description of the HA-CD44 interaction under unambiguously known conditions.

Author Response:

Evaluation Summary:

Since DBS of the habenula is a new treatment, these are the first data of its kind and potentially of high interest to the field. Although the study mostly confirms findings from animal studies rather than bringing up completely new aspects of emotion processing, it certainly closes a knowledge gap. This paper is of interest to neuroscientists studying emotions and clinicians treating psychiatric disorders. Specifically the paper shows that the habenula is involved in processing of negative emotions and that it is synchronized to the prefrontal cortex in the theta band. These are important insights into the electrophysiology of emotion processing in the human brain.

The authors are very grateful for the reviewers’ positive comments on our study. We also thank all the reviewers for the comments which has helped to improve the manuscript.

Reviewer #1 (Public Review):

The study by Huang et al. report on direct recordings (using DBS electrodes) from the human habenula in conjunction with MEG recordings in 9 patients. Participants were shown emotional pictures. The key finding was a transient increase in theta/alpha activity with negative compared to positive stimuli. Furthermore, there was a later increase in oscillatory coupling in the same band. These are important data, as there are few reports of direct recordings from the habenula together with the MEG in humans performing cognitive tasks. The findings do provide novel insight into the network dynamics associated with the processing of emotional stimuli and particular the role of the habenula.

Recommendations:

How can we be sure that the recordings from the habenula are not contaminated by volume conduction; i.e. signals from neighbouring regions? I do understand that bipolar signals were considered for the DBS electrode leads. However, high-frequency power (gamma band and up) is often associated with spiking/MUA and considered less prone to volume conduction. I propose to also investigate that high-frequency gamma band activity recorded from the bipolar DBS electrodes and relate to the emotional faces. This will provide more certainty that the measured activity indeed stems from the habenula.

We thank the reviewer for the comment. As the reviewer pointed out, bipolar macroelectrode can detect locally generated potentials, as demonstrated in the case of recordings from subthalamic nucleus and especially when the macroelectrodes are inside the subthalamic nucleus (Marmor et al., 2017). However, considering the size of the habenula and the size of the DBS electrode contacts, we have to acknowledge that we cannot completely exclude the possibility that the recordings are contaminated by volume conduction of activities from neighbouring areas, as shown in Bertone-Cueto et al. 2019. We have now added extra information about the size of the habenula and acknowledged the potential contamination of activities from neighbouring areas through volume conduction in the ‘Limitation’:

"Another caveat we would like to acknowledge that the human habenula is a small region. Existing data from structural MRI scans reported combined habenula (the sum of the left and right hemispheres) volumes of ~ 30–36 mm3 (Savitz et al., 2011a; Savitz et al., 2011b) which means each habenula has the size of 2~3 mm in each dimension, which may be even smaller than the standard functional MRI voxel size (Lawson et al., 2013). The size of the habenula is also small relative to the standard DBS electrodes (as shown in Fig. 2A). The electrodes used in this study (Medtronic 3389) have electrode diameter of 1.27 mm with each contact length of 1.5 mm, and contact spacing of 0.5 mm. We have tried different ways to confirm the location of the electrode and to select the contacts that is within or closest to the habenula: 1.) the MRI was co-registered with a CT image (General Electric, Waukesha, WI, USA) with the Leksell stereotactic frame to obtain the coordinate values of the tip of the electrode; 2.) Post-operative CT was co-registered to pre-operative T1 MRI using a two-stage linear registration using Lead-DBS software. We used bipolar signals constructed from neighbouring macroelectrode recordings, which have been shown to detect locally generated potentials from subthalamic nucleus and especially when the macroelectrodes are inside the subthalamic nucleus (Marmor et al., 2017). Considering that not all contacts for bipolar LFP construction are in the habenula in this study, as shown in Fig. 2, we cannot exclude the possibility that the activities we measured are contaminated by activities from neighbouring areas through volume conduction. In particular, the human habenula is surrounded by thalamus and adjacent to the posterior end of the medial dorsal thalamus, so we may have captured activities from the medial dorsal thalamus. However, we also showed that those bipolar LFPs from contacts in the habenula tend to have a peak in the theta/alpha band in the power spectra density (PSD); whereas recordings from contacts outside the habenula tend to have extra peak in beta frequency band in the PSD. This supports the habenula origin of the emotional valence related changes in the theta/alpha activities reported here."

We have also looked at gamma band oscillations or high frequency activities in the recordings. However, we didn’t observe any peak in high frequency band in the average power spectral density, or any consistent difference in the high frequency activities induced by the emotional stimuli (Fig. S1). We suspect that high frequency activities related to MUA/spiking are very local and have very small amplitude, so they are not picked up by the bipolar LFPs measured from contacts with both the contact area for each contact and the between-contact space quite large comparative to the size of the habenula.

A

B

Figure S1. (A) Power spectral density of habenula LFPs across all time period when emotional stimuli were presented. The bold blue line and shadowed region indicates the mean ± SEM across all recorded hemispheres and the thin grey lines show measurements from individual hemispheres. (B) Time-frequency representations of the power response relative to pre-stimulus baseline for different conditions showing habenula gamma and high frequency activity are not modulated by emotional

References:

Savitz JB, Bonne O, Nugent AC, Vythilingam M, Bogers W, Charney DS, et al. Habenula volume in post-traumatic stress disorder measured with high-resolution MRI. Biology of Mood & Anxiety Disorders 2011a; 1(1): 7.

Savitz JB, Nugent AC, Bogers W, Roiser JP, Bain EE, Neumeister A, et al. Habenula volume in bipolar disorder and major depressive disorder: a high-resolution magnetic resonance imaging study. Biological Psychiatry 2011b; 69(4): 336-43.

Lawson RP, Drevets WC, Roiser JP. Defining the habenula in human neuroimaging studies. NeuroImage 2013; 64: 722-7.

Marmor O, Valsky D, Joshua M, Bick AS, Arkadir D, Tamir I, et al. Local vs. volume conductance activity of field potentials in the human subthalamic nucleus. Journal of Neurophysiology 2017; 117(6): 2140-51.

Bertone-Cueto NI, Makarova J, Mosqueira A, García-Violini D, Sánchez-Peña R, Herreras O, et al. Volume-Conducted Origin of the Field Potential at the Lateral Habenula. Frontiers in Systems Neuroscience 2019; 13:78.

Figure 3: the alpha/theta band activity is very transient and not band-limited. Why refer to this as oscillatory? Can you exclude that the TFRs of power reflect the spectral power of ERPs rather than modulations of oscillations? I propose to also calculate the ERPs and perform the TFR of power on those. This might result in a re-interpretation of the early effects in theta/alpha band.

We agree with the reviewer that the activity increase in the first time window with short latency after the stimuli onset is very transient and not band-limited. This raise the question that whether this is oscillatory or a transient evoked activity. We have now looked at this initial transient activity in different ways: 1.) We quantified the ERP in LFPs locked to the stimuli onset for each emotional valence condition and for each habenula. We investigated whether there was difference in the amplitude or latency of the ERP for different stimuli emotional valence conditions. As showing in the following figure, there is ERP with stimuli onset with a positive peak at 402 ± 27 ms (neutral stimuli), 407 ± 35 ms (positive stimuli), 399 ± 30 ms (negative stimuli). The flowing figure (Fig. 3–figure supplement 1) will be submitted as figure supplement related to Fig. 3. However, there was no significant difference in ERP latency or amplitude caused by different emotional valence stimuli. 2.) We have quantified the pure non-phase-locked (induced only) power spectra by calculating the time-frequency power spectrogram after subtracting the ERP (the time-domain trial average) from time-domain neural signal on each trial (Kalcher and Pfurtscheller, 1995; Cohen and Donner, 2013). This shows very similar results as we reported in the main manuscript, as shown in Fig. 3–figure supplement 2. These further analyses show that even though there were event related potential changes time locked around the stimuli onset, and this ERP did NOT contribute to the initial broad-band activity increase at the early time window shown in plot A-C in Figure 3. The figures of the new analyses and following have now been added in the main text:

"In addition, we tested whether stimuli-related habenula LFP modulations primarily reflect a modulation of oscillations, which is not phase-locked to stimulus onset, or, alternatively, if they are attributed to evoked event-related potential (ERP). We quantified the ERP for each emotional valence condition for each habenula. There was no significant difference in ERP latency or amplitude caused by different emotional valence stimuli (Fig. 3–figure supplement 1). In addition, when only considering the non phase-locked activity by removing the ERP from the time series before frequency-time decomposition, the emotional valence effect (presented in Fig. 3–figure supplement 2) is very similar to those shown in Fig.3. These additional analyses demonstrated that the emotional valence effect in the LFP signal is more likely to be driven by non-phase-locked (induced only) activity."

A

B

Fig. 3–figure supplement 1. Event-related potential (ERP) in habenula LFP signals in different emotional valence (neutral, positive and negative) conditions. (A) Averaged ERP waveforms across patients for different conditions. (B) Peak latency and amplitude (Mean ± SEM) of the ERP components for different conditions.

Fig. 3–figure supplement 2. Non-phase-locked activity in different emotional valence (neutral, positive and negative) conditions (N = 18). (A) Time-frequency representation of the power changes relative to pre-stimulus baseline for three conditions. Significant clusters (p < 0.05, non-parametric permutation test) are encircled with a solid black line. (B) Time-frequency representation of the power response difference between negative and positive valence stimuli, showing significant increased activity the theta/alpha band (5-10 Hz) at short latency (100-500 ms) and another increased theta activity (4-7 Hz) at long latencies (2700-3300 ms) with negative stimuli (p < 0.05, non-parametric permutation test). (C) Normalized power of the activities at theta/alpha (5-10 Hz) and theta (4-7 Hz) band over time. Significant difference between the negative and positive valence stimuli is marked by a shadowed bar (p < 0.05, corrected for multiple comparison).

References:

Kalcher J, Pfurtscheller G. Discrimination between phase-locked and non-phase-locked event-related EEG activity. Electroencephalography and Clinical Neurophysiology 1995; 94(5): 381-4.

Cohen MX, Donner TH. Midfrontal conflict-related theta-band power reflects neural oscillations that predict behavior. Journal of Neurophysiology 2013; 110(12): 2752-63.

Figure 4D: can you exclude that the frontal activity is not due to saccade artifacts? Only eye blink artifacts were reduced by the ICA approach. Trials with saccades should be identified in the MEG traces and rejected prior to further analysis.

We understand and appreciate the reviewer’s concern on the source of the activity modulations shown in Fig. 4D. We tried to minimise the eye movement or saccade in the recording by presenting all figures at the centre of the screen, scaling all presented figures to similar size, and presenting a white cross at the centre of the screen preparing the participants for the onset of the stimuli. Despite this, participants my still make eye movements and saccade in the recording. We used ICA to exclude the low frequency large amplitude artefacts which can be related to either eye blink or other large eye movements. However, this may not be able to exclude artefacts related to miniature saccades. As shown in Fig. 4D, on the sensor level, the sensors with significant difference between the negative vs. positive emotional valence condition clustered around frontal cortex, close to the eye area. However, we think this is not dominated by saccades because of the following two reasons:

1.) The power spectrum of the saccadic spike artifact in MEG is characterized by a broadband peak in the gamma band from roughly 30 to 120 Hz (Yuval-Greenberg et al., 2008; Keren et al., 2010). In this study the activity modulation we observed in the frontal sensors are limited to the theta/alpha frequency band, so it is different from the power spectra of the saccadic spike artefact.

2.) The source of the saccadic spike artefacts in MEG measurement tend to be localized to the region of the extraocular muscles of both eyes (Carl et al., 2012).We used beamforming source localisation to identify the source of the activity modulation reported in Fig. 4D. This beamforming analysis identified the source to be in the Broadmann area 9 and 10 (shown in Fig. 5). This excludes the possibility that the activity modulation in the sensor level reported in Fig. 4D is due to saccades. In addition, Broadman area 9 and 10, have previously been associated with emotional stimulus processing (Bermpohl et al., 2006), Broadman area 9 in the left hemisphere has also been used as the target for repetitive transcranial magnetic stimulation (rTMS) as a treatment for drug-resistant depression (Cash et al., 2020). The source localisation results, together with previous literature on the function of the identified source area suggest that the activity modulation we observed in the frontal cortex is very likely to be related to emotional stimuli processing.

References:

Yuval-Greenberg S, Tomer O, Keren AS, Nelken I, Deouell LY. Transient induced gamma-band response in EEG as a manifestation of miniature saccades. Neuron 2008; 58(3): 429-41.

Keren AS, Yuval-Greenberg S, Deouell LY. Saccadic spike potentials in gamma-band EEG: characterization, detection and suppression. NeuroImage 2010; 49(3): 2248-63.

Carl C, Acik A, Konig P, Engel AK, Hipp JF. The saccadic spike artifact in MEG. NeuroImage 2012; 59(2): 1657-67.

Bermpohl F, Pascual-Leone A, Amedi A, Merabet LB, Fregni F, Gaab N, et al. Attentional modulation of emotional stimulus processing: an fMRI study using emotional expectancy. Human Brain Mapping 2006; 27(8): 662-77.

Cash RFH, Weigand A, Zalesky A, Siddiqi SH, Downar J, Fitzgerald PB, et al. Using Brain Imaging to Improve Spatial Targeting of Transcranial Magnetic Stimulation for Depression. Biological Psychiatry 2020.

The coherence modulations in Fig 5 occur quite late in time compared to the power modulations in Fig 3 and 4. When discussing the results (in e.g. the abstract) it reads as if these findings are reflecting the same process. How can the two effect reflect the same process if the timing is so different?

As the reviewer pointed out correctly, the time window where we observed the coherence modulations happened quite late in time compared to the initial power modulations in the frontal cortex and the habenula (Fig. 4). And there was another increase in the theta band activities in the habenula area even later, at around 3 second after stimuli onset when the emotional figure has already disappeared. Emotional response is composed of a number of factors, two of which are the initial reactivity to an emotional stimulus and the subsequent recovery once the stimulus terminates or ceases to be relevant (Schuyler et al., 2014). We think these neural effects we observed in the three different time windows may reflect different underlying processes. We have discussed this in the ‘Discussion’:

"These activity changes at different time windows may reflect the different neuropsychological processes underlying emotion perception including identification and appraisal of emotional material, production of affective states, and autonomic response regulation and recovery (Phillips et al., 2003a). The later effects of increased theta activities in the habenula when the stimuli disappeared were also supported by other literature showing that, there can be prolonged effects of negative stimuli in the neural structure involved in emotional processing (Haas et al., 2008; Puccetti et al., 2021). In particular, greater sustained patterns of brain activity in the medial prefrontal cortex when responding to blocks of negative facial expressions was associated with higher scores of neuroticism across participants (Haas et al., 2008). Slower amygdala recovery from negative images also predicts greater trait neuroticism, lower levels of likability of a set of social stimuli (neutral faces), and declined day-to-day psychological wellbeing (Schuyler et al., 2014; Puccetti et al., 2021)."

References:

Schuyler BS, Kral TR, Jacquart J, Burghy CA, Weng HY, Perlman DM, et al. Temporal dynamics of emotional responding: amygdala recovery predicts emotional traits. Social Cognitive and Affective Neuroscience 2014; 9(2): 176-81.

Phillips ML, Drevets WC, Rauch SL, Lane R. Neurobiology of emotion perception I: The neural basis of normal emotion perception. Biological Psychiatry 2003a; 54(5): 504-14.

Haas BW, Constable RT, Canli T. Stop the sadness: Neuroticism is associated with sustained medial prefrontal cortex response to emotional facial expressions. NeuroImage 2008; 42(1): 385-92.

Puccetti NA, Schaefer SM, van Reekum CM, Ong AD, Almeida DM, Ryff CD, et al. Linking Amygdala Persistence to Real-World Emotional Experience and Psychological Well-Being. Journal of Neuroscience 2021: JN-RM-1637-20.

Be explicit on the degrees of freedom in the statistical tests given that one subject was excluded from some of the tests.

We thank the reviewers for the comment. The number of samples used for each statistics analysis are stated in the title of the figures. We have now also added the degree of freedom in the main text when parametric statistical tests such as t-test or ANOVAs have been used. When permutation tests (which do not have any degrees of freedom associated with it) are used, we have now added the number of samples for the permutation test.

Reviewer #2 (Public Review):

In this study, Huang and colleagues recorded local field potentials from the lateral habenula in patients with psychiatric disorders who recently underwent surgery for deep brain stimulation (DBS). The authors combined these invasive measurements with non-invasive whole-head MEG recordings to study functional connectivity between the habenula and cortical areas. Since the lateral habenula is believed to be involved in the processing of emotions, and negative emotions in particular, the authors investigated whether brain activity in this region is related to emotional valence. They presented pictures inducing negative and positive emotions to the patients and found that theta and alpha activity in the habenula and frontal cortex increases when patients experience negative emotions. Functional connectivity between the habenula and the cortex was likewise increased in this band. The authors conclude that theta/alpha oscillations in the habenula-cortex network are involved in the processing of negative emotions in humans.

Because DBS of the habenula is a new treatment tested in this cohort in the framework of a clinical trial, these are the first data of its kind. Accordingly, they are of high interest to the field. Although the study mostly confirms findings from animal studies rather than bringing up completely new aspects of emotion processing, it certainly closes a knowledge gap.

In terms of community impact, I see the strengths of this paper in basic science rather than the clinical field. The authors demonstrate the involvement of theta oscillations in the habenula-prefrontal cortex network in emotion processing in the human brain. The potential of theta oscillations to serve as a marker in closed-loop DBS, as put forward by the authors, appears less relevant to me at this stage, given that the clinical effects and side-effects of habenula DBS are not known yet.

We thank the reviewers for the favourable comments about the implication of our study in basic science and about the value of our study in closing a knowledge gap. We agree that further studies would be required to make conclusions about the clinical effects and side-effects of habenula DBS.

Detailed comments:

The group-average MEG power spectrum (Fig. 4B) suggests that negative emotions lead to a sustained theta power increase and a similar effect, though possibly masked by a visual ERP, can be seen in the habenula (Fig. 3C). Yet the statistics identify brief elevations of habenula theta power at around 3s (which is very late), a brief elevation of prefrontal power a time 0 or even before (Fig. 4C) and a brief elevation of Habenula-MEG theta coherence around 1 s. It seems possible that this lack of consistency arises from a low signal-to-noise ratio. The data contain only 27 trails per condition on average and are contaminated by artifacts caused by the extension wires.

With regard to the nature of the activity modulation with short latency after stimuli onset: whether this is an ERP or oscillation? We have now investigated this. In summary, by analysing the ERP and removing the influence of the ERP from the total power spectra, we didn’t observe stimulus emotional valence related modulation in the ERP, and the modulation related to emotional valence in the pure induced (non-phase-locked) power spectra was similar to what we have observed in the total power shown in Fig. 3. Therefore, we argue that the theta/alpha increase with negative emotional stimuli we observed in both habenula and prefrontal cortex 0-500 ms after stimuli onset are not dominated by visual or other ERP.

With regard to the signal-to-noise ratio from only 27 trials per condition on average per participant: We have tried to clean the data by removing the trials with obvious artefacts characterised by increased measurements in the time domain over 5 times the standard deviation and increased activities across all frequency bands in the frequency domain. After removing the trials with artefacts, we have 27 trials per condition per subject on average. We agree that 27 trials per condition on average is not a high number, and increasing the number of trials would further increase the signal-to-noise ratio. However, our studies with EEG recordings and LFP recordings from externalised patients have shown that 30 trials was enough to identify reduction in the amplitude of post-movement beta oscillations at the beginning of visuomotor adaption in the motor cortex and STN (Tan et al., 2014a; Tan et al., 2014b). These results of motor error related modulation in the post-movement beta have been repeated by other studies from other groups. In Tan et al. 2014b, with simultaneous EEG and STN LFP measurements and a similar number of trials (around 30), we also quantified the time-course of STN-motor cortex coherence during voluntary movements. This pattern has also been repeated in a separate study from another group with around 50 trials per participant (Talakoub et al., 2016). In addition, similar behavioural paradigm (passive figure viewing paradigm) has been used in two previous studies with LFP recordings from STN from different patient groups (Brucke et al., 2007; Huebl et al., 2014). In both studies, a similar number of trials per condition around 27 was used. The authors have identified meaningful activity modulation in the STN by emotional stimuli. Therefore, we think the number of trials per condition was sufficient to identify emotional valence induced difference in the LFPs in the paradigm.

We agree that the measurement of coherence can be more susceptible to noise and suffer from the reduced signal-to-noise ratio in MEG recording. In Hirschmann et al. 2013, 5 minutes of resting recording and 5 minutes of movement recording from 10 PD patients were used to quantify movement related changes in STN-cortical coherence and how this was modulated by levodopa (Hirschmann et al., 2013). Litvak et al. (2012) have identified movement-related changes in the coherence between STN LFP and motor cortex with recording with simultaneous STN LFP and MEG recordings from 17 PD patients and 20 trials in average per participant per condition (Litvak et al., 2012). With similar methods, van Wijk et al. (2017) used recordings from 9 patients and around on average in 29 trials per hand per condition, and they identified reduced cortico-pallidal coherence in the low-beta decreases during movement (van Wijk et al., 2017). So the trial number per condition participant we used in this study are comparable to previous studies.

The DBS extension wires do reduce signal-to-noise ratio in the MEG recording. therefore the spatiotemporal Signal Space Separation (tSSS) method (Taulu and Simola, 2006) implemented in the MaxFilter software (Elekta Oy, Helsinki, Finland) has been applied in this study to suppress strong magnetic artifacts caused by extension wires. This method has been proved to work well in de-noising the magnetic artifacts and movement artifacts in MEG data in our previous studies (Cao et al., 2019; Cao et al., 2020). In addition, the beamforming method proposed by several studies (Litvak et al., 2010; Hirschmann et al., 2011; Litvak et al., 2011) has been used in this study. In Litvak et al., 2010, the artifacts caused by DBS extension wires was detailed described and the beamforming was demonstrated to effectively suppress artifacts and thereby enable both localization of cortical sources coherent with the deep brain nucleus. We have now added more details and these references about the data cleaning and the beamforming method in the main text. With the beamforming method, we did observe the standard movement-related modulation in the beta frequency band in the motor cortex with 9 trials of figure pressing movements, shown in the following figure for one patient as an example (Figure 5–figure supplement 1). This suggests that the beamforming method did work well to suppress the artefacts and help to localise the source with a low number of trials. The figure on movement-related modulation in the motor cortex in the MEG signals have now been added as a supplementary figure to demonstrate the effect of the beamforming.

Figure 5–figure supplement 1. (A) Time-frequency maps of MEG activity for right hand button press at sensor level from one participant (Case 8). (B) DICS beamforming source reconstruction of the areas with movement-related oscillation changes in the range of 12-30 Hz. The peak power was located in the left M1 area, MNI coordinate [-37, -12, 43].

References:

Tan H, Jenkinson N, Brown P. Dynamic neural correlates of motor error monitoring and adaptation during trial-to-trial learning. Journal of Neuroscience 2014a; 34(16): 5678-88.

Tan H, Zavala B, Pogosyan A, Ashkan K, Zrinzo L, Foltynie T, et al. Human subthalamic nucleus in movement error detection and its evaluation during visuomotor adaptation. Journal of Neuroscience 2014b; 34(50): 16744-54.

Talakoub O, Neagu B, Udupa K, Tsang E, Chen R, Popovic MR, et al. Time-course of coherence in the human basal ganglia during voluntary movements. Scientific Reports 2016; 6: 34930.

Brucke C, Kupsch A, Schneider GH, Hariz MI, Nuttin B, Kopp U, et al. The subthalamic region is activated during valence-related emotional processing in patients with Parkinson's disease. European Journal of Neuroscience 2007; 26(3): 767-74.

Huebl J, Spitzer B, Brucke C, Schonecker T, Kupsch A, Alesch F, et al. Oscillatory subthalamic nucleus activity is modulated by dopamine during emotional processing in Parkinson's disease. Cortex 2014; 60: 69-81.

Hirschmann J, Ozkurt TE, Butz M, Homburger M, Elben S, Hartmann CJ, et al. Differential modulation of STN-cortical and cortico-muscular coherence by movement and levodopa in Parkinson's disease. NeuroImage 2013; 68: 203-13.

Litvak V, Eusebio A, Jha A, Oostenveld R, Barnes G, Foltynie T, et al. Movement-related changes in local and long-range synchronization in Parkinson's disease revealed by simultaneous magnetoencephalography and intracranial recordings. Journal of Neuroscience 2012; 32(31): 10541-53.

van Wijk BCM, Neumann WJ, Schneider GH, Sander TH, Litvak V, Kuhn AA. Low-beta cortico-pallidal coherence decreases during movement and correlates with overall reaction time. NeuroImage 2017; 159: 1-8.

Taulu S, Simola J. Spatiotemporal signal space separation method for rejecting nearby interference in MEG measurements. Physics in Medicine and Biology 2006; 51(7): 1759-68.

Cao C, Huang P, Wang T, Zhan S, Liu W, Pan Y, et al. Cortico-subthalamic Coherence in a Patient With Dystonia Induced by Chorea-Acanthocytosis: A Case Report. Frontiers in Human Neuroscience 2019; 13: 163.

Cao C, Li D, Zhan S, Zhang C, Sun B, Litvak V. L-dopa treatment increases oscillatory power in the motor cortex of Parkinson's disease patients. NeuroImage Clinical 2020; 26: 102255.

Litvak V, Eusebio A, Jha A, Oostenveld R, Barnes GR, Penny WD, et al. Optimized beamforming for simultaneous MEG and intracranial local field potential recordings in deep brain stimulation patients. NeuroImage 2010; 50(4): 1578-88.

Litvak V, Jha A, Eusebio A, Oostenveld R, Foltynie T, Limousin P, et al. Resting oscillatory cortico-subthalamic connectivity in patients with Parkinson's disease. Brain 2011; 134(Pt 2): 359-74.

Hirschmann J, Ozkurt TE, Butz M, Homburger M, Elben S, Hartmann CJ, et al. Distinct oscillatory STN-cortical loops revealed by simultaneous MEG and local field potential recordings in patients with Parkinson's disease. NeuroImage 2011; 55(3): 1159-68.

I doubt that the correlation between habenula power and habenula-MEG coherence (Fig. 6C) is informative of emotion processing. First, power and coherence in close-by time windows are likely to to be correlated irrespective of the task/stimuli. Second, if meaningful, one would expect the strongest correlation for the negative condition, as this is the only condition with an increase of theta coherence and a subsequent increase of theta power in the habenula. This, however, does not appear to be the case.

The authors included the factors valence and arousal in their linear model and found that only valence correlated with electrophysiological effects. I suspect that arousal and valence scores are highly correlated. When fed with informative yet highly correlated variables, the significance of individual input variables becomes difficult to assess in many statistical models. Hence, I am not convinced that valence matters but arousal not.

For the correlation shown in Fig. 6C, we used a linear mixed-effect modelling (‘fitlme’ in Matlab) with different recorded subjects as random effects to investigate the correlations between the habenula power and habenula-MEG coherence at an earlier window, while considering all trials together. Therefore the reported value in the main text and in the figure (k = 0.2434 ± 0.1031, p = 0.0226, R2 = 0.104) show the within subjects correlation that are consistent across all measured subjects. The correlation is likely to be mediated by emotional valence condition, as negative emotional stimuli tend to be associated with both high habenula-MEG coherence and high theta power in the later time window tend to happen in the trials with.

The arousal scores are significantly different for the three valence conditions as shown in Fig. 1B. However, the arousal scores and the valence scores are not monotonically correlated, as shown in the following figure (Fig. S2). The emotional neutral figures have the lowest arousal value, but have the valence value sitting between the negative figures and the positive figures. We have now added the following sentence in the main text:

"This nonlinear and non-monotonic relationship between arousal scores and the emotional valence scores allowed us to differentiate the effect of the valence from arousal."

Table 2 in the main text show the results of the linear mixed-effect modelling with the neural signal as the dependent variable and the valence and arousal scores as independent variables. Because of the non-linear and non-monotonic relationship between the valence and arousal scores, we think the significance of individual input variables is valid in this statistical model. We have now added a new figure (shown below, Fig. 7) with scatter plots showing the relationship between the electrophysiological signal and the arousal and emotional valence scores separately using Spearman’s partial correlation analysis. In each scatter plot, each dot indicates the average measurement from one participant in one emotional valence condition. As shown in the following figure, the electrophysiological measurements linearly correlated with the valence score, but not with the arousal scores. However, the statistics reported in this figure considered all the dots together. The linear mixed effect modelling taking into account the interdependency of the measurements from the same participant. So the results reported in the main text using linear mixed effect modelling are statistically more valid, but supplementary figure here below illustrate the relationship.

Figure S2. Averaged valence and arousal ratings (mean ± SD) for figures of the three emotional condition. (B) Scatter plots showing the relationship between arousal and valence scores for each emotional condition for each participant.

Figure 7. Scatter plots showing how early theta/alpha band power increase in the frontal cortex (A), theta/alpha band frontal cortex-habenula coherence (B) and theta band power increase in habenula stimuli (C) changed with emotional valence (left column) and arousal (right column). Each dot shows the average of one participant in each categorical valence condition, which are also the source data of the multilevel modelling results presented in Table 2. The R and p value in the figure are the results of partial correlation considering all data points together.

Page 8: "The time-varying coherence was calculated for each trial". This is confusing because coherence quantifies the stability of a phase difference over time, i.e. it is a temporal average, not defined for individual trials. It has also been used to describe the phase difference stability over trials rather than time, and I assume this is the method applied here. Typically, the greatest coherence values coincide with event-related power increases, which is why I am surprised to see maximum coherence at 1s rather than immediately post-stimulus.

We thank the reviewer for pointing out this incorrect description. As the reviewer pointed out correctly, the method we used describe the phase difference stability over trials rather than time. We have now clarified how coherence was calculated and added more details in the methods:

"The time-varying cross trial coherence between each MEG sensor and the habenula LFP was first calculated for each emotional valence condition. For this, time-frequency auto- and cross-spectral densities in the theta/alpha frequency band (5-10 Hz) between the habenula LFP and each MEG channel at sensor level were calculated using the wavelet transform-based approach from -2000 to 4000 ms for each trial with 1 Hz steps using the Morlet wavelet and cycle number of 6. Cross-trial coherence spectra for each LFP-MEG channel combination was calculated for each emotional valence condition for each habenula using the function ‘ft_connectivityanalysis’ in Fieldtrip (version 20170628). Stimulus-related changes in coherence were assessed by expressing the time-resolved coherence spectra as a percentage change compared to the average value in the -2000 to -200 ms (pre-stimulus) time window for each frequency."

In the Morlet wavelet analysis we used here, the cycle number (C) determines the temporal resolution and frequency resolution for each frequency (F). The spectral bandwidth at a given frequency F is equal to 2F/C while the wavelet duration is equal to C/F/pi. We used a cycle number of 6. For theta band activities around 5 Hz, we will have the spectral bandwidth of 25/6 = 1.7 Hz and the wavelet duration of 6/5/pi = 0.38s = 380ms.

As the reviewer noticed, we observed increased activities across a wide frequency band in both habenula and the prefrontal cortex within 500 ms after stimuli onset. But the increase of cross-trial coherence starts at around 300 ms. The increase of coherence in a time window without increase of power in either of the two structures indicates a phase difference stability across trials in the oscillatory activities from the two regions, and this phase difference stability across trials was not secondary to power increase.

Reviewer #3 (Public Review):

This paper describes the oscillatory activity of the habenula using local field potentials, both within the region and, through the use of MEG, in connection to the prefrontal cortex. The characteristics of this activity were found to vary with the emotional valence but not with arousal. Sheding light on this is relevant, because the habenula is a promising target for deep brain stimulation.

In general, because I am not much on top of the literature on the habenula, I find difficult to judge about the novelty and the impact of this study. What I can say is that I do find the paper is well-written and very clear; and the methods, although quite basic (which is not bad), are sound and rigourous.

We thank the reviewer for the positive comments about the potential implication of our study and on the methods we used.

On the less positive side, even though I am aware that in this type of studies it is difficult to have high N, the very low N in this case makes me worry about the robustness and replicability of the results. I'm sure I have missed it and it's specified somewhere, but why is N different for the different figures? Is it because only 8 people had MEG? The number of trials seems also a somewhat low. Therefore, I feel the authors perhaps need to make an effort to make up for the short number of subjects in order to add confidence to the results. I would strongly recommend to bootstrap the statistical analysis and extract non-parametric confidence intervals instead of showing parametric standard errors whenever is appropriate. When doing that, it must be taken into account that each two of the habenula belong to the same person; i.e. one bootstraps the subjects not the habenula.

We do understand and appreciate the concern of the reviewer on the low sample numbers due to the strict recruitment criteria for this very early stage clinical trial: 9 patients for bilateral habenula LFPs, and 8 patients with good quality MEGs. Some information to justify the number of trials per condition for each participant has been provided in the reply to the Detailed Comments 1 from Reviewer 2. The sample number used in each analysis was included in the figures and in the main text.

We have used non-parametric cluster-based permutation approach (Maris and Oostenveld, 2007) for all the main results as shown in Fig. 3-5. Once the clusters (time window and frequency band) with significant differences for different emotional valence conditions have been identified, parametric statistical test was applied to the average values of the clusters to show the direction of the difference. These parametric statistics are secondary to the main non-parametric permutation test.

In addition, the DICS beamforming method was applied to localize cortical sources exhibiting stimuli-related power changes and cortical sources coherent with deep brain LFPs for each subject for positive and negative emotional valence conditions respectively. After source analysis, source statistics over subjects was performed. Non-parametric permutation testing with or without cluster-based correction for multiple comparisons was applied to statistically quantify the differences in cortical power source or coherence source between negative and positive emotional stimuli.

References:

Maris E, Oostenveld R. Nonparametric statistical testing of EEG- and MEG-data. Journal of Neuroscience Methods 2007; 164(1): 177-90.

Related to this point, the results in Figure 6 seem quite noisy, because interactions (i.e. coherence) are harder to estimate and N is low. For example, I have to make an effort of optimism to believe that Fig 6A is not just noise, and the result in Fig 6C is also a bit weak and perhaps driven by the blue point at the bottom. My read is that the authors didn't do permutation testing here, and just a parametric linear-mixed effect testing. I believe the authors should embed this into permutation testing to make sure that the extremes are not driving the current p-value.

We have now quantified the coherence between frontal cortex-habenula and occipital cortex-habenula separately (please see more details in the reply to Reviewer 2 (Recommendations for the authors 6). The new analysis showed that the increase in the theta/alpha band coherence around 1 s after the negative stimuli was only observed between prefrontal cortex-habenula and not between occipital cortex-habenula. This supports the argument that Fig. 6A is not just noise.

Author Response

Reviewer #1:

Köster and colleagues present a brief report in which they study in 9 month-old babies the electrophysiological responses to expected and unexpected events. The major finding is that in addition to a known ERP response, an NC present between 400-600 ms, they observe a differential effect in theta oscillations. The latter is a novel result and it is linked to the known properties of theta oscillations in learning. This is a nice study, with novel results and well presented. My major reservation however concerns the push the authors make for the novelty of the results and their interpretation as reflecting brain dynamics and rhythms. The reason for that is, that any ERP, passed through the lens of a wavelet/FFT etc, will yield a response at a particular frequency. This is especially the case for families of ERP responses related to unexpected event e.g., MMR, and NC, etc. For which there is plenty of literature linking them to responses to surprising event, and in particular in babies; and which given their timing will be reflected in delta/theta oscillations. The reason why I am pressing on this issue, is because there is an old, but still ongoing debate attempting to dissociate intrinsic brain dynamics from simple event related responses. This is by no means trivial and I certainly do not expect the authors to resolve it, yet I would expect the authors to be careful in their interpretation, to warn the reader that the result could just reflect the known ERP, to avoid introducing confusion in the field.

We would like to thank the author for highlighting the novelty of the results. Critically, there is one fundamental difference in investigating the ERP response and the trial-wise oscillatory power, which we have done in the present analysis: when looking at the evoked oscillatory response (i.e., the TF characteristics of the ERP), the signal is averaged over trials first and then subjected to a wavelet transform. However, when looking at the ongoing (or total) oscillatory response, the wavelet transform is applied at the level of the single trial, before the TF response of the single trials is averaged across the trials of one condition trials (for a classical illustration, see Tallon-Baudry & Bertrand, 1999; TICS, Box 2). We have now made this distinction more salient throughout the manuscript.

In the present study, the results did not suggest a relation between the ERP and the ongoing theta activity, because the topography, temporal evolution, and polarity of the ERP and the theta response were very dissimilar: Looking at Figure 2 (A and B) and Figure 3 (B and C), the Nc peaks at central electrodes, but the theta response is more distributed, and the expected versus unexpected difference was specific for the .4 to .6 s time window, but the theta difference lasted the whole trial. Furthermore, the NC was higher for expected versus unexpected, which should (due to the low frequency) rather lead to a higher theta power for unexpected, in contrast to expected events for the time frequency analysis for the Nc. To verify this intuition, we now ran a wavelet analysis on the evoked response (i.e., the ERP) and, for a direct comparison, also plotted the ongoing oscillatory response for the central electrodes (see Additional Figure 1). These additional analyses nicely illustrate that the trial-wise theta response provides a fundamentally different approach to analyze oscillatory brain dynamics.

Because this is likely of interest to many readers, we also report the results of the wavelet analysis of the ERP versus the analysis of the ongoing theta activity at central electrodes and the corresponding statistics in the result section, and have also included the Additional Figure in the supplementary materials, as Figure S2.

Additional Figure 1. Comparison of the topography and time course for the 4 – 5 Hz activity for the evoked (A, B) and the ongoing (C, D) oscillatory response at central electrodes (400 – 600 ms; Cz, C3, C4; baseline: -100 – 0 ms). (A) Topography for the difference between unexpected and expected events in the evoked oscillatory response. (B) The corresponding time course at central electrodes, which did not reveal a significant difference between 400 – 600 ms, t(35) = 1.57, p = .126. (C) Topography for the same contrast in the ongoing oscillatory response and (D) the corresponding time course at central electrodes, which did likewise not reveal a significant difference between 400 – 600 ms, t(35) = -1.26, p = .218. The condition effects (unexpected - expected) were not correlated between the evoked and the ongoing response, r = .23, p = .169.

A second aspect that I would like the authors to comment on is the power of the experimental design to measure surprise. From the methods, I gathered that the same stimulus materials and with the same frequency were presented as expected and unexpected endings. If that is the case, what is the measure of surprise? For once the same materials are shown causing habituation and reducing novelty and second the experiment introduces a long-term expectation of a 50:50 proportion of expected/unexpected events. I might be missing something here, which is likely as the methods are quite sparse in the description of what was actually done.

We have used 4 different stimuli types (variants) in each of the 4 different domains, with either an expected or unexpected outcome. This resulted in 32 distinct stimulus sequences, which we presented twice, resulting in (up to) 64 trials. We have now described this approach and design in more detail and have also included all stimuli as supplementary material (Figure S1). In particular, we have used multiple types in each domain to reduce potential habituation or expectation effects. Still, we agree that one difficulty may be that, over time, infants got used to the fact that expected and unexpected outcomes were to be similarly “expected” (i.e., 50:50). However, if this was the case it would have resulted in a reduction (or disappearance) of the condition effect, and would thus also reduce the condition difference that we found, rather than providing an alternative explanation. We now included this consideration in the method section (p. 7).

Two more comments concerning the analysis choices:

1) The statistics for the ERP and the TF could be reported using a cluster size correction. These are well established statistical methods in the field which would enable to identify the time window/topography that maximally distinguished between the expected and the unexpected condition both for ERP and TF. Along the same lines, the authors could report the spatial correlation of the ERP/TF effects.

For the ERP analysis we used the standard electrodes typically analyzed for the Nc in order to replicate effects found in former research (Langeloh et al., 2020; see also, Kayhan et al., 2019; Reynolds and Richards, 2005; Webb et al., 2005). For the TF analyses we used the most conservative criterion, namely all scalp recorded electrodes and the whole time window from 0 to 2000 ms, such that we did not make any choice regarding time window or the electrodes (i.e., which could be corrected for against other choices). We have now made those choices clearer in the method section, and why we think that, under these condition a multiple comparison correction is not needed/applicable (p. 10). Regarding the spatial correlation of the ERP and TF effects, we explained in response to the first comment the very different nature of the TF decomposition of the ERP and ongoing oscillatory activity and also that these were found to be interdependent (i.e., uncorrelated). We hope that with the additional analysis included in response to this comment that this difference is much clearer now.

2) While I can see the reason why the authors chose to keep the baseline the same between the ERP and the TF analysis, for time frequency analysis it would be advisable to use a baseline amounting to a comparable time to the frequency of interest; and to use a period that does not encroach in the period of interest i.e., with a wavelet = 7 and a baseline -100:0 the authors are well into the period of interested.

The difficulty in choosing the baseline in the present study was two-fold. First, we were interested in the ERP and the change in neural oscillations upon the onset of an outcome picture within a continuous presentation of pictures, forming a sequence. Second, we wanted to use a similar baseline for both analyses, to make them comparable. Because the second picture (the picture before the outcome picture) also elicited both an ERP and an oscillatory response at ~ 4 Hz (see Additional Figure 2), we choose a baseline just before the onset of the outcome stimulus, from -100 to 0 ms. Also we agree that the possibility to take a longer and earlier baseline, in particular for the TF results would have been favorable, but still consider that the -100 to 0 ms is still the best choice for the present analysis. Notably, because we found an increase in theta oscillations and the critical difference relies on a higher theta rhythm in one compared to the other condition, the effects of the increase in theta, if they effected the baseline, this effect would counteract rather than increase the current effect. We now explain this choice in more detail (p.10).

Additional Figure 1. Display of the grand mean signals prior to the -100 to 0 baseline and outcome stimulus. (A) The time-frequency response across all scalp-recorded electrodes, as well as (B) the ERP at the central electrodes (Cz, C3, C4) across both conditions show a similar response to the 2. picture like the outcome picture. Thus a baseline just prior to the stimulus of interest was chosen, consistent for both analyses.

Reviewer #2:

The manuscript reports increases in theta power and lower NC amplitude in response to unexpected (vs. expected) events in 9-month-olds. The authors state that the observed increase in theta power is significant because it is in line with an existing theory that the theta rhythm is involved in learning in mammals. The topic is timely, the results are novel, the sample size is solid, the methods are sound as far as I can tell, and the use of event types spanning multiple domains (e.g. action, number, solidity) is a strength. The manuscript is short, well-written, and easy to follow.

1) The current version of the manuscript states that the reported findings demonstrate that the theta rhythm is involved in processing of prediction error and supports the processing of unexpected events in 9-month-old infants. However, what is strictly shown is that watching at least some types of unexpected events enhance theta rhythm in 9-month-old infants, i.e. an increase in the theta rhythm is associated with processing unexpected events in infants, which suggests that an increase in the theta rhythm is a possible neural correlate of prediction error in this age range. While the present novel findings are certainly suggestive, more data and/or analyses would be needed to corroborate/confirm the role of the observed infant theta rhythm in processing prediction error, or document whether and how this increase in the theta rhythm supports the processing of unexpected events in infants. (As an example, since eye-tracking data were collected, are trial-by-trial variations in theta power increases to unexpected outcomes related to how long individual infants looked to the unexpected outcome pictures?) If it is not possible to further confirm/corroborate the role of the theta rhythm with this dataset, then the discussion, abstract, and title should be revised to more closely reflect what the current data shows (as the wording of the conclusion currently does), and clarify how future research may test the hypothesis that the infant theta rhythm directly supports the processing of prediction error in response to unexpected events.

We would like to thank the reviewer for acknowledging the merit of the present research.

On the one hand, we have revised our manuscript and are now somewhat more careful with our conclusion, in particular with regard to the refinement of basic expectations. On the other hand, we consider the concept of “violation to expectation” (VOE), which is one of the most widely used concepts in infancy research, very closely linked to the concept of a prediction error processing, namely a predictive model is violated. In particular, we have made this conceptual link in a recent theoretical paper (Köster et al., 2020), and based on former theoretical considerations about the link between these two concepts (e.g., see Schubotz 2015; Prediction and Expectation). In particular, in the present study we used a set of four different domains of violation of expectation paradigms, which are among the best established domains of infants core knowledge (e.g., action, solidity, cohesion, number; cf. Spelke & Kinzler, 2007). It was our specific goal not to replicate, for another time, that infants possess expectations (i.e., make predictions) in these domains, but to “flip the coin around” and investigate infants’ prediction error more generally, independent of the specific domain. We have now made the conceptual link between VOE and prediction error processing more explicit in the introduction of the manuscript and also emphasize that we choose a variety of domains to obtain a more general neural marker for infant processing of prediction errors.

Having said this, indeed, we planned to assess and compare both infants gaze behavior and EEG response. Unfortunately, this was not very successful and the concurrent recording only worked for a limited number of infants and trials. This led us to the decision to make the eye-tracking study a companion study and to collect more eye-tracking data in an independent sample of infants after the EEG assessment was completed, such that a match between the two measures was not feasible. We now make this choice more explicit in the method section (p. 7). In addition, contrary to our basic assumption we did not find an effect in the looking time measure. Namely, there was no difference between expected and unexpected outcomes. We assume that this is due to the specificities of the current design that was rather optimized for EEG assessments: We used a high number of repetitions (64), with highly variable domains (4), and restricted the time window for potential looking time effects to 5 seconds, which is highly uncommon in the field and therefore not directly comparable with former studies.

Finally, besides the ample evidence from former studies using VOE paradigms, if it were not the unexpected vs. expected (i.e., unpredicted vs. predicted) condition contrast which explains the differences we found in the ERP and the theta response, there would need to be an alternative explanation for the differential responses in the EEG, which produce the hypothesized effects. (Please also note that there are many studies relying their VOE assumption on ERPs alone, here we have two independent measures suggesting that infants discriminated between those conditions.)

2) The current version of the manuscript states "The ERP effect was somewhat consistent across conditions, but the effect was mainly driven by the differences between expected and unexpected events in the action and the number domain (Figure S1). The results were more consistent across domains for the condition difference in the 4 - 5 Hz activity, with a peak in the unexpected-expected difference falling in the 4 - 5 Hz range across all electrodes (Figure S2)". However, the similarity/dissimilarity of NC and theta activity responses across domains was not quantified or tested. Looking at Figures S1 and S2, it is not that obvious to me that theta responses were more consistent across domains than NC responses. I understand that there were too few trials to formally test for any effect of domain (action, number, solidity, cohesion) on NC and theta responses, either alone or in interaction with outcome (expected, unexpected). It may still be possible to test for correlations of the topography and time-course of the individual average unexpected-expected difference in NC and theta responses across domains at the group level, or to test for an effect of outcome (expected, unexpected) in individual domains for subgroups of infants who contributed enough trials. Alternatively, claims of consistency across domains may be altered throughout, in which case the inability to test whether the theta and/or NC signatures of unexpected event processing found are consistent across domains (vs. driven by some domains) should be acknowledged as a limitation of the present study.

We agree that this statement rather reflected our intuition and would not surpass statistical analysis given the low number of trials. So we are happy to refrain from this claim and simply refer to the supplementary material for the interested reader and also mention this as a perspective for future research in the discussion (p. 12; p. 15).

As outlined in our previous response, it was also not our goal to draw conclusions about each single domain, but rather to present a diversity of stimulus types from different core knowledge domains to gain a more generalized neural marker for infants’ processing of unexpected, i.e., unpredicted events.

Reviewer #3:

General assessment:

In this manuscript, the authors bring up a contemporary and relevant topic in the field, i.e. theta rhythm as a potential biomarker for prediction error in infancy. Currently, the literature is rich on discussions about how, and why, theta oscillations in infancy implement the different cognitive processes to which they have been linked. Investigating the research questions presented in this manuscript could therefore contribute to fill these gaps and improve our understanding of infants' neural oscillations and learning mechanisms. While we appreciate the motivation behind the study and the potential in the authors' research aim, we find that the experimental design, analyses and conclusions based on the results that can be drawn thereafter, lack sufficient novelty and are partly problematic in their description and implementation. Below, we list our major concerns in more detail, and make suggestions for improvements of the current analyses and manuscript.

Summary of major concerns:

1) Novelty:

(a) It is unclear how the study differs from Berger et al., 2006 apart from additional conditions. Please describe this study in more detail and how your study extends beyond it.

We would like to thank the reviewers for emphasizing the timeliness and relevance of the study.

The critical difference between the present study and the study by Berger et al. 2006 was that the authors applied, as far as we understand this from Figure 4 and the method section of their study, the wavelet analysis to the ERP signal. In contrast, in the present study, we applied the wavelet analysis at the level of single trials. We now explain the difference between the two signals in more detail in the revised manuscript and also included an additional comparison between the evoked (i.e., ERP) and the ongoing (i.e., total) oscillatory response (for more details, please see the first response to the first comment of reviewer 1).

(b) Seemingly innovative aspects (as listed below), which could make the study stand out among previous literature, but are ultimately not examined. Consequently, it is also not clear why they are included.

-Relation between Nc component and theta.

-Consistency of the effect across different core knowledge domains.

-Consistency of the effect across the social and non-social domains.

-Link between infants looking at time behavior and theta.

We are thankful for these suggestions, which are closely related to the points raised by reviewer 1 and 2. With regard to the relation between the Nc and the theta response, we have now included a direct comparison of these signals (see Additional Figure 1, i.e., novel Figure S2; for details, please see the first response to the first comment of reviewer 1). Regarding the consistency of effects across domains, we have explained in response to point 1 by reviewer 2 that this was not the specific purpose of the present study, but we aimed at using a diversity of VOE stimuli to obtain a more general neural signature for infants’ prediction error processing, and explain this in more detail in the revised manuscript. Having said this, we agree that the question of consistency of effects between conditions is highly interesting, but we would not consider the data robust enough to confidently test these differences given the limited number of trials available per stimulus category. We now discuss this as a direction for future research (p. 15). Finally, we also agree with regard to the link between looking times and the theta rhythm. As also outlined in response to point 1 by reviewer 2 (paragraph 2), we initially had this plan, but did not succeed in obtaining a satisfactory number of trials in the dual recording of EEG and eye-tracking, which made us change these plans. This is now explained in detail in the method section (p. 7).

(c) The reason to expect (or not) a difference at this age, compared to what is known from adult neural processing, is not adequately explained.

-Potentially because of neural generators in mid/pre-frontal cortex? See Lines 144-146.

The overall aim of the present study was to identify the neural signature for prediction error processing in the infant brain, which has, to the best of our knowledge, not been done this explicitly and with a focus on the ongoing theta activity and across a variety of violations in infants’ core knowledge domains. Because we did not expect a specific topography of this effect, in particular across multiple domains, we included all electrodes in the analyses. We have now clarified this in the method section (p. 10).

(d) The study is not sufficiently embedded in previous developmental literature on the functionality of theta. That is, consider theta's role in error processing, but also the increase of theta over time of an experiment and it's link to cognitive development. See, for example: Braithwaite et al., 2020; Conejero et al., 2018; Adam et al., 2020.

We are thankful that the reviewer indicated these works and have now included them in the introduction and discussion. Closest to the present study is the study by Conejero et al., 2018. However, this study is also based on theta analyses of the ERP, not of the ongoing oscillatory response and it includes considerably older infants (i.e., 16-month-olds instead of 9-month-olds as in the present study).

2) Methodology:

(a) Design: It is unclear what exactly a testing session entails.

-Was the outcome picture always presented for 5secs? The methods section suggests that, but the introduction of the design and Figure 1 do not. This might be misleading. Please change in Figure 1 to 5sec if applicable.

Yes, the final images were shown for 5s in order to simultaneously assess infants’ looking times. However, we included trials in the EEG analysis if infants looked for 2s, so this is the more relevant info for the analysis. We now clarified this in the method section (p. 7) and have also added this info in the figure caption.

-Were infants' eye-movements tracked simultaneously to the EEG recording? If so, please present findings on their looking time and (if possible) pupil size. Also examine the relation to theta power. This would enhance the novelty and tie these findings to the larger looking time literature that the authors refer to in their introduction.

Yes, in response to the second reviewer (comment 1) we explained in more detail why the joint analysis of the EEG and looking time data was not possible: We planned to assess both, infants gaze behavior and EEG response. Unfortunately, this was not very successful and the dual recording only worked for a few infants and trials. This led us to collect more eye-tracking data after the EEG assessment was completed, such that a match between the two measures was not feasible. We now clarified this in the method section (p. 7).

(b) Analysis:

-In terms of extracting theta power information: The baseline of 100ms is extremely short for a comparison in the frequency domain, since it does not even contain half a cycle of the frequency of interest, i.e. 4Hz. We appreciate the thought to keep the baseline the same as in the ERP analysis (which currently is hardly focused on in the manuscript), but it appears problematic for the theta analysis. Also, if we understand the spectral analysis correctly, the window the authors are using to estimate their spectral estimates is largely overlapping between baseline and experimental window. The question arises whether a baseline is even needed here, or if a direct contrast between conditions might be better suited.

Please see our explanation about the choice of the baseline in our response to reviewer 1, comment 2. Because our stimulus sequences were highly variable, likely leading to highly variable overall theta activity, and our specific interest was in the change in theta activity upon the onset of the unexpected versus unpredicted outcome, we still consider it useful to take a baseline here. Also because this makes the study more closely comparable to the existing literature. We now clarified this in the method section (p. 9)

-In terms of statistical testing

-It appears that the authors choose the frequency band that will be entered in the statistical analysis from visual inspection of the differences between conditions. They write: "we found the strongest difference between 4 - 5 Hz (see lower panel of Figure 3). Therefore, and because this is the first study of this kind, we analyzed this frequency range." ll. 277-279). This approach seems extremely problematic since it poses a high risk for 'double-dipping'. This is crucial and needs to be addressed. For instance, the authors could run non-parametric permutation tests on the time-frequency domain using FDR correction or cluster-based permutation tests on the topography.

-Lack of examining time- / topographic specificity.

Please also note the sentence before this citation, which states our initial hypothesis: “While our initial proposal was to look at the difference in the 4 Hz theta rhythm between conditions (Köster et al., 2019), we found the strongest difference between 4 – 5 Hz (see lower panel of Figure 3).” Note that the hypothesis of 4 Hz can be clearly derived from our 2019 study. We would maintain that the center frequency we took for the analysis 4.5Hz (i.e., 4 – 5Hz) is very close to this original hypothesis and, considering that we applied a novel design and analyses in very young infants, could indeed hardly have fallen more closely to this initial proposal. The frequency choice is also underlined, as the reviewer remarks, by the consistency of this peak across domains, peaking at 4Hz (cohesion), 4.5Hz (action), and 5Hz (solidity, number). Importantly, please note that we have chosen the electrodes and time window very conservatively, namely by including the whole time period and all electrodes, which we now explain in more detail on p. 10. Please also see our response to reviewer 1, comment “1)”.

3) Interpretation of results:

(a) The authors interpret the descriptive findings of Figure S1 as illustration of the consistency of the results across the four knowledge domains. While we would partly agree with this interpretation based on column A of that figure (even though also there the peak shifts between domains), columns B and C do not picture a consistent pattern of data. That is, the topography appears very different between domains and so does the temporal course of the 4-5Hz power, with only showing higher power in the action and number domain, not in the other two. Since none of these data were compared statistically, any interpretation remains descriptive. Yet, we would like to invite the authors to critically reconsider their interpretation. You also might want to consider adding domain (action, number etc.) as a covariate to your statistical model.

We agree with the reviewers (reviewer 2 and reviewer 3) that our initial interpretation of the data regarding the consistency of effects across domains may have been too strong. Thus, in the revised version of the manuscript, we do not state that the TF analysis revealed more consistent results. Given that the analysis was based on a different subsample and highly variable in trial numbers, we did not enter them as a covariate in the statistical model.

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

Learn more at Review Commons

Reply to the reviewers

Manuscript number: RC-2025-03220

Corresponding author(s): Ryusuke Niwa, Yuko Shimada-Niwa, and Wei Sun

Dear Editors,

We are pleased to submit our revised manuscript of RC-2025-03220R. The reviewers’ comments from Review Commons are presented in italic.

For submission of our current revised manuscript, we provide two Word files, which are the “clean” and “Track-and-Change” files. Page and line numbers described below correspond to those of the “clean” file. The “Track-and-Change” file might be helpful for Reviewers to find what we have changed for the current revision.

We hope that the revised version is now suitable for the next stage of evaluation.

Sincerely,

Ryusuke Niwa, Yuko Shimada-Niwa, and Wei Sun

1. General Statements [optional]

We sincerely thank the reviewers for their thoughtful feedback on our initial submission. Experiments that we will conduct and the revisions on the manuscript that have already been incorporated are detailed below in the point-by-point response. For this revised submission, two versions of the manuscript are provided: a clean copy and a tracked-changes file. Page and line numbers mentioned below refer to the clean version, while the tracked-changes file is intended to help reviewers easily identify the revisions made.

In preparing the revision plan, we have included additional data, some of which were generated in collaboration with new contributors. Accordingly, we would like to propose adding Yuichi Shichino and Shintaro Iwasaki as co-authors to acknowledge their contributions.

2. Description of the planned revisions__ __

__

- Also, the authors show that two different RNAi lines for NudC give the same defects - it would be good to know if the RNAi lines target the same or different sequences in the NudC transcripts. Alternatively, it would be equally good to show that trans-allelic combinations of NudC mutants have the same defects in the prothoracic glands and the salivary glands as the RNAi. Instead, they examine only overall body size, developmental delays and lethality in the trans-hetero allelic NudC mutants.

Author response:

In response to the second part of the criticism, we will further validate the observed phenotypes by examining tissue and nuclear size, chromosomal structure, and the levels of Fibrillarin and RpS6 proteins in the prothoracic glands and salivary glands of NudC mutants.

__

- It would be quite helpful to characterize the "5 blob" and "shortened polytene chromosome arm" defects shown in Figure 2 and Figure 6. Are these partially polytenized chromosomes or are large sections of the chromosomes missing or just underreplicated? What do the chromosomes look like if you lyse the nuclei, spread the chromosomes and stain with DAPI or Hoechst - this is a pretty standard practice and would reveal much more about the structure of the polytene chromosomes.

Author response:

To address these structural concerns more clearly, we plan to apply established protocols to obtain higher-resolution images and gather more detailed information on chromosome morphology.

__ - Discussion, line 468. I don't think the authors have provided evidence of DNA damage. With the experiments they have shown, the chromosomes look abnormal - not clear what is abnormal.

Author response:

To further confirm DNA damage in NudC knockdown salivary gland cells, we plan to perform a TUNEL assay, which detects DNA fragmentation associated with damage.

We would like to note that, in the current manuscript, we have shown that depletion of NudC, eIF5, RpLP0-like, or Nopp140 increased γH2Av levels, suggesting activation of the DNA damage response (Figures 6B and 6C).

__

*The authors claim that NudC has a dual role as a cell cycle/cytoskeleton regulator and as a ribosome biogenesis factor. However, because NudC knockdown reduces nuclear size and ploidy (Figures 1F and 2H-2I), the authors cannot exclude that decreased rDNA dosage and nucleolar volume contribute to reduced rRNA signals and that the effects seen are due to a NudC involvement in endoreplication, the rRNA reduction being a consequence of lower polyploidy. Different allelic combinations of NudC induce larval growth defects (Figure S5), consistent with a NudC role in endoreplication. To circumvent this, the authors could genetically modulate endocycle progression (e.g., E2F or Fzr overexpression) in the NudC RNAi background to test whether inducing endoreplication rescues rRNA production and nucleolar volume. This would establish causality between the endocycle state and rRNA output and clarify whether NudC's primary role is in RiBi or endocycle control. *

Author response: In response to Reviewer #2’s suggestion, we plan to genetically modify the progression of the endocycle by inducing continuous expression of Cyclin E (CycE), E2F1, and Fzr in NudC RNAi salivary glands to test whether promoting endoreplication can restore rRNA production and nucleolar volume.

In fact, we have attempted to rescue the developmental arrest in animals with NudC-deficient prothoracic glands (PGs) by inducing continuous expression of CycE. Two constructs, UAS-CycE-1 (BDSC#30725) and UAS-CycE-2 (BDSC#30924), were used. UAS-CycE-1 has previously been shown to rescue developmental arrest in PG-specific TOR loss-of-function animals (Ohhara, Kobayashi, and Yamanaka. PLoS Genetics 13 (1): e1006583, 2017). We introduced each construct into NudC knockdown PGs. However, continuous expression of CycE did not restore development (Figure A as shown below), suggesting that NudC functions in the polyploid cells extend beyond endocycle regulation. We do not currently plan to include the PG data shown in Figure A in the revised manuscript. We will evaluate whether it would be meaningful to present PG data alongside salivary gland results once we have obtained and analyzed data from the salivary gland rescue experiment.

__Figure A. _Survival and developmental progression following continuous expression of CycE._ __Control (phtm>dicer2, +), NudC knockdown (phtm>dicer2, NudC RNAi), and NudC RNAi + CycE (phtm>dicer2, NudC RNAi, CycE) flies were analyzed at 10 days after hatching (10 dAH). Dead indicates dead larvae; L3 denotes third-instar larvae. Sample sizes (number of flies) are shown below each bar.

__

*The conclusion that NudC maintains rRNA levels is derived from salivary gland RNAi phenotypes with strong reductions in ITS1/ITS2 and 18S/28S signals (Figure 4B-4K) and reduced 28S by Northern (Figure 4L), plus corroboration in fat body cells (Figure S7). The authors verified knockdown using two independent RNAi lines for growth phenotypes and NudC::GFP reduction (Figure S2) and generated a UAS-FLAG::NudC transgene (Key Resources), but rRNA measurements were reported for only one RNAi line without rescue. Rescue of the rRNA phenotype by transgenic NudC re-expression, or replication of the rRNA decrease with a second, non-overlapping RNAi, would directly attribute the effect to NudC. In the absence of these standard validation controls, an off-target explanation remains plausible. *

Author response:

We plan to analyze rRNA FISH signals in salivary glands and fat bodies using a second, non-overlapping RNAi strain to confirm the reproducibility of the observed effects.

__ - The authors report in Fig. 2 elevated γH2Av in SG cells upon NudC knockdown and interpret this as evidence of chromosome destabilization. They also state that apoptosis is not observed in Fig S10. However, the increase in γH2Av could reflect transient or early apoptotic events or other stress responses triggered by NudC depletion, rather than direct defects in endoreplication or genome stability. I suggest that the authors clarify this important point, for example, by co-expressing apoptotic inhibitors such as P35, or by using the TUNEL assay, which is more sensitive than anti-Caspase3 or Dcp1 antibodies.

Author response:

We plan to perform a TUNEL assay on salivary gland cells to evaluate apoptosis associated with NudC depletion.

__ - Activation of the JNK pathway is often accompanied by apoptosis. It would strengthen the conclusions if the authors included a positive control to confirm that apoptosis is not induced under these experimental conditions, ensuring that the observed effects are specific to autophagy and not confounded by cell death.

Author response:

We will analyze pJNK and autophagy levels in animals expressing a constitutively-active form of hemipterous (hep) (hep[CA] ) under the control of fkh-GAL4 driver as a positive control. hep encodes the Drosophila JNK kinase, and it is well established that forced expression of hep[CA] induces JNK phosphorylation and activation.

__ - In Figure S1, reduction of NudC in the fat body appears to induce a starvation-like phenotype, suggesting a potential impairment of metabolic or nutrient-sensing pathways. It would be important to determine whether modulation of nutrient-responsive signaling could rescue this phenotype. Specifically, have the authors examined whether activation of the TOR or PI3K pathways mitigates the effects of NudC knockdown? Assessing pathway activity (e.g., via phospho-S6K or phospho-Akt levels) or performing genetic rescue experiments with pathway activators could clarify whether the observed phenotypes are mediated through disrupted nutrient signaling rather than a secondary effect of general cellular stress. Such analyses could also provide a mechanistic explanation for the increased autophagy observed in these cells.

Author response:

1. We will analyze phospho-S6K levels in salivary glands and fat bodies by immunostaining.
2. To activate the TOR pathway in NudC RNAi fat bodies, we will overexpress Rheb, an established upstream activator of the TOR pathway in Drosophila, which has been shown to robustly increase TOR signaling and S6K phosphorylation.

   __ - The current images of autophagic vesicles in the SG in Fig. 8B are not clearly visible and quantified. Considering the large size of these polyploid cells, higher-resolution images or alternative imaging approaches should be presented to better visualize and quantify autophagy. This would make the conclusions regarding enhanced autophagy more convincing. In addition, this data could be further strengthened by expanding the analysis of autophagy to other cell types. For example, examining autophagy in fat body cells, where autophagy plays a primary physiological role associated with rRNA accumulation (Fig. S7), rather than a reduction like in SG (Fig. 4), could provide a useful comparison for the function of NudC between polyploid cells.

Author response:

In response to the second part of the reviewer’s comment, we will conduct additional experiments using anti-Atg8a immunostaining and/or LysoTracker staining to analyze autophagy in NudC RNAi fat bodies and prothoracic glands. These experiments will help further characterize the cellular responses associated with NudC depletion.

3. Description of the revisions that have already been incorporated in the transferred manuscript

__

-The title is a bit problematic since they haven't shown that NudC doesn't also affect normal mitotic cells - they only look at polyploid cells, but that doesn't mean normal mitotic cells are not also affected.

Author response:

In response to the suggestion from Reviewer #1, we have revised the title from “NudC moonlights in ribosome biogenesis and homeostasis in Drosophila melanogaster polyploid cells” to “NudC moonlights in ribosome biogenesis and homeostasis in polyploid cells of Drosophila melanogaster” to place greater emphasis on “polyploid cells.”

Regarding mitotic cells, we have added new data in the revised manuscript (Figure S7; lines 249–256 and 417–418) demonstrating that NudC regulates apoptosis and stress responses in mitotic imaginal wing disc cells. However, as the main focus of our study remains polyploid cells, we have chosen to retain the emphasis in the title.

__

- Also, the authors show that two different RNAi lines for NudC give the same defects - it would be good to know if the RNAi lines target the same or different sequences in the NudC transcripts. Alternatively, it would be equally good to show that trans-allelic combinations of NudC mutants have the same defects in the prothoracic glands and the salivary glands as the RNAi. Instead, they examine only overall body size, developmental delays and lethality in the trans-hetero allelic NudC mutants.

Author response:

In response to the first half of criticism, the two RNAi lines used for NudC target distinct sequences. We have added the corresponding RNAi target sites to Figure S4A for clarity.

__

- Results: Lines 261 - 266. Seeing electron dense structures in TEMs and seeing increased Me31B staining by confocal imaging in the cytoplasm is insufficient evidence that the electron dense structures are P-bodies. They could be the P-bodies but they could also be aggregated ribosomes; there is insufficient evidence to "confirm" that they are P-bodies - maybe just say "suggests".

Author response:

In response to Reviewer #1’s suggestion, we have revised lines 261–262 to avoid using the word "confirm." The new sentence reads: “Immunostaining with the P-body marker Me31B reveals numerous cytoplasmic P-bodies in NudC-deficient SG cells,” which appears in lines 293–295.

__

- Abstract, lines 28 - 31. I think this gene has been identified before. The authors probably want to say they have discovered a role for this gene in RiBi.

Author response:

We have followed Reviewer #1’s suggestion and revised the sentence in lines 35–37 to: “In this study, we discovered a role for the gene NudC (nuclear distribution C, dynein complex regulator) in RiBi within polyploid cells of Drosophila melanogaster larvae.”

__

- Introduction, line 66. The protein is imported into the nucleus, where it localizes to the nucleolus - technically the protein is not imported into the nucleolus.

Author response:

To correct the misrepresentation in line 66, we have revised the sentence to: “RP mRNAs are synthesized by RNA polymerase II, and exported to the cytoplasm for translation. Then, RPs are imported into the nucleus, where they localize to the nucleolus.” in lines 70–73.

__ - Introduction, line 70. To be comprehensive in the description of ribosome biogenesis, the authors may want to mention that the 40S and 60S subunits are then exported from the nucleus and form the 80S subunit in the cytoplasm during translation.

Author response:

To improve the representation, we have revised the sentences in lines 73 – 78 as follows: “Within the nucleolus, rRNAs and RPs assemble into pre-40S and pre-60S subunits. immature versions of the small (40S) and large (60S) subunits, respectively, that undergo maturation with numerous ribosome biogenesis factors (RBFs) (Greber, 2016). The 40S and 60S subunits are then transported separately to the cytoplasm, where they combine to form functional 80S ribosomes, capable of sustaining protein synthesis (Pelletier et al., 2018).”

__ - Introduction, line 98. May want to cite paper showing that Minute mutations turn out to be mutations in individual ribosomal protein genes.

Author response:

As Reviewer #1 suggested, we have cited two, Marygold et al. (2007) entitled “The ribosomal protein genes and Minute loci of Drosophila melanogaster” and Recasens-Alvarez et al. (2021) entitled “Ribosomopathy-associated mutations cause proteotoxic stress that is alleviated by TOR inhibition” along with He et al. (2015). The inappropriate citation to Brehme (1939) has been removed.

__ - Results, lines 292. Since they didn't knock down NudC in the fat body cells in this experiment, this comment seems irrelevant.

Author response:

We would like to clarify that the phenotype observed with fkh-GAL4-driven NudC RNAi was specific to salivary glands, and no obvious phenotypes were detected in the surrounding fat body cells, which do not express fkh-GAL4. In this context, the adjacent fat body cells serve as an internal control.

In the revised manuscript, the sentence has been rewritten as: “In contrast, the fat body cells surrounding NudC-deficient SGs did not show this reduction (Figure S9),” in lines 323–324.

__ - Figure 6A. Hoechst is misspelled.

__

- Fig. 2 I - Hoeschest should be Hoescht.

Author response:

We have fixed the error.

__ *- Given that prothoracic gland (PG) size influences ecdysone production, the finding that NudC knockdown alters PG cell size, morphology, and cytoskeletal organization raises the possibility that ecdysone synthesis or signaling may also be affected. This, in turn, could explain the delayed maturation phenotype observed in Figure 1. I recommend testing whether ectopic activation of ecdysone signaling, for instance through 20-hydroxyecdysone (20E) supplementation, can rescue the defects in PG size and developmental timing. Such an experiment would strengthen the link between NudC function, PG morphology, and ecdysone-dependent developmental progression. *

Author response:

We have conducted experiments showing that developmental defects in NudC RNAi animals can be partially rescued by administering 20E. Approximately 32% of NudC RNAi larvae fed with 20E completed pupariation. These new data have been added to Figure S1B and are described in the main text (lines 165-168).

Regarding PG size, our experiments show that PG growth remains inhibited following 20E administration (Figure B as shown below). This observation indicates that treatment with exogenous 20E does not restore PG growth in NudC RNAi animals, suggesting that other factors may be required for normal PG development beyond ecdysone supplementation.

Because this analysis is not the main focus of our manuscript, we currently plan not to include these data in the revised manuscript.

Figure B. Prothoracic gland (PG) size ____after 20E administration.

To assess whether 20E supplementation could restore PG size, control (phtm>dicer2, +) and NudC RNAi (phtm>dicer2, NudC RNAi) larvae were transferred at 60 hours after hatching (hAH) to standard medium containing 20E dissolved in 100% ethanol. Control groups were transferred to medium containing the same volume of 100% ethanol at the same time point. PG size was quantified at the wandering stage. Sample sizes (number of glands) are shown below each bar. Bars represent mean ± SD. **p * *

__ - Additionally, qRT-PCR can be performed to assess the expression levels of ecdysone precursors or target genes in whole larvae, serving as a readout of ecdysone activity, including dilp8, which is usually upregulated when ecdysone levels are reduced.

Author response: To investigate ecdysone biosynthesis, Halloween genes including nvd, spok, sro, phm, dib, and sad were measured by conducting qRT-PCR. In NudC RNAi animals, nvd, sro and phm were suppressed at late L3 stage, indicating that NudC in the PG is required for ecdysone biosynthesis. The new data are described in Figure S1A and in the main text (lines 159-164) in the revised manuscript.

__ - The current images of autophagic vesicles in the SG in Fig. 8B are not clearly visible and quantified. Considering the large size of these polyploid cells, higher-resolution images or alternative imaging approaches should be presented to better visualize and quantify autophagy. This would make the conclusions regarding enhanced autophagy more convincing.

Author response:

Regarding the image quality issue, we have provided improved images of anti-Atg8a immunostaining in the salivary gland mosaic clones (Figure 8B) and included additional data from SG-specific knockdown cells (Supplemental Figures S13A-S13F) to provided quantitative results.

__ - Furthermore, including experiments in other cell types, such as imaginal disc cells, where apoptosis is more readily induced, would help determine whether the effects of NudC knockdown are specific to polyploid cells or are more broadly applicable.

Author response: We found that apoptosis was observed in NudC RNAi wing discs. In the revised manuscript, we have included this data in Figure S7 and referenced it in the main text (lines 249–256).

4. Description of analyses that authors prefer not to carry out

__ - Results, lines 285 to 298. In situs with multiple probes that detect all parts of both the pre-rRNA and processed rRNA indicate that all are down in the SG in NudC knockdowns, but that the 18S and 28S rRNAs are down the internal transcribed spacers go up - can the authors explain or hypothesize how this could happen?

Author response:

As Reviewer #1 indicated, we indeed observed that internal transcribed spacer (ITS) levels decrease in NudC knockdown salivary glands, but increase in knockdown fat bodies. Our hypothesis is that, as noted in the Discussion (lines 529–534), ribosome abundance is typically linked to protein synthesis. Salivary gland cells, which are highly active in protein production, may be particularly sensitive to disruptions in ribosome biogenesis. Therefore, NudC may maintain appropriate levels of rRNA with its impact varying according to the specific regulatory mechanisms of each cell type. We do not have a further explanation for this phenomenon, and therefore we have retained the original sentences without adding new ones.

__ - The data presented in Fig 4 show that NudC knockdown reduces pre-rRNA (ITS1/ITS2) and mature 18S/28S rRNAs in a tissue-specific manner. However, it remains unclear whether these reductions have functional consequences for ribosome assembly and translation. I recommend that the authors perform polysome profiling or an equivalent assay to assess the impact of NudC loss on actively translating ribosomes. This approach would provide a quantitative readout of translation efficiency and clarify whether the observed rRNA defects lead to impaired protein synthesis. Additionally, polysome profiling could help explain the tissue-specific differences observed between salivary glands and fat body cells.

Author response:

We performed ribosome fractionation using wild-type salivary glands and repeated the experiment three times with 56–62 gland pairs per sample. As shown in Figure C, the polyribosome peaks (grey lines) are not prominent, indicating that a much larger number of glands would be required for robust polysome profiling. Given that NudC RNAi salivary glands are significantly smaller than wild-type glands, collecting enough tissue for equivalent profiling would be technically difficult. Therefore, we concluded that obtaining sufficient RNAi samples for polysome profiling is extremely challenging, and these data have not been included in the revised manuscript.

On the other hand, we would like to emphasize that we observed a significant reduction in O-propargyl puromycin (OPP) labeling in NudC-deficient salivary gland cells (Figure 3B), which provides strong evidence for reduced translational activity.

__Figure C. Ribosomal fraction profiles of wild-type salivary glands. __Salivary glands from the late L3 larvae were dissected for analysis. Polyribosome peaks are indicated in grey. The number of salivary gland pairs used for each sample is shown above each bar.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

Referee #3

Evidence, reproducibility and clarity

Summary:

NudC (Nuclear Distribution Protein C) is a conserved, dynein-associated protein that plays a critical role in nuclear positioning and neuronal development. It functions as a co-chaperone, stabilizing components of the dynein-motor complex, thereby facilitating proper microtubule-dependent nuclear migration and intracellular transport. In developing neurons, NudC is essential for correct dendritic morphogenesis, ensuring nuclei and dendritic processes attain their proper spatial organization. Loss or knockdown of nudC leads to defects in nuclear localization, aberrant dendritic architecture, and mitotic stress, which can predispose cells to apoptosis. Highlighting NudC as a pivotal regulator of intracellular dynamics, cytoskeletal organization, In this paper, the authors propose a role for the gene in regulating ribosomal biogenesis. However, the interpretation of these results remains somewhat unclear, as the observed effects on ribosome biogenesis could potentially result from nonspecific cellular stress or toxicity caused by gene knockdown in polyploid cells. At this stage, the link between NudC and the regulation of ribosomal biogenesis is not fully convincing. Additional experiments could help clarify whether this relationship is direct or secondary to other cellular effects. I suggest conducting additional experiments to strengthen this hypothesis; for example, by examining whether knocking down NudC would give similar effects as observed for other genes that regulate RiBi in other organs and tissues where ribosomal biogenesis and stress responses have been well-characterized, such as the imaginal discs. Comparing the results across these different tissues would help clarify whether the effects of gene knockdown are specific to polyploid cells or represent a more general cellular response.

Suggested experiments to sustain the paper:

1. Given that prothoracic gland (PG) size influences ecdysone production, the finding that NudC knockdown alters PG cell size, morphology, and cytoskeletal organization raises the possibility that ecdysone synthesis or signaling may also be affected. This, in turn, could explain the delayed maturation phenotype observed in Figure 1. I recommend testing whether ectopic activation of ecdysone signaling, for instance through 20-hydroxyecdysone (20E) supplementation, can rescue the defects in PG size and developmental timing. Such an experiment would strengthen the link between NudC function, PG morphology, and ecdysone-dependent developmental progression.
2. Additionally, qRT-PCR can be performed to assess the expression levels of ecdysone precursors or target genes in whole larvae, serving as a readout of ecdysone activity, including dilp8, which is usually upregulated when ecdysone levels are reduced.
3. The authors report in Fig. 2 elevated γH2Av in SG cells upon NudC knockdown and interpret this as evidence of chromosome destabilization. They also state that apoptosis is not observed in Fig S10. However, the increase in γH2Av could reflect transient or early apoptotic events or other stress responses triggered by NudC depletion, rather than direct defects in endoreplication or genome stability. I suggest that the authors clarify this important point, for example, by co-expressing apoptotic inhibitors such as P35, or by using the TUNEL assay, which is more sensitive than anti-Caspase3 or Dcp1 antibodies.
4. The data presented in Fig 4 show that NudC knockdown reduces pre-rRNA (ITS1/ITS2) and mature 18S/28S rRNAs in a tissue-specific manner. However, it remains unclear whether these reductions have functional consequences for ribosome assembly and translation. I recommend that the authors perform polysome profiling or an equivalent assay to assess the impact of NudC loss on actively translating ribosomes. This approach would provide a quantitative readout of translation efficiency and clarify whether the observed rRNA defects lead to impaired protein synthesis. Additionally, polysome profiling could help explain the tissue-specific differences observed between salivary glands and fat body cells.
5. Activation of the JNK pathway is often accompanied by apoptosis. It would strengthen the conclusions if the authors included a positive control to confirm that apoptosis is not induced under these experimental conditions, ensuring that the observed effects are specific to autophagy and not confounded by cell death.
6. In Figure S1, reduction of NudC in the fat body appears to induce a starvation-like phenotype, suggesting a potential impairment of metabolic or nutrient-sensing pathways. It would be important to determine whether modulation of nutrient-responsive signaling could rescue this phenotype. Specifically, have the authors examined whether activation of the TOR or PI3K pathways mitigates the effects of NudC knockdown? Assessing pathway activity (e.g., via phospho-S6K or phospho-Akt levels) or performing genetic rescue experiments with pathway activators could clarify whether the observed phenotypes are mediated through disrupted nutrient signaling rather than a secondary effect of general cellular stress. Such analyses could also provide a mechanistic explanation for the increased autophagy observed in these cells.
7. The current images of autophagic vesicles in the SG in Fig. 8B are not clearly visible and quantified. Considering the large size of these polyploid cells, higher-resolution images or alternative imaging approaches should be presented to better visualize and quantify autophagy. This would make the conclusions regarding enhanced autophagy more convincing. In addition, this data could be further strengthened by expanding the analysis of autophagy to other cell types. For example, examining autophagy in fat body cells, where autophagy plays a primary physiological role associated with rRNA accumulation (Fig. S7), rather than a reduction like in SG (Fig. 4), could provide a useful comparison for the function of NudC between polyploid cells.
8. Furthermore, including experiments in other cell types, such as imaginal disc cells, where apoptosis is more readily induced, would help determine whether the effects of NudC knockdown are specific to polyploid cells or are more broadly applicable.

Significance

NudC is a conserved dynein-associated protein essential for nuclear positioning, dendritic morphogenesis, and intracellular transport. This study suggests a novel role for NudC in regulating ribosome biogenesis, potentially linking cytoskeletal organization with protein synthesis and cellular homeostasis. Validating this connection across different tissues could reveal whether NudC serves as a general coordinator of intracellular architecture and translational capacity, providing new insights into how cells integrate structural and biosynthetic functions.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

Referee #1

Evidence, reproducibility and clarity

Summary: This manuscript describes evidence for a role for the Nuclear distribution C dynein complex regulator (NudC) in ribosome biogenesis (RiBi) independent of its role in microtubule-associated dynein function.

Evidence: NudC was picked up in a screen for genes affecting ecdysteroid biosynthesis, a process that occurs in the prothoracic gland (PG; an endocrine organ). In the absence of ecdysone, larvae fail to pupate. Consistent with this finding, the authors find that prothoracic RNAi knockdown of NudC results in a failure in pupation and a decrease in total PG size. They also show defects in polytene chromosome architecture and a mild decrease in overall DNA content. They then turn to the salivary gland (SG) to further characterize the phenotypes associated with NudC knockdown. First, they show that an endogenously tagged version of NudC is abundant in the cytosol and has very weak nuclear staining in the region of the nucleolus (marked by the very low levels of DAPI staining). Knockdown of NudC using RNAi results in reduced NudC-GFP staining, a reduction in SG size, and a reduction in nuclear size. They also find that the SG polytene chromosomes are abnormal and that the production of a SG glue protein as measured by Sgs3-GFP levels and electron dense secretory granules is significantly reduced with NudC knockdown. Interestingly, they also observe the presence of abundant virus-like particles in the nucleus (these structures are thought to originate from retrotransposons and are an indicator of stress). Consistent with increased cellular stress, the authors show activation of JNK signalling. Ultrastructural analysis reveals an abnormally organized ER with an apparent loss of ER-associated ribosomes. They do see other electron dense structures in the cytosol, which they provide evidence (see below) of being P-bodies (structures associated with mRNA). They show that, consistent with a decrease in ribosomes, protein translation is reduced. This is supported by FISH experiments where they show significant decreases in ribosomal RNA (rRNA) transcript levels and decreased translation. Seeing the significant decreases in rRNA levels prompted them to look at overall changes in gene expression, where they discovered that both ribosomal protein gene expression as well as expression of other genes involved in ribosome biogenesis (RiBi) are upregulated with knockdown of NudC. They confirm the changes in mRNA for two genes by showing that levels of the corresponding proteins are also upregulated based on immunostaining of SG cells in which NudC is knocked down. Linking NudC function to a response to defects in RiBi, they shown that SG knockdown of several ribosomal biogenesis factors (RBFs) have similar chromosome structural defects and result in an increase in expression of ribosomal protein genes and of NudC itself. Finally, they show that knock down of genes encoding proteins linked to NudC function in microtubule dynamics do not have any of the same phenotypes as knockdown of NudC and RBFs. Altogether, their data support a moonlighting function for NudC in ribosome biogenesis. Moreover, defects in RiBi wherein ribosomal RNAs are decreased seem to result in compensatory changes where both RBFs and ribosomal protein genes are upregulated.

Major issues:

The title is a bit problematic since they haven't shown that NudC doesn't also affect normal mitotic cells - they only look at polyploid cells, but that doesn't mean normal mitotic cells are not also affected.

Also, the authors show that two different RNAi lines for NudC give the same defects - it would be good to know if the RNAi lines target the same or different sequences in the NudC transcripts. Alternatively, it would be equally good to show that trans-allelic combinations of NudC mutants have the same defects in the prothoracic glands and the salivary glands as the RNAi. Instead, they examine only overall body size, developmental delays and lethality in the trans-hetero allelic NudC mutants.

Results: Lines 261 - 266. Seeing electron dense structures in TEMs and seeing increased Me31B staining by confocal imaging in the cytoplasm is insufficient evidence that the electron dense structures are P-bodies. They could be the P-bodies but they could also be aggregated ribosomes; there is insufficient evidence to "confirm" that they are P-bodies - maybe just say "suggests".

It would be quite helpful to characterize the "5 blob" and "shortened polytene chromosome arm" defects shown in Figure 2 and Figure 6. Are these partially polytenized chromosomes or are large sections of the chromosomes missing or just underreplicated? What do the chromosomes look like if you lyse the nuclei, spread the chromosomes and stain with DAPI or Hoechst - this is a pretty standard practice and would reveal much more about the structure of the polytene chromosomes.

Minor points:

Abstract, lines 28 - 31. I think this gene has been identified before. The authors probably want to say they have discovered a role for this gene in RiBi.

Introduction, line 66. The protein is imported into the nucleus, where it localizes to the nucleolus - technically the protein is not imported into the nucleolus.

Introduction, line 70. To be comprehensive in the description of ribosome biogenesis, the authors may want to mention that the 40S and 60S subunits are then exported from the nucleus and form the 80S subunit in the cytoplasm during translation.

Introduction, line 98. May want to cite paper showing that Minute mutations turn out to be mutations in individual ribosomal protein genes.

Results, lines 285 to 298. In situs with multiple probes that detect all parts of both the pre-rRNA and processed rRNA indicate that all are down in the SG in NudC knockdowns, but that the 18S and 28S rRNAs are down the internal transcribed spacers go up - can the authors explain or hypothesize how this could happen?

Results, lines 292. Since they didn't knock down NudC in the fat body cells in this experiment, this comment seems irrelevant.

Discussion, line 468. I don't think the authors have provided evidence of DNA damage. With the experiments they have shown, the chromosomes look abnormal - not clear what is abnormal.

Figure 6A. Hoechst is misspelled.

Referee cross-commenting

I think the other reviewers have valid criticisms. I think among the most critical issues to sort out is (1) what is wrong with the chromosomes, (2) are diploid tissues also affected, (3) are the RIBI phenotypes a primary or secondary consequence of nudC loss. I'm not sure how easy it is to do ribosomal profiling on tissues dissected from larvae as the third reviewer is suggesting.

Significance

It is a novel discovery that a protein regulating microtubule dynamics is moonlighting, presumably in the nucleolus, to regulate rRNA synthesis or stabilization. A little information regarding mechanism of action would make this a much more exciting paper - how does it do it? Right now, it is unclear whether rRNA synthesis or maintenance is being regulated and there are no hypotheses regarding how this protein localizes to nucleoli and exactly what it is doing there. Is it regulating all RNA Pol I-dependent transcription? Is it involved in processing or stabilizing rRNAs? The description of the chromosomal defects also fall short of satisfying. As is, this paper probably of most interest to those who study ribosome biogenesis - an important topic, but without more mechanistic insight, not so interesting to a more general audience.

My expertise

I am an experienced Drosophila biologist who is familiar with the system and who fully understands all of the experiments presented in this manuscript and the relevance of the findings.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

Referee #1

Evidence, reproducibility and clarity

Summary: This manuscript describes evidence for a role for the Nuclear distribution C dynein complex regulator (NudC) in ribosome biogenesis (RiBi) independent of its role in microtubule-associated dynein function.

Evidence: NudC was picked up in a screen for genes affecting ecdysteroid biosynthesis, a process that occurs in the prothoracic gland (PG; an endocrine organ). In the absence of ecdysone, larvae fail to pupate. Consistent with this finding, the authors find that prothoracic RNAi knockdown of NudC results in a failure in pupation and a decrease in total PG size. They also show defects in polytene chromosome architecture and a mild decrease in overall DNA content. They then turn to the salivary gland (SG) to further characterize the phenotypes associated with NudC knockdown. First, they show that an endogenously tagged version of NudC is abundant in the cytosol and has very weak nuclear staining in the region of the nucleolus (marked by the very low levels of DAPI staining). Knockdown of NudC using RNAi results in reduced NudC-GFP staining, a reduction in SG size, and a reduction in nuclear size. They also find that the SG polytene chromosomes are abnormal and that the production of a SG glue protein as measured by Sgs3-GFP levels and electron dense secretory granules is significantly reduced with NudC knockdown. Interestingly, they also observe the presence of abundant virus-like particles in the nucleus (these structures are thought to originate from retrotransposons and are an indicator of stress). Consistent with increased cellular stress, the authors show activation of JNK signalling. Ultrastructural analysis reveals an abnormally organized ER with an apparent loss of ER-associated ribosomes. They do see other electron dense structures in the cytosol, which they provide evidence (see below) of being P-bodies (structures associated with mRNA). They show that, consistent with a decrease in ribosomes, protein translation is reduced. This is supported by FISH experiments where they show significant decreases in ribosomal RNA (rRNA) transcript levels and decreased translation. Seeing the significant decreases in rRNA levels prompted them to look at overall changes in gene expression, where they discovered that both ribosomal protein gene expression as well as expression of other genes involved in ribosome biogenesis (RiBi) are upregulated with knockdown of NudC. They confirm the changes in mRNA for two genes by showing that levels of the corresponding proteins are also upregulated based on immunostaining of SG cells in which NudC is knocked down. Linking NudC function to a response to defects in RiBi, they shown that SG knockdown of several ribosomal biogenesis factors (RBFs) have similar chromosome structural defects and result in an increase in expression of ribosomal protein genes and of NudC itself. Finally, they show that knock down of genes encoding proteins linked to NudC function in microtubule dynamics do not have any of the same phenotypes as knockdown of NudC and RBFs. Altogether, their data support a moonlighting function for NudC in ribosome biogenesis. Moreover, defects in RiBi wherein ribosomal RNAs are decreased seem to result in compensatory changes where both RBFs and ribosomal protein genes are upregulated.

Major issues:

The title is a bit problematic since they haven't shown that NudC doesn't also affect normal mitotic cells - they only look at polyploid cells, but that doesn't mean normal mitotic cells are not also affected.

Also, the authors show that two different RNAi lines for NudC give the same defects - it would be good to know if the RNAi lines target the same or different sequences in the NudC transcripts. Alternatively, it would be equally good to show that trans-allelic combinations of NudC mutants have the same defects in the prothoracic glands and the salivary glands as the RNAi. Instead, they examine only overall body size, developmental delays and lethality in the trans-hetero allelic NudC mutants.

Results: Lines 261 - 266. Seeing electron dense structures in TEMs and seeing increased Me31B staining by confocal imaging in the cytoplasm is insufficient evidence that the electron dense structures are P-bodies. They could be the P-bodies but they could also be aggregated ribosomes; there is insufficient evidence to "confirm" that they are P-bodies - maybe just say "suggests".

It would be quite helpful to characterize the "5 blob" and "shortened polytene chromosome arm" defects shown in Figure 2 and Figure 6. Are these partially polytenized chromosomes or are large sections of the chromosomes missing or just underreplicated? What do the chromosomes look like if you lyse the nuclei, spread the chromosomes and stain with DAPI or Hoechst - this is a pretty standard practice and would reveal much more about the structure of the polytene chromosomes.

Minor points:

Abstract, lines 28 - 31. I think this gene has been identified before. The authors probably want to say they have discovered a role for this gene in RiBi.

Introduction, line 66. The protein is imported into the nucleus, where it localizes to the nucleolus - technically the protein is not imported into the nucleolus.

Introduction, line 70. To be comprehensive in the description of ribosome biogenesis, the authors may want to mention that the 40S and 60S subunits are then exported from the nucleus and form the 80S subunit in the cytoplasm during translation.

Introduction, line 98. May want to cite paper showing that Minute mutations turn out to be mutations in individual ribosomal protein genes.

Results, lines 285 to 298. In situs with multiple probes that detect all parts of both the pre-rRNA and processed rRNA indicate that all are down in the SG in NudC knockdowns, but that the 18S and 28S rRNAs are down the internal transcribed spacers go up - can the authors explain or hypothesize how this could happen?

Results, lines 292. Since they didn't knock down NudC in the fat body cells in this experiment, this comment seems irrelevant.

Discussion, line 468. I don't think the authors have provided evidence of DNA damage. With the experiments they have shown, the chromosomes look abnormal - not clear what is abnormal.

Figure 6A. Hoechst is misspelled.

Referee cross-commenting

I think the other reviewers have valid criticisms. I think among the most critical issues to sort out is (1) what is wrong with the chromosomes, (2) are diploid tissues also affected, (3) are the RIBI phenotypes a primary or secondary consequence of nudC loss. I'm not sure how easy it is to do ribosomal profiling on tissues dissected from larvae as the third reviewer is suggesting.

Significance

It is a novel discovery that a protein regulating microtubule dynamics is moonlighting, presumably in the nucleolus, to regulate rRNA synthesis or stabilization. A little information regarding mechanism of action would make this a much more exciting paper - how does it do it? Right now, it is unclear whether rRNA synthesis or maintenance is being regulated and there are no hypotheses regarding how this protein localizes to nucleoli and exactly what it is doing there. Is it regulating all RNA Pol I-dependent transcription? Is it involved in processing or stabilizing rRNAs? The description of the chromosomal defects also fall short of satisfying. As is, this paper probably of most interest to those who study ribosome biogenesis - an important topic, but without more mechanistic insight, not so interesting to a more general audience.

My expertise

I am an experienced Drosophila biologist who is familiar with the system and who fully understands all of the experiments presented in this manuscript and the relevance of the findings.

Author Response

We thank the editors and the reviewers for a number of useful criticisms and suggestions, and for the opportunity given to us, as authors, to publicly reply to the comments. This is a useful exercise, which brings to the attention of the reader lights, but also shadows of the reviewing process, and that we hope will lead in future to develop a better approach to it. Here, we will reply to a number of selected issues which appear to us to be of particular relevance.

Reviewer 1

Reviewer 1 disqualifies our work altogether, based on her/his statement that: “In the paper by Mercurio et al, the authors examine the role of SOX2 in the development of mouse hippocampal dentate gyrus. Using conditionally mutant SOX2 mice the authors show that early, but not late, deletion of SOX2 leads to developmental impairments of the dentate gyrus. A drawback of their study is that these findings have been reported previously by the group (Favaro et al. 2009; Ferri et al. 2013).”

The statement reported in bold is simply not true. In Favaro et al. 2009 (Nat Neurosci 12:1248), we demonstrated that nes-Cre-mediated Sox2 deletion leads to defects in postnatal, but not embryonic, hippocampal neurogenesis. In Ferri et al. 2013 (Development 140:1250), we demonstrated that FoxG1Cre-mediated Sox2 deletion leads to defective development of the VENTRAL forebrain. The presence, at the end of gestation, of hippocampal defects was just mentioned in one sentence: - “the hippocampus, at E18.5, was severely underdeveloped (not shown)” (line 1, page 1253)-, and not analyzed any further. In the present work, we describe in detail, starting from E12.5, up to E18.5, how the hippocampal defect develops, and undertake a detailed study of downstream gene expression and cellular defects arising in mutants.

It is unfortunate that the reviewer further insists on the same misleading, and unfounded statement – see her/his comment 3, highlighted in bold character: “the authors state "...remarkably, in the FoxG1-Cre cKO, the DG appears to be almost absent (Figure 2A).". The question is why this finding is remarkable as it already was published in (Ferri et al. 2013)”. As mentioned above, we only remark, in Ferri et al., that the hippocampus was severely underdeveloped (not shown).

Reviewer 2

Reviewer 2 states, already at the beginning: “I am concerned about a major confounding issue (see below).” ... “The authors rely on Foxg1-Cre for their main evidence that very early deletion of Sox2 leads to near loss of the dentate. However, it doesn't appear that the authors are aware that Foxg1 het mice have a fairly significant dentate phenotype (see this paper).”

The reviewer refers to the fact that, to delete Sox2, we need to express a Cre gene “knocked-in” into the Foxg1 gene; hence, heterozygous and homozygous Sox2 deletions will be accompanied by heterozygous loss of Foxg1. If Foxg1 is important for hippocampus development, the absence of a Foxg1 allele will affect the phenotype.

Unfortunately, the statement of the reviewer is subtly misleading, and leads the reader who has not checked the data reported in the cited paper (Shen et al., 2006) to erroneously believe that heterozygous loss of Foxg1 may be responsible for the effects that we report upon homozygous Sox2 deletion. In contrast to the statement made by the reviewer, the paper cited by the reviewer documents that, while heterozygous loss of Foxg1 leads to important POSTNATAL dentate gyrus abnormalities, the PRENATAL development of the dentate gyrus is essentially normal (Figure 6) (“a subtle and inconsistent defect” of the ventral blade observed in about 50% of the mice at E18.5, according to the authors of that paper). Compare “subtle and inconsistent defect” by Shen et al. with “fairly significant dentate phenotype”, as stated by the reviewer. As our paper is entirely focused on defects seen in PRENATAL development in Foxg1Cre; Sox2 mutants, the subtle and inconsistent defects seen by Shen et al. are in sharp contrast with the deep defects seen in embryonic development in our Foxg1Cre;Sox2-/- mutants, and in agreement with the similarity we observe between wild type and heterozygous Foxg1Cre;Sox2+/- embryos (page 5, lines 140-145, of the version of the Full Submission for publication on August 30). An example showing the comparison between a Wild type, a FoxG1 +/- heterozygote;Sox2+/- heterozygote and a FoxG1 heterozygote;Sox2-/- homozygote is now shown in the accompanying figure.

Obviously the incorrect statement kills our paper by itself. If the reviewer had doubts, we could have provided plenty of additional data demonstrating the lack of significant differences between Foxg1CRE Sox2+/- and wild type (Sox2+/+) embryos, as we stated in our paper.

There is an additional interesting comment by Reviewer 2 (see points 2 and 6). The reviewer argues that “The only two direct targets they find don't seem likely to be important players in the phenotypes they describe”. The Reviewer excludes the Gli3 gene (a direct Sox2 target, see Fig. 6), as a possible important player, in spite of the observation that Gli3 is decreased, at early developmental stages, in the cortical hem (Figure 5). The reviewer says “The Gli3 [mutation] phenotypes that have been published are quite distinct from this”. We object that the Gli3 phenotypes are indeed more severe than the phenotype of our mutant, and include failure to develop a dentate gyrus. However, this observation does not preclude the hypothesis that the decreased expression of Gli3 in our mutant is directly responsible for the phenotype we observe. The more severe phenotype of the Gli3 mutants is in fact due to a germ-line null mutation, whereas, in our Foxg1-Cre Sox2 mutants, we observe only a reduction of Gli3 expression, around E12.5 (Fig. 5), that is compatible with a less severe dentate gyrus phenotype. The Reviewer adds that Wnt3A, based on the phenotype of the knock-out mice, similar to that of our Sox2 deleted mice, is a more relevant gene, but it is not a direct target of Sox2. However, the fact that Wnt3A is apparently not directly regulated by Sox2 is not necessarily to be considered a “minus”; Sox2, being a transcription factor, is expected to directly regulate a multiplicity of genes, whose expression will affect the expression of other genes. Indeed, we presented in Fig 6D the hypothesis that decreased expression of Gli3 may contribute to decreased expression of Wnt3A, as already proposed by Grove et al. (1998) based on the observation that Gli3 null mutants lose the expression of Wnt3A (and other Wnt factors) from the cortical hem. The additional suggestion made by the Reviewer, in the context of the Wnt3A hypothesis, to investigate LEF1, as a potential direct Sox2 target, and its expression, is certainly interesting, but, as stated by the reviewer, LEF1 is downstream to Wnt3A, and, by itself, its hypothetical regulation by Sox2 would not explain the downregulation of Wnt3A. Moreover, we already have evidence that Sox2 does not directly regulate Wnt3A (unpublished).

Reviewer 1 and 2

Both Reviewer 1 and 2 have questions about the timing of Sox2 ablation in the Sox2 mutants obtained with the three different Cre deleters. As we state in the text (pages 4, 6), Foxg1-Cre deletes at E.9.5 (Ferri et al., 2013; Hébert and McConnell, 2000); Emx1-Cre deletes from E10.5 onwards, but not at E9.5 (Gorski et al., 2002; see also Shetty AS et al., PNAS 2013, E4913); Nestin-Cre deletes at later stages, around E12.5 (Favaro et al. 2009).

Reviewer 3

We thank Reviewer 3 for the useful considerations and suggestions, which constructively help to improve the paper.

Evidence that Sox2+/-;FoxG1+/- hippocampi at E18.5 do not significantly differ from wild type (Sox2+/+, FoxG1+/+) controls. In contrast, Sox2-/-;FoxG1+/- hippocampi are severely defective. (A) GFAP immunofluorescence at E18.5 on coronal sections of control and FoxG1-Cre cKO hippocampi (controls n=6, mutants n=4). (B) In situ hybridization at E18.5 for NeuroD (controls n=4, mutants n=3) on coronal sections of control and FoxG1-Cre cKO hippocampi. Arrows indicate dentate gyrus (DG); note the strong decrease of the dentate gyrus, and the radial glia (GFAP) disorganization in cKO.<br /> The Sox2flox/flox genotype corresponds to wild type mice (Sox2+/+). The Sox2+/flox ; FoxG1Cre genotype corresponds to Sox2+/-; FoxG1+/- controls. The Sox2flox/flox ; FoxG1Cre genotype corresponds to Sox2-/-; FoxG1+/- mutants.

Author Response

Reviewer #1:

Hutchings et al. report an updated cryo-electron tomography study of the yeast COP-II coat assembled around model membranes. The improved overall resolution and additional compositional states enabled the authors to identify new domains and interfaces--including what the authors hypothesize is a previously overlooked structural role for the SEC31 C-Terminal Domain (CTD). By perturbing a subset of these new features with mutants, the authors uncover some functional consequences pertaining to the flexibility or stability of COP-II assemblies.

Overall, the structural and functional work appears reliable, but certain questions and comments should be addressed prior to publication. However, this reviewer failed to appreciate the conceptual advance that warrants publication in a general biology journal like eLIFE. Rather, this study provides a valuable refinement of our understanding of COP-II that I believe is better suited to a more specialized, structure-focused journal.

We agree that in our original submission our description of the experimental setup, indeed similar to previous work, did not fully capture the novel findings of this paper. Rather than being simply a higher resolution structure of the COPII coat, in fact we have discovered new interactions in the COPII assembly network, and we have probed their functional roles, significantly changing our understanding of the mechanisms of COPII-mediated membrane curvature. In the revised submission we have included additional genetic data that further illuminate this mechanism, and have rewritten the text to better communicate the novel aspects of our work.

Our combination of structural, functional and genetic analyses goes beyond refining our textbook understanding of the COPII coat as a simple ‘adaptor and cage’, but rather it provides a completely new picture of how dynamic regulation of assembly and disassembly of a complex network leads to membrane remodelling.

These new insights have important implications for how coat assembly provides structural force to bend a membrane but is still able to adapt to distinct morphologies. These questions are at the forefront of protein secretion, where there is debate about how different types of carriers might be generated that can accommodate cargoes of different size.

Major Comments: 1) The authors belabor what this reviewer thinks is an unimportant comparison between the yeast reconstruction of the outer coat vertex with prior work on the human outer coat vertex. Considering the modest resolution of both the yeast and human reconstructions, the transformative changes in cryo-EM camera technology since the publication of the human complex, and the differences in sample preparation (inclusion of the membrane, cylindrical versus spherical assemblies, presence of inner coat components), I did not find this comparison informative. The speculations about a changing interface over evolutionary time are unwarranted and would require a detailed comparison of co-evolutionary changes at this interface. The simpler explanation is that this is a flexible vertex, observed at low resolution in both studies, plus the samples are very different.

We do agree that our proposal that the vertex interface changes over evolutionary time is speculative and we have removed this discussion. We agree that a co-evolutionary analysis will be enlightening here, but is beyond the scope of the current work.

We respectfully disagree with the reviewer’s interpretation that the difference between the two vertices is due to low resolution. The interfaces are clearly different, and the resolutions of the reconstructions are sufficient to state this. The reviewer’s suggestion that the difference in vertex orientation might be simply attributable to differences in sample, such as inclusion of the membrane, cylindrical versus spherical morphology, or presence of inner coat components were ruled out in our original submission: we resolved yeast vertices on spherical vesicles (in addition to those on tubes) and on membrane-less cages. These analyses clearly showed that neither the presence of a membrane, nor the change in geometry (tubular vs. spherical) affect vertex interactions. These experiments are presented in Supplementary Fig 4 (Supplementary Fig. 3 in the original version). Similarly, we discount that differences might be due to the presence or absence of inner coat components, since membrane-less cages were previously solved in both conditions and are no different in terms of their vertex structure (Stagg et al. Nature 2006 and Cell 2008).

We believe it is important to report on the differences between the two vertex structures. Nevertheless, we have shifted our emphasis on the functional aspects of vertex formation and moved the comparison between the two vertices to the supplement.

2) As one of the major take home messages of the paper, the presentation and discussion of the modeling and assignment of the SEC31-CTD could be clarified. First, it isn't clear from the figures or the movies if the connectivity makes sense. Where is the C-terminal end of the alpha-solenoid compared to this new domain? Can the authors plausibly account for the connectivity in terms of primary sequence? Please also include a side-by-side comparison of the SRA1 structure and the CTD homology model, along with some explanation of the quality of the model as measured by Modeller. Finally, even if the new density is the CTD, it isn't clear from the structure how this sub-stoichiometric and apparently flexible interaction enhances stability. Hence, when the authors wrote "when the [CTD] truncated form was the sole copy of Sec31 in yeast, cells were not viable, indicating that the novel interaction we detect is essential for COPII coat function." Maybe, but could this statement be a leap to far? Is it the putative interaction essential, or is the CTD itself essential for reasons that remain to be fully determined?

The CTD is separated from the C-terminus of the alpha solenoid domain by an extended domain (~350 amino acids) that is predicted to be disordered, and contains the PPP motifs and catalytic fragment that contact the inner coat. This is depicted in cartoon form in Figures 3A and 7, and discussed at length in the text. This arrangement explains why no connectivity is seen, or expected. We could highlight the C-terminus of the alpha-solenoid domain to emphasize where the disordered region should emerge from the rod, but connectivity of the disordered domain to the CTD could arise from multiple positions, including from an adjacent rod.

The reviewer’s point about the essentiality of the CTD being independent of its interaction with the Sec31 rod, is an important one. The basis for our model that the CTD enhances stability or rigidity of the coat is the yeast phenotype of Sec31-deltaCTD, which resembles that of a sec13 null. Both mutants are lethal, but rescued by deletion of emp24, which leads to more easily deformable membranes (Čopič et al. Science 2012). We agree that even if this model is true, the interaction of the CTD with Sec31 that our new structure reveals is not proven to drive rigidity or essentiality. We have tempered this hypothesis and added alternative possibilities to the discussion.

We have included the SRA1 structure in Supplementary Fig 5, as requested, and the model z-score in the Methods. The Z-score, as calculated by the proSA-web server is -6.07 (see figure below, black dot), and falls in line with experimentally determined structures including that of the template (PDB 2mgx, z-score = -5.38).

3) Are extra rods discussed in Fig. 4 are a curiosity of unclear functional significance? This reviewer is concerned that these extra rods could be an in vitro stoichiometry problem, rather than a functional property of COP-II.

This is an important point, that, as we state in the paper, cannot be answered at the moment: the resolution is too low to identify the residues involved in the interaction. Therefore we are hampered in our ability to assess the physiological importance of this interaction. We still believe the ‘extra’ rods are an important observation, as they clearly show that another mode of outer coat interaction, different from what was reported before, is possible.

The concern that interactions visualised in vitro might not be physiologically relevant is broadly applicable to structural biology approaches. However, our experimental approach uses samples that result from active membrane remodelling under near-physiological conditions, and we therefore expect these to be less prone to artefacts than most in vitro reconstitution approaches, where proteins are used at high concentrations and in high salt buffer conditions.

4) The clashsccore for the PDB is quite high--and I am dubious about the reliability of refining sidechain positions with maps at this resolution. In addition to the Ramchandran stats, I would like to see the Ramachandran plot as well as, for any residue-level claims, the density surrounding the modeled side chain (e.g. S742).

The clashscore is 13.2, which, according to molprobity, is in the 57th percentile for all structures and in the 97th for structures of similar resolutions. We would argue therefore that the clashscore is rather low. In fact, the model was refined from crystal structures previously obtained by other groups, which had worse clashscore (17), despite being at higher resolution. Our refinement has therefore improved the clashscore. During refinement we have chosen restraint levels appropriate to the resolution of our map (Afonine et al., Acta Cryst D 2018)

The Ramachandran plot is copied here and could be included in a supplemental figure if required. We make only one residue-level claim (S742), the density for which is indeed not visible at our resolution. We claim that S742 is close to the Sec23-23 interface, and do not propose any specific interactions. Nevertheless we have removed reference to S742 from the manuscript. We included this specific information because of the potential importance of this residue as a site of phosphorylation, thereby putting this interface in broader context for the general eLife reader.

Minor Comments:

1) The authors wrote "To assess the relative positioning of the two coat layers, we analysed the localisation of inner coat subunits with respect to each outer coat vertex: for each aligned vertex particle, we superimposed the positions of all inner coat particles at close range, obtaining the average distribution of neighbouring inner coat subunits. From this 'neighbour plot' we did not detect any pattern, indicating random relative positions. This is consistent with a flexible linkage between the two layers that allows adaptation of the two lattices to different curvatures (Supplementary Fig 1E)." I do not understand this claim, since the pattern both looks far from random and the interactions depend on molecular interactions that are not random. Please clarify.

We apologize for the confusion: the pattern of each of the two coats are not random. Our sentence refers to the positions of inner and outer coats relative to each other. The two lattices have different parameters and the two layers are linked by flexible linkers (the 350 amino acids referred to above). We have now clarified the sentence.

2) Related to major point #1, the author wrote "We manually picked vertices and performed carefully controlled alignments." I do now know what it means to carefully control alignments, and fear this suggests human model bias.

We used different starting references for the alignments, with the precise aim to avoid model bias. For both vesicle and cage vertex datasets, we have aligned the subtomograms against either the vertex obtained from tubules, or the vertex from previously published membrane-less cages. In all cases, we retrieved a structure that resembles the one on tubules, suggesting that the vertex arrangement we observe isn’t simply the result of reference bias. This procedure is depicted in Supplementary Fig 4 (Supplementary Fig. 3 in the original manuscript), but we have now clarified it also in the methods section.

3) Why do some experiments use EDTA? I may be confused, but I was surprised to see the budding reaction employed 1mM GMPPNP, and 2.5mM EDTA (but no Magnesium?). Also, for the budding reaction, please replace or expand upon the "the 10% GUV (v/v)" with a mass or molar lipid-to-protein ratio.

We regret the confusion. As stated in the methods, all our budding reactions are performed in the presence of EDTA and Magnesium, which is present in the buffer (at 1.2 mM). The reason is to facilitate nucleotide exchange, as reported and validated in Bacia et al., Scientific Reports 2011.

Lipids in GUV preparations are difficult to quantify. We report the stock concentrations used, but in each preparation the amount of dry lipid that forms GUVs might be different, as is the concentration of GUVs after hydration. However since we analyse reactions where COPII proteins have bound and remodelled individual GUVs, we do not believe the protein/lipid ratio influences our structures.

4) Please cite the AnchorMap procedure.

We cite the SerialEM software, and are not aware of other citations specifically for the anchor map procedure.

5) Please edit for typos (focussing, functionl, others)

Done

Reviewer #2:

The manuscript describes new cryo-EM, biochemistry, and genetic data on the structure and function of the COPII coat. Several new discoveries are reported including the discovery of an extra density near the dimerization region of Sec13/31, and "extra rods" of Sec13/31 that also bind near the dimerization region. Additionally, they showed new interactions between the Sec31 C-terminal unstructured region and Sec23 that appear to bridge multiple Sec23 molecules. Finally, they increased the resolution of the Sec23/24 region of their structure compared to their previous studies and were able to resolve a previously unresolved L-loop in Sec23 that makes contact with Sar1. Most of their structural observations were nicely backed up with biochemical and genetic experiments which give confidence in their structural observations. Overall the paper is well-written and the conclusions justified.

However, this is the third iteration of structure determination of the COPII coat on membrane with essentially the same preparation and methods. Each time, there has been an incremental increase in resolution and new discoveries, but the impact of the present study is deemed to be modest. The science is good, but it may be more appropriate for a more specialized journal. Areas of specific concern are described below.

As described above, we respectfully disagree with this interpretation of the advance made by the current work. This work improves on previous work in many aspects. The resolution of the outer coat increases from over 40A to 10-12A, allowing visualisation of features that were not previously resolved, including a novel vertex arrangement, the Sec31 CTD, and the outer coat ‘extra rods’. An improved map of the inner coat also allows us to resolve the Sec23 ‘L-loop’. We would argue that these are not just extra details, but correspond to a suite of novel interactions that expand our understanding of the complex COPII assembly network. Moreover, we include biochemical and genetic experiments that not only back up our structural observations but bring new insights into COPII function. As pointed out in response to reviewer 1, we believe our work contributes a significant conceptual advance, and have modified the manuscript to convey this more effectively.

1) The abstract is vague and should be re-written with a better description of the work.

We have modified the abstract to specifically outline what we have done and the major new discoveries of this paper.

2) Line 166 - "Surprisingly, this mutant was capable of tubulating GUVs". This experiment gets to one of the fundamental unknown questions in COPII vesiculation. It is not clear what components are driving the membrane remodeling and at what stages during vesicle formation. Isn't it possible that the tubulation activity the authors observe in vitro is not being driven at all by Sec13/31 but rather Sec23/24-Sar1? Their Sec31ΔCTD data supports this idea because it lacks a clear ordered outer coat despite making tubules. An interesting experiment would be to see if tubules form in the absence of all of Sec13/31 except the disordered domain of Sec31 that the authors suggest crosslinks adjacent Sec23/24s.

This is an astute observation, and we agree with the reviewer that the source of membrane deformation is not fully understood. We favour the model that budding is driven significantly by the Sec23-24 array. To further support this, we have performed a new experiment, where we expressed Sec31ΔN in yeast cells lacking Emp24, which have more deformable membranes and are tolerant to the otherwise lethal deletion of Sec13. While Sec31ΔN in a wild type background did not support cell viability, this was rescued in a Δemp24 yeast strain, strongly supporting the hypothesis that a major contributor to membrane remodelling is the inner coat, with the outer coat becoming necessary to overcome membrane bending resistance that ensues from the presence of cargo. We now include these results in Figure 1.

However, we must also take into account the results presented in Fig. 6, where we show that weakening the Sec23-24 interface still leads to budding, but only if Sec13-31 is fully functional, and that in this case budding leads to connected pseudo-spherical vesicles rather than tubes. When Sec13-31 assembly is also impaired, tubes appear unstructured. We believe this strongly supports our conclusions that both inner and outer coat interactions are fundamental for membrane remodelling, and it is the interplay between the two that determines membrane morphology (i.e. tubes vs. spheres).

To dissect the roles of inner and outer coats even further, we have done the experiment that the reviewer suggests: we expressed Sec31768-1114, but the protein was not well-behaved and co-purified with chaperones. We believe the disordered domain aggregates when not scaffolded by the structured elements of the rod. Nonetheless, we used this fragment in a budding reaction, and could not see any budding. We did not include this experiment as it was inconclusive: the lack of functionality of the purified Sec31 fragment could be attributed to the inability of the disordered region to bind its inner coat partner in the absence of the scaffolding Sec13-31 rod. As an alternative approach, we have used a version of Sec31 that lacks the CTD, and harbours a His tag at the N-terminus (known from previous studies to partially disrupt vertex assembly). We think this construct is more likely to be near native, since both modifications on their own lead to functional protein. We could detect no tubulation with this construct by negative stain, while both control constructs (Sec31ΔCTD and Nhis-Sec31) gave tubulation. This suggests that the cross-linking function of Sec31 is not sufficient to tubulate GUV membranes, but some degree of functional outer coat organisation (either mediated by N- or C-terminal interactions) is needed. It is also possible that the lack of outer coat organisation might lead to less efficient recruitment to the inner coat and cross-linking activity. We have added this new observation to the manuscript.

3) Line 191 - "Inspecting cryo-tomograms of these tubules revealed no lozenge pattern for the outer 192 coat" - this phrasing is vague. The reviewer thinks that what they mean is that there is a lack of order for the Sec13/31 layer. Please clarify.

The reviewer is correct, we have changed the sentence.

4) Line 198 - "unambiguously confirming this density corresponds to 199 the CTD." This only confirms that it is the CTD if that were the only change and the Sec13/31 lattice still formed. Another possibility is that it is density from other Sec13/31 that only appears when the lattice is formed such as the "extra rods". One possibility is that the density is from the extra rods. The reviewer agrees that their interpretation is indeed the most likely, but it is not unambiguous. The authors should consider cross-linking mass spectrometry.

We have removed the word ‘unambiguously’, and changed to ‘confirming that this density most likely corresponds to the CTD’. Nonetheless, we believe that our interpretation is correct: the extra rods bind to a different position, and themselves also show the CTD appendage. In this experiment, the lack of the CTD was the only biochemical change.

5) In the Sec31ΔCTD section, the authors should comment on why ΔCTD is so deleterious to oligomer organization in yeast when cages form so abundantly in preparations of human Sec13/31 ΔC (Paraan et al 2018).

We have added a comment to address this. “Interestingly, human Sec31 proteins lacking the CTD assemble in cages, indicating that either the vertex is more stable for human proteins and sufficient for assembly, or that the CTD is important in the context of membrane budding but not for cage formation in high salt conditions.”

6) The data is good for the existence of the "extra rods", but significance and importance of them is not clear. How can these extra densities be distinguished from packing artifacts due to imperfections in the helical symmetry.

Please also see our response to point 3 from reviewer 1. Regarding the specific concern that artefacts might be a consequence of imperfection in the helical symmetry, we would argue such imperfections are indeed expected in physiological conditions, and to a much higher extent. For this reason interactions seen in the context of helical imperfections are likely to be relevant. In fact, in normal GTP hydrolysis conditions, we expect long tubes would not be able to form, and the outer coat to be present on a wide range of continuously changing membrane curvatures. We think that the ability of the coat to form many interactions when the symmetry is imperfect might be exactly what confers the coat its flexibility and adaptability.

7) Figure 5 is very hard to interpret and should be redone. Panels B and C are particularly hard to interpret.

We have made a new figure where we think clarity is improved.

8) The features present in Sec23/24 structure do not reflect the reported resolution of 4.7 Å. It seems that the resolution is overestimated.

We report an average resolution of 4.6 Å. In most of our map we can clearly distinguish beta strands, follow the twist of alpha helices and see bulky side chains. These features typically become visible at 4.5-5A resolution. We agree that some areas are worse than 4.6 Å, as typically expected for such a flexible assembly, but we believe that the average resolution value reported is accurate. We obtained the same resolution estimate using different software including relion, phenix and dynamo, so that is really the best value we can provide. To further convince ourselves that we have the resolution we claim, we sampled EM maps from the EMDB with the same stated resolution (we just took the 7 most recent ones which had an associated atomic model), and visualised their features at arbitrary positions. For both beta strands and alpha helices, we do not feel our map looks any worse than the others we have examined. We include a figure here.

9) Lines 315/316 - "We have combined cryo-tomography with biochemical and genetic assays to obtain a complete picture of the assembled COPII coat at unprecedented resolution (Fig. 7)"

10) Figure 7. is a schematic model/picture the authors should reference a different figure or rephrase the sentence.

We now refer to Fig 7 in a more appropriate place.

Reviewer #3:

The manuscript by Hutchings et al. describes several previously uncharacterised molecular interactions in the coats of COP-II vesicles by using a reconstituted coats of yeast COPI-II. They have improved the resolution of the inner coat to 4.7A by tomography and subtomogram averaging, revealing detailed interactions, including those made by the so-called L-loop not observed before. Analysis of the outer layer also led to new interesting discoveries. The sec 31 CTD was assigned in the map by comparing the WT and deletion mutant STA-generated density maps. It seems to stabilise the COP-II coats and further evidence from yeast deletion mutants and microsome budding reconstitution experiments suggests that this stabilisation is required in vitro. Furthermore, COP-II rods that cover the membrane tubules in right-handed manner revealed sometimes an extra rod, which is not part of the canonical lattice, bound to them. The binding mode of these extra rods (which I refer to here a Y-shape) is different from the canonical two-fold symmetric vertex (X-shape). When the same binding mode is utilized on both sides of the extra rod (Y-Y) the rod seems to simply insert in the canonical lattice. However, when the Y-binding mode is utilized on one side of the rod and the X-binding mode on the other side, this leads to bridging different lattices together. This potentially contributes to increased flexibility in the outer coat, which maybe be required to adopt different membrane curvatures and shapes with different cargos. These observations build a picture where stabilising elements in both COP-II layers contribute to functional cargo transport. The paper makes significant novel findings that are described well. Technically the paper is excellent and the figures nicely support the text. I have only minor suggestions that I think would improve the text and figure.

We thank the reviewer for helpful suggestions which we agree improve the manuscript.

Minor Comments:

L 108: "We collected .... tomograms". While the meaning is clear to a specialist, this may sound somewhat odd to a generic reader. Perhaps you could say "We acquired cryo-EM data of COP-II induced tubules as tilt series that were subsequently used to reconstruct 3D tomograms of the tubules."

We have changed this as suggested

L 114: "we developed an unbiased, localisation-based approach". What is the part that was developed here? It seems that the inner layer particle coordinates where simply shifted to get starting points in the outer layer. Developing an approach sounds more substantial than this. Also, it's unclear what is unbiased about this approach. The whole point is that it's biased to certain regions (which is a good thing as it incorporates prior knowledge on the location of the structures).

We have modified the sentence to “To target the sparser outer coat lattice for STA, we used the refined coordinates of the inner coat to locate the outer coat tetrameric vertices”, and explain the approach in detail in the methods.

L 124: "The outer coat vertex was refined to a resolution of approximately ~12 A, revealing unprecedented detail of the molecular interactions between Sec31 molecules (Supplementary Fig 2A)". The map alone does not reveal molecular interactions; the main understanding comes from fitting of X-ray structures to the low-resolution map. Also "unprecedented detail" itself is somewhat problematic as the map of Noble et al (2013) of the Sec31 vertex is also at nominal resolution of 12 A. Furthermore, Supplementary Fig 2A does not reveal this "unprecedented detail", it shows the resolution estimation by FSC. To clarify, these points you could say: "Fitting of the Sec31 atomic model to our reconstruction vertex at 12-A resolution (Supplementary Fig 2A) revealed the molecular interactions between different copies of Sec31 in the membrane-assembled coat.

We have changed the sentence as suggested.

L 150: Can the authors exclude the possibility that the difference is due to differences in data processing? E.g. how the maps amplitudes have been adjusted?

Yes, we can exclude this scenario by measuring distances between vertices in the right and left handed direction. These measurements are only compatible with our vertex arrangement, and cannot be explained by the big deviation from 4-fold symmetry seen in the membrane-less cage vertices.

L 172: "that wrap tubules either in a left- or right-handed manner". Don't they do always both on each tubule? Now this sentence could be interpreted to mean that some tubules have a left-handed coat and some a right-handed coat.

We have changed this sentence to clarify. “Outer coat vertices are connected by Sec13-31 rods that wrap tubules both in a left- and right-handed manner.”

L276: "The difference map" hasn't been introduced earlier but is referred to here as if it has been.

We now introduce the difference map.

L299: Can "Secondary structure predictions" denote a protein region "highly prone to protein binding"?

Yes, this is done through DISOPRED3, a feature include in the PSIPRED server we used for our predictions. The reference is: Jones D.T., Cozzetto D. DISOPRED3: precise disordered region predictions with annotated protein-binding activity Bioinformatics. 2015; 31:857–863. We have now added this reference to the manuscript.

L316: It's true that the detail in the map of the inner coat is unprecedented and the model presented in Figure 7 is partially based on that. But here "unprecedented resolution" sounds strange as this sentence refers to a schematic model and not a map.

We have changed this by moving the reference to Fig 7 to a more appropriate place

L325: "have 'compacted' during evolution" -> remove. It's enough to say it's more compact in humans and less compact in yeast as there could have been different adaptations in different organisms at this interface.

We have changed as requested. See also our response to reviewer 1, point 1.

L327: What's exactly meant by "sequence diversity or variability at this density".

We have now clarified: “Since multiple charge clusters in yeast Sec31 may contribute to this interaction interface (Stancheva et al., 2020), the low resolution could be explained by the fact that the density is an average of different sequences.”

L606-607: The description of this custom data processing approach is difficult to follow. Why is in-plane flip needed and how is it used here?

Initially particles are picked ignoring tube directionality (as this cannot be assessed easily from the tomograms due to the pseudo-twofold symmetry of the Sec23/24/Sar1 trimer). So the in plane rotation of inner coat subunit could be near 0 or 180°. For each tube, both angles are sampled (in-plane flip). Most tubes result in the majority of particles being assigned one of the two orientations (which is then assumed as the tube directionality). Particles that do not conform are removed, and rare tubes where directionality cannot be determined are also removed. We have re-written the description to clarify these points: “Initial alignments were conducted on a tube-by-tube basis using the Dynamo in-plane flip setting to search in-plane rotation angles 180° apart. This allowed to assign directionality to each tube, and particles that were not conforming to it were discarded by using the Dynamo dtgrep_direction command in custom MATLAB scripts”

L627: "Z" here refers to the coordinate system of aligned particles not that of the original tomogram. Perhaps just say "shifted 8 pixels further away from the membrane".

Changed as requested.

L642-643: How can the "left-handed" and "right-handed" rods be separated here? These terms refer to the long-range organisation of the rods in the lattice it's not clear how they were separated in the early alignments.

They are separated by picking only one subset using the dynamo sub-boxing feature. This extracts boxes from the tomogram which are in set positions and orientation relative to the average of previously aligned subtomograms. From the average vertex structure, we sub-box rods at 4 different positions that correspond to the centre of the rods, and the 2-fold symmetric pairs are combined into the same dataset. We have clarified this in the text: “The refined positions of vertices were used to extract two distinct datasets of left and right-handed rods respectively using the dynamo sub-boxing feature.”

Figure 2B. It's difficult to see the difference between dark and light pink colours.

We have changed colours to enhance the difference.

Figure 3C. These panels report the relative frequency of neighbouring vertices at each position; "intensity" does not seem to be the right measure for this. You could say that the colour bar indicates the "relative frequency of neighbouring vertices at each position" and add detail how the values were scaled between 0 and 1. The same applies to SFigure 1E.

Changed as requested.

Figure 4. The COP-II rods themselves are relatively straight, and they are not left-handed or right-handed. Here, more accurate would be "architecture of COPII rods organised in a left-handed manner". (In the text the authors may of course define and then use this shorter expression if they so wish.) Panel 4B top panel could have the title "left-handed" and the lower panel should have the title "right-handed" (for consistency and clarity).

We have now defined left- and right-handed rods in the text, and have changed the figure and panel titles as requested.

Author Response

We thank the reviewers for their comments, which will improve the quality of our manuscript.

Our study describes a novel approach to the identification of GTPase binding-partners. We recapitulated and augmented previous protein-protein interaction data for RAB18 and presented data validating some of our findings. In aggregate, our dataset suggested that RAB18 modulates the establishment of membrane contact sites and the transfer of lipid between closely apposed membranes.

In the original version of our manuscript, we stated that we were exploring the possibility that RAB18 contributes to cholesterol biosynthesis by mobilizing substrates or products of the Δ8-Δ7 sterol isomerase emopamil binding protein (EBP). While our manuscript was under review, we profiled sterols in wild-type and RAB18-null cells and assayed cholesterol biosynthesis in a panel of cell lines (Figure 1).

Our new data show that an EBP-product, lathosterol, accumulates in RAB18-null cells (p<0.01). Levels of a downstream cholesterol intermediate, desmosterol, are reduced in these cells (p<0.01) consistent with impaired delivery of substrates to post-EBP biosynthetic enzymes (Figure 1A). Further, our preliminary data suggests that cholesterol biosynthesis is substantially reduced when RAB18 is absent or dysregulated (4 technical replicates, one independent experiment) (Figure 1B).

Because of the clinical overlap between Micro syndrome and cholesterol biosynthesis disorders including Smith-Lemli-Opitz syndrome (SLOS; MIM 270400) and lathosterolosis (MIM 607330), our new findings suggest that impaired cholesterol biosynthesis may partly underlie Warburg Micro syndrome pathology. Therapeutic strategies have been developed for the treatment of SLOS and lathosterolosis, and so confirmation of our findings may spur development of similar strategies for Micro syndrome.

Our new findings provide further functional validation of our methodology and support our interpretation of our protein interaction data.

Response to Reviewer #1

Reply to point 1)

Tetracycline-induced expression of wild-type and mutant BirA*-RAB18 fusion proteins in the stable HEK293 cell lines was quantified by densitometry (Figure 2).

For the HEK293 BioID experiments, tetracycline dosage was adjusted to ensure comparable expression levels. We will include these data in supplemental material in an updated version of our manuscript.

The localization of wild-type and mutant forms of RAB18 in HEK293 cells is somewhat different consistent with previous reports (Ozeki et al. 2005)(Figure 3).

We feel that this may reflect the differential localization of ‘active’ and ‘inactive’ RAB18, with wild-type RAB18 corresponding to a mixture of the two. We will include these data in supplemental material in an updated version of our manuscript.

We acknowledge that the differential localization of wild-type and mutant BirA*-RAB18 might influence the compliment of proteins labeled by these constructs. Nevertheless, we feel that the RAB18(S22N):RAB18(WT) ratios are useful since they distinguish a number of previously-identified RAB18-interactors (manuscript, Figure 1B).

Reply to point 2)

For the HEK293 dataset, spectral counts are provided and for the HeLa dataset LFQ intensities were provided in the manuscript (manuscript, Tables S1 and S2 respectively). However, we did not find that these were useful classifiers for ranking functional interactions when used in isolation.

The extent of labelling produced in a BioID experiment is not wholly determined by the kinetics of protein-protein associations. It is also influenced by, for example, protein abundance, the number and location of exposed surface lysine residues, and protein stability over the timcourse of labelling. We feel that RAB18(S22N):RAB18(WT) and GEF-null:wild-type ratios were helpful in controlling for these factors. Further, that our comparative approach was effective in highlighting known RAB18-interactors and in identifying novel ones.

We acknowledge that our approach may omit some bona fide functional RAB18-interactions, but would argue that our aims were to augment existing functional RAB18-interaction data and avoid false-positives, rather than to emphasise completeness.

Reply to point 3)

We will include representative fluorescence images for the SEC22A, NBAS and ZW10 knockdown experiments in an updated version of our manuscript.

Unfortunately, a suitable antibody for determining knockdown efficiency of SEC22A at the protein level is not commercially available. We will determine SEC22A knockdown efficiency at the mRNA level using qPCR.

Reply to point 4)

Expression levels of wild-type and mutant RAB18 in the stable CHO cell lines generated for this study were determined by Western blotting and found to be comparable (Figure 4).

We will include these data in supplemental material in an updated version of our manuscript.

The levels of [14C]-CE were higher in RAB18(Gln67Leu) cells than in the other cell lines following loading with [14C]-oleate for 24 hours. We will amend the text to make this explicit. Our interpretation of the data is that ‘active’ RAB18 facilitates the mobilization of cholesterol. When cells are loaded with oleate, this promotes generation and storage of CE. Conversely, when cells are treated with HDL, it promotes more rapid efflux.

Our new data implicating RAB18 in the mobilization of lathosterol supports our interpretation of our loading and efflux experiments. In the light of our new data showing that de novo cholesterol biosynthesis is impaired when RAB18 is absent or dysregulated, it will be interesting to determine whether cholesterol synthesis is increased in the RAB18(Gln67Leu) cells.

Response to Reviewer #2

Reply to point 1)

We anticipate that the approach of comparative proximity biotinylation in GEF-null and wild-type cell lines will be broadly useful in small GTPase research.

While RAB18 has previously been implicated in regulating membrane contacts, the identification of SEC22A as a RAB18-interactor adds to the previous model for their assembly.

While ORP2 and INPP5B have previously been shown to mediate cholesterol mobilization, the novel finding that they both interact with RAB18 adds to this work. We argue that RAB18-ORP2-INPP5B functions in an analogous manner to ARF1-OSBP-SAC1 in mediating sterol exchange. The broad Rab-binding specificity of multiple OSBP-homologs, and that of multiple phosphoinositide phosphatase enzymes, suggests that this may be a common conserved relationship.

Our new data indicating that RAB18 coordinates generation of sterol intermediates by EBP and their delivery to post-EBP biosynthetic enzymes reveals a new role for Rab proteins in lipid biogenesis. Most importantly, our new findings that RAB18 deficiency is associated with impaired cholesterol biogenesis suggest that Warburg Micro syndrome is a cholesterol biogenesis disorder. Further, that it may be amenable to therapeutic intervention.

Reply to point 2)

Recognising that the effect of RAB18 on cholesterol esterification and efflux could arise from various causes, we previously carried out Western blotting of the CHO cell lines for ABCA1 to determine whether this protein was involved (Figure 5).

Similar levels of ABCA1 expression in these lines suggests it is not. We will include these data in supplemental material in an updated version of our manuscript.

We feel that our new data implicating RAB18 in lathosterol mobilization provides important insight into its role in cholesterol biogenesis. Further, it supports our previous suggestion that RAB18 mediates cholesterol mobilization.

Reply to point 3)

We agree that the established roles for ORP2, TMEM24/C2CD2L and PIP2 at the plasma membrane make this an extremely interesting area for future research; it is one we are actively investigating. However, we respectfully feel that to comprehensively explore the subcellular locations of RAB18-mediated sterol/PIP2 exchange requires another study and is beyond the scope of the present report.

Response to Reviewer #3

Reply to point 1)

The RAB18-SPG20 interaction has already been validated with a co-immunoprecipitation experiment (Gillingham et al. 2014). We will update the text of our manuscript to make this more explicit, but do not feel it is necessary to recapitulate this work.

We argue in the manuscript that RAB18 may coordinate the assembly of a non-canonical SNARE complex incorporating SEC22A, STX18, BNIP1 and USE1. However, this role may be mediated through prior interaction with the NBAS-RINT1-ZW10 (NRZ) tethering complex and the SM-protein SCFD2 rather than through a direct interaction. We therefore feel that a RAB18-SEC22A interaction may be difficult to validate by conventional means.

The reciprocal experiments with BioID2(Gly40S)-SEC22A did provide tentative confirmation of the interaction together with evidence that a subset of SEC22A-interactions are attenuated when RAB18 is absent or dysregulated. In the light of our new findings reinforcing a role for RAB18 in sterol mobilization at membrane contact sites, it is interesting that one of these is DHRS7, an enzyme with steroids among its putative substrates.

Reply to point 2)

We previously analysed the localization of the BirA*-RAB18 fusion protein in HeLa cells (Figure 6).

It shows a reticular staining pattern consistent with the reported localization of RAB18 to the ER (Gerondopoulos et al. 2014; Ozeki et al. 2005). We will include these data in supplemental material in an updated version of our manuscript.

Heterologous expression of the BirA*-RAB18 fusion protein in HeLa cells identified the interactions between RAB18 and EBP, ORP2 and INPP5B, for which we now have supportive functional evidence. Since the evidence for impaired lathosterol mobilization and cholesterol biosynthesis was derived from experiments on null-cells, in which endogenous protein expression is absent, we feel that rescue experiments are not necessary in the present study. However, such experiments could be highly useful in future studies.

Reply to point 3)

Our screening approach did use both a RAB3GAP-null:wild-type comparison (manuscript, Figure 2, Table S2) and also a RAB18(S22N):RAB18(WT) comparison (manuscript, Figure 1, Table S1). Differences should be expected between these datasets, since they used different cell lines and slightly different methodologies. Nevertheless, proteins identified in both datasets included the known RAB18 effectors NBAS, RINT1, ZW10 and SCFD2, and the novel potential effectors CAMSAP1 and FAM134B.

There is prior evidence for 12 of the 25 RAB3GAP-dependent RAB18 interactions we identified (manuscript, Figure 2D). Among the 6 lipid modifying/mobilizing proteins found exclusively in our HeLa dataset, we previously presented direct evidence for the interaction of RAB18 with TMCO4. We now also have strong functional evidence for its interaction with EBP, ORP2 and INPP5B.

Reply to point 4)

It has been reported that knockdown of SEC22B does not affect the size distribution of lipid droplets (Xu et al. 2018) Figure 8H). Nevertheless, we will carry out qPCR experiments to determine whether the SEC22A siRNAs used in our study affect SEC22B expression. We have found that exogenous expression of SEC22A can cause cellular toxicity. Rescue experiments would therefore be difficult to perform.

Reply to point 5)

The background fluorescence measured in SPG20-null cells and presented in Figure 4B in the manuscript does not imply that the SPG20 antibody shows significant cross-reactivity. Rather, it reflects the fact that fluorescence intensity is recorded by our Operetta microscope in arbitrary units.

Above (Figure 7), is a version of the panel in which fluorescence from staining cells with only the secondary antibody is included (recorded in a previous experiment and expressed as a proportion of total SPG20 fluorescence in this experiment).

We have found that comparative fluorescence microscopy is more sensitive than immunoblotting. The SPG20 antibody we used to stain the HeLa cells has previously been used in quantitative fluorescence microscopy (Nicholson et al. 2015).

Furthermore, we showed corresponding, significantly reduced, expression of SPG20 in RAB18- and TBC1D20-null RPE1 cells, using quantitative proteomics (manuscript, Table S3).

We acknowledge that quantification of SPG20 transcript levels would clarify the level at which it is downregulated and will carry out qPCR experiments accordingly.

Reply to point 6)

We interpret both the enhanced CE-synthesis following oleate-loading and the rapid efflux upon incubation with HDL (manuscript, Figure 7A) as resulting from increased cholesterol mobilization. Our new data implicating RAB18 in the mobilization of lathosterol support this interpretation.

In the [3H]-cholesterol efflux assay (manuscript, Figure 7B) total [3H]-cholesterol loading at t=0 was 156392±8271 for RAB18(WT) cells, 168425±9103 for RAB18(Gln67Leu) cells and 148867±7609 (cpm determined through scintillation counting). Normalizing to total cellular radioactivity assured that differences in loading between replicates did not skew the results.

The candidate effector likely to directly mediate cholesterol mobilization is ORP2. It has been shown that ORP2 overexpression drives cholesterol to the plasma membrane (Wang et al. 2019). Further, there is evidence for reduced plasma membrane cholesterol in ORP2-null cells (Wang et al. 2019).

We previously carried out Western blotting of the CHO cell lines for ABCA1 to determine whether this protein was involved in altered efflux (Figure 5, above). Similar levels of ABCA1 expression in these lines suggests it is not. We will include these data in supplemental material in an updated version of our manuscript.

References

Gerondopoulos, A., R. N. Bastos, S. Yoshimura, R. Anderson, S. Carpanini, I. Aligianis, M. T. Handley, and F. A. Barr. 2014. 'Rab18 and a Rab18 GEF complex are required for normal ER structure', J Cell Biol, 205: 707-20.

Gillingham, A. K., R. Sinka, I. L. Torres, K. S. Lilley, and S. Munro. 2014. 'Toward a comprehensive map of the effectors of rab GTPases', Dev Cell, 31: 358-73.

Nicholson, J. M., J. C. Macedo, A. J. Mattingly, D. Wangsa, J. Camps, V. Lima, A. M. Gomes, S. Doria, T. Ried, E. Logarinho, and D. Cimini. 2015. 'Chromosome mis-segregation and cytokinesis failure in trisomic human cells', eLife, 4.

Ozeki, S., J. Cheng, K. Tauchi-Sato, N. Hatano, H. Taniguchi, and T. Fujimoto. 2005. 'Rab18 localizes to lipid droplets and induces their close apposition to the endoplasmic reticulum-derived membrane', J Cell Sci, 118: 2601-11.

Wang, H., Q. Ma, Y. Qi, J. Dong, X. Du, J. Rae, J. Wang, W. F. Wu, A. J. Brown, R. G. Parton, J. W. Wu, and H. Yang. 2019. 'ORP2 Delivers Cholesterol to the Plasma Membrane in Exchange for Phosphatidylinositol 4, 5-Bisphosphate (PI(4,5)P2)', Mol Cell, 73: 458-73 e7.

Xu, D., Y. Li, L. Wu, Y. Li, D. Zhao, J. Yu, T. Huang, C. Ferguson, R. G. Parton, H. Yang, and P. Li. 2018. 'Rab18 promotes lipid droplet (LD) growth by tethering the ER to LDs through SNARE and NRZ interactions', J Cell Biol, 217: 975-95.

Author Response

Reviewer #1:

This paper addresses the very interesting topic of genome evolution in asexual animals. While the topic and questions are of interest, and I applaud the general goal of a large-scale comparative approach to the questions, there are limitations in the data analyzed. Most importantly, as the authors raise numerous times in the paper, questions about genome evolution following transitions to asexuality inherently require lineage-specific controls, i.e. paired sexual species to compare with the asexual lineages. Yet such data are currently lacking for most of the taxa examined, leaving a major gap in the ability to draw important conclusions here. I also do not think the main positive results, such as the role of hybridization and ploidy on the retention and amount of heterozygosity, are novel or surprising.

We agree with the reviewer that having the sexual outgroups would improve the interpretations; this is one of the points we make in our manuscript. Importantly however, all previous genome studies of asexual species focus on individual asexual lineages, generally without sexual species for comparison. Yet reported genome features have been interpreted as consequences of asexuality (e.g., Flot et al. 2013). By analysing and comparing these genomes, we can show that these features are in fact lineage-specific rather than general consequences of asexuality. Unexpectedly, we find that asexuals that are not of hybrid origin are largely homozygous, independently of the cellular mechanism underlying asexuality. This contrasts with the general view that cellular mechanisms such as central fusion (which facilitates heterozygosity retention between generation) promotes the evolutionary success of asexual lineages relative to mechanisms such as gamete duplication (which generate complete homozygosity) by delaying the expression of the recessive load. We also do not observe the expected relationship between cellular mechanism of asexuality and heterozygosity retention in species of hybrid origin. Thus we respectfully disagree that our results are not surprising. Reviewer #2 found our results “interesting” and a “potentially important contribution”, and reviewer #3 wrote that we “call into question the generality of the theoretical expectations, and suggest that the genomic impacts of asexuality may be more complicated than previously thought”.

We also make it very clear that some of the patterns we uncover (e.g. low TE loads in asexual species) cannot be clearly evaluated with asexuals alone. Our study emphasizes the importance of the fact that asexuality is a lineage-level trait and that comparative analyses using asexuals requires lineage-level replication in addition to comparisons to sexual species.

References

Flot, Jean-François, et al. "Genomic evidence for ameiotic evolution in the bdelloid rotifer Adineta vaga." Nature 500.7463 (2013): 453-457.

Reviewer #2:

[...] Major Issues and Questions:

1) The authors choose to refer to asexuality when describing thelytokous parthenogenesis. Asexuality is a very general term that can be confusing: fission, vegetative reproduction could also be considered asexuality. I suggest using parthenogenesis throughout the manuscript for the different animal clades studied here. Moreover, in thelytokous parthenogenesis meiosis can still occur to form the gametes, it is therefore not correct to write that "gamete production via meiosis... no longer take place" (lines 57-58). Fertilization by sperm indeed does not seem to take place (except during hybridogenesis, a special form of parthenogenesis).

We will clarify more explicitly what asexuality refers to in our manuscript. Notably our study does not include species that produce gametes which are fertilized (which is the case under hybridogenesis, which sensu stricto is not a form of parthenogenesis). Even though many forms of parthenogenesis do indeed involve meiosis (something we explain in much detail in box 2), there is no production of gametes.

2) The cellular mechanisms of asexuality in many asexual lineages are known through only a few, old cytological studies and could be inaccurate or incomplete (for example Triantaphyllou paper of 1981 of Meloidogyne nematodes or Hsu, 1956 for bdelloid rotifers). The authors should therefore mention in the introduction the lack of detailed and accurate cellular and genetic studies to describe the mode of reproduction because it may change the final conclusion.

For example, for bdelloid rotifers the literature is scarce. However the authors refer in Supp Table 1 to two articles that did not contain any cytological data on oogenesis in bdelloid rotifers to indicate that A. vaga and A. ricciae use apomixis as reproductive mode. Welch and Meselson studied the karyotypes of bdelloid rotifers, including A. vaga, and did not conclude anything about absence or presence of chromosome homology and therefore nothing can be said about their reproduction mode. In the article of Welch and Meselson the nuclear DNA content of bdelloid species is measured but without any link with the reproduction mode. The only paper referring to apomixis in bdelloids is from Hsu (1956) but it is old and new cytological data with modern technology should be obtained.

We will correct the rotifer citations and thank the reviewer for picking up the error. We agree that there are uncertainties in some cytological studies, but the same is true for genomic studies (which is why we base our analyses as much as possible on raw reads rather than assemblies because the latter may be incorrect). We in fact excluded cytological studies where the findings could not be corroborated. For example, we discarded the evidence for meiosis and diploidy by Handoo at al. 2004 for its incompatibility with genomic data because this study does not provide any verifiable evidence (there are no data or images, only descriptions of observations). We provide all the references in the supplementary material concerning the cytological evidence used.

3) In the section on Heterozygosity, the authors compute heterozygosity from kmer spectra analysis from reads to "avoid biases from variable genome assembly qualities" (page 16). But such kmer analysis can be biased by the quality and coverage of sequencing reads. While such analyses are a legitimate tool for heterozygosity measurements, this argument (the bias of genome quality) is not convincing and the authors should describe the potential limits of using kmer spectra analyses.

We excluded all the samples with unsuitable quality of data (e.g. one tardigrade species with excessive contamination or the water flea samples for insufficient coverage), and T. Rhyker Ranallo Benavidez, the author of the method we used, collaborated with us on the heterozygosity analyzes. However, we will clarify the limitations of the method for species with extremely low or high heterozygosity (see also comment 5 of this reviewer).

4) The authors state that heterozygosity levels “should decay over time for most forms of meiotic asexuality". This is incorrect, as this is not expected with "central fusion" or with "central fusion automixis equivalent" where there is no cytokinesis at meiosis I.

Our statement is correct. Note that we say “most” and not “all” because certain forms of endoduplication in F1 hybrids result in the maintenance of heterozygosity. Central fusion is expected to fully retain heterozygosity only if recombination is completely suppressed (see for example Suomalainen et al. 1987 or Engelstädter 2017).

5) I do not fully agree with the authors’ statement that: "In spite of the prediction that the cellular mechanism of asexuality should affect heterozygosity, it appears to have no detectable effect on heterozygosity levels once we control for the effect of hybrid origins (Figure 2)." (page 17)

The scaling on Figure 2 is emphasizing high values, while low values are not clearly separated. By zooming in on the smaller heterozygosity % values we may observe a bigger difference between the "asexuality mechanisms". I do not see how asexuality mechanism was controlled for, and if you look closely at intra group heterozygosity, variability is sometimes high.

It is expected that hybrid origin leads to higher heterozygosity levels but saying that asexuality mechanism is not important is surprising: on Figure 2 the orange (central fusion) is always higher than yellow (gamete duplication).

As we explain in detail in the text, the three comparatively high heterozygosity values under spontaneous origins of asexuality (“orange” points in the bottom left corner of the figure) are found in an only 40-year old clone of the Cape bee. Among species of hybrid origin, we see no correlation between asexuality mechanism and heterozygosity. These observations suggest that the asexuality mechanism may have an impact on genome-wide heterozygosity in recent incipient asexual lineages, but not in established asexual lineages.

Also, the variability found within rotifers could be an argument against a strong importance of asexuality origin on heterozygosity levels: the four bdelloid species likely share the same origin but their allelic heterozygosity levels appears to range from almost 0 to almost 6% (Fig 2 and 3, however the heterozygosity data on Rotaria should be confirmed, see below).

We prefer not using the data from rotifers for making such arguments, given the large uncertainty with respect to genome features in this group (including the possibility of octoploidy in some species which we describe in the supplemental information). One could even argue that the highly variable genome structure among rotifer species could indicate repeated transitions to asexuality and/or different hybridization events, but the available genome data would make all these arguments highly speculative.

The authors’ main idea (i.e. asexuality origin is key) seems mostly true when using homoeolog heterozygosity and/or composite heterozygosity which is not what most readers will usually think as "heterozygosity". This should be made clear by the authors mostly because this kind of heterozygosity does not necessarily undergo the same mechanism as the one described in Box 2 for allelic heterozygosity. If homoeolog heterozygosity is sometimes not distinguishable from allelic heterozygosity, then it would be nice to have another box showing the mechanisms and evolution pattern for such cases (like a true tetraploid, in which all copies exist).

The heterozygosity between homoeologs is always high in this study while it appears low between alleles, but since the heterozygosity between homeologs can only be measured when there is a hybrid origin, the only heterozygosity that can be compared between ALL the asexual groups is the one between alleles.

By definition, homoeologs have diverged between species, while alleles have diverged within species. So indeed divergence between homoeologs will generally exceed divergence between alleles. We will consider adding expected patterns in perfect tetraploid species for Box 2.

Both in the results and the conclusion the authors should not over interpret the results on heterozygosity. The variation in allelic heterozygosity could be small (although not in all asexuals studied) also due to the age of the asexual lineages. This is not mentioned here in the result/discussion section..

We explain in section Overview of species and genomes studied that age effects are important but that we do not consider them quantitatively because age estimates are not available for the majority of asexual species in our paper.

6) Regarding the section on Heterozygosity structure in polyploids

There is inconsistency in many of the numbers. For example, A. vaga heterozygosity is estimated at 1.42% in Figure 1, but then appears to show up around 2% in Figure 2, and then becomes 2.4% on page 20. It is unclear is this is an error or the result of different methods.

It is also unclear how homologs were distinguished from homeologs. How are 21 bp k-mers considered homologous? In the method section. the authors describe extracting unique k-mer pairs differing by one SNP, so does this mean that no more than one SNP was allowed to define heterozygous homologous regions? Does this mean that homologues (and certainly homoeologs) differing by more than 5% would not be retrieved by this method. If so, then It is not surprising that for A.vaga is classified as a diploid.

Figure 1 a presents the values reported in the original genome studies, not our results. This is explained in the corresponding figure legend. Hence, 1.42 is the value reported by Flot at al. 2013. 2.4 is the value we measure and it is consistent in Figures 2 and 3.

We used k-mer pairs differing by one SNP to estimate ploidy (smudgeplot). The heterozygosity estimates were estimated from kmer spectra (GenomeScope 2.0). The kmers that are found in 1n must be heterozygous between homologs, as the homoeolog heterozygosity would produce 2n kmers, We used the kmer approach to estimate heterozygosity in all other cases than homoeologs of rotifers, which were directly derived from the assemblies. We explain this in the legend to Figure 3, but we will add the information also to the Methods section for clarification.

The result for A. ricciae is surprising and I am still not convinced by the octoploid hypothesis. In Fig S2. there is a first peak at 71x coverage that still could be mostly contaminants. It would be helpful to check the GC distribution of k-mers in the first haploid peak of A. ricciae to check whether there are contaminants. The karyotypes of 12 chromosomes indeed do not fit the octoploid hypothesis. I am also surprised by the 5.5% divergence calculated for A. ricciae, this value should be checked when eliminating potential contaminants (if any). In general, these kind of ambiguities will not be resolved without long-read sequencing technology to improve the genome assemblies of asexual lineages.

We understand the scepticism of the reviewer regarding the octoploidy hypothesis, but it is important to note that we clearly present it as a possible explanation for the data that needs to be corroborated, i.e., we state that the data are better consistent with octo- than tetraploidy. Contamination seems quite unlikely, as the 71.1x peak represents nearly exactly half the coverage of the otherwise haploid peak (142x). Furthermore, the Smudgeplot analysis shows that some of the kmers from the 71x peak pair with genomic kmers of the main peaks. We also performed KAT analysis (not presented in the manuscript) showing that these kmers are also represented in the decontaminated assembly. We will add this clarification regarding possible contamination to the supplementary materials.

7) Regarding the section on palindromes and gene conversion

The authors screened all the published genomes for palindromes, including small blocks, to provide a more robust unbiased view. However, the result will be unbiased and robust if all the genomes compared were assembled using the same sequencing data (quality, coverage) and assembly program. While palindromes appear not to play a major role in the genome evolution of parthenogenetic animals since only few palindromes were detected among all lineages, mitotic (and meiotic) gene conversion is likely to take place in parthenogens and should indeed be studied among all the clades.

We agree with the reviewer that gene conversion might be one of the key aspects of asexual genome evolution. Our study merely pointed out that genomes of asexual animals do not show organisation in palindromes, indicating that palindromes might not be of general importance in asexual genome evolution. Note also that we clearly point out that these analyses are biased by the quality of the available genome assemblies.

8) Regarding the section on transposable elements

The authors are aware that the approach used may underestimate the TEs present in low copy numbers, therefore the comparison might underestimate the TE numbers in certain asexual groups.

Yes. We clearly explain this limitation in the manuscript. The currently available alternatives are based on assembled genomes, so the results are biased by the quality of the assemblies (and similarities to TEs in public databases) and our aim was to broadly compare genomes in the absence of assembly-generated biases.

9) Regarding the section on horizontal gene transfer. For the HGTc analysis, annotated genes were compared to the UniRef90 database to identify non-metazoan genes and HGT candidates were confirmed if they were on a scaffold containing at least one gene of metazoan origin. While this method is indeed interesting, it is also biased by the annotation quality and the length of the scaffolds which vary strongly between studies.

Yes, this is true and we explain many limitations in the supplemental information, but re-assembling and re-annotating all these genomes would be beyond reasonable computational possibilities.

10) Regarding the use of GenomeScope2.0

When homologues are very divergent (as observed in bdelloid rotifers) GenomeScope probably considers these distinct haplotypes as errors, making it difficult to model the haploid genome size and giving a high peak of errors in the GenomeScope profile. Moreover, due to the very divergent copies in A. vaga, GenomeScope indeed provides a diploid genome (instead of tetraploid).

For A. vaga, the heterozygosity estimated par GenomeScope2.0. on our new sequencing dataset is 2% (as shown in this paper). This % corresponds to the heterozygosity between k-mers but does not provide any information on the heterogeneity in heterozygosity measurements along the genome. A limitation of GenomeScope2.0. (which the authors should mention here) is that it is assuming that the entire genome is following the same theoretical k-mer distribution.

The model of estimating genome wide heterozygosity indeed assumes a random distribution of heterozygous loci and indeed is unable to estimate divergence over a certain threshold, which is the reason why we used genome assemblies for the estimation of divergence of homoeologs. Regarding estimates in all other genomes, the assumptions are unlikely to fundamentally change the output of the analysis. GenomeScope2 is described in detail in a recent paper (Ranallo-Benavidez et al. 2019), where the assumption that heterozygosity rates are constant across the genome is explicitly mentioned.

References

Engelstädter, Jan. "Asexual but not clonal: evolutionary processes in automictic populations." Genetics 206.2 (2017): 993-1009.

Flot, Jean-François, et al. "Genomic evidence for ameiotic evolution in the bdelloid rotifer Adineta vaga." Nature 500.7463 (2013): 453-457.

Handoo, Z. A., et al. "Morphological, molecular, and differential-host characterization of Meloidogyne floridensis n. sp.(Nematoda: Meloidogynidae), a root-knot nematode parasitizing peach in Florida." Journal of nematology 36.1 (2004): 20.

Suomalainen, Esko, Anssi Saura, and Juhani Lokki. Cytology and evolution in parthenogenesis. CRC Press, 1987.

Ranallo-Benavidez, Timothy Rhyker, Kamil S. Jaron, and Michael C. Schatz. "GenomeScope 2.0 and Smudgeplots: Reference-free profiling of polyploid genomes." BioRxiv (2019): 747568. 

Reviewer #3:

Jaron and collaborators provide a large-scale comparative work on the genomic impact of asexuality in animals. By analysing 26 published genomes with a unique bioinformatic pipeline, they conclude that none of the expected features due to the transition to asexuality is replicated across a majority of the species. Their findings call into question the generality of the theoretical expectations, and suggest that the genomic impacts of asexuality may be more complicated than previously thought.

The major strengths of this work is (i) the comparison among various modes and origins of asexuality across 18 independent transitions; and (ii) the development of a bioinformatic pipeline directly based on raw reads, which limits the biases associated with genome assembly. Moreover, I would like to acknowledge the effort made by the authors to provide on public servers detailed methods which allow the analyses to be reproduced. That being said, I also have a series of concerns, listed below:

We thank this reviewer for the relevant comments and for providing many constructive suggestions in the points below. We will take them into account for our final version of the manuscript.

1) Theoretical expectations

As far as I understand, the aim of this work is to test whether 4 classical predictions associated with the transition to asexuality and 5 additional features observed in individual asexual lineages hold at a large phylogenetic scale. However, I think that these predictions are poorly presented, and so they may be hardly understood by non-expert readers. Some of them are briefly mentioned in a descriptive way in the Introduction (L56 - 61), and with a little more details in the Boxes 1 and 2. However, the evolutive reasons why one should expect these features to occur (and under which assumptions) is not clearly stated anywhere in the Introduction (but only briefly in the Results & Discussion). I think it is important that the authors provide clear-cut quantitative expectations for each genomic feature analysed and under each asexuality origin and mode (Box 1 and 2). Also highlighting the assumptions behind these expectations will help for a better interpretation of the observed patterns.

We will clarify the expectations for non expert readers.

2) Mutation accumulation & positive selection

A subtlety which is not sufficiently emphasized to my mind is that the different modes of asexuality encompass reproduction with or without recombination (Box 2), which can lead to very different genetic outcomes. For example, it has been shown that the Muller's ratchet (the accumulation of deleterious mutations in asexual populations) can be stopped by small amounts of recombination in large-sized populations (Charlesworth et al. 1993; 10.1017/S0016672300031086). Similarly a new recessive beneficial mutation can only segregate at a heterozygous state in a clonal lineage (unless a second mutation hits the same locus); whereas in the presence of recombination, these mutations will rapidly fix in the population by the formation of homozygous mutants (Haldane's Sieve, Haldane 1927; 10.1017/S0305004100015644). Therefore, depending on whether recombination occurs or not during asexual reproduction, the expectations may be quite different; and so they could deviate from the "classical predictions". In this regard, I would like to see the authors adjust their conclusions. Moreover, it is also not very clear whether the species analysed here are 100% asexuals or if they sometimes go through transitory sexual phases, which could reset some of the genomic effects of asexuality.

Yes, the predictions regarding the efficiency of selection are indeed influenced by cellular modes of asexuality. Adding some details or at least a good reference would certainly increase the readability of the section. We thank the reviewer for this suggestion.

3) Transposable elements

I found the predictions regarding the amount of TEs expected under asexuality quite ambiguous. From one side, TEs are expected not to spread because they cannot colonize new genomes (Hickey 1982); but on the other side TEs can be viewed as any deleterious mutation that will accumulate in asexual genome due to the Muller's ratchet. The argument provided by the authors to justify the expectation of low TE load in asexual lineages is that "Only asexual lineages without active TEs, or with efficient TE suppression mechanisms, would be able to persist over evolutionary timescales". But this argument should then equally be applied to any other type of deleterious mutations, and so we won't be able to see Muller's ratchet in the first place. Therefore, not observing the expected pattern for TEs in the genomic data is not so surprising as the expectation itself does not seem to be very robust. I would like the authors to better acknowledge this issue, which actually goes into their general idea that the genomic consequences of asexuality are not so simple.

Indeed, the survivorship bias should affect all genomic features. Nothing that is incompatible with the viability of the species will ever be observed in nature. Perhaps the difference between Muller’s ratchet and the dynamics of accumulation of transposable elements (TEs) is that TEs are expected to either propagate very fast or not at all (Dolgin and Charlesworth 2006), while the effects of Muller’s ratchet are expected to vary among different populations and cellular mechanisms of asexuality. We will rephrase the text to better reflect the complexity of the predicted consequences of TE dynamics.

4) Heterozygosity

Due to the absence of recombination, asexual populations are expected to maintain a high level of diversity at each single locus (heterozygosity), but a low number of different haplotypes. However, as presented by the authors in the Box 2, there are different modes of parthenogenesis with different outcomes regarding heterozygosity: (1) preservation at all loci; (2) reduction or loss at all loci; (3) reduction depending on the chromosomal position relative to the centromere (distal or proximal). Therefore, the authors could benefit from their genome-based dataset to explore in more detail the distribution of heterozygosity along the chromosomes, and further test whether it fits with the above predictions. If the differing quality of the genome assemblies is an issue, the authors could at least provide the variance of the heterozygosity across the genome. The mode #3 (i.e. central fusions and terminal fusions) would be particularly interesting as one would then be able to compare, within the same genome, regions with large excess vs. deficit of heterozygosity and assess their evolutive impacts.

Moreover, the authors should put more emphasis on the fact that using a single genome per species is a limitation to test the subtle effects of asexuality on heterozygosity (and also on "mutation accumulation & positive selection"). These effects are better detected using population-based methods (i.e. with many individuals, but not necessarily many loci). For example, the FIS value of a given locus is negative when its heterozygosity is higher than expected under random mating, and positive when the reverse is true (Wright 1951; 10.1111/j.1469-1809.1949.tb02451.x).

We agree with the reviewer that the analysis of the distribution of heterozygosity along the chromosomes would be very interesting. However, the necessary data is available only for the Cape honey bee, and its analysis has been published by Smith et al. 2018. Calculating the probability distribution of heterozygosities would be possible, but it would require SNP calling for each of the datasets. Such an analysis would be computationally intensive and prone to biases by the quality of the genome assemblies.

5) Absence of sexual lineages

A second limit of this work is the absence of sexual lineages to use as references in order to control for lineage-specific effects. I do not agree with the authors when they say that "the theoretical predictions pertaining to mutation accumulation, positive selection, gene family expansions, and gene loss are always relative to sexual species [...] and cannot be independently quantified in asexuals." I think that this is true for all the genomic features analysed, because the transition to asexuality is going to affect the genome of asexual lineages relative to their sexual ancestors. This is actually acknowledged at the end of the Conclusion by the authors.

To give an example, the authors say that "Species with an intraspecific origin of asexuality show low heterozygosity levels (0.03% - 0.83%), while all of the asexual species with a known hybrid origin display high heterozygosity levels (1.73% - 8.5%)". Interpreting these low vs. high heterozygosity values is difficult without having sexual references, because the level of genetic diversity is also heavily influenced by the long term life history strategies of each species (e.g. Romiguier et al. 2014; 10.1038/nature13685).

I understand that the genome of related sexual species are not available, which precludes direct comparisons with the asexual species. However, I think that the results could be strengthened if the authors provided for each genomic feature that they tested some estimates from related sexual species. Actually, they partially do so along the Result & Discussion section for the palindromes, transposable elements and horizontal gene transfers. I think that these expectations for sexual species (and others) could be added to Table 1 to facilitate the comparisons.

Our statement "the theoretical predictions pertaining to mutation accumulation, positive selection, gene family expansions, and gene loss are always relative to sexual species [...] and cannot be independently quantified in asexuals." specifically refers to methodology: analyses to address these predictions require orthologs between sexual and asexual species. We fully agree that in addition to methodological constraints, comparisons to sexual species are also conceptually relevant - which is in fact one of the major points of our paper. We will clarify these points.

6) Regarding statistics, I acknowledge that the number of species analysed is relatively low (n=26), which may preclude getting any significant results if the effects are weak. However, the authors should then clearly state in the text (and not only in the reporting form) that their analyses are descriptive. Also, their position regarding this issue is not entirely clear as they still performed a statistical test for the effect of asexuality mode / origin on TE load (Figure 2 - supplement 1). Therefore, I would like to see the same statistical test performed on heterozygosity (Figure 2).

We will unify the sections and add an appropriate test everywhere where suited.

7) As you used 31 individuals from 26 asexual species, I was wondering whether you make profit of the multi-sample species. For example, were the kmer-based analyses congruent between individuals of the same species?

Unfortunately, some of the 31 individuals do not have publicly available reads (some of the root-knot nematode datasets are missing), others do not have sufficient quality (the coverage for some water flea samples is very low). Our analyses were consistent for the few cases where we have multiple datasets available.

References

Dolgin, Elie S., and Brian Charlesworth. "The fate of transposable elements in asexual populations." Genetics 174.2 (2006): 817-827.

Smith, Nicholas MA, et al. "Strikingly high levels of heterozygosity despite 20 years of inbreeding in a clonal honey bee." Journal of evolutionary biology 32.2 (2019): 144-152.

Reviewer #2 (Public review):

Summary:

The manuscript describes various conformational states and structural dynamics of the Insulin degrading enzyme (IDE), a zinc metalloprotease by nature. Both open and closed state structures of IDE have been previously solved using crystallography and cryo-EM which reveal a dimeric organization of IDE where each monomer is organized into N and C domains. C-domains form the interacting interface in the dimeric protein while the two N-domains are positioned on the outer sides of the core formed by C-domains. It remains elusive how the open state is converted into the closed state but it is generally accepted that it involves large-scale movement of N-domains relative to the C-domains. Authors here have used various complementary experimental techniques such as cryo-EM, SAXS, size-exclusion chromatography and enzymatic assays to characterize the structure and dynamics of IDE protein in the presence of substrate protein insulin whose density is captured in all the structures solved. The experimental structural data from cryo-EM suffered from high degree of intrinsic motion amongst the different domains and consequently, the resultant structures were moderately resolved at 3-4.1 Å resolution. Total five structures were generated in the originally submitted manuscript using cryo-EM. Another cryo-EM reconstruction (sixth) at 5.1Å resolution was mentioned after first revision which was obtained using time-resolved cryo-EM experiments. Authors have extensively used Molecular dynamics simulation to fish out important inter-subunit contacts which involves R668, E381, D309, etc residues. In summary, authors have explored the conformational dynamics of IDE protein using experimental approaches which are complimented and analyzed in atomic details by using MD simulation studies. The studies are meticulously conducted and lay ground for future exploration of protease structure-function relationship.

Comments after first peer-review:

The authors have addressed all my concerns, and have added new data and explanations in terms of time-resolved cryo-EM (Fig. 7) and upside simulations (Fig. 8) which in my opinion have strengthened the merit of the manuscript.

Author response:

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

Reviewer #1 (Public review): 

Summary: 

Mancl et al. present cryo-EM structures of the Insulin Degrading Enzyme (IDE) dimer and characterize its conformational dynamics by integrating structures with SEC-SAXS, enzymatic activity assays, and all-atom molecular dynamics (MD) simulations. They present five cryo-EM structures of the IDE dimer at 3.0-4.1 Å resolution, obtained with one of its substrates, insulin, added to IDE in a 1:2 ratio. The study identified R668 as a key residue mediating the open-close transition of IDE, a finding supported by simulations and experimental data. The work offers a refined model for how IDE recognizes and degrades amyloid peptides, incorporating the roles of IDE-N rotation and charge-swapping events at the IDE-N/C interface. 

Strengths: 

The study by Mancl et al. uses a combination of experimental (cryoEM, SEC-SAXS, enzymatic assays) and computational (MD simulations, multibody analysis, 3DVA) techniques to provide a comprehensive characterization of IDE dynamics. The identification of R668 as a key residue mediating the open-to-close transition of IDE is a novel finding, supported by both simulations and experimental data presented in the manuscript. The work offers a refined model for how IDE recognizes and degrades amyloid peptides, incorporating the roles of IDE-N rotation and chargeswapping events at the IDE-N/C interface. The study identifies the structural basis and key residues for IDE dynamics that were not revealed by static structures. 

Weaknesses: 

Based on MD simulations and enzymatic assays of IDE, the authors claim that the R668A mutation in IDE affects the conformational dynamics governing the open-closed transition, which leads to altered substrate binding and catalysis. The functional importance of R668 would be substantiated by enzymatic assays that included some of the other known substrates of IDE than insulin such as amylin and glucagon. 

We have included amyloid beta in our enzymatic assays, as shown in Figure 5D, and have updated the manuscript text accordingly. The R668A mutation results in a loss of dose-dependent competition with amyloid beta, but not with insulin. To further substantiate this unexpected finding, we plan to undertake a comprehensive biochemical characterization of the R668A mutation across a variety of substrates, followed by structural analysis of this mutant. However, these investigations are beyond the scope of the current study and, if successful, warrant a separate publication.

It is unclear to what extent the force field (FF) employed in the MD simulations favors secondary structures and if the lack of any observed structural changes within the IDE domains in the simulations - which is taken to suggest that the domains behave as rigid bodies - stems from bias by the FF. 

We utilized the widely adopted CHARMM36 force field, whose parameters have been validated by thousands of previous studies. As shown in Figure 2A, our simulations reveal small but noticeable fluctuations in intradomain RMSD values. However, after careful examination, we found that these changes do not correspond to any biologically meaningful motions based on previously reported structural and biophysical characterizations of IDE (e.g., Shen et al., Nature 2006; Noinaj et al., PLOS One 2011; McCord et al., PNAS 2013; Zhang et al., eLife 2018, and references therein).

Reviewer #2 (Public review): 

Summary: 

The manuscript describes various conformational states and structural dynamics of the Insulin degrading enzyme (IDE), a zinc metalloprotease by nature. Both open and closed-state structures of IDE have been previously solved using crystallography and cryo-EM which reveal a dimeric organization of IDE where each monomer is organized into N and C domains. C-domains form the interacting interface in the dimeric protein while the two N-domains are positioned on the outer sides of the core formed by Cdomains. It remains elusive how the open state is converted into the closed state but it is generally accepted that it involves large-scale movement of N-domains relative to the C-domains. The authors here have used various complementary experimental techniques such as cryo-EM, SAXS, size-exclusion chromatography, and enzymatic assays to characterize the structure and dynamics of IDE protein in the presence of substrate protein insulin whose density is captured in all the structures solved. The experimental structural data from cryo-EM suffered from a high degree of intrinsic motion among the different domains and consequently, the resultant structures were moderately resolved at 3-4.1 Å resolution. A total of five structures were generated by cryo-EM. The authors have extensively used Molecular dynamics simulation to fish out important inter-subunit contacts which involve R668, E381, D309, etc residues. In summary, authors have explored the conformational dynamics of IDE protein using experimental approaches which are complemented and analyzed in atomic details by using MD simulation studies. The studies are meticulously conducted and lay the ground for future exploration of the protease structure-function relationship. 

Reviewer #1 (Recommendations for the authors): 

The manuscript reads well, however, there are minor details throughout that would tighten it up and, in some cases, make it easier to approach for a broader readership: 

Abstract 

(1) R668 is referred to by its one-letter code throughout the main text but referred to as arginine-668 in the abstract. The abstract should be corrected to R668. 

This has been corrected.

(2) The authors should consider reordering the significance of their work as it is listed at the end of the abstract. As the work first and foremost "offers the molecular basis of unfoldase activity of IDE and provides a new path forward towards the development of substrate-specific modulators of IDE activity" these should come before "the power of integrating experimental and computational methodologies to understand protein dynamics". 

We have revised abstract substantially to incorporate the new findings. Consequently, the sentence for "the power of integrating experimental and computational methodologies to understand protein dynamics" has been removed.  

Main text 

(1) Cryo-EM is consistently referred to as cryoEM throughout the text. The commonly accepted format for referring to cryogenic electron microscopy is cryo-EM. The authors are asked to consider revising the text accordingly. 

The text has been revised.

(2) Introduction: The authors are asked to consider including a figure (panel) that provides the general reader with an overview of IDE architecture and topology as a point of reference in the introduction to understanding the pseudo symmetry in IDE, domains, and IDE-C relative to IDE-N, etc. This is relevant for reading most of the figures. 

We have added a new figure 1 to provide the background and questions to be answered.

(3) The authors should consider renaming some of the headers in the results section to include the main conclusion. For instance, "CryoEM structures of IDE in the presence of a sub-saturating concentration of insulin" is not really helpful for the reader to understand the work, while "R668A mediates IDE conformational dynamics in vitro" is. 

The headings have been altered in an effort to be more informative.

(4) It is unclear what the timescale for insulin cleavage is for IDE. Clearly, it is possible for the authors to capture an insulin-bound IDE from within the 7 million particles, but what is the chance of this? The authors emphasize the IDE:insulin ratio relative to previous experiments, but surely the kinetics would be the same in the two experiments that were presumably set up exactly the same way. In the context of this, the authors should disclose how concentrations were estimated experimentally. The authors are encouraged to touch upon the subject of time scales to tie up cryo-EM and enzyme experiments with MD simulations. 

Both reviewers posted the question about time-scale relevant to IDE catalysis. In response to this request, we have revised the manuscript to address the relevance of key kinetic timescales. Specifically, we now discuss the open/closed transition (~0.1 second) and insulin cleavage (~2/sec), both established experimentally in prior studies (McCord et al PNAS 2013). 

IDE concentrations were determined by spectrometry (Nanodrop and/or Bradford assay), and its purity was confirmed to be greater than 90% by SDS-PAGE. Insulin was purchased commercially, weighed, and dissolved in buffer, with its concentration subsequently verified using Nanodrop. Catalytically inactive IDE and insulin were mixed and incubated for at least 30 minutes. Given IDE’s low nanomolar affinity for insulin, and the sub-stoichiometric insulin concentrations used, sufficient time was allowed for insulin to bind IDE and remain bound.

To distinguish between IDE’s unfoldase and protease activities, all structural analyses were performed in the presence of EDTA, which chelates catalytic zinc, thereby inactivating IDE. This approach inhibits the enzyme’s catalytic cycle and allows us to capture the fully unfolded state of insulin bound to IDE in its closed conformation, representing the endpoint of the reaction. Under these conditions, the only meaningful kinetic parameter available for investigation was the unfolding of insulin by IDE.

To elaborate the interaction between IDE and insulin in the catalytically relevant time regime, we investigated IDE–insulin interactions within the millisecond time regime by rapidly mixing IDE with a large molar excess of insulin for approximately 120 milliseconds for the cryo-EM single particle analysis. Under these conditions, we observed that both IDE subunits in the dimer predominantly adopt open states, which are distinct from those previously reported. This observation suggests a potential mechanism of allostery in IDE function. 

(5) It should be included in the main text that the data was processed with C1 symmetry and not just in Table 1. This is more useful information for understanding the study than the number of micrographs.  

We have stated that the data was processed with C1 symmetry at the start of the results section.

(6) The authors should consider adding speculation on what the approximately 6 million particles that did not yield a high-resolution structure represent. 

In cryo-EM single particle analysis, particle selection is typically performed automatically using software such as Relion. Due to the low signal-to-noise ratio, many “junk particles”—originating from contaminants such as ice, impurities, aggregates, or incomplete particles—are inevitably included along with the particles of interest. It is standard practice to filter out these junk particles during data processing. In our case, we estimate that the majority of the 6 million particles are likely junk. However, we cannot fully exclude the possibility that some of these particles may originate from IDE and carry potentially useful information about its conformational heterogeneity. Nonetheless, current cryo-EM single particle analysis methods face significant challenges in objectively recovering and interpreting such particles.

Reviewer #2 (Recommendations for the authors): 

I have some minor comments regarding the manuscript which are given below. 

(1) For O/O state, it will be great to see an explanation regarding why the values are dissimilar for 0.5 and 0.143 FSC. 

All of our IDE structures (including previously published data) demonstrate a dip/plateau at moderate resolution in their FSCs. We interpret this an indicator of structural heterogeneity, as the dip/plateau is smallest in the pC/pC state, becomes larger when one of the subunits is open, and is largest when both subunits are open. Because both subunits within the O/O state are highly heterogeneous, the FSC dipped below the 0.5 threshold. Other states, such as the O/pO, display the same FSC trend, the dip remains slightly above the 0.5 threshold.

(2) O/pO state is moderately resolved at 4.1 Å, but this state is populated with many particles (328,870). Can the resolution be improved by more extensive sorting of heterogenous particles which intrinsically causes misalignment amongst particles? 

Unfortunately, no. As shown by the local resolution maps in Figure 1-figure supplement 1, the primary source of misalignment is the IDE-N region in the open subunit. We have found that IDE-N is nearly unconstrained in its conformational flexibility in the open state, and does not appear to adopt discrete states, our attempts to better classify particles have failed. We speculate that this may be a failing in kmeans cluster based classification, and this is part of the driving force behind our exploration of advanced methods of heterogeneity analysis.

(3) Given the observation that capturing a substrate-bound open state is difficult, it can be assumed that the substrate capture in the catalytic cleft is a fast event. Please comment on the possible time frame of unfolding of substrate and catalysis. Can authors comment on any cryo-EM experiments that can deal with such a short time frame? If there is a possibility to include data from such experiments, then it may be considered.

This has been addressed in conjunction with the previous reviewer’s comment (see above). Specifically, we now discuss the open/closed transition (~0.1 second) and insulin cleavage (~2/sec), both established experimentally in prior studies. Additionally, we investigated IDE–insulin interactions by rapidly mixing IDE with a large molar excess of insulin for approximately 120 milliseconds for the cryo-EM single particle analysis. Under these conditions, we observed that both IDE subunits in the dimer predominantly adopt open states, which are distinct from those previously reported. This observation suggests a potential mechanism of allostery in IDE function. 

(4) How long was incubation time after adding any substrates, such as insulin? Can different incubation times be tested to generate additional information regarding other conformational states that lie in between open and closed states?  

The incubation time for IDE with insulin prior to cryo-EM grid freezing was approximately 30 minutes. We agree that it would be exciting to explore shorter time frames to identify new conformational states. As discussed above, we have rapidly mixed IDE with a large molar excess of insulin for approximately 120 milliseconds for the cryo-EM single particle analysis. Under these conditions, we observed that both IDE subunits in the dimer predominantly adopt open states, which are distinct from those previously reported. This observation suggests a potential mechanism of allostery in IDE function.

(5) A complex network of hydrogen bonding interaction initiated by R668 latching onto N-domain is mentioned in MD simulation studies but it is not clear why cryo-EM experiments did not capture such stabilized structures. 

We believe that two main factors have prevented us from observing the hydrogen bonding network in our cryo-EM structures. The first factor is the requirement to freeze the sample in liquid ethane. According to the second law of thermodynamics, lowering the temperature reduces the effect of entropy. Our findings suggest that residue R668 interacts with several neighboring residues through a network of polar and electrostatic interactions, rather than being limited to a single partner. These interactions facilitate both the open-closed transitions and rotational movements between IDE-N and IDE-C. From a thermodynamic perspective, these interactions have both enthalpic and entropic components, and cooling the sample diminishes the entropic contribution. In line with this, we observe that the closed-state domains in our cryo-EM studies are positioned closer together than in our MD simulations, though not as tightly as in crystal structures of IDE. This implies that cryogenic data collection may constrain the interface between IDE-N and IDE-C, which can further alter the equilibrium for the network of R668 mediated interactions.

Secondly, our cryo-EM structures represent ensemble averages of tens to hundreds of thousands of particles. MD simulations indicate that IDE-N and IDE-C can rotate relative to one another, resulting in considerable variability in residue interactions. However, the level of particle density in our cryo-EM data does not permit sufficiently fine classification to resolve these differences. As a result, distinct hydrogen bonding networks are likely averaged out in the ensemble structure, particularly in the case of R668, which is indicated to interact with multiple neighboring residues in the conformation-dependent manner. This averaging effect may also contribute to our inability to achieve resolutions below 3 Å.

(6) Despite the observation that IDE is an intrinsically flexible protein, it seems probable that differently-sized substrates might reveal additional interaction networks formed by other novel key players apart from just R668. Will it be helpful to first try this computationally using MD simulations and then try to replicate this in cryo-EM experiments? If needed, additional simulation time may be added to the MD analysis. Please comment!  

We agree that this is an exciting avenue to explore. Doubly so when considered in light of our R668A enzymatic results with amyloid beta. However, several challenges must be overcome before we can explore this direction effectively:

(1) We lack experimental knowledge of the initial interaction event between IDE and substrate. All substrate-bound IDE structures have been obtained after unfolding and positioning for cleavage has occurred. Without a solid foundational model for the initial interaction event between IDE and substrate, the interpretation of subsequent MD simulations is open to question.

(2) We have previously observed minimal effect of substrate on IDE in all-atom MD simulations. We believe that observable effects would require a much longer time scale than is currently achievable with all-atom MD, so have turned to Upside, a coarse-grained method to overcome these limitations, but Upside handles side chains with presumptive modeling, which prevent the identification of potential novel residue interactions.

(3) Due to the conformational heterogeneity present within IDE cryo-EM datasets, we struggle to obtain sufficient resolution to clearly identify side chain interactions at the domain interface (see response to 5).

Given these challenges, we plan to explore these directions in future manuscripts.

(7) What is the possibility of water interaction networks and dynamism in this network to contribute to the overall dynamics of the protein in the presence and absence of substrates? How symmetric these networks be in the four domains of dimeric IDE? 

This is an interesting idea that we have begun to explore, but consider to be outside the scope of this work. Currently, we do not have any MD simulations containing substrate with explicit solvent (Upside uses implicit solvent), and solvent atoms were removed from our all-atom simulations prior to analysis to speed up processing. That being said, preliminary WAXS data suggests that there may be a difference in water interaction interfaces between WT and R668A IDE, and this is a lead we plan to pursue in future work.

(8) Line 214: Please fix the typo which wrongly describes closed = pO. 

This is not a typo, but it is confusing. The pO state has previously been defined as the closed state of IDE lacking bound substrate as determined by cryo-EM. This differentiates the pO state from the pC state, where the pC state contains density indicative of bound substrate. As the MD simulations were conducted with the apo-state, the closed state the simulations were initialized from was the pO state structure, which represents the substrate-free closed state as determined by cryo-EM. We realize that this difference is probably unnecessary to the majority of readers, and have removed the (pO) specificity to avoid confusion.

(9) It is not clear why a cryo-EM structure was not attempted for the R668A mutant. If the authors have tried to generate such a structure, it should be mentioned in the manuscript. Such a structure should yield more information when compared to SAXS experiments.

We have not attempted to obtain a cryo-EM structure for the R668A mutant. Our SAXS analysis suggests a transition from a dominant O/pO state to a dominant O/O state. The O/O state is known to exhibit the highest degree of conformational heterogeneity, which severely limits structural insights. We are working to better handle the sample preparation of IDE and perform such analysis without the need to use Fab. We plan to further characterize IDE R668A biochemically and potentially explore other mutations that would provide insights in how IDE works. Armed with that, we will perform the structural analysis of such IDE mutant(s).

Reviewer #3 (Public review):

Summary:

Here, Bykov et al move the bi-genomic split-GFP system they previously established to the genome-wide level in order to obtain a more comprehensive list of mitochondrial matrix and inner membrane proteins. In this very elegant split-GFP system, the longer GFP fragment, GFP1-10, is encoded in the mitochondrial genome and the shorter one, GFP11, is C-terminally attached to every protein encoded in the genome of yeast Saccharomyces cerevisiae. GFP fluorescence can therefore only be reconstituted if the C-terminus of the protein is present in the mitochondrial matrix, either as part of a soluble protein, a peripheral membrane protein or an integral inner membrane protein. The system, combined with high-throughput fluorescence microscopy of yeast cells grown under six different conditions, enabled the authors to visualize ca. 400 mitochondrial proteins, 50 of which were not visualised before and 8 of which were not shown to be mitochondrial before. The system appears to be particularly well suited for analysis of dually localized proteins and could potentially be used to study sorting pathways of mitochondrial inner membrane proteins.

Strengths:

Many fluorescence-based genome-wide screen were previously performed in yeast and were central to revealing the subcellular location of a large fraction of yeast proteome. Nonetheless, these screens also showed that tagging with full-length fluorescent proteins (FP) can affect both the function and targeting of proteins. The strength of the system used in the current manuscript is that the shorter tag is beneficial for detection of a number of proteins whose targeting and/or function is affected by tagging with full length FPs.

Furthermore, the system used here can nicely detect mitochondrial pools of dually localized proteins. It is especially useful when these pools are minor and their signals are therefore easily masked by the strong signals coming from the major, nonmitochondrial pools of the proteins.

Weaknesses:

My only concern is that the biological significance of the screen performed appears limited. The dataset obtained is largely in agreement with several previous proteomic screens but it is, unfortunately, not more comprehensive than them, rather the opposite. For proteins that were identified inside mitochondria for the first time here or were identified in an unexpected location within the organelle, it remains unclear whether these localizations represent some minor, missorted pools of proteins or are indeed functionally important fractions and/or productive translocation intermediates. The authors also allude to several potential applications of the system but do little to explore any of these directions.

Comments on revisions:

The revised version of the manuscript submitted by Bykov et al addresses the comments and concerns raised by the Reviewers. It is a pity that the verification of the newly obtained data and its further biological exploration is apparently more challenging than perhaps anticipated.

Author response:

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

Reviewer #1 (Public Review):

Summary:

The study conducted by the Schuldiner's group advances the understanding of mitochondrial biology through the utilization of their bi-genomic (BiG) split-GFP assay, which they had previously developed and reported. This research endeavors to consolidate the catalog of matrix and inner membrane mitochondrial proteins. In their approach, a genetic framework was employed wherein a GFP fragment (GFP1-10) is encoded within the mitochondrial genome. Subsequently, a collection of strains was created, with each strain expressing a distinct protein tagged with the GFP11 fragment. The reconstitution of GFP fluorescence occurs upon the import of the protein under examination into the mitochondria.

We are grateful for the positive evaluation. We would like to clarify that the bi-genomic (BiG) split-GFP assay was developed by the labs of H. Becker and Roza Kucharzyk by highly laborious construction of the strain with mtDNA-encoded GFP_1-10 (Bader et al, 2020). 

Strengths:

Notably, this assay was executed under six distinct conditions, facilitating the visualization of approximately 400 mitochondrial proteins. Remarkably, 50 proteins were conclusively assigned to mitochondria for the first time through this methodology. The strains developed and the extensive dataset generated in this study serve as a valuable resource for the comprehensive study of mitochondrial biology. Specifically, it provides a list of 50 "eclipsed" proteins whose role in mitochondria remains to be characterized.

Weaknesses:

The work could include some functional studies of at least one of the newly identified 50 proteins.

In response to this we have expanded the characterization of phenotypic effects resulting from changing the targeting signal and expression levels of the dually localized Gpp1 protein and expanded the data in Fig. 3, panels H and I.

Reviewer #2 (Public Review):

The authors addressed the question of how mitochondrial proteins that are dually localized or only to a minor fraction localized to mitochondria can be visualized on the whole genome scale. For this, they used an established and previously published method called BiG split-GFP, in which GFP strands 1-10 are encoded in the mitochondrial DNA and fused the GFP11 strand C-terminally to the yeast ORFs using the C-SWAT library. The generated library was imaged under different growth and stress conditions and yielded positive mitochondrial localization for approximately 400 proteins. The strength of this method is the detection of proteins that are dually localized with only a minor fraction within mitochondria, which so far has hampered their visualization due to strong fluorescent signals from other cellular localizations. The weakness of this method is that due to the localization of the GFP1-10 in the mitochondrial matrix, only matrix proteins and IM proteins with their C-termini facing the matrix can be detected. Also, proteins that are assembled into multimeric complexes (which will be the case for probably a high number of matrix and inner membrane-localized proteins) resulting in the C-terminal GFP11 being buried are likely not detected as positive hits in this approach. Taking these limitations into consideration, the authors provide a new library that can help in the identification of eclipsed protein distribution within mitochondria, thus further increasing our knowledge of the complete mitochondrial proteome. The approach of global tagging of the yeast genome is the logical consequence after the successful establishment of the BiG split-GFP for mitochondria. The authors also propose that their approach can be applied to investigate the topology of inner membrane proteins, however, for this, the inherent issue remains that it cannot be excluded that even the small GFP11 tag can impact on protein biogenesis and topology. Thus, the approach will not overcome the need to assess protein topology analysis via biochemical approaches on endogenous untagged proteins.

Reviewer #3 (Public Review):

Summary:

Here, Bykov et al move the bi-genomic split-GFP system they previously established to the genomewide level in order to obtain a more comprehensive list of mitochondrial matrix and inner membrane proteins. In this very elegant split-GFP system, the longer GFP fragment, GFP1-10, is encoded in the mitochondrial genome and the shorter one, GFP11, is C-terminally attached to every protein encoded in the genome of yeast Saccharomyces cerevisiae. GFP fluorescence can therefore only be reconstituted if the C-terminus of the protein is present in the mitochondrial matrix, either as part of a soluble protein, a peripheral membrane protein, or an integral inner membrane protein. The system, combined with high-throughput fluorescence microscopy of yeast cells grown under six different conditions, enabled the authors to visualize ca. 400 mitochondrial proteins, 50 of which were not visualised before and 8 of which were not shown to be mitochondrial before. The system appears to be particularly well suited for analysis of dually localized proteins and could potentially be used to study sorting pathways of mitochondrial inner membrane proteins.

Strengths:

Many fluorescence-based genome-wide screens were previously performed in yeast and were central to revealing the subcellular location of a large fraction of yeast proteome. Nonetheless, these screens also showed that tagging with full-length fluorescent proteins (FP) can affect both the function and targeting of proteins. The strength of the system used in the current manuscript is that the shorter tag is beneficial for the detection of a number of proteins whose targeting and/or function is affected by tagging with full-length FPs.

Furthermore, the system used here can nicely detect mitochondrial pools of dually localized proteins. It is especially useful when these pools are minor and their signals are therefore easily masked by the strong signals coming from the major, nonmitochondrial pools of the proteins.

Weaknesses:

My only concern is that the biological significance of the screen performed appears limited. The dataset obtained is largely in agreement with several previous proteomic screens but it is, unfortunately, not more comprehensive than them, rather the opposite. For proteins that were identified inside mitochondria for the first time here or were identified in an unexpected location within the organelle, it remains unclear whether these localizations represent some minor, missorted pools of proteins or are indeed functionally important fractions and/or productive translocation intermediates. The authors also allude to several potential applications of the system but do little to explore any of these directions.

We agree with the reviewer that a single method may not be used for the construction of the complete protein inventory of an organelle or its sub-compartment. We suggest that the value of our assay is in providing a complementary view to the existing data and approaches. For example, we confirm the matrix localization of several proteins that were only found in the two proteomic data and never verified before (Vögtle et al, 2017; Morgenstern et al, 2017). Given that proteomics is a very sensitive technique and false positives are hard to completely exclude, our complementary verification is valuable.

Reviewer #1 (Recommendations for the authors):

In my opinion, the manuscript can be published as it is, and I would expect that future work will advance the functional properties of the newly found mitochondrial proteins.

We thank the reviewer for their positive evaluation

Reviewer #2 (Recommendations for the authors)

(1) Due to the localization of the GFP1-10 in the matrix, only matrix and IM proteins with C-termini facing the matrix can be detected, this should be added e.g. in the heading of the first results part and discussed earlier in the manuscript. In addition, the limitation that assembly into protein complexes will likely preclude detection of matrix and IM proteins needs to be discussed.

To address the first point, we edited the title of the first section to only mention the visualization of the matrix-facing proteome and remove the words “inner membrane”. We also clarified early in the Results section that we only consider the matrix-facing C-termini by extending the sentence early in the results section “To compare our findings with published data, we created a unified list of 395 proteins that are observed with high confidence using our assay indicating that their C-terminus is positioned in the matrix (Fig. 2 – figure supplement 1B-D, Table S1).” (P. 6 Lines 1-3). Concluding the comparison with the earlier proteomic studies we also added the sentence “Many proteins are missing because their C-termini are facing the IMS” (P.8 Line 2). 

To address the second point concerning the possible interference of the complex assembly and protein detection by our assay, we conducted an additional analysis. The analysis takes advantage of the protein complexes with known structures where we could estimate if the C-terminus with the GFP₁₁ tag would be available for GFP1-10 binding. We added the additional figure (Figure 3 – figure supplement 2) and following text in the Results section (P.7 Lines 22-34): 

“To examine the influence of protein complex assembly on the performance of the BiG Mito-Split assay we analyzed the published structures of the mitoribosome and ATP synthase (Desai et al, 2017; Srivastava et al, 2018; Guo et al, 2017) and classified all proteins as either having C-termini in, or out of,  the complex. There was no difference between the “in” and “out” groups in the percentage observed in the BiG Mito-Split collection (Fig. 3 – figure supplement 2A) suggesting that the majority of the GFP11tagged proteins have a chance to interact with GFP1-10 before (or instead of) assembling into the complex. PCR and western blot verification of eight strains with the tagged complex subunits for which we observed no signal showed that mitoribosomal proteins were incorrectly tagged or not expressed, and the ATP synthase subunits Atp7, Atp19, and Atp20 were expressed (Fig. 3 – Supplement 2B). Atp19 and Atp20 have their C-termini most likely oriented towards the IMS (Guo et al, 2017) while Atp7 is completely in the matrix and may be the one example of a subunit whose assembly into a complex prevents its detection by the BiG Mito-Split assay.”

We also consider related points on the interference of the tag and the influence of protein essentiality in the replies to points 3) and 12) of these reviews.

(2) The imaging data is of high quality, but the manuscript would greatly benefit from additional analysis to support the claims or hypothesis brought forward by the authors. The idea that the nonmitochondrial proteins are imported due to their high sequence similarity to MTS could be easily addressed at least for some of these proteins via import studies, as also suggested by the authors.

The idea that non-mitochondrial proteins may be imported into mitochondria due to occasional sequence similarity was recently demonstrated experimentally by (Oborská-Oplová et al, 2025). We incorporate this information in the Discussion section as follows (P. 14 Lines 10-16):

“It was also recently shown that the r-protein uS5 (encoded by RPS2 in yeast) has a latent MTS that is masked by a special mitochondrial avoidance segment (MAS) preceding it (Oborská-Oplová et al, 2025). The removal of the MAS leads to import of uS5 into mitochondria killing the cells. The case of uS5 is an example of occasional similarity between an r-protein and an MTS caused by similar requirements of positive charges for rRNA binding and mitochondrial import. It remains unclear if other r-proteins have a MAS and if there are other mechanisms that protect mitochondria from translocation of cytosolic proteins.”

We also conducted additional analysis to substantiate the claim that ribosomal (r)-proteins are similar in their physico-chemical properties to MTS-containing mitochondrial proteins. For this we chose not to use prediction algorithms like TartgetP and MitoFates that were already trained on the same dataset of yeast proteins to discriminate cytosolic and mitochondrial localization. Instead, we extended the analysis earlier made by (Woellhaf et al, 2014) and calculated several different properties such as charge, hydrophobicity, hydrophobic moment and amino acid content for mitochondrial MTS-containing proteins, cytosolic non-ribosomal proteins, and r-proteins. The analysis showed striking similarity of r-proteins and mitochondrial proteins. We incorporate a new Figure 3 – figure supplement 3 and the following text in the Results section (P. 8 Lines14-22): 

“Five out of eight proteins are components of the cytosolic ribosome (r-proteins). In agreement with previous reports (Woellhaf et al, 2014) we find that their unique properties, such as charge, hydrophobicity and amino acid content, are indeed more similar to mitochondrial proteins than to cytosolic ones (Fig. 3 – figure supplement 3). Additional experiments with heterologous protein expression and in vitro import will be required to confirm the mitochondrial import and targeting mechanisms of these eight non-mitochondrial proteins. The data highlights that out of hundreds of very abundant proteins with high prediction scores only few are actually imported and highlights the importance of the mechanisms that help to avoid translocation of wrong proteins (Oborská-Oplová et al, 2025).”

To further prove the possibility of r-protein import into mitochondria we aimed to clone the r-proteins identified in this work for cell-free expression and import into purified mitochondria. Despite the large effort, we have succeeded in cloning and efficiently expressing only Rpl23a (Author response image 1 A). Rpl23a indeed forms proteinase-protected fractions in a membrane potential-dependent manner when incubated with mitochondria. The inverse import dynamics of Rpl23a could be either indicative of quick degradation inside mitochondria or of background signal during the import experiments (Author response image 1.A). To address the r-protein degradation possibility, we measured how does GFP signal change in the BiG Mito-Split diploid collection strains after blocking cytosolic translation with cycloheximide (CHX). For this we selected Mrpl12a, that had one of the highest signals. We did not detect any drop in fluorescence signal for Rpl12a and the control protein Mrpl6 (Author response image 1 B). This might indicate the lack of degradation, or the degradation of the whole protein except GFP₁₁ that remains connected to GFP_1-10. Due to time constrains we could not perform all experiments for the whole set of potentially imported r-proteins. Since more experiments are required to clearly show the mechanisms of mitochondrial r-protein import, degradation, and toxicity, or possible moonlighting functions (such as import into mitochondria derived from pim1∆ strain, degradation assays, fractionations, and analyses with antibodies for native proteins) we decided not to include this new data into the manuscript itself.

Author response image 1.

The import of r-proteins into mitochondria and their stability. (A) Rpl23 was synthesized in vitro (Input), radiolabeled, and imported into mitochondria isolated from BY4741 strain as described before (Peleh et al, 2015); the import was performed for 5,10, or 15 minutes and mitochondria were treated with proteinase K (PK) to degrade nonimported proteins; some reactions were treated with the mix of valinomycin, antimycin, and oligomycin (VAO) to dissipate mitochondrial membrane potential; the proteins were visualized by SDS-PAGE and autoradiography (B) The strains from the diploid BiG Mito-Split collection were grown in YPD to mid-logarithmic growth phase, then CHX was added to block translation and cell aliquots were taken from the culture and analyzed by fluorescence microscopy at the indicated time points. Scale bar is 5 µm.

(3) The claim that the approach can be used to assess the topology of inner membrane proteins is problematic as the C-terminal tag can alter the biogenesis pathway of the protein or impact on the translocation dynamics (in particular as the imaging method applied here does not allow for analysis of dynamics). The hypothesis that the biogenesis route can be monitored is therefore far-reaching. To strengthen the hypothesis the authors should assess if the C-terminal GFP11 influences protein solubility by assessing protein aggregation of e.g. Rip1.

We agree with the reviewer that the tag and assembly of GFP_1-10/11 can further complicate the assessment of topology of the IM proteins that already have complex biogenesis routes (lateral transfer, conservative, and a Rip1-specific Bcs1 pathway). To emphasize that the assessment of the steady state topology needs to be backed up by additional biochemical approaches, we edited the beginning of the corresponding Results sections as follows (P. 11 Lines 2-6): 

“Studying membrane protein biogenesis requires an accurate way to determine topology in vivo. The mitochondrial IM is one of the most protein-rich membranes in the cell supporting a wide variety of TMD topologies with complex biogenesis pathways. We aimed to find out if our BiG Mito-Split collection can accurately visualize the steady-state localization of membrane protein C-termini protruding into the matrix or trap protein transport intermediates” (inserted text is underlined).

The collection that we studied by microscopy is diploid and contains one WT copy of each 3xGFP₁₁tagged gene. To assess the influence of the tag on the protein function we performed growth assays with haploid strains which have one 3xGFP₁₁-tagged gene copy and no GFP_1-10. We find that Rip13xGFP₁₁ displays slower growth on glycerol at 30˚C and even slower at 37˚C while tagged Qcr8, Qcr9, and Qcr10 grow normally (Author response image 2 A). Based on the growth assays and microscopy it is not possible to conclude whether the “Qcr” proteins’ biogenesis is affected by the tag. It may be that laterally sorted proteins are functional with the tag and constitute the majority while only a small portion is translocated into the matrix, trapped and visualized with GFP_1-10. In case of Rip1 it was shown that C-terminal tag can affect its interaction with the chaperone Mzm1 and promote Rip1 aggregation (Cui et al, 2012). The extent of Rip1 function disruption can be different and depends on the tag. We hypothesize that our split-assay may trap the pre-translocation intermediate of Rip1 and can be helpful to study its interactors. To test this, we performed anti-GFP immune-precipitation (IP) using GFP-Trap beads (Author response image 2 B).

Author response image 2.

The influence of 3x-GFP11 on the function and processing of the inner membrane proteins. (A) Drop dilution assays with haploid strains from C-SWAT 3xGFP<Sub>11</sub> library on fermentative (YPD) and respiratory (YPGlycerol) media at different temperatures. (B) Immuno-precipitation with GFP-Trap agarose was performed on haploid strain that has only Rip1-3xGFP₁₁ and on the diploid strain derived from this haploid mated with BiG Mito-Split strain containing mtGFP_1-10 and WT untagged Rip1 using the lysis (1% TX-100) and washing protocols provided by the manufacturer; the total (T) and eluted with the Laemmli buffer (IP) samples were analyzed by immunoblotting with polyclonal rabbit antibodies against GFP (only visualizes GFP<Sub>11</sub> in these samples) and Rip1 (visualizes both tagged and WT Rip1). Polyclonal home-made rabbit antisera for GFP and Rip1 were kindly provided by Johannes Herrmann (Kaiserslautern) and Thomas Becker (Bonn); the antisera were diluted 1:500 for decorating the membranes.

We find that the haploid strain with Rip1-3xGFP₁₁ contains not only mature (m) and intermediate (i) forms but also an additional higher Mw band that we interpreted as precursor that was not cleaved by MPP. WT Rip1 in the diploid added two more lower Mw bands: (m) and (i) forms of the untagged Rip1. IP successfully enriched GFP_1-10 fragment as visualized by anti-GFP staining. Interestingly only the highest Mw Rip1-3xGFP₁₁ band was also enriched when anti-Rip1 antibodies were used to analyze the samples. This suggests that Rip1 precursor gets completely imported and interacts with GFP_1-10 and can be pulled down. It is however not processed. Processed Rip1 is not interacting with GFP_1-10. Based on the literature we expect all Rip1 in the matrix to be cleaved by MPP including the one interacting with GFP. Due to this discrepancy, we did not include this data in the manuscript. This is however clear that the assay may be useful to analyze biogenesis intermediates of the IM and matrix proteins. To emphasize this, we added information on the C-terminal tagging of Rip1 in the Results section (P. 11 Lines 18-20):

“It was shown that a C-terminal tag on Rip1 can prevent its interaction with the chaperone Mzm1 and promote aggregation in the matrix (Cui et al, 2012). It is also possible that our assay visualizes this trapped biogenesis intermediate.”

We also added a note on biogenesis intermediates in the Discussion (P. 14 Line 36 onwards): 

“It is possible that the proteins with C-termini that are translocated into the IMS from the matrix side can be trapped by the interaction with GFP_1-10. In that case, our assay can be a useful tool to study these pre-translocation intermediates.”

(4) The hypothesis that the method can reveal new substrates for Bcs1 is interesting, and it would strongly increase the relevance for the scientific community if this would be directly tested, e.g. by deleting BCS1 and testing if more IM proteins are then detected by interaction with the matrix GFP110.

we attempted to move the BiG Mito-Split assay into haploid strains where BCS1 and other factors can be deleted, however, this was not successful. Since this was a big effort (We cloned 10 potential substrate proteins but none of them were expressed) we decided not to pursue this further.

(5) The screening of six different growth conditions reflects the strength of the high-throughput imaging readout. However, the interpretation of the data and additional follow-up on this is rather short and would be a nice addition to the present manuscript. In addition, one wonders, what was the rationale behind these six conditions (e.g. DTT treatment)? The direct metabolic shift from fermentation to respiration to boost mitochondrial biogenesis would be a highly interesting condition and the authors should consider adding this in the present manuscript.

we agree with the reviewer that the analysis of different conditions is a strength of this work. However, we did not reveal any clear protein groups with strong conditional import and thus it was hard to select a follow-up candidate. The selection of conditions was partially driven by the technical possibilities: the media change is challenging on the robotic system; heat shock conditions make microscope autofocus unstable; library strain growth on synthetic respiratory media is very slow and the media cannot be substituted with rich media due to its autofluorescence. However, the usage of the spinning disc confocal microscope allowed us to screen directly in synthetic oleate media which has a lot of background on widefield systems due to oil micelles. We extended the explanation of condition choice as follows (P. 4 Line 34 onwards): 

“The diploid BiG Mito-Split collection was imaged in six conditions representing various carbon sources and a diversity of stressors the cells can adapt to: logarithmic growth on glucose as a control carbon source and oleic acid as a poorly studied carbon source; post-diauxic (stationary) phase after growth on glucose where mitochondria, are more active and inorganic phosphate (Pi) depletion that was recently described to enhance mitochondrial membrane potential (Ouyang et al, 2024); as stress conditions we chose growth on glucose in the presence of 1 mM dithiothreitol (DTT) that might interfere with the disulfide relay system in the IMS, and nitrogen starvation as a condition that may boost biosynthetic functions of mitochondria. DTT and nitrogen starvation were earlier used for a screen with the regular C’-GFP collection (Breker et al, 2013). Another important consideration for selecting the conditions was the technical feasibility to implement them on automated screening setups.”

Reviewer #3 (Recommendations for the authors )

(6) This is a very elegant and clearly written study. As mentioned above, my only concern is that the biological significance of the obtained data, at this stage, is rather limited. It would have been nice if the authors explored one of the potential applications of the system they propose. For example, it should be relatively easy to analyze whether Cox26, Qcr8, Qcr9, or Qcr10 are new substrates of Bsc1, as the authors speculate.

we thank the reviewer for their positive feedback. We addressed the biological application of the screen by including new data on metabolite concentrations in the strains where Gpp1 N-terminus was mutated leading to loss of the mitochondrial form. We added panels H and I to Figure 4, the new Supplementary Table S2 and appended the description of these results at the end of the third Results subsection (P. 10 Lines 19-35). Our data now show a role for the mitochondrial fraction of Gpp1 which adds mechanistic insight into this dually localized protein.

We also were interested in the applications of our system to the study of mitochondrial import. However, the study of Cox26, Qcr8, Qcr9, and Qcr10 was not successful (also related to point 4, Reviewer #2). We thus decided to investigate the import mechanisms of the poorly studied dually localized proteins Arc1, Fol3, and Hom6 (related to Figure 4 of the original manuscript). To this end, we expressed these proteins in vitro, radiolabeled, and performed import assays with purified mitochondria. Arc1 was not imported, Fol3 and Hom6 gave inconclusive results (Author response image 3). Since it is known that even some genuine fully or dually localized mitochondrial proteins such as Fum1 cannot be imported in vitro post-translationally (Knox et al, 1998), we cannot draw conclusions from these experiments and left them out of the revised manuscript. Additional investigation is required to clarify if there exist special cytosolic mechanisms for the import of these proteins that were not reconstituted in vitro such as co-translational import.

Author response image 3.

In vitro import of poorly studies dually localized proteins. Arc1, Fol3, and Hom6 were cloned into pGEM4 plasmid, synthesized in vitro (Input), radiolabeled, and imported into mitochondria isolated from BY4741 strain as described before (Peleh et al, 2015); the import was performed for 5,10, or 15 minutes and mitochondria were treated with proteinase K (PK) to degrade non-imported proteins; some reactions were treated with the mix of valinomycin, antimycin, and oligomycin (VAO) to dissipate mitochondrial membrane potential. The proteins were separated by SDS-PAGE and visualized by autoradiography.

Minor comments:

(7) It is unclear why the authors used the six growth conditions they used, and why for example a nonfermentable medium was not included at all.

we address this shortcoming in the reply to the previous point 5 (Reviewer #2).

(8) Page 2, line 17 - "Its" should be corrected to "its".

Changed

(9) Page 2, line 25 to the end of the paragraph - the authors refer to the TIM complex when actually the TIM23 complex is probably meant. Also, it would be clearer if the TIM22 complex was introduced as well, especially in the context of the sentence stating that "the IM is a major protein delivery destination in mitochondria".

This was corrected.

(10) Page 5, line 35 - "who´s" should be corrected to "whose".

This was corrected.

(11) Page 9, line 5 - "," after Gpp1 should probably be "and".

This was corrected.

(12) Page 11 - the authors discuss in several places the possible effects of tags and how they may interfere with "expression, stability and targeting of proteins". Protein function may also be dramatically affected by tags - a quick look into the dataset shows that several mitochondrial matrix and inner membrane proteins that are essential for cell viability were not identified in the screen, likely because their function is impaired.

we agree with the reviewer that the influence of tags needs to be carefully evaluated. This is not always possible in the context of whole genomic screens. Sometimes, yeast collections (and proteomic datasets) can miss well-known mitochondrial residents without a clear reason. To address this important point we conducted an additional analysis to look specifically at the essential proteins. We indeed found that several of the mitochondrial proteins that are essential for viability were absent from the collection at the start, but for those present, their essentiality did not impact the likelihood to be detected in our assay. To describe the analysis we added the following text and a Fig. 3 – figure supplement 2. Results now read (P.7 Lines 8-21): 

“Next, we checked the two categories of proteins likely to give biased results in high-throughput screens of tagged collections: proteins essential for viability, and molecular complex subunits. To look at the first category we split the proteomic dataset of soluble matrix proteins (Vögtle et al. 2017) into essential and non-essential ones according to the annotations in the Saccharomyces Genome Database (SGD) (Wong et al, 2023). We found that there was no significant difference in the proportion of detected proteins in both groups (17 and 20 % accordingly), despite essential proteins being less represented in the initial library (Fig. 3 – figure supplement 2A). From the three essential proteins of the (Vögtle et al. 2017) dataset for which the strains present in our library but showed no signal, two were nucleoporins Nup57 and Nup116, and one was a genuine mitochondrial protein Ssc1. Polymerase chain reaction (PCR) and western blot verification showed that the Ssc1 strain was incorrect (Fig. 3 – figure supplement 2B). We conclude that essential proteins are more likely to be absent or improperly tagged in the original C’-SWAT collection, but the essentiality does not affect the results of the BiG Mito-Split assay.” 

Discussion (P. 13 Lines 23-26): 

“We did not find that protein complex components or essential proteins are more likely to be falsenegatives. However, some essential proteins were absent from the collection to start with (Fig. 3 – figure supplement 2A). Thus, a small tag allows visualization of even complex proteins.” 

From our data it is difficult to estimate the effect of tagging on protein function. We also addressed the effect of tagging Rip1 as well as performed growth assays on the tagged small “Qcr proteins” in the reply to point 3 (Reviewer #2). It is also difficult to estimate the effect of GFP_1-10 and ₁₁ complex assembly on protein function since the presence of functional, unassembled GFP₁₁ tagged pool cannot be ruled out in our assay. 

Other changes

Figure and table numbers changed after new data additions.

A sentence added in the abstract to highlight the additional experiments on Gpp1 function: “We use structure-function analysis to characterize the dually localized protein Gpp1, revealing an upstream start codon that generates a mitochondrial targeting signal and explore its unique function.”

The reference to the PCR verification (Fig. 3 – Supplement 2B) of correct tagging of Ycr102c was added to the Results section (P.8 Line 6), western blot verification added on.

Added the Key Resources Table at the beginning of the Methods section.

Small grammar edits, see tracked changes.

References:

Bader G, Enkler L, Araiso Y, Hemmerle M, Binko K, Baranowska E, De Craene J-O, Ruer-Laventie J, Pieters J, Tribouillard-Tanvier D, et al (2020) Assigning mitochondrial localization of dual localized proteins using a yeast Bi-Genomic Mitochondrial-Split-GFP. eLife 9: e56649

Cui T-Z, Smith PM, Fox JL, Khalimonchuk O & Winge DR (2012) Late-Stage Maturation of the Rieske Fe/S Protein: Mzm1 Stabilizes Rip1 but Does Not Facilitate Its Translocation by the AAA ATPase Bcs1. Mol Cell Biol 32: 4400–4409

Desai N, Brown A, Amunts A & Ramakrishnan V (2017) The structure of the yeast mitochondrial ribosome. Science 355: 528–531

Guo H, Bueler SA & Rubinstein JL (2017) Atomic model for the dimeric FO region of mitochondrial ATP synthase. Science 358: 936–940

Knox C, Sass E, Neupert W & Pines O (1998) Import into Mitochondria, Folding and Retrograde Movement of Fumarase in Yeast. J Biol Chem 273: 25587–25593

Morgenstern M, Stiller SB, Lübbert P, Peikert CD, Dannenmaier S, Drepper F, Weill U, Höß P, Feuerstein R, Gebert M, et al (2017) Definition of a High-Confidence Mitochondrial Proteome at Quantitative Scale. Cell Rep 19: 2836–2852

Oborská-Oplová M, Geiger AG, Michel E, Klingauf-Nerurkar P, Dennerlein S, Bykov YS, Amodeo S, Schneider A, Schuldiner M, Rehling P, et al (2025) An avoidance segment resolves a lethal nuclear–mitochondrial targeting conflict during ribosome assembly. Nat Cell Biol 27: 336–346

Peleh V, Ramesh A & Herrmann JM (2015) Import of Proteins into Isolated Yeast Mitochondria. In Membrane Trafficking: Second Edition, Tang BL (ed) pp 37–50. New York, NY: Springer

Srivastava AP, Luo M, Zhou W, Symersky J, Bai D, Chambers MG, Faraldo-Gómez JD, Liao M & Mueller DM (2018) High-resolution cryo-EM analysis of the yeast ATP synthase in a lipid membrane. Science 360: eaas9699

Vögtle F-N, Burkhart JM, Gonczarowska-Jorge H, Kücükköse C, Taskin AA, Kopczynski D, Ahrends R, Mossmann D, Sickmann A, Zahedi RP, et al (2017) Landscape of submitochondrial protein distribution. Nat Commun 8: 290

Woellhaf MW, Hansen KG, Garth C & Herrmann JM (2014) Import of ribosomal proteins into yeast mitochondria. Biochem Cell Biol 92: 489–498

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

Learn more at Review Commons

Reply to the reviewers

1. General Statements

*We thank the reviewers for their valuable comments. A common suggestion by all reviewers was that the manuscript would benefit from restructuring. Following their recommendation we have restructured this manuscript to improve its readability. *

2. Point-by-point description of the revisions

__Reviewer #1 (Evidence, reproducibility and clarity (Required)): __ The paper from Louka et al. studies the function of Cep104 during the development of Xenopus embryos. They perform overexpression and knock down experiments and address the consequences on neural tube closure, on ciliogenesis, and MT stability and on apical intercalation. There is a lot of data presented on a wide range of topics. While the data on MTs tracks reasonably well with other reports on Cep104, there are some concerns regarding the quality of some of the data and the interpretations based on the experimental results.

Specific Points: It is difficult to assess the effect on apical constriction with the data provided. Please show zoomed in higher mag images. Also this should be coupled with a quantification of cell number and proliferation rates, as it is possible that Cep104 mildly affects proliferation / cell division which could affect cell size. Overall this experiment is not really addressing apical constriction since there is no before and after data. Lots of things could affect apical surface area, most notably proliferation rates which one might predict would be affected by subtle changes to MT dynamics.

__Response: __Following the reviewer's recommendation we now show zoomed in higher magnification images to more clearly demonstrate the larger cell surface area in the morpholino injected neural plate compared to the control non-injected side in the same embryo. We agree with the reviewer that defects in cell proliferation could affect the cell size. If the effect of Cep104 on the cell surface area is caused by defects in cell proliferation, then we would expect this phenotype to persist in other tissues such as the ectoderm. However, we show that this phenotype is specific to the neural plate. On the other hand, if the cell surface area defect is caused by defects in apical constriction, we would expect this phenotype to be stage specific. Following the reviewer's recommendation, we compared the surface area of neuroectoderm cells before and after extensive apical constriction takes. The new data is shown in Figure S2. Our results show no difference in the surface area of neuroectoderm cells in control tracer injected and morpholino injected neuroepithelial cells at stage 13, before extensive apical constriction whereas significant differences are observed in stage 15 embryos during which cells undergo apical constriction. This data strengthens our conclusion that downregulation of Cep104 affects apical constriction.

"This defect was rescued with expression of exogenous human CEP104-GFP mRNA (300pg mRNA) (Figure 1D-E)." This was partially rescued as the control and the rescue are significantly different.

__Response: __We thank the reviewer for this important clarification. We edited the text to more clearly reflect our data.

I am unclear what is being depicted in Figure 1F and G. What is the intense red staining? Is that the blastopore? Which would imply that the stage of analysis is quite different between C and F which is concerning. The same stages should be used.

__Response: __This is an image of the anterior most region of a stage 15 embryo. Occasionally some embryos do display intense phalloidin staining at the neural plate. We replaced the image with a more clear one and moved this data to Figure S2C.

S1A has a boxed region as if there was going to be a zoomed in image, but there is not. It would be nice to see it zoomed in. While the localization is indeed at the base and tips of cilia the base looks too dispersed and big to be the basal body?

__Response: __Following the reviewer's recommendation we now show a zoomed in image of a primary cilium. The boxed area in figure S2A shows the cilium that was used to generate the fluorescence intensity profile plot shown in S2B. The Cep104 signal at the basal body is much stronger compared to the ciliary tip signal. Exposure that allows simultaneous detection of both the base and the tip signal results in overexposure of the signal at the base. This is consistent with observations in primary cilia in cell culture (please refer to Figure 4 in Frikstad et al. 2019 and Figure 3 in Yamazoe et al 2020).

In other systems the depletion of Cep104 decreases primary cilia length. While the authors claim that neural tube cilia are normal there is no quantification to support that and the provided image is hard to assess.

__Response: __Following the reviewer's recommendation we now show quantifications of the length of floor plate cilia (Figure S3C). Floor plate cilia are longer than the cilia found elsewhere in the neural tube. This inherent variability in the length of cilia will likely prevent the detection of small changes in the cilium length elicited by downregulation of Cep104. Therefore, we chose to examine the length of floor plate cilia only, in control and morpholino injected cells. Our results show that downregulation of Cep104 leads to the formation of shorter floor plate cilia which is in agreement with published data in other systems.

While the authors claim broad expression in humans and MO effects in cells without cilia, there is little data supporting the expression of Cep104 in the Xenopus cells being assayed (e.g. goblet cells).

__Response: __We agree with the reviewer that there is little evidence supporting the expression of Cep104 in Xenopus goblet cells. Cep104 is a very low abundance protein and thus very difficult to detect it at endogenous levels For example, Ryniawec et al. (2023) raised an antibody against Drosophila Cep104 that failed to detect the native (endogenous) protein via western blot or immunofluorescence, but successfully recognized the overexpressed (transgenic) Cep104. A proteomic study by Peshkin et al. 2019 showed that Cep104 levels remain relatively constant throughout Xenopus development suggesting that this protein is expressed ubiquitously. This data is shown in Figure 4 where we plot the relative expression levels of Cep104 along with two motile cilia specific genes: hydin and RSPH9.

The data in Figure 2 regarding the explants is difficult to understand and I think missing some key data. The text refers to the level of Gli increasing in the BF injected explants compared to uninjected explants, but the presentation of that is odd as the levels are normalized against uninjected rather than directly compared. And there are no stats for this key experiment. However, I think a bigger concern is the lack of information regarding the presence of cilia. While elongation and Sox2 expression are important they don't address if this tissue is similar to the neural tube in terms of cilia which is key to the interpretations.

__Response: __Following the reviewer's recommendation we changed the presentation of this data. GLI1 levels are now normalized to XBF2 injected explants. The results are the same, Gli1 levels are 25% lower in morphant XBF2 explants (ttest pWe understand the reviewer's concern regarding the presence of cilia in the explants. To our knowledge there are currently no reports on the presence of cilia in the neural ectoderm in Xenopus. We have made several attempts to determine if cilia are present in this tissue during neurulation. However, we have not been able to detect cilia based on immunofluorescence staining for acetylated tubulin and Arl13b in the neural ectoderm. We conclude from this experiment that downregulation of Cep104 negatively affects hedgehog signaling and it remains to be addressed whether this is due to defects in primary cilia.

The localization of Cep104 GFP in the epidermis and the neuroepithelium does not look similar as stated. Ones does not really see the punctate pattern in the neuroectoderm.

Response: We thank the reviewer for pointing this out. To more clearly present this data we now show a plot of the fluorescence profile of Cep104-GFP along cell-cell junctions to demonstrate the punctate localization in the neuroepithelium.

The experiments linking Cep104 to the tips of paused MTs is not particularly convincing. The depolymerization of MTs with nocodazole, will decrease all MTs as well as MT trafficking which could affect Cep104. Comparing this experiment with taxol treatment to stabilize MTs (and decrease dynamics) would be more convincing. Plus the image provided does not support the claim that the leftover EMTB is marked with Cep104.

__Response: __Following the reviewer's recommendation we have examined the effect of taxol on the density of Cep104 apical puncta. We injected embryos with CEP104-GFP and EMTB-scarlet and exposed them to 20 μm taxol and imaged them live at stage 38. Embryos non treated with taxol served as the control. As shown in Figure S4 treatment with taxol led to an increase in the density of Cep104 puncta. This further supports our conclusion that Cep104 localizes to the ends of stable or paused microtubules. We also revised Figure 5 to more clearly show that Cep104 remains associated with the ends of nocodazole resistant EMTB labeled microtubules.

The data in Figure 6 is very difficult to interpret / believe. The quantified effects on MTs are pretty subtle (which is fine...that is why you quantify), but the massive experimental variability questions the meaningfulness of those quantifications. In Fig 6B There are cells with lots of MTs right next to cells with no MTs and both have similar expression levels of Cep104. The staining just doesn't look consistent enough to accurately quantify. Also the effect of Nocodozole on MT stability is quite rapid, on the order of seconds to minutes, it is unclear what ON treatment with nocodazole would even be measuring since in that time there would be lots of secondary effects.

__Response: __We thank the reviewer for this comment. Some cells in the epidermis lack apical microtubules as the reviewer correctly points out. Cells without strong apical microtubule staining are seen in both control and morpholino injected cells. Here we quantified the number of control and morphant cells per embryo that lack apical microtubules (DMSO treated embryos). Our results show that similar numbers of control and morphant cells per embryo appear to lack apical microtubules. We think that the heterogeneity in tubulin signal is not an artifact of immunofluorescence staining since these cells are adjacent to cells with clear tubulin staining. Although the source of this variability is still unknown, the fact that an equal number of control and morphant cells show this phenotype suggests that this is unlikely to be linked to the injections or drug treatment. Those cells were excluded from the quantifications shown in Figures 6C and 6D It is possible that these cells are preparing to enter mitosis.

We think that the reviewer refers to the acute effects of nocodazole seen in cell cultures. However, in Xenopus tadpoles we didn't observe any effect on microtubules after short nocodazole treatment at low temperatures.

The authors propose that overexpressing Cep104 would lead to stabilized MTs which is a reasonable hypothesis, however, they test this in multiciliated cells that already have a ton of acetylated MTs. If their hypothesis is correct it should lead to an increase in acetylated tubulin in non multiciliated cells which don't have much to begin with. This would be a marked improvement as the side projection quantification seems a little suspect as the analysis requires a precises ROI that eliminates the strong cilia acetylation staining. While I believe that could be done, the image provided looks as if it might cut off some of the apical surface which highlights the challenge.

__Response: __Following the reviewer's recommendation, we examined the effect of Cep104 overexpression in non-MCCs on Xenopus epidermis. We show in Figure 7 that overexpression of Cep104 leads to a significant increase in the levels of acetylated tubulin in the cytoplasm of non-MCCs. We also show that overexpression of GFP alone did not have an effect on microtubule acetylation (Figure S5A). We moved the data on the cytoplasmic levels of acetylated microtubules in MCCs to figure S5B. We would like to clarify that the ROI to mark the cell body of MCCs was drawn right below the apical phalloidin signal to ensure that no signal derived from motile cilia will be included in the quantifications. A more detailed explanation of the quantification methods is included in this revised manuscript.

Minor: Overall the color choice of images does not conform to the color blind favorable options that are becoming standard in the field. Also to the extent possible the colors should be consistent (e.g. Fig 4 A Cep104-GFP is green but in B it is red).

__Response: __We thank the reviewer for this comment. We have changed the color choices in the figures to conform to the color blind.

The recent Xenopus Cep104 paper was referenced with two references, and the wording of those two sentences was redundant.

__Response: __We thank the reviewer for this comment. We edited the text accordingly.

---

__Reviewer #2 (Evidence, reproducibility and clarity (Required)): __ This study by Louka et al., investigates the function of Cep104, a protein associated with Joubert syndrome, in Xenopus. Several aspects are studied at different scales. Loss of function of this protein suggests a role in neural tube closure, apical constriction, and HH signaling. Moving on in the study, the authors investigate the localization of Cep104 in the primary cilia of the neural tube before focusing on its localization in multiciliated cells. They then look at the consequences of loss of function on motile cilia and conclude that it plays a role in the length of the distal segment. They then show an association of Cep104 with cytoplasmic microtubules in non-multiciliated cells of the Xenopus epidermis. They then analyze the function of Cep104 on these microtubules and show that loss of Cep104 function increases the speed of EB1 comets. They then looked at the impact of loss of function on microtubule stability and finally the impact of gain of function. Finally, they returned to the multiciliated cells and described an intercalation defect that correlated with decreases in acetylated tubulin. I think that certain controls are missing and that the choice of illustrations should be reconsidered (better quality, appropriate zoom). In terms of form, the text is not easy to read and the manuscript would benefit from reformatting to highlight the logical links between the different experiences and avoid a catalog-like effect. I would advise the authors to revise their introduction to make it less disjointed and guide readers toward the questions addressed by the manuscript.

Response: We thank the reviewer for the constructive criticism. We have revised the introduction to make it easier to read.

Below are specific comments and remarks: Figure 1: Why the conclusion is a "delay" in neural tube closure? At what stage is this analyzed? Is there a recovery of NT closure at later stage? A: I would suggest to provide control picture of non-injected and tracer only injected embryos. B: Statistics are missing on the graph D: mention what was injected instead of "+ rescue". Close up picture would allow a better appreciation of the differences in surface area.

Response: We thank the reviewer for this comment. The image shown in Figure 1A is from late neurula embryos, stage 18. We conclude that it is a delay in neural tube closure because the neural tube does close and the embryos develop to tailbud stages. To demonstrate the delay in neural tube closure we now include a time lapse sequence of a neurula stage embryo injected with the morpholino unilaterally which shows that the morpholino injected side moves towards the midline slower compared to the control uninjected side (movie 1). We also included a representative image of the dorsal side of a tailbud embryo injected unilaterally with the CEP104 morpholino to show that the neural tube has closed and the embryos develop to tailbud stages (figure S1D).

Following the reviewer's recommendation, we also show images of embryos injected unilaterally with the tracer alone (Figure S2), we included the statistical analysis for graph 1D, revised image 1D to show that the embryo is injected with the morpholino and CEP104-GFP and provide close ups to allow for better appreciation of the differences in surface area.

Figure S1: To illustrate the claim that cilia are not affected, it would be good to show injection of tracer alone and compare to tracer + morpholino. Also, to provide a measure of the cilia size.

__Response: __Following the reviewer's recommendation we quantified the length of floor plate cilia in the neural tube of control and morpholino injected embryos. As explained in our response to a comment by reviewer 1, the floor plate cilia are longer than the cilia found elsewhere in the neural tube. This inherent variability in the length of cilia will likely prevent the detection of small changes in the cilium length elicited by downregulation of Cep104. Therefore, we chose to examine the length of floor plate cilia only in control and morpholino injected cells. Our results show that downregulation of Cep104 leads to the formation of shorter floor plate cilia which is in agreement with published data in other systems (Figure S3C).

Figure 2: Please provide pictures to illustrate graph D.

---

__Response: __The graph in Figure 2D shows RT-qPCR results for CEP104 in BF2 and BF2 and morpholino injected explants as compared to non-injected explants. We do not have a working antibody that would allow us to show the downregulation at the protein level.

Figure 5: "Interestingly, most of the nocodazole-resistant stable microtubules were positive for Cep104 (Figure 5C, arrows). " The variation in density of Cep104-GFP signal is not visible on the pictures provided in C. I would suggest to show higher magnifications. Also, in the DMSO treated picture the Cep104GFP signal looks really different when compared to Cep104-GFP signal shown in B. Arrows should be reported on all channels. However, it not clear what we should see with this arrows. 5C: it seems that in nocodazole treated condition the Cep104-GFP is at the cilia base in MCCs which is different from the DMSO control condition. The basal body signal was not seen in the figure 3A which analyze the localization of Cep104-GFP in MCCs. Why not comment on this? Is it a phenotype on MCCs ?

Response: __Following the reviewer's recommendation, we now show higher magnifications of the images shown in Figure 5C. We removed the arrows as most reviewers found them confusing. To demonstrate the presence of Cep104 at the ends of nocodazole resistant EMTB labeled microtubules we show zoomed images and a representative fluorescence intensity profile plot. __Figure 5B shows an image of a non-MCC whereas Figure 5C shows a larger area on the tadpole epidermis which includes both MCCs and non-MCCs. We thank the reviewer for pointing out that the localization of Cep104 in 5C looks different from 3A. We do not think this is a phenotype on MCCs. In Figure 3A we imaged only the tips of cilia which is why it looks different from 5C in which we imaged the apical surface of the cells as well. We disagree with the reviewer regarding the comment '5C: it seems that in nocodazole treated condition the Cep104-GFP is at the cilia base in MCCs which is different from the DMSO control condition'. The basal body localization of Cep104 is shown in the DMSO image as well. We hope that it will be clear in this revised figure.

Figure 6: Intriguingly, morphant non-MCCs have significantly more mean β-tubulin signal compared to control non-MCCs in embryos treated with DMSO (Figure 6C). impossible to appreciate on the figures. Please specify on the figure what is considered as a morphant non-MCC versus a control non-MCC. The membrane-cherry positive cells (supposedly morphant? it has to be clarified show very heterogenous tubulin expression) If the point here is to show that microtubules are more sensitive to nocodazole in morphant cells as compared to control. I would suggest to show all conditions on a same graph. At least annotate more the graph for a self-explanatory figure (DMSO , Nocodazole).

__Response: __We agree with the reviewer that it impossible to appreciate the difference in β-tubulin signal between control and morphant non-MCCs. Based on the quantifications of mean β-tubulin fluorescence intensity there is 5% difference in the fluorescence intensity between the two groups. Statistical analysis using t-test shows that although very small, this difference is statistically significant which is why we mention it in the manuscript. We have removed this statement and data from the revised manuscript because this is a very subtle phenotype, and it is beyond the scope of this experiment.

Following the reviewer's recommendation, we clarify that mem-cherry positive cells contain the morpholino and mem-cherry negative cells are the control cells. We marked with a white asterisk the morphant non-MCCs. To address the heterogenous tubulin levels we provide quantifications which show that a similar number of control and morphant cells appear to lack microtubules. We think that the heterogeneity in tubulin signal is not an artifact of immunofluorescence staining since these cells are adjacent to cells with clear tubulin staining. Although the source of this variability is still unknown, the fact that an equal number of control and morphant cells show this phenotype suggests that this is unlikely to be linked to the injections or drug treatment. Those cells were excluded from the quantifications shown in figure 6. It is possible that these cells are preparing to enter mitosis. The reviewer is correct; the point of this experiment is to examine the effect of Cep104 downregulation on the sensitivity of microtubules to nocodazole. To more clearly present the results of this experiment we normalize the β-tubulin fluorescence Intensity in morphant cells to the one in control cells in the same embryo and we compare the normalized intensity in DMSO and nocodazole treated embryos.

Figure 7: Statistics are missing on Graph B

__ ____Response: __Following the reviewer's recommendation, we added the statistics on the graph.

Comment on the text: "Cep104 signal shows the characteristic two dot pattern in motile cilia (Figure 3A) that was also observed in a recent study using Xenopus Cep10465 and in the cilia of Tetrahymena50. This is in agreement with a recent study showing the characteristic two dot pattern for Xenopus Cep104 as well66 " ref 65 and 66b are the same (Hong et al., preprint)

__ ____Response: __We thank the reviewer for pointing this out. We edited the text to avoid repetition and corrected the references.

"This data suggests that downregulation of CEP104 affects the stability of cytoplasmic microtubules." I would suggest a more precise conclusion by stating how is it affected? More stable? Less stable? Important for the follow-up demonstration.

__ _Response: _We edited the text according to the reviewer's recommendation to precisely conclude that downregulation of Cep104 makes cytoplasmic microtubules less stable. __

Movies: Please annotate properly movie 2 and 3 so the reader can know what he/she is looking.

---

__Response: __Following the reviewer's comment, we revised the movie annotations to help the reader know what they are looking.

---

__Reviewer #3 (Evidence, reproducibility and clarity (Required)): __ The manuscript entitled "Ciliary and non-ciliary functions of Cep104 in Xenopus" by Louka et al. investigate roles for the centriole and cilia tip protein Cep104 in Xenopus embryos. The authors show that depletion of Cep104 prevents neural tube closure due to inefficient apical constriction of neural cells and defective hedgehog signaling. Cep104 depletion also resulted in structural and functional ciliary defects in multi-ciliated cells. Surprisingly, the authors discover a role for Cep104 in stabilizing cytoplasmic microtubules in non-ciliated and multi-ciliated cells. Reduced microtubule stability in Cep104-depleted cells correlated with reduced apical intercalation of multi-ciliated cells in the epidermis.

Overall, I find this manuscript difficult to understand because the experiments lack description of the findings within a normal developmental context and the findings are not developed into a cohesive narrative. I do find the study to be potentially impactful as the authors characterize Cep104 in a novel system (previous peer-reviewed studies have investigated Cep104 in human cell lines, Drosophila, zebrafish, Tetrahymena, and Chlamydomonas) with disease-relevant biology (neural development); however, mechanistic links are not properly explored. Over the course of their investigation, the authors made the novel finding that Cep104 controls the dynamics of cytoplasmic microtubules. However, this is not directly tested and potential pleiotropic effects of the developmental defects caused by Cep104 depletion confound the results.

Response: We thank the reviewer for their comments. We tried to address this by restructuring the manuscript to describe the results in more detail within a normal developmental context.

Major Critiques: The developmental context of experiments is not made clear. The authors use different tissues at varying developmental stages to perform experiments. However, these findings are not explored in depth and, therefore, the manuscript does not advance our understanding of Cep104's role in any of the processes explored.

__ ____Response:__ We thank the reviewer for their comment. We took advantage of different tissues during Xenopus development to understand the cellular and molecular function of this protein in vivo. In this manuscript we show that Cep104 is involved in neural tube closure likely through its effect on apical constriction. Our data show that Cep104 is important for the stability of cytoplasmic microtubules and this is further demonstrated through its role in apical intercalation of multiciliated cells, a process known to depend on stable microtubules. Although our data do not advance our understanding on developmental processes such as apical constriction and MCC apical intercalation, they do improve our understanding of how Cep104 impacts cytoplasmic microtubules which has not been addressed in vivo yet.

While the potential role of Cep104 in cytoplasmic microtubule regulation is intriguing, the experiments in the manuscript do not directly test this function. Because Cep104 depletion appears to have a profound developmental effect, it is difficult to interpret changes to EB1 velocity as directly attributed to Cep104 function. Additionally, the only evidence for Cep104 localization occurs in cells overexpressing human Cep104. The authors must directly visualize endogenous Cep104 to conclude microtubule or membrane localization, which they can also use to demonstrate Cep104 depletion in the morpholino experiments. Additionally, the assertion that Cep104 is binding plus-ends of cytoplasmic microtubules is not experimentally supported.

__ ____Response: __Unfortunately, we cannot directly visualize endogenous Cep104 because there is no commercially available antibody that works in Xenopus. Cep104 is a very low abundance protein, and this is highlighted in the study by John M.Ryniawec et al. 2023, where they generated an antibody against the drosophila Cep104 which detected the GFP-tagged DmCep104 but failed to detect the endogenous protein. Given that the ciliary and basal body signal of Cep104 represents the cumulative signal from nine microtubules, one can appreciate the difficulty of observing the Cep104 signal in individual microtubules. None of the commercially available Cep104 antibodies that we have tested worked against the Xenopus protein in immunofluorescence or western blot experiments. We agree with the reviewer that we do not experimentally test the binding of Cep104 to the microtubule plus-end. This has been demonstrated by others. In Jiang et al. 2012 it was showed that GFP-Cep104 co-immunoprecipitates with GST-EB1 but not with GST-EB1 that lacks the tail which contains the SxIP binging motif. In Yamazoe et al. 2020 study it was shown that exogenous Cep104 co-immunoprecipitates with exogenous EB1 and Cep104 with mutated SxIP motif (SKNN) fails to co-immunoprecipitate with EB1. This shows that Cep104 interacts with EB1 through its SxIP motif. In addition, overexpression of Cep104 recruits Cep97 to microtubule tips suggesting that it acts as a +TIP protein. A recent study by Saunders et al. 2025 showed that in in vitro microtubule reconstitution assays, Cep104 could not autonomously bind the microtubule plus-end at low concentrations but in the presence of EB3 it could bind the microtubule plus-end and block microtubule polymerization at the same low concentration. This shows that Cep104 interacts with EB3, localizes to the microtubule plus-end and affects its dynamics in vitro. We added this information in the manuscript to more clearly show that the interaction of Cep104 and EB proteins is well documented. We anticipate that this interaction will hold true in all cell types where the two proteins are co-expressed.

Additional Critiques: Figure S1. I only see the emergence of a shorter product after Cep104 depletion. Should PCR using Exon5-7 still work in successful knockdown? If not, then it is unclear what was quantified to determine Cep104 depletion as morpholino bands appear no different than control.

__ ____Response: __We thank the reviewer for this comment. PCR using exon5-7 will not work when splice blocking by the morpholino takes place. This is a knockdown approach and the efficiency of the morpholino is about 90%. Upon completion of the RT-qPCR cycle the samples were analyzed by gel electrophoresis to demonstrate that 1) alternative splicing took place (see two products with exon 3-7 primers) and 2) the presence of a single product for all primer sets used.

Figure 1A. Is this an example of an open or closed NTC? Show data used to determine the statement "no difference during convergent extension".

__ ____Response: __This is an example of an embryo that was unilatterally injected with the morpholino. The left side is the control non-injected side and the right side is the morpholino injected. We added this information on the figure to make it more self-explanatory. In Figure 2 the elongation of the BF2 injected explants is due to convergent extension. The statement "no difference during convergent extension" was removed from the revised manuscript.

Figure S2C. What does "Does not effect formation of cilia" mean? Does Cep104 depletion does not effect number, length, etc? Show quantitation used to determine this?

__ ____Response:__ Following the reviewer's recommendation, we quantified the length of floor plate cilia in control and morpholino injected embryos. As mentioned in our response to reviewer 1 and 2, floor plate cilia are longer than the cilia found elsewhere in the neural tube. This inherent variability in the length of cilia will likely prevent the detection of small changes in the cilium length elicited by downregulation of Cep104. Therefore, we chose to examine the length of floor plate cilia only, in control and morpholino injected cells. Our results show that downregulation of Cep104 leads to the formation of shorter floor plate cilia which is in agreement with published data in other systems.

Figure 5B. Along with strong Cep104 localization to membranes, there also appears to be strong EMTB localization. Is this also present in beta-tubulin immunostaining? Are these localizing to a cortical population of microtubules or to the membrane?

__ ____Response: __We thank the reviewer for their comment. The Cep104 puncta at the cell periphery, are reduced/lost upon nocodazole treatment thus we conclude that Cep104 localizes to microtubules and not the cell membrane (Figure 5C, zoomed images). Of course, we cannot exclude the possibility that microtubules are required to target CEP104 to the plasma membrane. We edited the text to clearly state this conclusion.

Figure 6C and 6D. These two panels have the same labels. The authors should denote that 6D is in nocodazole-treated explants.

__ ____Response:__ We thank the reviewer for this comment. We edited this figure to more clearly present the results of this experiment: We normalized the β -tubulin levels in morphant cells to that of control cells in the same embryo (mosaic morphant embryos were used in this experiment). The graph shows the mean normalized β -tubulin levels per embryo treated with DMSO or nocodazole.

Figure 7. What are Cep104 levels at stage 18-19?

__ ____Response: __Following the reviewer's comment we now show the Cep104 protein expression levels during Xenopus development as reported on Xenbase (Figure 4). Cep104 is expressed at low levels from gastrulation to tailbud stages (Figure 4D).

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

Referee #3

Evidence, reproducibility and clarity

The manuscript entitled "Ciliary and non-ciliary functions of Cep104 in Xenopus" by Louka et al. investigate roles for the centriole and cilia tip protein Cep104 in Xenopus embryos. The authors show that depletion of Cep104 prevents neural tube closure due to inefficient apical constriction of neural cells and defective hedgehog signaling. Cep104 depletion also resulted in structural and functional ciliary defects in multi-ciliated cells. Surprisingly, the authors discover a role for Cep104 in stabilizing cytoplasmic microtubules in non-ciliated and multi-ciliated cells. Reduced microtubule stability in Cep104-depleted cells correlated with reduced apical intercalation of multi-ciliated cells in the epidermis.

Overall, I find this manuscript difficult to understand because the experiments lack description of the findings within a normal developmental context and the findings are not developed into a cohesive narrative. I do find the study to be potentially impactful as the authors characterize Cep104 in a novel system (previous peer-reviewed studies have investigated Cep104 in human cell lines, Drosophila, zebrafish, Tetrahymena, and Chlamydomonas) with disease-relevant biology (neural development); however, mechanistic links are not properly explored. Over the course of their investigation, the authors made the novel finding that Cep104 controls the dynamics of cytoplasmic microtubules. However, this is not directly tested and potential pleiotropic effects of the developmental defects caused by Cep104 depletion confound the results.

Major Critiques:

The developmental context of experiments is not made clear. The authors use different tissues at varying developmental stages to perform experiments. However, these findings are not explored in depth and, therefore, the manuscript does not advance our understanding of Cep104's role in any of the processes explored.

While the potential role of Cep104 in cytoplasmic microtubule regulation is intriguing, the experiments in the manuscript do not directly test this function. Because Cep104 depletion appears to have a profound developmental effect, it is difficult to interpret changes to EB1 velocity as directly attributed to Cep104 function. Additionally, the only evidence for Cep104 localization occurs in cells overexpressing human Cep104. The authors must directly visualize endogenous Cep104 to conclude microtubule or membrane localization, which they can also use to demonstrate Cep104 depletion in the morpholino experiments. Additionally, the assertion that Cep104 is binding plus-ends of cytoplasmic microtubules is not experimentally supported.

Additional Critiques:

Figure S1. I only see the emergence of a shorter product after Cep104 depletion. Should PCR using Exon5-7 still work in successful knockdown? If not, then it is unclear what was quantified to determine Cep104 depletion as morpholino bands appear no different than control.

Figure 1A. Is this an example of an open or closed NTC? Show data used to determine the statement "no difference during convergent extension".

Figure S2C. What does "Does not effect formation of cilia" mean? Does Cep104 depletion does not effect number, length, etc? Show quantitation used to determine this?

Figure 5B. Along with strong Cep104 localization to membranes, there also appears to be strong EMTB localization. Is this also present in beta-tubulin immunostaining? Are these localizing to a cortical population of microtubules or to the membrane?

Figure 6C and 6D. These two panels have the same labels. The authors should denote that 6D is in nocodazole-treated explants.

Figure 7. What are Cep104 levels at stage 18-19?

Significance

Overall, I find this manuscript difficult to understand because the experiments lack description of the findings within a normal developmental context and the findings are not developed into a cohesive narrative. I do find the study to be potentially impactful as the authors characterize Cep104 in a novel system (previous peer-reviewed studies have investigated Cep104 in human cell lines, Drosophila, zebrafish, Tetrahymena, and Chlamydomonas) with disease-relevant biology (neural development); however, mechanistic links are not properly explored. Over the course of their investigation, the authors made the novel finding that Cep104 controls the dynamics of cytoplasmic microtubules. However, this is not directly tested and potential pleiotropic effects of the developmental defects caused by Cep104 depletion confound the results.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

Referee #2

Evidence, reproducibility and clarity

This study by Louka et al., investigates the function of Cep104, a protein associated with Joubert syndrome, in Xenopus. Several aspects are studied at different scales. Loss of function of this protein suggests a role in neural tube closure, apical constriction, and HH signaling. Moving on in the study, the authors investigate the localization of Cep104 in the primary cilia of the neural tube before focusing on its localization in multiciliated cells. They then look at the consequences of loss of function on motile cilia and conclude that it plays a role in the length of the distal segment. They then show an association of Cep104 with cytoplasmic microtubules in non-multiciliated cells of the Xenopus epidermis. They then analyze the function of Cep104 on these microtubules and show that loss of Cep104 function increases the speed of EB1 comets. They then looked at the impact of loss of function on microtubule stability and finally the impact of gain of function. Finally, they returned to the multiciliated cells and described an intercalation defect that correlated with decreases in acetylated tubulin. I think that certain controls are missing and that the choice of illustrations should be reconsidered (better quality, appropriate zoom). In terms of form, the text is not easy to read and the manuscript would benefit from reformatting to highlight the logical links between the different experiences and avoid a catalog-like effect. I would advise the authors to revise their introduction to make it less disjointed and guide readers toward the questions addressed by the manuscript.

Below are specific comments and remarks:

Figure 1:

Why the conclusion is a "delay" in neural tube closure? At what stage is this analyzed? Is there a recovery of NT closure at later stage? A: I would suggest to provide control picture of non-injected and tracer only injected embryos. B: Statistics are missing on the graph D: mention what was injected instead of "+ rescue". Close up picture would allow a better appreciation of the differences in surface area.

Figure S1:

To illustrate the claim that cilia are not affected, it would be good to show injection of tracer alone and compare to tracer + morpholino. Also, to provide a measure of the cilia size.

Figure 2:

Please provide pictures to illustrate graph D.

Figure 5:

"Interestingly, most of the nocodazole-resistant stable microtubules were positive for Cep104 (Figure 5C, arrows). " - The variation in density of Cep104-GFP signal is not visible on the pictures provided in C. I would suggest to show higher magnifications. Also, in the DMSO treated picture the Cep104GFP signal looks really different when compared to Cep104-GFP signal shown in B. Arrows should be reported on all channels. However, it not clear what we should see with this arrows. 5C: it seems that in nocodazole treated condition the Cep104-GFP is at the cilia base in MCCs which is different from the DMSO control condition. The basal body signal was not seen in the figure 3A which analyze the localization of Cep104-GFP in MCCs. Why not comment on this? Is it a phenotype on MCCs ? Figure 6: Intriguingly, morphant non-MCCs have significantly more mean β-tubulin signal compared to control non-MCCs in embryos treated with DMSO (Figure 6C). - impossible to appreciate on the figures. Please specify on the figure what is considered as a morphant non-MCC versus a control non-MCC. The membrane-cherry positive cells (supposedly morphant? it has to be clarified show very heterogenous tubulin expression)

If the point here is to show that microtubules are more sensitive to nocodazole in morphant cells as compared to control. I would suggest to show all conditions on a same graph. At least annotate more the grap for a self-explanatory figure (DMSO , Nocodazole). Figure 7: Statistics are missing on Graph B Comment on the text: "Cep104 signal shows the characteristic two dot pattern in motile cilia (Figure 3A) that was also observed in a recent study using Xenopus Cep10465 and in the cilia of Tetrahymena50. This is in agreement with a recent study showing the characteristic two dot pattern for Xenopus Cep104 as well66 " - ref 65 and 66b are the same (Hong et al., preprint)

"This data suggests that downregulation of CEP104 affects the stability of cytoplasmic microtubules." - I would suggest a more precise conclusion by stating how is it affected? More stable? Less stable? Important for the follow-up demonstration.

Movies:

Please annotate properly movie 2 and 3 so the reader can know what he/she is looking.

Referees cross-commenting

Similar feeling that reviews are consistent

Significance

This study investigates the role of the proprotein Cep104 in Xenopus. Cep104 is a protein associated with Joubert syndrome, whose role in primary cilia has been extensively documented. While its localization at the tip of motile cilia has also been reported, this study provides functional evidence for the role of Cep104 in motile cilia. In addition, the study looks at the role of Cep104 on non-cilial microtubules, which is the original aspect of the paper and may ultimately lead to a better understanding of Joubert syndrome. However, I believe that the evidence provided (controls, illustrations) needs to be improved. This paper will be of interest to a specialized audience with an interest in proteins associated with cilia and microtubules.

I am a cell biologist specialized in the study of multiciliated cells using advanced imaging methods and Xenopus and mice as models. I believe my expertise was a perfect match for this manuscript.

Author Response:

Reviewer #1:

This is a very interesting study that examines the neural processes underlying age-related changes in the ability to prioritize memory for value information. The behavioral results show that older subjects are better able to learn which information is valuable (i.e., more frequently presented) and are better at using value to prioritize memory. Importantly, prioritizing memory for high-value items is accompanied by stronger neural responses in the lateral PFC, and these responses mediate the effects of age on memory.

Strengths of this paper are the large sample size and the clever learning tasks. The results provide interesting insights into potential neurodevelopmental changes underlying the prioritization of memory.

There are also a few weaknesses:

First, the effects of age on repetition suppression in the parahippocampal cortex are relatively modest. It is not clear why repetition suppression effects should only be estimated using the first and last but not all presentations. The consideration of linear and quadratic effects of repetition number could provide a more reliable estimate and provide insights into age-related differences in the dynamics of frequency learning across multiple repetitions.

Thank you for this helpful suggestion. As recommended, we have now computed neural activation within our parahippocampal region of interest not just for the first and last appearance of each item during frequency learning, but for all appearances. Specifically we extended our repetition suppression analysis described in the manuscript to include all image repetitions (p. 36 - 37). Our new methods description reads:

“For each stimulus in the high-frequency condition, we examined repetition suppression by measuring activation within a parahippocampal ROI during the presentation of each item during frequency-learning. We defined our ROI by taking the peak voxel (x = 30, y = -39, z = -15) from the group-level first > last item appearance contrast for high-frequency items during frequency-learning and drawing a 5 mm sphere around it. This voxel was located in the right parahippocampal cortex, though we observed widespread and largely symmetric activation in bilateral parahippocampal cortex. To encompass both left and right parahippocampal cortex within our ROI, we mirrored the peak voxel sphere. For each participant, we modeled the neural response to each appearance of each item using the Least Squares-Separate approach (Mumford et al., 2014). Each first-level model included a regressor for the trial of interest, as well as separate regressors for the onsets of all other items, grouped by repetition number (e.g., a regressor for item onsets on their first appearance, a regressor for item onsets on their second appearance, etc.). Values that fell outside five standard deviations from the mean level of neural activation across all subjects and repetitions were excluded from subsequent analyses (18 out of 10,320 values; .01% of observations). In addition to examining neural activation as a function of stimulus repetition, we also computed an index of repetition suppression for each high-frequency item by computing the difference in mean beta values within our ROI on its first and last appearance.”

As suggested, we ran a mixed effects model examining the influence of linear and quadratic age and linear and quadratic repetition number on neural activation. In line with our whole-brain analysis, we observed a robust effect of linear and quadratic repetition number, suggesting that neural activation decreased non-linearly across stimulus repetitions. In addition, we observed significant interactions between our age and repetition number terms, suggesting that repetition suppression increased into early adulthood. Thus, although the relation we observed between age and repetition suppression is modest, the results from our new analyses suggest it is robust. Because these results largely aligned with the pattern of age-related change we observed in our analysis of repetition suppression indices, we continued to use that compressed metric in subsequent analyses looking at relations with behavior. However, we have updated our results section to include the full analysis taking into account all item repetitions, as suggested. Our updated manuscript now reads (p. 9):

“We next examined whether repetition suppression in the parahippocampal cortex changed with age. We defined a parahippocampal region of interest (ROI) by drawing a 5mm sphere around the peak voxel from the group-level first > last appearance contrast (x = 30, y = -39, z = -15), and mirrored it to encompass both right and left parahippocampal cortex (Figure 2C). For each participant, we modeled the neural response to each appearance of each high-frequency item. We then examined how neural activation changed as a function of repetition number and age. To account for non-linear effects of repetition number, we included linear and quadratic repetition number terms. In line with our whole-brain analysis, we observed a main effect of repetition number, F(1, 5016.0) = 30.64, p < .001, indicating that neural activation within the parahippocampal ROI decreased across repetitions. Further, we observed a main effect of quadratic repetition number, F(1, 9881.0) = 7.47, p = .006, indicating that the reduction in neural activity was greatest across earlier repetitions (Fig 3A). Importantly, the influence of repetition number on neural activation varied with both linear age, F(1, 7267.5) = 7.2, p = .007 and quadratic age , F(1, 7260.8) = 6.9, p = .009. Finally, we also observed interactions between quadratic repetition number and both linear and quadratic age (ps < .026). These age-related differences suggest that repetition suppression was greatest in adulthood, with the steepest increases occurring from late adolescence to early adulthood (Figure 3).”

"For each participant for each item, we also computed a “repetition suppression index” by taking the difference in mean beta values within our ROI on each item’s first and last appearance (Ward et al., 2013). These indices demonstrated a similar pattern of age- related variance — we found that the reduction of neural activity from the first to last appearance of the items varied positively with linear age, F(1, 78.32) = 3.97, p = .05, and negatively with quadratic age, F(1, 77.55) = 4.8, p = .031 (Figure 3B). Taken together, our behavioral and neural results suggest that sensitivity to the repetition of items in the environment was prevalent from childhood to adulthood but increased with age.”

In addition, in the main text on p. 10, we have now included the suggested scatter plot (see new Fig. 3B, below) as well as a modified version of our previous figure S2 to show neural activation across all repetitions in the parahippocampal cortex (see new Fig 3A). We thank the reviewer for this helpful suggestion, as we believe these new figures much more clearly illustrate the repetition suppression effects we observed during frequency learning.

Fig 3. (A) Neural activation within a bilateral parahippocampal cortex ROI decreased across stimulus repetitions both linearly, F(1, 5015.9) = 30.64, p < .001, and quadratically, F(1, 9881.0) = 7.47, p = .006. Repetition suppression increased with linear age, F(1, 7267.5) = 7.2, p = .007, and quadratic age F(1, 7260.8) = 6.9, p = .009. The horizontal black lines indicate median neural activation values. The lower and upper edges of the boxes indicate the first and third quartiles of the grouped data, and the vertical lines extend to the smallest value no further than 1.5 times the interquartile range. Grey dots indicate data points outside those values. (B) The decrease in neural activation in the bilateral PHC ROI from the first to fifth repetition of each item also increased with both linear age, F(1, 78.32) = 3.97, p = .05, and quadratic age, F(1, 77.55) = 4.8, p = .031.

Second, the behavioral data show effects of age on both initial frequency learning and the effects of item frequency on memory. It is not clear whether the behavioral findings reflect the effects of age on the ability to use value information to prioritize memory or simply better initial learning of value-related information on older subjects.

Thank you for raising this important point. Indeed, one of our main findings is that older participants are better both at learning the structure of their environments and also at using structured knowledge to strategically prioritize memory. In our original manuscript, we described results of a model that included participants’ explicit frequency reports as a predictor of memory. Model comparison revealed that participants’ frequency reports — which we interpret as reflecting their beliefs about the structure of the environment — predicted memory more strongly than the item’s true frequency. In other words, participants’ beliefs about the structure of the environment (even if incorrect) more strongly influenced their memory encoding than the true structure of the environment. Critically, however, frequency reports interacted with age to predict memory (Fig 8). Even when we accounted for age-related differences in knowledge of the structure of the environment, older participants demonstrated a stronger influence of frequency on memory, suggesting they were better able to use their beliefs to control subsequent associative encoding. We have now clarified our interpretation of this model in our discussion on p. 23:

“Importantly, though we observed age-related differences in participants’ learning of the structure of their environment, the strengthening of the relation between frequency reports and associative memory with increasing age suggests that age differences in learning cannot fully account for age differences in value-guided memory. Even when accounting for individual differences in participants’ explicit knowledge of the structure of the environment, older participants demonstrated a stronger relation between their beliefs about item frequency and associative memory, suggesting that they used their beliefs to guide memory to a greater degree than younger participants.”

As noted by the reviewer, however, our initial memory analysis did not account for age-related differences in participants’ initial, online learning of item frequency, and our neural analyses further did not account for age differences in explicit frequency reports. We have now run additional control analyses to account for the potential influence of individual differences in frequency learning on associative memory. Specifically, for each participant, we computed three metrics: 1.) their overall accuracy during frequency-learning, 2.) their overall accuracy for the last presentation of each item during frequency-learning (as suggested by Reviewer 2), and 3.) the mean magnitude of the error in their frequency reports. We then included these metrics as covariates in our memory analyses.

When we include these control variables in our model, we continue to observe a robust effect of frequency condition (p < .001) as well as robust interactions between frequency condition and linear and quadratic age (ps < .003) on associative memory accuracy. We also observed a main effect of frequency error magnitude on memory accuracy (p < .001). Here, however, we no longer observe main effects of age or quadratic age on overall memory accuracy. Given the relation we observed between frequency error magnitudes and age, the results from this model suggests that there may be age-related improvements in overall memory that influence both memory for associations as well as learning of and memory for item frequencies. The fact that age no longer relates to overall memory when controlling for frequency error magnitudes suggest that age-related variance in memory for item frequencies and memory for associations are strongly related within individuals. Importantly, however, age-related variance in memory for item frequencies did not explain age-related variance in the influence of frequency condition on associative memory, suggesting that there are developmental differences in the use of knowledge of environmental structure to prioritize valuable information in memory that persist even when controlling for age-related differences in initial learning of environmental regularities. Given the importance of this analysis in elucidating the relation between the learning of environmental structure and value-guided memory, we have now updated the results in the main text of our manuscript to include them. Specifically, on p. 13, we now write:

“Because we observed age-related differences in participants’ online learning of item frequencies and in their explicit frequency reports, we further examined whether these age differences in initial learning could account for the age differences we observed in associative memory. To do so, we ran an additional model in which we included each participant’s mean frequency learning accuracy, mean frequency learning accuracy on the last repetition of each item, and explicit report error magnitude as covariates. Here, explicit report error magnitude predicted overall memory performance, χ2(1) =13.05, p < .001, and we did not observe main effects of age or quadratic age on memory performance (ps > .20). However, we continued to observe a main effect of frequency condition, χ2(1) = 19.65 p < .001, as well as significant interactions between frequency condition and both linear age χ2(1) = 10.59, p = .001, and quadratic age χ2(1) = 9.15, p = .002. Thus, while age differences in initial learning related to overall memory performance, they did not account for age differences in the use of environmental regularities to strategically prioritize memory for valuable information.”

In addition, as suggested by the reviewer, we also included the three covariates as control variables in our mediation analysis. When controlling for online frequency learning and explicit frequency report errors, PFC activity continued to mediate the relation between age and memory difference scores. We have now included these results on p. 16 - 17 of the main text:

“Further, when we included quadratic age, WASI scores, online frequency learning accuracy, online frequency learning accuracy on the final repetition of each item, and mean explicit frequency report error magnitudes as control variables in the mediation analysis, PFC activation continued to mediate the relation between linear age and memory difference scores (standardized indirect effect: .56, 95% confidence interval: [.06, 1.35], p = .023; standardized direct effect; 1.75, 95% confidence interval: [.12, .3.38], p = .034).”

We also refer to these analyses when we interpret our findings in our discussion. On p. 23, we write:

“In addition, we continued to observe a robust interaction between age and frequency condition on associative memory, even when controlling for age-related change in the accuracy of both online frequency learning and explicit frequency reports. Thus, though we observed age differences in the learning of environmental regularities and in their influence on subsequent associative memory encoding, our developmental memory effects cannot be fully explained by differences in initial learning.”

We thank the reviewer for this constructive suggestion, as we believe these control analyses strengthen our interpretation of age differences in both the learning and use of environmental regularities to prioritize memory.

Reviewer #2:

Nussenbaum and Hartley provide novel neurobehavioral evidence of how individuals differentially use incrementally acquired information to guide goal-relevant memory encoding, highlighting roles for the medial temporal lobe during frequency learning, and the lateral prefrontal cortex for value-guided encoding/retrieval. This provides a novel behavioral phenomenology that gives great insight into the processes guiding adaptive memory formation based on prior experience. However, there were a few weaknesses throughout the paper that undermined an overall mechanistic understanding of the processes.

First, there was a lack of anatomical specificity in the discussion and interpretation of both prefrontal and striatal targets, as there is great heterogeneity across these regions that would infer very different behavioral processes.

We agree with the reviewer that our introduction and discussion would benefit from more anatomical granularity, and we did indeed have a priori predictions about more specific neural regions that might be involved in our task.

First, we expected that both the ventral and dorsal striatum might be responsive to stimulus value across our age range. Prior work has suggested that activity in the ventral striatum often correlates with the intrinsic value of a stimulus, whereas activity in the dorsal striatum may reflect goal-directed action values (Liljeholm & O’Doherty, 2012). In our task, we expected that high-frequency items may acquire intrinsic value during frequency-learning that is then reflected in the striatal response to these items during encoding. However, because participants were not rewarded when they encountered these images, but rather incentivized to encode associations involving them, we hypothesized that the dorsal striatum may represent the value of the ‘action’ of remembering each pair. In line with this prediction, the dorsal striatum, and the caudate in particular, have also been shown to be engaged during value-guided cognitive control (Hikosaka et al., 2014; Insel et al., 2017).

We have now revised our introduction to include greater specificity in our anatomical predictions on p. 3:

“When individuals need to remember information associated with previously encountered stimuli (e.g., the grocery store aisle where an ingredient is located), frequency knowledge may be instantiated as value signals, engaging regions along the mesolimbic dopamine pathway that have been implicated in reward anticipation and the encoding of stimulus and action values. These areas include the ventral tegmental area (VTA) and the ventral and dorsal striatum (Adcock et al., 2006; Liljeholm & O’Doherty, 2012; Shigemune et al., 2014).”

Though we initially predicted that encoding of high-value information would be associated with increased activation in both the ventral and dorsal striatum, the activation we observed was largely within the dorsal striatum, and specifically, the caudate. We have now revised our discussion accordingly on p. 26:

“Though we initially hypothesized that both the ventral and dorsal striatum may be involved in encoding of high-value information, the activation we observed was largely within the dorsal striatum, a region that may reflect the value of goal-directed actions (Liljeholm & O’Doherty, 2012). In our task, rather than each stimulus acquiring intrinsic value during frequency-learning, participants may have represented the value of the ‘action’ of remembering each pair during encoding.”

Second, while the ventromedial PFC often reflects value, given the control demands of our task, we expected to see greater activity in the dorsolateral PFC, which is often engaged in tasks that require the implementation of cognitive control (Botvinick & Braver, 2015). Thus, we hypothesized that individuals would show increased activation in the dlPFC during encoding of high- vs. low-value information, and that this activation would vary as a function of age. We have now clarified this hypothesis on p. 3:

“Value responses in the striatum may signal the need for increased engagement of the dorsolateral prefrontal cortex (dlPFC) (Botvinick & Braver, 2015), which supports the implementation of strategic control.”

In our discussion, we review disparate findings in the developmental literature and discuss factors that may contribute to these differences across studies. For example, in our discussion of Davidow et al. (2016), we highlight differences between their task design and the present study, focusing on how their task involved immediate receipt of reward at the time of encoding, while our task incentivized memory accuracy. We further note that studies that involve reward delivery at the time of encoding may engage different neural pathways than those that promote goal-directed encoding. Beyond Davidow et al. (2016), there are no other neuroimaging studies that examine the influence of reward on memory across development. Thus, we cannot relate our present neural findings to prior work on the development of value-guided memory. As we note in our discussion (p. 28), “Further work is needed to characterize both the influence of different types of reward signals on memory across development, as well as the development of the neural pathways that underlie age-related change in behavior.”

Second, age-related differences in neural activation emerged both during the initial frequency learning as well as during memory-guided adaptive encoding. While data from this initial phase was used to unpack the behavioral relationships on adaptive memory, a major weakness of the paper was not connecting these measures to neural activity during memory encoding/retrieval. This would be especially relevant given that both implicit and explicit measures of frequency predicted subsequent performance, but it is unclear which of these measures was guiding lateral PFC and caudate responses.

Thank you for this valuable suggestion. We agree that it would be interesting to link frequency- learning behavior to neural activity at encoding. As such, we have now conducted additional analyses to explore these relations.

In the original version of our manuscript, we examined behavior at the item level through mixed- effects models, and neural activation during encoding at the participant level. Thus, to examine the relation between frequency-learning metrics and neural activation at encoding, we created two additional participant-level metrics. For each participant we computed their average repetition suppression index, and a measure of frequency distance. The average repetition suppression index reflects the overall extent to which the participant demonstrated repetition suppression in response to the fifth presentation of the high-frequency items, and is computed by averaging each participant’s repetition suppression indices across items. We hypothesized that participants who demonstrated the greatest degree of repetition suppression might be the most sensitive to the difference between the 1- and 5-frequency items, and therefore, show the greatest differences in striatal and PFC activation during encoding of high- vs. low-value information. The frequency distance metric reflects the average distance between participants’ explicit frequency reports for items that appeared once and items that appeared five times, and is computed by averaging their explicit frequency reports for items in each frequency condition, and then subtracting the average reports in the low-frequency condition from those in the high- frequency condition. We hypothesized that participants with the largest frequency distances might similarly be the most sensitive to the difference between the 1- and 5-frequency items, and therefore, show the greatest differences in striatal and PFC activation during encoding of high- vs. low-value information.

We first wanted to confirm that the relations we observed between repetition suppression, frequency reports, and age, could also be observed at the participant level. In line with our prior, behavioral analyses, we found that age related to both mean repetition suppression indices (marginally; linear age: p = .067; quadratic age: p = .042); and frequency distances (linear and quadratic age: ps < .001).

In addition, we further tested whether these two metrics related to memory performance. In contrast to our item-level findings, we did not observe a significant relation between repetition suppression indices and memory (p = .83). We did observe an effect of frequency distance on memory performance. Specifically, we observed significant interactions between frequency distance and age (p = .014) and frequency distance and quadratic age (p = .021) on memory difference scores, such that the influence of frequency distance on memory difference scores increased with increasing age from childhood to adolescence.

We next examined how mean repetition suppression indices and frequency distances related to differential neural activation during encoding of high- and low-value pairs. In line with our memory findings, we did not observe any significant relations between mean repetition suppression indices and neural activation in the caudate or prefrontal cortex during encoding (ps > .15).

Frequency distance did not relate to caudate activation during encoding nor did we observe a frequency distance x age interaction effect (ps > .16). Frequency distance did, however, relate to differential PFC activation during encoding of high- vs. low-value pairs. Specifically, we observed a main effect of frequency distance on PFC activation (p = .0012), such that participants whose explicit reports of item frequency, were on average, more distinct across frequency conditions, demonstrated increased PFC activation during encoding of pairs involving high- vs. low-frequency items. Interestingly, when we included frequency distance in our model, we no longer observed a significant effect of age on differential PFC activation, nor did we observe a significant frequency distance x age interaction (ps > .13). These findings suggest that PFC activation during encoding may have, in part, reflected participants’ beliefs about the structure of the environment, with participants demonstrating stronger differential engagement of control processes across conditions when their representations of the conditions themselves were more distinct.

Finally, we examined how age, frequency distance, and PFC activation related to memory difference scores. Here, even when controlling for both frequency distance and PFC activation, we continued to observe main effects of age and quadratic age on memory difference scores (linear age: p = .006; quadratic age: p = .001). In line with our analysis of the relation between frequency reports and memory, these results suggest that age-related variance in value-guided memory may depend on both knowledge of the structure of the environment and use of that knowledge to effectively control encoding.

We have now added these results to our manuscript on p. 13 - 14. We write:

“Given the relations we observed between memory and both repetition suppression and frequency reports, we examined whether they related to neural activation in both our caudate and PFC ROI during encoding. To do so, we computed each participant’s average repetition suppression index, and their “frequency distance” — or the average difference in their explicit reports for items in the high- and low-frequency conditions. We expected that participants with greater average repetition suppression indices and greater frequency distances represented the high- and low-frequency items as more distinct from one another and therefore would show greater differences in neural activation at encoding across frequency conditions. In line with our prior analyses, both metrics varied with age (though repetition suppression only marginally (linear age: p = .067; quadratic age: p = .042); Appendix 3 y Tables 22 and 25), suggesting that older participants demonstrated better learning of the structure of the environment. We ran linear regressions examining the relations between each metric, age, and their interaction on neural activation in both the caudate and PFC. We observed no significant effects or interactions of average repetition suppression indices on neural activation (ps > .15; Appendix 3 Tables 23 and 24). We did, however, observe a significant effect of frequency distance on PFC activation (β = .42, SE = .12, p = .0012), such that participants who believed that average frequencies of the high- and low-frequency items were further apart also demonstrated greater PFC activation during encoding of pairs with high- vs. low-frequency items. Here, we did not observe a significant effect of age on PFC activation (β = -.03, SE = .13, p = .82), suggesting that age-related variance in PFC activation may be related to age differences in explicit frequency beliefs. Importantly, however, even when we accounted for both PFC activation and frequency distances, we continued to observe an effect of age on memory difference scores (β = .56, SE = .20, p = .006), which, together with our prior analyses, suggest that developmental differences in value-guided memory are not driven solely by age differences in beliefs about the structure of the environment but also depend on the use of those beliefs to guide encoding.”

We have added the full model results to Appendix 3: Full Model Specification and Results.

Given these results, we have now revised our interpretation of our neural data. Our memory analyses demonstrate that across our age range, we observed age-related differences in both the acquisition of knowledge of the structure of the environment and in its use. Originally, we interpreted the PFC activation as reflecting the use of learned value to guide memory. However, the strong relation we found between frequency distance and PFC activation suggests that the age differences in PFC activation that we observed may also be related to age differences in knowledge of the structure of the environment that governs when control processes should be engaged most strongly. However, these results must be interpreted cautiously. Participants provided explicit frequency reports after they completed the encoding and retrieval tasks, and so explicit frequency reports may have been influenced not only by participants’ memories of online frequency learning, but also by the strength with which they encoded the item and its paired associate, and the experience of successfully retrieving it.

We have now revised our discussion to consider these results. On p. 23, we now write,

“Our neural results further suggest that developmental differences in memory were driven by both knowledge of the structure of the environment and use of that knowledge to guide encoding.”

On p. 24, we write,

“The development of adaptive memory requires not only the implementation of encoding and retrieval strategies, but also the flexibility to up- or down-regulate the engagement of control in response to momentary fluctuations in information value (Castel et al., 2007, 2013; Hennessee et al., 2017). Importantly, value-based modulation of lateral PFC engagement during encoding mediated the relation between age and memory selectivity, suggesting that developmental change in both the representation of learned value and value-guided cognitive control may underpin the emergence of adaptive memory prioritization. Prior work examining other neurocognitive processes, including response inhibition (Insel et al., 2017) and selective attention (Störmer et al., 2014), has similarly found that increases in the flexible upregulation of control in response to value cues enhance goal-directed behavior across development (Davidow et al., 2018), and may depend on the engagement of both striatal and prefrontal circuitry (Hallquist et al., 2018; Insel et al., 2017). Here, we extend these past findings to the domain of memory, demonstrating that value signals derived from the structure of the environment increasingly elicit prefrontal cortex engagement and strengthen goal-directed encoding across childhood and into adolescence.”

And on p. 25, we have added an additional paragraph:

“Further, we also demonstrate that in the absence of explicit value cues, the engagement of prefrontal control processes may reflect beliefs about information value that are learned through experience. Here, we found that differential PFC activation during encoding of high- vs. low-value information reflected individual and age-related differences in beliefs about the structure of the environment; participants who represented the average frequencies of the low- and high-frequency items as further apart also demonstrated greater value-based modulation of lateral PFC activation. It is important to note, however, that we collected explicit frequency reports after associative encoding and retrieval. Thus the relation between PFC activation and explicit frequency reports may be bidirectional — while participants may have increased the recruitment of cognitive control processes to better encode information they believed was more valuable, the engagement of more elaborative or deeper encoding strategies that led to stronger memory traces may have also increased participants’ subjective sense of an item’s frequency (Jonides & Naveh-Benjamin, 1987).”

Third, more discussion is warranted on the nature of age-related changes given that some findings followed quadratic functions and others showed linear. Further interpretation of the quadratic versus linear fits would provide greater insight into the relative rates of maturation across discrete neurobehavioral processes.

We agree with the reviewer that more discussion is warranted here. While many cognitive processes tend to improve with increasing age, the significant interaction between quadratic age and frequency condition on memory accuracy could reflect a number of different patterns of developmental variance. Because quadratic curves are U-shaped, the significant interaction between quadratic age and frequency condition could reflect a peak in value-guided memory in adolescence. However, the combination of linear and quadratic effects can also capture “plateauing” effects, where the influence of age on a particular cognitive process decreases at a particular developmental timepoint. To determine how to interpret the quadratic effect of age on value-guided memory — and specifically, to test for the presence of an adolescent peak — we ran an additional analysis.

To test for an adolescent peak in value-guided memory, we first fit our memory accuracy model without any age terms, and then extracted the random slope across frequency conditions for each subject. We then conducted a ‘two lines test’ (Simonsohn, 2018) to examine the relation between age and these random slopes. In brief, the two-lines test fits the data with two linear models — one with a positive slope and one with a negative slope, algorithmically determining the breakpoint in the estimates where the signs of the slopes change. When we analyzed our memory data in this way, we found a robust, positive relation between age and value-guided memory (see newly added Appendix 2 Figure 3, also below) from childhood to mid- adolescence, that peaked around age 16 (age 15.86). From age ~16 to early adulthood, however, we observed only a marginal negative relation between age and value-guided memory (p = .0567). Thus, our findings do not offer strong evidence in support of an adolescent peak in value-guided memory — instead, they suggest that improvements in value-guided memory are strongest from childhood to adolescence.

Appendix 2 - Figure 3. Results from the two-lines test (Simonsohn, 2018) revealed that the influence of frequency condition on memory accuracy increased throughout childhood and early adolescence, and did not significantly decrease from adolescence into early adulthood.

To more clearly demonstrate the relation between age and value-guided memory, we have now included the results of the two-lines test in the results section of our main text. On p. 12 - 13, we write:

“In line with our hypothesis, we observed a main effect of frequency condition on memory, χ2(1) = 21.51, p <.001, indicating that individuals used naturalistic value signals to prioritize memory for high-value information. Critically, this effect interacted with both linear age (χ2(1) = 11.03, p < .001) and quadratic age (χ2(1) = 9.51, p = .002), such that the influence of frequency condition on memory increased to the greatest extent throughout childhood and early adolescence. To determine whether the interaction between quadratic age and frequency condition on memory accuracy reflected an adolescent peak in value-guided memory prioritization, we re-ran our memory accuracy model without including any age terms, and extracted each participant’s random slope across frequency conditions. We then submitted these random slopes to the “two-lines” test (Simonsohn, 2018), which fits two regression lines with oppositely signed slopes to the data, algorithmically determining where the sign flip should occur. The results of this analysis revealed that the influence of frequency condition on memory significantly increased from age 8 to age 15.86 (b = .03, z = 2.71, p = .0068; Appendix 2 – Figure 3), but only marginally decreased from age 15.86 to age 25 (b = -.02, z = 1.91, p = .0576). Thus, the interaction between frequency condition and quadratic age on memory performance suggests that the biggest age differences in value-guided memory occurred through childhood and early adolescence, with older adolescents and adults performing similarly.”

That said, this developmental trajectory is likely specific to the particular demands of our task. In our previous behavioral study that used a very similar paradigm (Nussenbaum, Prentis, & Hartley, 2018), we observed only a linear relation between age and value-guided memory.

Although the task used in our behavioral study was largely similar to the task we employed here, there were subtle differences in the design that may have extended the age range through which we observed improvements in memory prioritization. In particular, in our previous behavioral study, the memory test required participants to select the correct associate from a grid of 20 options (i.e., 1 correct and 19 incorrect options), whereas here, participants had to select the correct associate from a grid of 4 options (1 correct and 3 incorrect options). In our prior work, the need to differentiate the ‘correct’ option from many more foils may have increased the demands on either (or both) memory encoding or memory retrieval, requiring participants to encode and retrieve more specific representations that would be less confusable with other memory representations. By decreasing the task demands in the present study, we may have shifted the developmental curve we observed toward earlier developmental timepoints.

We originally did not emphasize our quadratic findings in the discussion of our manuscript because, given the marginal decrease in memory selectivity we observed from age 16 to age 25 and the different age-related findings across our two studies, we did not want to make strong claims about the specific shape of developmental change. However, we agree with the reviewer that these points are worthy of discussion within the manuscript. We have now amended our discussion on p. 25 accordingly:

“We found that memory prioritization varied with quadratic age, and our follow-up tests probing the quadratic age effect did not reveal evidence for significant age-related change in memory prioritization between late adolescence and early adulthood. However, in our prior behavioral work using a very similar paradigm (Nussenbaum et al., 2020), we found that memory prioritization varied with linear age only. In line with theoretical proposals (Davidow et al., 2018), subtle differences in the control demands between the two tasks (e.g., reducing the number of ‘foils’ presented on each trial of the memory test here relative to our prior study), may have shifted the age range across which we observed differences in behavior, with the more demanding variant of our task showing more linear age-related improvements into early adulthood. In addition, the specific control demands of our task may have also influenced the age at which value- guided memory emerged. Future studies should test whether younger children can modulate encoding based on the value of information if the mnemonic demands of the task are simpler.”

We thank the reviewer for this helpful suggestion, and believe our additions that expand on the quadratic age effects help clarify our developmental findings.

Although hippocamapal and PHC results did not show a main effect of value, it seems by the introduction that this region would be critical for the processes under study. I would suggest including these regions as ROIs of interest guiding age-related differences during the memory encoding and retrieval phases. Even reporting negative findings for these regions would be helpful to readers, especially given the speculation of the negative findings in the discussion.

Thank you for this suggestion. We have now examined how differential neural activation within the hippocampus and parahippocampal cortex during encoding of high- vs. low-value information varies with age. To do so, we followed the same approach as with our PFC and caudate ROI analyses. Specifically, we first identified the voxel within both the hippocampus and parahippocampal cortex with the highest z-statistic from our group-level 5 > 1 encoding contrast. We then drew a 5-mm sphere around these voxels and examined how mean beta weights within these spheres varied with age.

We did not observe any relation between differential hippocampal or parahippocampal cortex activation during encoding of high- vs. low-value information and age (ps > .50). We agree with the reviewer that these results are informative, and have now added them to Appendix 2: Supplementary Analyses, which we refer to in the main text (p. 15). In Appendix 2, we write:

“Hippocampal and parahippocampal cortex activation during encoding A priori, we expected that regions in the medial temporal lobe that have been linked to successful memory formation, including the hippocampus and parahippocampal cortex (Davachi, 2006), may be differentially engaged during encoding of high- vs. low- value information. Further, we hypothesized that the differential engagement of these regions across age may contribute to age differences in value-guided memory. Though we did not see any significant clusters of activation in the hippocampus or parahippocampal cortex in our group level high value vs. low value encoding contrast, we conducted additional ROI analyses to test these hypotheses. As with our other ROI analyses, we first identified the peak voxel (based on its z-statistic; hippocampus: x = 24, y = 34, z = 23; parahippocampal cortex: x = 22, y = 41, z = 16) in each region from our group-level contrast, and then drew 5-mm spheres around them. We then examined how average parameter estimates within these spheres related to both age and memory difference scores.

First, we ran a linear regression modeling the effects of age, WASI scores, and their interaction on hippocampal activation. We did not observe a main effect of age on hippocampal activation, (β = .00, SE = .10, p > .99). We did, however, observe a significant age x WASI score interaction effect (β = .30, SE = .10, p = .003). Next, we conducted another linear regression to examine the effects of hippocampal activation, age, WASI scores, and their interaction on memory difference scores. In contrast to our prefrontal cortex activation results, activation in the hippocampus did not relate to memory difference scores, (β = -.02, SE = .03, p = .50).

We repeated these analyses with our parahippocampal cortex sphere. Here, we did not observe any significant effects of age on parahippocampal activation (β = -.07, SE = .11, p = .50), nor did we observe any effects of parahippocampal activation on memory difference scores (β = .01, SE = .03, p = .25).”

Reviewer #3:

This paper investigated age differences in the neurocognitive mechanisms of value-based memory encoding and retrieval across children, adolescents and young adults. It used a novel experimental paradigm in combination with fMRI to disentangle age differences in determining the value of information based on its frequency from the usage of these learned value signals to guide memory encoding. During value learning, younger participants demonstrated a stronger effect of item repetition on response accuracy, whereas repetition suppression effects in a parahippocampal ROI were strongest in adults. Item frequency modulated memory accuracy such that associative memory was better for previously high-frequency value items. Notably, this effect increased with age. Differences in memory accuracy between low- and high-frequency items were associated with left lateral PFC activation which also increased with age. Accordingly, a mediation analyses revealed that PFC activation mediated the relation between age and memory benefit for high- vs. low-frequency items. Finally, both participants' representations of item frequency (which were more likely to deviate in younger children) and repetition suppression in the parahippocampal ROI were associated with higher memory accuracy. Together, these results data add to the still scarce literature examining how information value influences memory processes across development.

Overall, the conclusions of the paper are well supported by the data, but some aspects of the data analysis need to be clarified and extended.

Empirical findings directly comparing cross-sectional and longitudinal effects have demonstrated that cross-sectional analyses of age differences do not readily generalize to longitudinal research (e.g., Raz et al., 2005; Raz & Lindenberger, 2012). Formal analyses have demonstrated that proportion of explained age-related variance in cross-sectional mediation models may stem from various factors, including similar mean age trends, within-time correlations between a mediator and an outcome, or both (Lindenberger et al., 2011; see also Hofer, Flaherty, & Hoffman, 2006; Maxwell & Cole, 2007). Thus, the results of the mediation analysis showing that PFC activation explains age-related variance in memory difference scores, cannot be taken to imply that changes in PFC activation are correlated with changes in value-guided memory. While the general limitations of a cross-sectional study are noted in the Discussion of the manuscript, it would be important to discuss the critical limitations of the mediation analysis. While the main conclusions of the paper do not critically depend on this analysis, it would be important to alert the reader to the limited information value in performing cross-sectional mediation analyses of age variance.

Thank you for raising this critical point. We have expanded our discussion to specifically note the limitations of our mediation analysis and to more strongly emphasize the need for future longitudinal studies to reveal how changes in neural circuitry may support the emergence of motivated memory across development. Specifically, on p. 26, we now write:

“One important caveat is that our study was cross-sectional — it will be important to replicate our findings in a longitudinal sample to more directly measure how developmental changes in cognitive control within an individual contribute to changes in their ability to selectively encode useful information. Our mediation results, in particular, must be interpreted with caution as simulations have demonstrated that in cross-sectional samples, variables can emerge as significant mediators of age-related change due largely to statistical artifact (Hofer, Flaherty, & Hoffman, 2006; Lindenberger et al., 2011). Indeed, our finding that PFC activation mediates the relation between age and value-guided memory does not necessarily imply that within an individual, PFC development leads to improvements in memory selectivity. Longitudinal work in which individuals’ neural activity and memory performance is sampled densely within developmental windows of interest is needed to elucidate the complex relations between age, brain development, and behavior (Hofer, Flaherty, & Hoffman, 2006; Lindenberger et al., 2011).”

It would be helpful to provide more information on how chance memory performance was handled during data analysis, especially as it is more likely to occur in younger participants. Related to this, please connect the points that belong to the same individual in Figure 3 to facilitate evaluation of individual differences in the memory difference scores.

Thank you for raising this important point. On each memory test trial, participants viewed the item (either a postcard or picture) above images of four possible paired associates (see Figure 1 on p. 6). On each memory test trial, participants had 6 seconds to select one of these items. If participants did not make a response within 6 seconds, that trial was considered ‘missed.’ Missed trials were excluded from behavioral analyses and regressed out in neural analyses. If participants selected the correct associate, memory accuracy was coded as ‘1;’ if they selected an incorrect associate, accuracy was coded as ‘0.’ On each trial, there was 1 correct option and 3 incorrect options. As such, chance-level memory performance was 25%. We have now clarified this on p. 34 and included a dashed line indicating chance-level performance within Fig. 4 (formerly Figure 3) on p. 12. In addition, we have also updated Figure 4 (see below) to connect the points belonging to the same participants, as suggested by the reviewer.

Figure 4. Participants demonstrated prioritization of memory for high-value information, as indicated by higher memory accuracy for associations involving items in the five- relative to the one-frequency condition (χ2(1) = 19.73, p <.001). The effects of item frequency on associative memory increased throughout childhood and into adolescence (linear age x frequency condition: χ2(1) = 10.74, p = .001; quadratic age x frequency condition: χ2(1) = 9.27, p = .002).

Out of 90 participants, 2 children performed at or below chance (<= 25% memory accuracy). Interpreting the behavior of the participants who responded to fewer than 12 out of 48 trials correctly is challenging. On the one hand, they might not have remembered anything and responded correctly on these trials due to randomly guessing. On the other hand, they may have implemented an encoding strategy of focusing only on a small number of pairs. Thus, a priori, based on the analysis approach we implemented in our prior, behavioral study (Nussenbaum et al., 2019), we decided to include all participants in our memory analyses, regardless of their overall accuracy. However, when we exclude these two participants from our memory analyses, our main findings still hold. Specifically, we continue to observe main effects of frequency condition and age, and interactions between frequency condition and both linear and quadratic age on associative memory accuracy (ps < .012).

We have now clarified these details about chance-level performance in the methods section of our manuscript on p. 34.

“For our memory analyses, trials were scored as ‘correct’ if the participant selected the correct association from the set of four possible options presented during the memory test, ‘incorrect’ if the participant selected an incorrect association, and ‘missed’ if the participant failed to respond within the 6-second response window. Missed trials were excluded from all analyses. Because participants had to select the correct association from four possible options, chance-level performance was 25%. Two child participants performed at or below chance-level on the memory test. They were included in all analyses reported in the manuscript; however, we report full details of the results of our memory analyses when we exclude these two participants in Appendix 3 (Table 15). Importantly, our main findings remain unchanged.”

In Appendix 3, we include a table with the full results from our memory model without these two participants:

Appendix Table 15: Associative memory accuracy by frequency condition (below chance subjects excluded)

I would like to see some consideration of how the different signatures of value learning, repetition suppression and reported item frequency, are related to the observed PFC and caudate effects during memory encoding. Such a discussion would help the reader connect the findings on learning and using information value across development.

Thank you for this valuable suggestion. We agree that it would be interesting to link frequency- learning behavior to neural activity at encoding. As such, we have now conducted additional analyses to explore these relations.

In the original version of our manuscript, we examined behavior at the item level through mixed- effects models, and neural activation during encoding at the participant level. Thus, to examine the relation between frequency-learning metrics and neural activation at encoding, we created two additional participant-level metrics. For each participant we computed their average repetition suppression index, and a measure of frequency distance. The average repetition suppression index reflects the overall extent to which the participant demonstrated repetition suppression in response to the fifth presentation of the high-frequency items, and is computed by averaging each participant’s repetition suppression indices across items. We hypothesized that participants who demonstrated the greatest degree of repetition suppression might be the most sensitive to the difference between the 1- and 5-frequency items, and therefore, show the greatest differences in striatal and PFC activation during encoding of high- vs. low-value information. The frequency distance metric reflects the average distance between participants’ explicit frequency reports for items that appeared once and items that appeared five times, and is computed by averaging their explicit frequency reports for items in each frequency condition, and then subtracting the average reports in the low-frequency condition from those in the high- frequency condition. We hypothesized that participants with the largest frequency distances might similarly be the most sensitive to the difference between the 1- and 5-frequency items, and therefore, show the greatest differences in striatal and PFC activation during encoding of high- vs. low-value information.

We first wanted to confirm that the relations we observed between repetition suppression, frequency reports, and age, could also be observed at the participant level. In line with our prior, behavioral analyses, we found that age related to both mean repetition suppression indices (marginally; linear age: p = .067; quadratic age: p = .042); and frequency distances (linear and quadratic age: ps < .001).

In addition, we further tested whether these two metrics related to memory performance. In contrast to our item-level findings, we did not observe a significant relation between repetition suppression indices and memory (p = .83). We did observe an effect of frequency distance on memory performance. Specifically, we observed significant interactions between frequency distance and age (p = .014) and frequency distance and quadratic age (p = .021) on memory difference scores, such that the influence of frequency distance on memory difference scores increased with increasing age from childhood to adolescence.

We next examined how mean repetition suppression indices and frequency distances related to differential neural activation during encoding of high- and low-value pairs. In line with our memory findings, we did not observe any significant relations between mean repetition suppression indices and neural activation in the caudate or prefrontal cortex during encoding (ps > .15).

Frequency distance did not relate to caudate activation during encoding nor did we observe a frequency distance x age interaction effect (ps > .16). Frequency distance did, however, relate to differential PFC activation during encoding of high- vs. low-value pairs. Specifically, we observed a main effect of frequency distance on PFC activation (p = .0012), such that participants whose explicit reports of item frequency, were on average, more distinct across frequency conditions, demonstrated increased PFC activation during encoding of pairs involving high- vs. low-frequency items. Interestingly, when we included frequency distance in our model, we no longer observed a significant effect of age on differential PFC activation, nor did we observe a significant frequency distance x age interaction (ps > .13). These findings suggest that PFC activation during encoding may have, in part, reflected participants’ beliefs about the structure of the environment, with participants demonstrating stronger differential engagement of control processes across conditions when their representations of the conditions themselves were more distinct.

Finally, we examined how age, frequency distance, and PFC activation related to memory difference scores. Here, even when controlling for both frequency distance and PFC activation, we continued to observe main effects of age and quadratic age on memory difference scores (linear age: p = .006; quadratic age: p = .001). In line with our analysis of the relation between frequency reports and memory, these results suggest that age-related variance in value-guided memory may depend on both knowledge of the structure of the environment and use of that knowledge to effectively control encoding.

We have now added these results to our manuscript on p. 13 - 14. We write:

“Given the relations we observed between memory and both repetition suppression and frequency reports, we examined whether they related to neural activation in both our caudate and PFC ROI during encoding. To do so, we computed each participant’s average repetition suppression index, and their “frequency distance” — or the average difference in their explicit reports for items in the high- and low-frequency conditions. We expected that participants with greater average repetition suppression indices and greater frequency distances represented the high- and low-frequency items as more distinct from one another and therefore would show greater differences in neural activation at encoding across frequency conditions. In line with our prior analyses, both metrics varied with age (though repetition suppression only marginally (linear age: p = .067; quadratic age: p = .042); Appendix 3 Tables 22 and 25), suggesting that older participants demonstrated better learning of the structure of the environment. We ran linear regressions examining the relations between each metric, age, and their interaction on neural activation in both the caudate and PFC. We observed no significant effects or interactions of average repetition suppression indices on neural activation (ps > .15; Appendix 3 Tables 23 and 24). We did, however, observe a significant effect of frequency distance on PFC activation (β = .42, SE = .12, p = .0012), such that participants who believed that average frequencies of the high- and low-frequency items were further apart also demonstrated greater PFC activation during encoding of pairs with high- vs. low-frequency items. Here, we did not observe a significant effect of age on PFC activation (β = -.03, SE = .13, p = .82), suggesting that age-related variance in PFC activation may be related to age differences in explicit frequency beliefs. Importantly, however, even when we accounted for both PFC activation and frequency distances, we continued to observe an effect of age on memory difference scores (β = .56, SE = .20, p = .006), which, together with our prior analyses, suggest that developmental differences in value-guided memory are not driven solely by age differences in beliefs about the structure of the environment but also depend on the use of those beliefs to guide encoding.”

We have added the full model results to Appendix 3.

Given these results, we have now revised our interpretation of our neural data. Our memory analyses demonstrate that across our age range, we observed age-related differences in both the acquisition of knowledge of the structure of the environment and in its use. Originally, we interpreted the PFC activation as reflecting the use of learned value to guide memory. However, the strong relation we found between frequency distance and PFC activation suggests that the age differences in PFC activation that we observed may also be related to age differences in knowledge of the structure of the environment that governs when control processes should be engaged most strongly. However, these results must be interpreted cautiously. Participants provided explicit frequency reports after they completed the encoding and retrieval tasks, and so explicit frequency reports may have been influenced not only by participants’ memories of online frequency learning, but also by the strength with which they encoded the item and its paired associate, and the experience of successfully retrieving it.

We have now revised our discussion to consider these results. On p. 23, we now write,

“Our neural results further suggest that developmental differences in memory were driven by both knowledge of the structure of the environment and use of that knowledge to guide encoding.”

n p. 24, we write,

“The development of adaptive memory requires not only the implementation of encoding and retrieval strategies, but also the flexibility to up- or down-regulate the engagement of control in response to momentary fluctuations in information value (Castel et al., 2007, 2013; Hennessee et al., 2017). Importantly, value-based modulation of lateral PFC engagement during encoding mediated the relation between age and memory selectivity, suggesting that developmental change in both the representation of learned value and value-guided cognitive control may underpin the emergence of adaptive memory prioritization. Prior work examining other neurocognitive processes, including response inhibition (Insel et al., 2017) and selective attention (Störmer et al., 2014), has similarly found that increases in the flexible upregulation of control in response to value cues enhance goal-directed behavior across development (Davidow et al., 2018), and may depend on the engagement of both striatal and prefrontal circuitry (Hallquist et al., 2018; Insel et al., 2017). Here, we extend these past findings to the domain of memory, demonstrating that value signals derived from the structure of the environment increasingly elicit prefrontal cortex engagement and strengthen goal-directed encoding across childhood and into adolescence.”

And on p. 25, we have added an additional paragraph:

“Further, we also demonstrate that in the absence of explicit value cues, the engagement of prefrontal control processes may reflect beliefs about information value that are learned through experience. Here, we found that differential PFC activation during encoding of high- vs. low-value information reflected individual and age-related differences in beliefs about the structure of the environment; participants who represented the average frequencies of the low- and high-frequency items as further apart also demonstrated greater value-based modulation of lateral PFC activation. It is important to note, however, that we collected explicit frequency reports after associative encoding and retrieval. Thus the relation between PFC activation and explicit frequency reports may be bidirectional — while participants may have increased the recruitment of cognitive control processes to better encode information they believed was more valuable, the engagement of more elaborative or deeper encoding strategies that led to stronger memory traces may have also increased participants’ subjective sense of an item’s frequency (Jonides & Naveh-Benjamin, 1987).”

A point worthy of discussion are the implications of the finding that younger participants demonstrated greater deviations in their frequency reports for the development of value learning, given that frequency reports were found to predict associative memory accuracy.

Thank you for raising this important point. Indeed, one of our main findings is that older participants are better both at learning the structure of their environments and also at using structured knowledge to strategically prioritize memory. In our original manuscript, we described results of a model that included participants’ explicit frequency reports as a predictor of memory. Model comparison revealed that participants’ frequency reports — which we interpret as reflecting their beliefs about the structure of the environment — predicted memory more strongly than the item’s true frequency. In other words, participants’ beliefs about the structure of the environment (even if incorrect) more strongly influenced their memory encoding than the true structure of the environment. Critically, however, frequency reports interacted with age to predict memory (Fig 8). Even when we accounted for age-related differences in knowledge of the structure of the environment, older participants demonstrated a stronger influence of frequency on memory, suggesting they were better able to use their beliefs to control subsequent associative encoding. We have now clarified our interpretation of this model in our discussion on p. 23:

“Importantly, though we observed age-related differences in participants’ learning of the structure of their environment, the strengthening of the relation between frequency reports and associative memory with increasing age suggests that age differences in learning cannot fully account for age differences in value-guided memory. Even when accounting for individual differences in participants’ explicit knowledge of the structure of the environment, older participants demonstrated a stronger relation between their beliefs about item frequency and associative memory, suggesting that they used their beliefs to guide memory to a greater degree than younger participants.”

As noted by the reviewer, however, our initial memory analysis did not account for age-related differences in participants’ initial, online learning of item frequency, and our neural analyses further did not account for age differences in explicit frequency reports. We have now run additional control analyses to account for the potential influence of individual differences in frequency learning on associative memory. Specifically, for each participant, we computed three metrics: 1.) their overall accuracy during frequency-learning, 2.) their overall accuracy for the last presentation of each item during frequency-learning (as suggested by Reviewer 2), and 3.) the mean magnitude of the error in their frequency reports. We then included these metrics as covariates in our memory analyses.

When we include these control variables in our model, we continue to observe a robust effect of frequency condition (p < .001) as well as robust interactions between frequency condition and linear and quadratic age (ps < .003) on associative memory accuracy. We also observed a main effect of frequency error magnitude on memory accuracy (p < .001). Here, however, we no longer observe main effects of age or quadratic age on overall memory accuracy. Given the relation we observed between frequency error magnitudes and age, the results from this model suggests that there may be age-related improvements in overall memory that influence both memory for associations as well as learning of and memory for item frequencies. The fact that age no longer relates to overall memory when controlling for frequency error magnitudes suggest that age-related variance in memory for item frequencies and memory for associations are strongly related within individuals. Importantly, however, age-related variance in memory for item frequencies did not explain age-related variance in the influence of frequency condition on associative memory, suggesting that there are developmental differences in the use of knowledge of environmental structure to prioritize valuable information in memory that persist even when controlling for age-related differences in initial learning of environmental regularities. Given the importance of this analysis in elucidating the relation between the learning of environmental structure and value-guided memory, we have now updated the results in the main text of our manuscript to include them. Specifically, on p. 13, we now write:

“Because we observed age-related differences in participants’ online learning of item frequencies and in their explicit frequency reports, we further examined whether these age differences in initial learning could account for the age differences we observed in associative memory. To do so, we ran an additional model in which we included each participant’s mean frequency learning accuracy, mean frequency learning accuracy on the last repetition of each item, and explicit report error magnitude as covariates. Here, explicit report error magnitude predicted overall memory performance, χ2(1) =13.05, p < .001, and we did not observe main effects of age or quadratic age on memory performance (ps > .20). However, we continued to observe a main effect of frequency condition, χ2(1) = 19.65 p < .001, as well as significant interactions between frequency condition and both linear age χ2(1) = 10.59, p = .001, and quadratic age χ2(1) = 9.15, p = .002. Thus, while age differences in initial learning related to overall memory performance, they did not account for age differences in the use of environmental regularities to strategically prioritize memory for valuable information.”

In addition, as suggested by the reviewer, we also included the three covariates as control variables in our mediation analysis. When controlling for online frequency learning and explicit frequency report errors, PFC activity continued to mediate the relation between age and memory difference scores. We have now included these results on p. 16 - 17 of the main text:

“Further, when we included quadratic age, WASI scores, online frequency learning accuracy, online frequency learning accuracy on the final repetition of each item, and mean explicit frequency report error magnitudes as control variables in the mediation analysis, PFC activation continued to mediate the relation between linear age and memory difference scores (standardized indirect effect: .56, 95% confidence interval: [.06, 1.35], p = .023; standardized direct effect; 1.75, 95% confidence interval: [.12, .3.38], p = .034).”

We also refer to these analyses when we interpret our findings in our discussion. On p. 23, we write:

“In addition, we continued to observe a robust interaction between age and frequency condition on associative memory, even when controlling for age-related change in the accuracy of both online frequency learning and explicit frequency reports. Thus, though we observed age differences in the learning of environmental regularities and in their influence on subsequent associative memory encoding, our developmental memory effects cannot be fully explained by differences in initial learning.”

We thank the reviewer for this constructive suggestion, as we believe these control analyses strengthen our interpretation of age differences in both the learning and use of environmental regularities to prioritize memory.

Author response:

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

Reviewer #1 (Public review):

Summary:

This work shows that a specific adenosine deaminase protein in Dictyostelium generates the ammonia that is required for tip formation during Dictyostelium development. Cells with an insertion in the ADGF gene aggregate but do not form tips. A remarkable result, shown in several different ways, is that the ADGF mutant can be rescued by exposing the mutant to ammonia gas. The authors also describe other phenotypes of the ADGF mutant such as increased mound size, altered cAMP signalling, and abnormal cell type differentiation. It appears that the ADGF mutant has defects in the expression of a large number of genes, resulting in not only the tip defect but also the mound size, cAMP signalling, and differentiation phenotypes.

Strengths:

The data and statistics are excellent.

(1) Weaknesses: The key weakness is understanding why the cells bother to use a diffusible gas like ammonia as a signal to form a tip and continue development.

Ammonia can come from a variety of sources both within and outside the cells and this can be from dead cells also. Ammonia by increasing cAMP levels, trigger collective cell movement thereby establishing a tip in Dictyostelium. A gaseous signal can act over long distances in a short time and for instance ammonia promotes synchronous development in a colony of yeast cells (Palkova et al., 1997; Palkova and Forstova, 2000). The slug tip is known to release ammonia probably favouring synchronized development of the entire colony of Dictyostelium. However, after the tips are established ammonia exerts negative chemotaxis probably helping the slugs to move away from each other ensuring equal spacing of the fruiting bodies (Feit and Sollitto, 1987).

It is well known that ammonia serves as a signalling molecule influencing both multicellular organization and differentiation in Dictyostelium (Francis, 1964; Bonner et al., 1989; Bradbury and Gross, 1989). Ammonia by raising the pH of the intracellular acidic vesicles of prestalk cells (Poole and Ohkuma, 1981; Gross et al, 1983), and the cytoplasm, is known to increase the speed of chemotaxing amoebae (Siegert and Weijer, 1989; Van Duijn and Inouye, 1991), inducing collective cell movement (Bonner et al., 1988, 1989), favoring tipped mound development.

Ammonia produced in millimolar concentrations during tip formation (Schindler and Sussman, 1977) could ward off other predators in soil. For instance, ammonia released by Streptomyces symbionts of leaf-cutting ants is known to inhibit fungal pathogens (Dhodary and Spiteller, 2021). Additionally, ammonia may be recycled back into amino acids, as observed during breast cancer proliferation (Spinelli et al., 2017). Such a process may also occur in starving Dictyostelium cells, supporting survival and differentiation. These findings suggest that ammonia acts as both a local and long-range regulatory signal, integrating environmental and cellular cues to coordinate multicellular development.

(2) The rescue of the mutant by adding ammonia gas to the entire culture indicates that ammonia conveys no positional information within the mound.

Ammonia reinforces or maintains the positional information by elevating cAMP levels, favoring prespore differentiation (Bradbury and Gross, 1989; Riley and Barclay, 1990; Hopper et al., 1993). Ammonia is known to influence rapid patterning of Dictyostelium cells confined in a restricted environment (Sawai et al., 2002). In adgf mutants that have low ammonia levels, both neutral red staining (a marker for prestalk and ALCs) (Figure. S3) and the prestalk marker ecmA/ ecmB expression (Figure. 7D) are higher than the WT and the mound arrest phenotype can be reversed by exposing the adgf mutant mounds to ammonia.

Prestalk cells are enriched in acidic vesicles, and ammonia, by raising the pH of these vesicles and the cytoplasm (Davies et al 1993; Van Duijn and Inouye 1991), plays an active role in collective cell movement during tip formation (Bonner et al., 1989).

(3) By the time the cells have formed a mound, the cells have been starving for several hours, and desperately need to form a fruiting body to disperse some of themselves as spores, and thus need to form a tip no matter what.

Exposure of adgf mounds to ammonia, led to tip development within 4 h (Figure. 5). In contrast, adgf controls remained at the mound stage for at least 30 h. This demonstrates that starvation alone is not the trigger for tip development and ammonia promotes the transition from mound to tipped mound formation.

Many mound arrest mutants are blocked in development and do not proceed to form fruiting bodies (Carrin et al., 1994). Further, not all the mound arrest mutants tested in this study were rescued by ADA enzyme (Figure. S4A), and they continue to stay as mounds.

(4) One can envision that the local ammonia concentration is possibly informing the mound that some minimal number of cells are present (assuming that the ammonia concentration is proportional to the number of cells), but probably even a minuscule fruiting body would be preferable to the cells compared to a mound. This latter idea could be easily explored by examining the fate of the ADGF cells in the mound - do they all form spores? Do some form spores?

Or perhaps the ADGF is secreted by only one cell type, and the resulting ammonia tells the mound that for some reason that cell type is not present in the mound, allowing some of the cells to transdifferentiate into the needed cell type. Thus, elucidating if all or some cells produce ADGF would greatly strengthen this puzzling story.

A fraction of adgf mounds form bulkier spore heads by the end of 36 h as shown in Figure. 2H. This late recovery may be due to the expression of other ADA isoforms. Mixing WT and adgf mutant cell lines results in a chimeric slug with mutants occupying the prestalk region (Figure. 8) and suggests that WT ADGF favours prespore differentiation. However, it is not clear if ADGF is secreted by a particular cell type, as adenosine can be produced by both cell types, and the activity of three other intracellular ADAs may vary between the cell types. To address whether adgf expression is cell type-specific, prestalk and prespore cells will be separated by fluorescence activated cell sorter (FACS), and thereafter, adgf expression will be examined in each population.

Reviewer #2 (Public review):

Summary:

The paper describes new insights into the role of adenosine deaminase-related growth factor (ADGF), an enzyme that catalyses the breakdown of adenosine into ammonia and inosine, in tip formation during Dictyostelium development. The ADGF null mutant has a pre-tip mound arrest phenotype, which can be rescued by the external addition of ammonia. Analysis suggests that the phenotype involves changes in cAMP signalling possibly involving a histidine kinase dhkD, but details remain to be resolved.

Strengths:

The generation of an ADGF mutant showed a strong mound arrest phenotype and successful rescue by external ammonia. Characterization of significant changes in cAMP signalling components, suggesting low cAMP signalling in the mutant and identification of the histidine kinase dhkD as a possible component of the transduction pathway. Identification of a change in cell type differentiation towards prestalk fate

(1) Weaknesses: Lack of details on the developmental time course of ADGF activity and cell type type-specific differences in ADGF expression.

adgf expression was examined at 0, 8, 12, and 16 h (Figure. 1), and the total ADA activity was assayed at 12 and 16 h (Figure. 3). Previously, the 12 h data was not included, and it’s been added now (Figure. 3A). The adgf expression was found to be highest at 16 h and hence, the ADA assay was carried out at that time point. Since the ADA assay will also report the activity of other three isoforms, it will not exclusively reflect ADGF activity.

Mixing WT and adgf mutant cell lines results in a chimeric slug with mutants occupying the prestalk region (Figure. 8) suggesting that WT adgf favours prespore differentiation. To address whether adgf expression is cell type-specific, prestalk and prespore cells will be separated by fluorescence activated cell sorter (FACS), and thereafter, adgf expression will be examined in each population.

(2) The absence of measurements to show that ammonia addition to the null mutant can rescue the proposed defects in cAMP signalling.

The adgf mutant in comparison to WT has diminished acaA expression (Fig. 6B) and reduced cAMP levels (Fig. 6A) both at 12 and 16 h of development. The cAMP levels were measured at 8 h and 12 h in the mutant.

We would like to add that ammonia is known to increase cAMP levels (Riley and Barclay, 1990; Feit et al., 2001) in Dictyostelium. Exposure to ammonia increases acaA expression in WT (Figure. 7B) and is likely to increase acaA expression/ cAMP levels in the mutant also (Riley and Barclay, 1990; Feit et al., 2001) thereby rescuing the defects in cAMP signalling. Based on the comments, cAMP levels will also be measured in the mutant after the rescue with ammonia.

(3) No direct measurements in the dhkD mutant to show that it acts upstream of adgf in the control of changes in cAMP signalling and tip formation.

cAMP levels will be quantified in the dhkD mutant after treatment with ammonia. The histidine kinases dhkD and dhkC are reported to modulate phosphodiesterase RegA activity, thereby maintaining cAMP levels (Singleton et al., 1998; Singleton and Xiong, 2013). By activating RegA, dhkD ensures proper cAMP distribution within the mound, which is essential for the patterning of prestalk and prespore cells, as well as for tip formation (Singleton and Xiong, 2013). Therefore, ammonia exposure to dhkD mutants is likely to regulate cAMP signalling and thereby tip formation.

Reviewer #1 (Recommendations for the authors):

(1) Lines: 47,48 - "The gradient of these morphogens along the slug axis determines the cell fate, either as prestalk (pst) or as prespore (psp) cells." - many workers have shown that this is not true - intrinsic factors such as cell cycle phase drive cell fate.

Thank you for pointing this out. We have removed the line and rephrased as “Based on cell cycle phases, there exists a dichotomy of cell types, that biases cell fate as prestalk or prespore (Weeks and Weijer, 1994; Jang and Gomer, 2011).

(2) Line 48 - PKA - please explain acronyms at first use.

Corrected

(3) Line 56 - The relationship between adenosine deaminase and ADGF is a bit unclear, please clarify this more.

Adenosine deaminase (ADA) is intracellular, whereas adenosine deaminase related growth factor (ADGF) is an extracellular ADA and has a growth factor activity (Li and Aksoy, 2000; Iijima et al., 2008).

(4) Figure 1 - where are these primers, and the bsr cassette, located with respect to the coding region start and stop sites?

The primer sequences are mentioned in the supplementary table S2. The figure legend is updated to provide a detailed description.

(5) Line 104 - 37.47% may be too many significant figures.

Corrected

(6) Line 123 - 1.003 Å may be too many significant figures.

Corrected

(7) Line 128 - Since the data are in the figure, you don't need to give the numbers, also too many significant figures.

Corrected

(8) Figure 3G - did the DCF also increase mound size? It sort of looks like it did.

Yes, the addition of DCF increases the mound size (now Figure. 2G).

(9) Figure 3I - the spore mass shown here for ADGF - looks like there are 3 stalks protruding from it; this can happen if a plate is handled roughly and the spore masses bang into each other and then merge

Thank you for pointing this out. The figure 3I (now Figure. 2I) is replaced.

(10) Lines 160-162 - since the data are in the figure, you don't need to give the numbers, also too many significant figures.

Corrected.

(11) Line 165 - ' ... that are involved in adenosine formation' needs a reference.

Reference is included.

(12) Line 205 - 'Addition of ADA to the CM of the mutant in one compartment.' - might clarify that the mutant is the ADGF mutant

Yes, revised to 'Addition of ADA to the CM of the adgf mutant in one compartment.'

(13) Lines 222-223 need a reference for caffeine acting as an adenosine antagonist.

Reference is included.

(14) Figure 8B - left - use a 0-4 or so scale so the bars are more visible.

Thank you for the suggestion. The scale of the y-axis is adjusted to 0-4 in Figure. 7B to enhance the visibility of the bars.

Reviewer #2 (Recommendations for the authors):

The paper describes new insights into the role of ADGF, an enzyme that catalyses the breakdown of adenosine in ammonia and inosine, in tip formation in Dictyostelium development.

A knockout of the gene results in a tipless mound stage arrest and the mounds formed are somewhat larger in size. Synergy experiments show that the effect of the mutation is non-cell autonomous and further experiments show that the mound arrest phenotype can be rescued by the provision of ammonia vapour. These observations are well documented. Furthermore, the paper contains a wide variety of experiments attempting to place the observed effects in known signalling pathways. It is suggested that ADGF may function downstream of DhkD, a histidine kinase previously implicated in ammonia signalling. Ammonia has long been described to affect different aspects, including differentiation of slug and culmination stages of Dictyostelium development, possibly through modulating cAMP signalling, but the exact mechanisms of action have not yet been resolved. The experiments reported here to resolve the mechanistic basis of the mutant phenotype need focusing and further work.

(1) The paper needs streamlining and editing to concentrate on the main findings and implications.

The manuscript will be revised extensively.

Below is a list of some more specific comments and suggestions.

(2) Introduction: Focus on what is relevant to understanding tip formation and the role of nucleotide metabolism and ammonia (see https://doi.org/10.1016/j.gde.2016.05.014).leading). This could lead to the rationale for investigating ADGF.

The manuscript will be revised extensively

(3) Lines 36-38 are not relevant. Lines 55-63 need shortening and to focus on ADGF, cellular localization, and substrate specificity.

The manuscript will be revised accordingly. Lines 36-38 will be removed, and the lines 55-63 will be shortened.

In humans, two isoforms of ADA are known including ADA1 and ADA2, and the Dictyostelium homolog of ADA2 is adenosine deaminase-related growth factor (ADGF). Unlike ADA that is intracellular, ADGF is extracellular and also has a growth factor activity (Li and Aksoy, 2000; Iijima et al., 2008). Loss-of-function mutations in ada2 are linked to lymphopenia, severe combined immunodeficiency (SCID) (Gaspar, 2010), and vascular inflammation due to accumulation of toxic metabolites like dATP (Notarangelo, 2016; Zhou et al., 2014).

(4) Results: This section would benefit from better streamlining by a separation of results that provide more mechanistic insight from more peripheral observations.

The manuscript will be revised and the peripheral observations (Figure. 2) will be shifted to the supplementary information.

(5) Line 84 needs to start with a description of the goal, to produce a knockout.

Details on the knockout will be elaborated in the revised manuscript. Line number 84 (now 75). Dictyostelium cell lines carrying mutations in the gene adgf were obtained from the genome wide Dictyostelium insertion (GWDI) bank and were subjected to further analysis to know the role of adgf during Dictyostelium development.

(6) Knockout data (Figure 1) can be simplified and combined with a description of the expression profile and phenotype Figure 3 F, G, and Figure 5. Higher magnification and better resolution photographs of the mutants would be desirable.

Thank you, as suggested the data will be simplified (section E will be removed) and combined with a description of the expression profile and, the phenotype images of Figure 3 F, G, and Figure 5 ( now Figure. 2 F, G, and Figure. 4) will be replaced with better images/ resolution.

(7) It would also be relevant to know which cells actually express ADGF during development, using in-situ hybridisation or promoter-reporter constructs.

To address whether adgf expression is cell type-specific, prestalk and prespore cells will be separated by fluorescence activated cell sorter (FACS), and thereafter, adgf expression will be examined in each population.

(8) Figure 2 - Information is less directly relevant to the topic of the paper and can be omitted (or possibly in Supplementary Materials).

Figure. 2 will be moved to supplementary materials.

(9) Figures 4A, B - It is shown that as could be expected ada activity is somewhat reduced and adenosine levels are slightly elevated. However, the fact that ada levels are low at 16hrs could just imply that differentiation of the ADGF- cells is blocked/delayed at an earlier time point. To interpret these data, it would be necessary to see an ada activity and adenosine time course comparison of wt and mutant, or to see that expression is regulated in a celltype specific manner that could explain this (see above). It would be good to combine this with the observation that ammonia levels are lower in the ADGF- mutant than wildtype and that the mutant phenotype, mound arrest can be rescued by an external supply of ammonia (Figure 6).

In Dictyostelium four isoforms of ADA including ADGF are present, and thus the time course of total ADA activity will also report the function of other isoforms. Further, a number of pathways, generate adenosine (Dunwiddie et al., 1997; Boison and Yegutkin, 2019). ADGF expression was examined at 0, 8, 12 and 16 h (Fig 1) and the ADA activity was assayed at 12 h, the time point where the expression gradually increases and reaches a peak at 16 h. Earlier, we have not shown the 12 h activity data which will be included in the revised version. ADGF expression was found to be highly elevated at 16 h and adenosine/ammonia levels were measured at the two points indicated in the mutant.

(10) Panel 4C could be combined with other measurements trying to arrive at more insight in the mechanisms by which ammonia controls tip formation.

Panel 4C (now 3C) illustrates the genes involved in the conversion of cAMP to adenosine. Since Figure. 3 focuses on adenosine levels and ADA activity in both WT and adgf mutants, we have retained Panel 3C in Figure. 3, for its relevance to the experiment.

(11) There is a large variety of experiments attempting to link the mutant phenotype and its rescue by ammonia to cAMP signalling, however, the data do not yet provide a clear answer.

It is well known that ammonia increases cAMP levels (Riley and Barclay, 1990; Feit et al., 2001) and adenylate cyclase activity (Cotter et al., 1999) in D. discoideum, and exposure to ammonia increases acaA expression (Fig 7B) suggesting that ammonia regulates cAMP signaling. To address the concerns, cAMP levels will be quantified in the mutant after ammonia treatment.

(12) The mutant is shown to have lower cAMP levels at the mound stage which ties in with low levels of acaA expression (Figures 7A and B), also various phosphodiesterases, the extracellular phosphodiesterase pdsa and the intracellular phosphodiesterase regA show increased expression. Suggesting a functional role for cAMP signalling is that the addition of di cGMP, a known activator of acaA, can also rescue the mound phenotype (Figure 7E). There appears to be a partial rescue of the mound arrest phenotype level by the addition of 8Br-cAMP (fig 7C), suggesting that intracellular cAMP levels rather than extracellular cAMP signalling can rescue some of the defects in the ADGF- mutant. Better images and a time course would be helpful.

The relevant images will be replaced and a developmental time course after 8-Br-cAMP treatment will be included in the revised manuscript (Figure. 6D).

(13) There is also the somewhat surprising observation that low levels of caffeine, an inhibitor of acaA activation also rescues the phenotype (Figure 7F).

With respect to caffeine action on cAMP levels, the reports are contradictory. Caffeine has been reported to increase adenylate cyclase expression thereby increasing cAMP levels (Hagmann, 1986) whereas Alvarez-Curto et al., (2007) found that caffeine reduced intracellular cAMP levels in Dictyostelium. Caffeine, although is a known inhibitor of ACA, is also known to inhibit PDEs (Nehlig et al., 1992; Rosenfeld et al., 2014). Therefore, if caffeine differentially affects ADA and PDE activity, it may potentially counterbalance the effects and rescue the phenotype.

(14) The data attempting to asses cAMP wave propagation in mounds (Fig 7H) are of low quality and inconclusive in the absence of further analysis. It remains unresolved how this links to the rescue of the ADGF- phenotype by ammonia. There are no experiments that measure any of the effects in the mutant stimulated with ammonia or di-cGMP.

The relevant images will be replaced (now Figure. 6H). Ammonia by increasing acaA expression (Figure. 7B), and cAMP levels (Figure. 7C) may restore spiral wave propagation, thereby rescuing the mutant.

(15) A possible way forward could also come from the observation that ammonia can rescue the wobbling mound arrest phenotype from the histidine kinase mutant dhkD null mutant, which has regA as its direct target, linking ammonia and cAMP signalling. This is in line with other work that had suggested that another histidine kinase, dhkC transduces an ammonia signal sensor to regA activation. A dhkC null mutant was reported to have a rapid development phenotype and skip slug migration (Dev. Biol. (1998) 203, 345). There is no direct evidence to show that dhkD acts upstream of ADGF and changes in cAMP signalling, for instance, measurements of changes in ADA activity in the mutant.

cAMP levels will be quantified in the dhkD mutant after ammonia treatment and accordingly, the results will be revised.

(16) The paper makes several further observations on the mutant. After 16 hrs of development the adgf- mutant shows increased expression of the prestalk cell markers ecmA and ecmB and reduced expression of the prespore marker pspA. In synergy experiments with a majority of wildtype, these cells will sort to the tip of the forming slug, showing that the differentiation defect is cell autonomous (Fig 9). This is interesting but needs further work to obtain more mechanistic insight into why a mutant with a strong tip/stalk differentiation tendency fails to make a tip. Here again, knowing which cells express ADGF would be helpful.

The adgf mutant shows increased prestalk marker expression in the mound but do not form a tip. It is well known that several mound arrest mutants form differentiated cells but are blocked in development with no tips (Carrin et al., 1994). This is addressed in the discussions (539). To address whether adgf expression is cell type-specific, prestalk and prespore cells will be separated by fluorescence activated cell sorter (FACS), and thereafter, adgf expression will be examined in each population.

(17) The observed large mound phenotype could as suggested possibly be explained by the low ctn, smlA, and high cadA and csA expression observed in the mutant (Figure 3). The expression of some of these genes (csA) is known to require extracellular cAMP signalling. The reported low level of acaA expression and high level of pdsA expression could suggest low levels of cAMP signalling, but there are no actual measurements of the dynamics of cAMP signalling in this mutant to confirm this.

The acaA expression was examined at 8 and 12 h (Figure. 6B) and cAMP levels were measured at 12 and 16 h in the adgf mutants (Figure. 6A). Both acaA expression and cAMP levels were reduced, suggesting that cells expressing adgf regulate acaA expression and cAMP levels. This regulation, in turn, is likely to influence cAMP signaling, collective cell movement within mounds, ultimately driving tip development. Exposure to ammonia led to increased acaA expression (Figure. 7B) in in WT. Based on the comments above, cAMP levels will be measured in the mutant before and after rescue with ammonia.

(18) Furthermore, it would be useful to quantify whether ammonia addition to the mutant reverses mound size and restores any of the gene expression defects observed.

Ammonia treatment soon after plating or six hours after plating, had no effect on the mound size (Figure. 5G).

(19) There are many experimental data in the supplementary data that appear less relevant and could be omitted Figure S1, S3, S4, S7, S8, S9, S10.

Figure S8, S9, S10 are omitted. We would like to retain the other figures

Figure S1 (now Figure. S2): It is widely believed that ammonia comes from protein (White and Sussman, 1961; Hames and Ashworth, 1974; Schindler and Sussman, 1977) and RNA (Walsh and Wright, 1978) catabolism. Figure. S2 shows no significant difference in protein and RNA levels between WT and adgf mutant strains, suggesting that adenosine deaminaserelated growth factor (ADGF) activity serves as a major source of ammonia and plays a crucial role in tip organizer development in Dictyostelium. Thus, it is important to retain this figure.

Figure S3 (now Figure. S4): The figure shows the treatment of various mound arrest mutants and multiple tip mutants with ADA enzyme and DCF, respectively, to investigate the pathway through which adgf functions. Additionally, it includes the rescue of the histidine kinase mutant dhkD with ammonia, indicating that dhkD acts upstream of adgf via ammonia signalling. Therefore, it is important to retain this figure.

Figure S4 (now Figure. S5): This figure represents the developmental phenotype of other deaminase mutants. Unlike adgf mutants, mutations in other deaminases do not result in complete mound arrest, despite some of these genes exhibiting strong expression during development. This underscores the critical role of adenosine deamination in tip formation. Therefore, let this figure be retained.

Figure S7 (now Figure. S8): Figure S8 presents the transcriptomic profile of ADGF during gastrulation and pre-gastrulation stages across different organisms, indicating that ADA/ADGF is consistently expressed during gastrulation in several vertebrates (Pijuan-Sala et al., 2019; Tyser et al., 2021). Notably, the process of gastrulation in higher organisms shares remarkable similarities with collective cell movement within the Dictyostelium mound (Weijer, 2009), suggesting a previously overlooked role of ammonia in organizer development. This implies that ADA may play a fundamental role in regulating morphogenesis across species, including Dictyostelium and vertebrates. Therefore, we would like to retain this figure.

(20). Given the current state of knowledge, speculation about the possible role of ADGF in organiser function in amniotes seems far-fetched. It is worth noting that the streak is not equivalent to the organiser. The discussion would benefit from limiting itself to the key results and implications.

The discussion is revised accordingly by removing the speculative role of ADGF in organizer function in amniotes. The lines “It is likely that ADA plays a conserved, fundamental role in regulating morphogenesis in Dictyostelium and other organisms including vertebrates” have been removed.

Author Response:

Reviewer #1 (Public Review):

The main finding - that the moment-to-moment relationship between excitability and perception is coupled to the body's slower respiratory oscillation - is novel, interesting, and important for advancing our understanding of how the brain-body system works as a whole. The experiment is simple and elegant, and the authors strike the right level of making the most of the data without doing too much and obscuring the main findings. The primary weakness, in my opinion, is the inability to distinguish between the possibility that respiration modulates excitability and the possibility that respiration modulates something boring like signal-to-noise ratio. In terms of conclusions, I thought the authors stuck pretty well to the data. The one place where the conclusions felt a little bold was in terms of the respiration <> alpha <> behavior relationship, where it felt the authors had already made up their minds re: causality. I agree that it probably makes more sense for respiration to influence something about the brain than vice versa, and the background presented in the Intro/Discussion supports this. However, the analysis only tells us that the behavioral performance was modulated by both alpha and respiration (and their interaction, but this is no way causal). Overall, it will be necessary to differentiate the current interpretation from the possibility that breathing and alpha are two unrelated time courses that influence behavior at the same time (and even interact in how they influence behavior, but just not interact with each other), and I do not believe the phase-amplitude coupling analysis is sufficient for this.

We thank the reviewer for their positive and constructive evaluation of our work.

Reviewer #2 (Public Review):

Kluger and colleagues investigated the influence of respiration on visual sensory perception in a near-threshold task and argue that the detected correlation between respiration phase and detection precision is liked to alpha power, which in turn is modulated by the phase of respiration. The experiments involved detecting a low-contrast visual stimulus to the left or right of a fixation point with contrast settings adjusted via an adaptive staircase approach to reach a desired 60% hit rate, resulting in an observed hit rate of 54%. The main findings are that mutual information between the discrete outcome of hit-or- miss and the continuous contrast variable is significantly increased when respiration phase is considered as well. Furthermore, results show that neuronal alpha oscillation power is modulated in phase with respiration and that perception accuracy is correlated with alpha power. Time resolved correlation analysis aligned on respiration phase shows that this correlation peaks during inspiration around the same phase where the psychometric function for the visual detection task reaches a minimum. The experimental design and data analysis seem solid but there are several concerns regarding the novelty of the findings and the interpretation of the results.

Major concerns: The finding that visual perception is modulated by the respiration cycle is not new (see e.g. Flexman et al. 1974 or Zelano et al. 2016).

There are multiple studies going back decades that show alpha oscillation power to be modulated by breathing (e.g. Stancák et al., 1993, Bing-Canar et al. 2016). Also, as the authors acknowledge, it is well-established that alpha power correlates with neuronal excitability and perception threshold. What seems to be new in this study is the use of a linear mixed effect model to analyze the relationship between alpha power, respiration phase and perception accuracy. However, the results mostly seem to confirm previous findings.

Thank you for giving us the opportunity to clarify our approach and the conceptual novelty it provides. First, not at all do we claim that our study is the first to demonstrate respiration-related alpha changes. Not only do we prominently cite the work by Zelano and colleagues (JNeuro, 2016) in the Introduction and Discussion sections, we also have previous work from our own lab demonstrating these effects (see Kluger & Gross, PLoS Biol 2021). Second, the reviewer’s comment that ‘the results mostly seem to confirm previous findings’ unfortunately appears to frame a critical proof-of-concept as a lack of novelty: In order for us to claim a triadic relationship between respiration, excitability, and behaviour, it is paramount to first demonstrate that assumptions about pairwise relations (such as respiration <> alpha power and alpha power <> behaviour) are supported, which of course means replicating known results in our data. Third, in order to evaluate the novelty of our present study, it is crucial to consider its core aim, which was to characterise how automatic respiration is related to lowest-level perception by means of respiration-induced modulation of neural oscillations. At this point, we respectfully disagree with the reviewer’s assessment of our results being mostly replicative, as the references they provide differ from our approach in various key aspects: The classic study by Flexman and colleagues (1974) merely differentiates between inspiration and expiration, critically without accounting for the asymmetry between the two respiratory phases. Zelano and colleagues (2016) did not investigate visual perception at all, but instead asked participants to categorise emotional face stimuli (termed ‘emotion recognition task’). Stancák and colleagues (1993) did not investigate automatic, but paced breathing, which involves continuous, conscious top-down control of one’s breathing rhythm - a demand that is not comparable to automatic, natural breathing we investigate here. The same is true for any kind of respiratory intervention or training like the ‘mindfulness-of-breathing exercise’ employed in the study by Bing-Canar and colleagues (2016). Once again, the oscillatory changes reported by the authors are not induced by automatic breathing, but instead reflect the outcome of a conscious manipulation of the breathing rhythm. In highlighting the key differences between previous studies and our approach, we do hope to have dispelled the reviewer’s initial concern regarding the novelty of our findings.

Magnetoencephalography captures broad band neuronal activity including gamma frequencies. As the authors show (Fig. 4) and other studies have shown, the power of neuronal oscillations across multiple frequency bands is modulated by respiration phase. Gamma and beta oscillations have been implicated in sensory processing as well. Support for the author's hypothesis that the perception threshold modulation with respiration is due to alpha power modulation would be strengthened if they could show that the power of oscillations in other frequency bands are not or only weakly linked to perception accuracy.

We thank the reviewer for their well-justified suggestion to extend the spectral scope of our analyses to include other frequency bands. In response to their comment, we have recomputed our analysis pipeline for the frequency range between 2 - 70Hz. While the whole analysis and results are described in a new Supplementary Text and Supplementary Figures (see below), we outline key findings here.

In keeping with the structure of our main analyses, we first computed cluster-corrected whole-scalp topographies for delta, theta, alpha, beta, and gamma bands for hits vs misses over time intervals 1s prior to stimulus presentation:

Fig. S4 | Band-specific topographies over time. Whole-scalp topographic distribution of normalised pre- and peristimulus power differences between hits and misses, separately for each frequency band. Channels with significant differences in the respective band are marked (cluster-corrected within the respective time frame). Related to Fig. 3.

Compared to the clear parieto-occipital topography of prestimulus alpha modulations, delta and theta effects were prominently shifted to anterior sensors, which renders their involvement in low-level visual processing highly unlikely. No significant effects were observed in the gamma range. In contrast, beta-band modulations were closest to the alpha effects in their topography, covering parietal as well as occipital sites. Although the size of normalised effects were markedly smaller in the beta band (compared to alpha frequencies, cf. colour scaling), the topographic distribution of prestimulus modulations as well as the spectral proximity of the two bands prompted further investigation of beta involvement. To this end, we computed the instantaneous correlation between individual beta power (over the respiration cycle) and respiratory phase, analogous to our main analysis shown in Fig. 4c. Consistent with the TFR analysis shown above, no significant correlation between oscillatory power and respiration time courses were found for delta, theta, and gamma bands. For the beta band, however, we found a significant correlation during the inspiratory phase, similar to the alpha correlation described in the main text (and shown for comparison in the new Supplementary Fig. S5):

Fig. S5 | Instantaneous correlation of beta power and perceptual sensitivity. Group-level correlation between individual beta and PsychF threshold courses (averaged between 14 - 30 Hz) with significant phase vector (length of seven time points) marked by dark grey dots (cluster-corrected). Correlation time course of the alpha band (see Fig. 4c) shown for reference in light grey. Related to Fig. 4.

While both alpha and beta power were correlated to the breathing signal during the inspiratory phase, the correlation time courses suggested that there might be differential effects in both frequency bands, as indicated by the phase shift visible in Supplementary Fig S5. Therefore, we finally recomputed the LMEM visualised in Fig. 4 with an additional factor for beta power. In this extended model, significant effects were found for both alpha (t(1790) = 3.27, p < .001) and beta power (t(1790) = 4.83, p < .001). Beta showed significant interactions with the sine of the respiratory signal (t(1790) = -3.52, p < .001) as well as with alpha power (t(1790) = -4.63, p < .001). Comparing the LMEM to the previous model which only contained alpha power (along with respiratory sine and cosine) confirmed the significant contribution of beta power in explaining PsychF threshold variation by means of a theoretical likelihood ratio test (χ²(4) = 60.43, p < .001). Overall, we thus found beta power to be i) significantly modulated by respiration (see Fig 1), ii) significantly suppressed over parieto-occipital sensors for hits vs misses (see Fig. S4), and iii) significantly contribute to variations in PsychF threshold (see Fig S5). Collectively, these findings suggest differential roles of alpha and beta power, which we discuss in the main text as well as in the Supplementary Text:

“Whole-scalp control analyses across all frequency bands demonstrated that this topographical pattern was unique to alpha and beta prestimulus power (see Supplementary Text 1 and Fig. S4).”

“Control analyses across all frequency bands yielded a significant instantaneous correlation between PsychF threshold and beta power as well, albeit at a slightly later phase (see Fig. S5). No significant correlations were found for the remaining frequency bands.”

“Accordingly, one recent study proposed that the alpha rhythm shapes the strength of neural stimulus representations by modulating excitability (Iemi et al., 2021). Previous work by Michalareas and colleagues (2016) as well as our own data (see Supplementary Material) point towards an interactions between alpha and beta bands, as beta oscillations have very recently been implicated in mediating top-down signals from the frontal eye field (FEF) that modulate excitability in the visual cortex during spatial attention (Veniero et al., 2021). Our findings suggest that this top-down signalling is modulated across the respiration cycle in a way that changes behavioural performance.”

In the discussion the authors speculate that respiration locked modulation of alpha power and associated neuronal excitability could be based on the modulation of blood CO2 levels. Most recent studies of respiratory modulation of brain activity have demonstrated significant differences between nasal and oral breathing, with nasal breathing (through activation of the olfactory bulb) typically resulting in a stronger influence of respiration on neuronal activity and behavioral performance than oral breathing. The authors only tested nasal breathing. If blood CO2 fluctuations are indeed responsible for the observed effect, there should be no difference in outcome between nasal and oral breathing. Comparing the two conditions would thus provide interesting additional information about the possible underlying mechanisms.

We appreciate the reviewer’s well-justified remarks regarding the differential effects for nasal and oral breathing and their implications on underlying mechanisms such as CO2. In revising the present as well as other manuscripts, it has become evident that fluctuations of CO2 alone (and, as we previously discussed, related changes in pH) cannot possibly explain the effects we and others are observing. Therefore, the revised manuscript no longer discusses CO2 as a potential mechanism. We have removed the corresponding paragraph and instead refer to the distinction between nasal and oral breathing to strengthen the argument for OB-induced cross-frequency coupling:

“As outlined in the introduction, there is broad consensus that cross-frequency coupling (Canolty and Knight, 2010; Jensen and Colgin, 2007) plays a central role in translating respiratory to neural rhythms: Respiration entrains neural activity within the olfactory tract via mechanoreceptors, after which the phase of this infraslow rhythm is coupled to the amplitude of faster oscillations (see Fontanini and Bower, 2006; Ito et al., 2014). While this mechanism is difficult to investigate directly in humans, converging evidence for the importance of bulbar rhythms comes from animal bulbectomy studies (Ito et al., 2014) and the fact that respiration-related changes in both oscillatory power and behaviour dissipate during oral breathing (Zelano et al., 2016; Perl et al., 2019). Thus, rhythmic nasal respiration conceivably aligns rhythmic brain activity across the brain, which in turn influences behaviour. In our present paradigm, transient phases of heightened excitability would then be explained by decreased inhibitory influence on neural signalling within the visual cortex, leading to increased postsynaptic gain and higher detection rates. Given that the breathing act is under voluntary control, the question then becomes to what extent respiration may be actively used to synchronise information sampling with phasic states of heightened excitability.”

Reviewer #3 (Public Review):

The topic is timely, the study is well-designed, and the work has been performed in a highly competent manner. The authors relate three variables: respiration, alpha power and perceptual performance, constituting a link between somatic and neuronal physiology and cognition. A particular strength is the temporal resolution of respiration effects on cognition (continuous analysis of the respiration cycle). Furthermore, results are well contextualized by very comprehensively written introduction and discussion sections (which, nevertheless, could be slightly shortened).

We do appreciate the reviewer’s positive evaluation of our manuscript and are thankful for their constructive remarks. We respond to their comments in detail below and have shortened the Discussion section in response to one of the reviewer’s remarks (kindly see points 1.1 and 2 below).

I have three points of criticism, all meant in a constructive way:

1. I wonder whether the authors could have gone one step further in the analysis of causal mechanisms, rather than correlations. The analysis of timing (Fig. 4d) and the last sentence of the abstract suggest that they imagine a causal role of respiratory feedback on cognitive performance, mediated via coordination of brain activity (in the specific case, by increasing excitability in visual areas). This could be made more explicit by appropriate experiments and data analysis:

1.1. Manipulating the input signal: former studies suggest that nasal respiration is crucial for effects on brain oscillations and/or performance (e.g. Yanovsky et al., 2014; Zelano et al., 2016). Thus, the causal inference could be easily checked by comparing nasal versus oral respiration, without changing gas- and pH-parameters of activity of brainstem centers. >Admittedly, this experiment may add significant work to the present data which, by themselves, are already very strong.

We thank the reviewer for their insightful comment regarding the question of causality. We acknowledge that our interpretation should have been phrased a little more cautiously. Therefore, we have rephrased corresponding paragraphs at various instances throughout the manuscript (kindly see below). Particular under current circumstances, we further appreciate the reviewer’s concern regarding the acquisition of additional data for a direct comparison of nasal vs oral breathing. Their comment is of course entirely valid and we were eager to address it, especially since it relates to CO2- and/or pH-related mechanisms of RMBOs we previously discussed. In light of the reviewer’s comments (also see their related comment #2 below) and convincing evidence from both animal and human studies that already compared nasal and oral breathing, we no longer feel that changes in CO2 provide a reasonable explanation for respiration-related oscillatory and behavioural effects we observed here. Consequently, we have removed the corresponding paragraph from the Discussion section which now reads as follows:

“As outlined in the introduction, there is broad consensus that cross-frequency coupling (Canolty and Knight, 2010; Jensen and Colgin, 2007) plays a central role in translating respiratory to neural rhythms: Respiration entrains neural activity within the olfactory tract via mechanoreceptors, after which the phase of this infraslow rhythm is coupled to the amplitude of faster oscillations (see Fontanini and Bower, 2006; Ito et al., 2014). While this mechanism is difficult to investigate directly in humans, converging evidence for the importance of bulbar rhythms comes from animal bulbectomy studies (Ito et al., 2014) and the fact that respiration-related changes in both oscillatory power and behaviour dissipate during oral breathing (Zelano et al., 2016; Perl et al., 2019). Thus, rhythmic nasal respiration conceivably aligns rhythmic brain activity across the brain, which in turn influences behaviour. In our present paradigm, transient phases of heightened excitability would then be explained by decreased inhibitory influence on neural signalling within the visual cortex, leading to increased postsynaptic gain and higher detection rates. Given that the breathing 17 act is under voluntary control, the question then becomes to what extent respiration may be actively used to synchronise information sampling with phasic states of heightened excitability.”

1.2. Temporal relations: The authors show that respiration-induced alpha modulation precedes behavioral modulation (Fig. 4d and related results text). Again, this finding suggests a causal influence of respiration on performance, mediated by alpha suppression (see results, lines 318-320). Could the data be directly tested for causality (e.g. by applying Granger causality, dynamic causal modelling or other methods)? If this is difficult, the question of causality should at least be discussed more explicitly.

We appreciate the reviewer’s constructive criticism and their suggestion to employ causal analyses. While we agree that the overall pattern of results strongly suggests a causal cascade of respiration -> excitability -> perception, our interpretation with regard to a dynamic mechanism was probably overly strong. Unfortunately, it is indeed difficult to use directional analyses like Granger causality or DCM on the current data, since these methods quantify the relationship between two time series. They would not allow us to investigate the triad of respiration, alpha power, and behaviour, as we have discrete responses (i.e., single events) instead of a continuous behavioural measure. In fact, we are currently preparing a directional analysis of respiration-brain coupling (in resting-state data without a behavioural component) for an upcoming manuscript. In response to the reviewer’s remarks, we have toned down our interpretation throughout the manuscript and explicitly discuss the question of causality in the Discussion section of the revised manuscript:

“The bootstrapping procedure yielded a confidence interval of [-33.17 -29.25] degrees for the peak effect of alpha power. While these results strongly suggest that respiration-alpha coupling temporally precedes behavioural consequences, they do not provide sufficient evidence for a strict causal interpretation (see Discussion)”

“Rigorous future work is needed to investigate potentially causal effects of respiration-brain coupling on behaviour, e.g. by means of directed connectivity within task-related networks. A second promising line of research considers top-down respiratory modulation as a function of stimulus characteristics (such as predictability). This would grant fundamental insights into whether respiration is actively adapted to optimise sensory sampling in different contexts, as suggested by the animal literature.”

1. At various instances, the authors suggest that respiration-induced changes in pH may be responsible for the changes in cortical excitability which, in turn, affect behavioral performance. In the discussion, they quote respective literature (lines 406-418). I glanced through the quoted papers by Feldman, Chesler, Lee, Dulla and Gourine - as far as I could see none of them suggests that the cyclic process of respiration induces significant cyclic shifts of pH in the brain parenchyma (if at all, this may occur in specialized chemosensory neurons in the brainstem). Moreover, recent real-time measurements by Zhang et al. (Chem. Sci 12:7369-7376) do also not reveal such cyclic changes in the cortex. Finally, translating oscillatory extracellular pH changes (if existent) into changes in inhibitory efficacy would require some time, potentially inducing delays and variance onto the cyclic changes at the network level. I feel that the evidence for the proposed mechanism is not sufficient, notwithstanding that it is a valid hypothesis. Please check and correct the interpretation of the cited literature if necessary.

We acknowledge the reviewer’s caution regarding our suggestion of pH involvement, which is closely related to their previous comment (kindly see 1.1 above). As the reviewer mentions themselves, there are several studies demonstrating an absence of both neural and behavioural modulations for oral (vs nasal) breathing. These reports provide direct evidence against a mechanism driven by changes in CO2 and/or pH, which would be identical for nasal and oral breathing. Moreover, a second valid criticism is the uncertain temporal delay introduced by the (hypothetical) translation of pH changes into neural signals, which would most likely be incompatible with the ‘online’ (i.e., within-cycle) effects we report here. Therefore, as outlined in our response above, we have removed the pH-related suggestions from the Discussion section.

1. Finally, some illustrations should be presented in a clearer way for those not familiar with the specifics of MEG analysis.

We appreciate the reviewer’s suggestions regarding the clarity of our manuscript.

Author Response:

Reviewer #1 (Public Review):

In this manuscript, the authors challenge the long-standing conclusion that Orco and IR-dependent olfactory receptor neurons are segregated into subtypes such that Orco and IR expression do not overlap. First, the authors generate new knock-in lines to tag the endogenous loci with an expression reporter system, QF/QUAS. They then compare the observed expression of these knock-ins with the widely used system of enhancer transgenes of the same receptors, namely Orco, IR8a, IR25a, and IR76b. Surprisingly, they observe an expansion of the expression of the individual knock-in reporters as compared to the transgenic reporters in more chemosensory neurons targeting more glomeruli per receptor type than previously reported. They verify the expression of the knock-in reporters with antibody staining, in situ hybridization and by mining RNA sequencing data.

Finally, they address the question of physiological relevance of such co-expression of receptor systems by combining optogenetic activation with single sensillum recordings and mutant analysis. Their data suggests that IR25a activation can modulate Orco-dependent signaling and activation of olfactory sensory neurons.

The paper is well written and easy to follow. The data are well presented and very convincing due in part to the combination of complementary methods used to test the same point. Thus, the finding that co-receptors are more broadly and overlappingly expressed than previously thought is very convincing and invites speculation of how this might be relevant for the animal and chemosensory processing in general. In addition, the new method to make knock-ins and the generated knock-ins themselves will be of interest to the fly community.

We thank the reviewer for their enthusiasm and support of our work!

The last part of the manuscript, although perhaps the most interesting, is the least developed compared to the other parts. In particular, the following points could be addressed:

• It would be good to see a few more traces and not just the quantifications. For instance, the trace of ethyl acetate in Fig. 6C, and penthyl acetate for 6G.

Thank you for the suggestion. We have added a new figure supplement (Figure 6-Figure Supplement 3) with additional example traces for all odorants from Figure 6 for which we found a statistically significant difference between the two genotypes (Ir25a versus wildtype).

• In Fig. 4D, the authors show the non-retinal fed control, which is great. An additional genetic control fed with retinal would have been nice.

For these experiments, we followed a standard practice in Drosophila optogenetics to test the same experimental genotype in the presence or absence of the essential cofactor all-trans-retinal. This controls for potential effects from the genetic background. It is possible our description of these experiments was unclear (as also suggested by comments from Reviewer 2). As such, we have clarified our experimental design for the optogenetic experiments in the revised manuscript:

Modified text: “No light-induced responses were found in control flies, which had the same genotype as experimental flies but were not fed all-trans retinal (-ATR), a necessary co-factor for channelrhodopsin function (see Methods).” and “Bottom trace is control animal, which has the same genotype as the experimental animal but was not fed the required all-trans retinal cofactor (-ATR).”

Figure 4-Figure Supplement 1 legend: “In all optogenetic experiments, control animals have the same genotypes as the corresponding experimental animals but have not been fed all-trans retinal.”

Methods: “For all optogenetic experiments, the control flies were of the same genotype as experimental flies but had not been fed all-trans retinal.”

• It appears that mostly IR25a is strongly co-expressed with other co-receptors. The provided experiments suggest a possible modulation between IR25a and Orco-dependent neuronal activity. However, what does this mean? How could this be relevant? And moreover, is this a feature of Drosophila melanogaster after many generations in laboratories?

We share this reviewer’s excitement regarding the numerous questions our work now raises. While testing additional functional ramifications of chemosensory co-receptor expression is beyond the scope of this work (but will undoubtedly be the focus of future studies), we did expand on what this might mean in the revised Discussion section of the revised manuscript. Previously, we had raised the hypothesis that chemoreceptor co-expression could be an evolutionary relic of Ir25a expression in all chemoreceptor neurons , or a biological mechanism to broaden the response profile of an olfactory neuron without sacrificing its ability to respond to specific odors. We now extend our discussion to raise additional possible ramifications. For example, we suggest that modulating Ir25a coexpression could alter the electrical properties of a neuron, making it more (or possibly less) sensitive to Orco-dependent responses. We also suggest that Ir25a coexpression might be an evolutionary mechanism to allow olfactory neurons to adjust their response activities. That is, that most Orco-positive olfactory neurons are already primed to be able to express a functional Ir receptor if one were to be expressed. Such co-expression in some olfactory neurons might present an evolutionary advantage by ensuring olfactory responses to a complex but crucial biologically relevant odor, like human odors to some mosquitoes.

Reviewer #2 (Public Review):

In the present study, the authors: 1) generated knock-in lines for Orco, Ir8a, Ir25a, and IR7ba, and examined their expression, with a main focus on the adult olfactory organs. 2) confirmed the expression of these receptors using antibody staining. 3) examined the innervation patterns of these knock-in lines in the nervous system. 4) identified a glomerulus, VM6, that is divided into three subdivisions. 5) examined olfactory responses of neurons co-expressing Orco and Ir25a

The results of the first four sets of experiments are well presented and support the conclusions, but the results of the last set of experiments (the electrophysiology part) need some details. Please find my detailed comments below.

We thank the reviewer for their support of our work and appreciating the importance of our findings. In the revised manuscript, we now provide the additional experimental details for the electrophysiology work as requested.

Major points

Line 167-171: I wonder if the authors also compared the Orco-T2A-QF2 knock-in with antibody staining of the antenna.

We did perform whole-mount anti-Orco antibody staining on Orco-T2A-QF2 > GFP antennae (example image below). We saw broad overlap between Orco+ and GFP+ cells, similar to the palps. However, we did not include these results since quantification of these tissues is challenging for the following reasons:

1. There are ~1,200 olfactory neurons in each antenna, many of which are Orco+.
2. The thickness of the tissue makes determinations of co-localization difficult in wholemount staining.
3. Co-localization is further complicated by the sub-cellular localization of the signals: Orco antibodies preferentially label dendrites and weakly label cell bodies, while our GFP reporter is cytoplasmic and preferentially labels cell bodies. For these reasons, we focused on the numerically simpler palps for quantification. For the Ir8aT2A-QF2 and Ir76b-T2A-QF2 lines, palp quantification was not an option as neither knock-in drove expression in the palps (and the available antibodies did not work with the whole-mount staining protocol). This is why we performed antennal cryosections to validate these lines. Below is an example image of the antennal whole-mount staining in the Orco-T2A-QF2 knock-in line, illustrating the quantification challenges enumerated above.

*Co-staining of anti-Orco and GFP in Orco-T2A-QF2 > 10xQUAS-6xGFP antenna *

Lines 316-319 (Figure 4D): It would be better if the authors compare the responses of Ir25a>CsChrimson to those of Orco>CsChrimson.

The goal of the optogenetic experiments was to provide experimental support for Ir25a expression in Orco+ neurons in an approach independent to previous methods. Our main question was whether we could activate what was previously considered Orco-only olfactory neurons using the Ir25a knock-in. These experiments were not designed to determine if this optogenetic activation recapitulated the normal activity of these neurons. For these reasons, we did not attempt the optogenetic experiments with Orco>CsChrimson flies.

Line 324-326: Why the authors tested control flies not fed all-trans retinal? They should test Ir25a-T2A-QF2>QUAS-CsChrimson not fed all-trans retinal as a control.

We apologize for the confusion. The “control” flies we used were indeed Ir25a-T2AQF2>QUAS-CsChrimson flies not fed all-trans retinal as suggested by the reviewer. This detail was in the methods, yet likely was not clear. We have amended the main text in multiple locations to state the full genotype of the control fly more clearly:

Modified text: “No light-induced responses were found in control flies, which had the same genotype as experimental flies but were not fed all-trans retinal (-ATR), a necessary co-factor for channelrhodopsin function (see Methods).” and “Bottom trace is control animal, which has the same genotype as the experimental animal but was not fed the required all-trans retinal cofactor (-ATR).”

Figure 4-Figure Supplement 1 legend: “In all optogenetic experiments, control animals have the same genotypes as the corresponding experimental animals but have not been fed all-trans retinal.”

Methods: “For all optogenetic experiments, the control flies were of the same genotype as experimental flies but had not been fed all-trans retinal.”

Line 478-500: I wonder if the observed differences between the wildtype and Ir25a2 mutant lines are due to differences in the genetic background between both lines. Did the authors backcross Ir25a2 mutant line with the used wildtype for at least five generations?

Yes, the mutants are outcrossed into the same genetic background as the wildtypes for at least five generations. Please see Methods, revised manuscript: “Ir25a2 and Orco2 mutant fly lines were outcrossed into the w1118 wildtype genetic background for at least 5 generations.”

Line 1602-1603: Does the identification of ab3 sensilla using fluorescent-guided SSR apply for ab3 sensilla in Orco mutant flies. How does this ab3 fluorescent-guided SSR work?

In fluorescence guided SSR (fgSSR; Lin and Potter, PloS One, 2015), the ab3 sensilla is GFPlabelled (genotype: Or22a-Gal4>UAS-mCD8:GFP), which allows this sensilla to be specifically identified under a microscope and targeted for SSR recordings. We generated fly stocks for fgSSR identification of ab3 in all three genetic backgrounds (wildtype, Orco mutant, Ir25a mutant).

These three genotypes are described in the methods:

“Full genotypes for ab3 fgSSR were:

Pin/CyO; Or22a-Gal4,15XUAS-IVS-mcd8GFP/TM6B (wildtype),

Ir25a2; Or22a-Gal4,15XUAS-IVS-mcd8GFP/TM6B (Ir25a2 mutant),

Or22a-Gal4/10XUAS-IVS-mcd8GFP (attp40); Orco2 (Orco2 mutant).”

Line 1602-1604: There is no mention of how the authors identified ab9 sensilla.

Information on the identification of ab9 sensilla is under the optogenetics section of the methods: “Identification of ab9 sensilla was assisted by fluorescence-guided Single Sensillum Recording (fgSSR) (Lin and Potter, 2015) using Or67b-Gal4 (BDSC #9995) recombined with 15XUAS-IVS-mCD8::GFP (BDSC #32193).”

Line 1648: what are the set of odorants that were used to identify the different coeloconic sensilla?

We have added the specific odorants used for sensillar identification for coeloconic SSR in the Methods. The protocol and odorants used were:

*2,3-butanedione (BUT), 1,4-diaminobutane (DIA), Ammonia (AM), hexanol (HEX), phenethylamine (PHEN), and propanal (PROP) to distinguish coeloconic sensilla:

o Wildtype flies: Strong DIA and BUT responses identify ac2 and rule out ac4. Absence of strong AM response rules out ac1, absence of HEX response rules out ac3, absence of PHEN response further rules out ac4.

o Ir25a mutant flies (amine responses lost, so cannot use PHEN and DIA as diagnostics): Strong BUT response and moderate PROP response identify ac2 and rule out ac4. Absence of strong AM response rules out ac1, absence of HEX response rules out ac3. Ac4 is further ruled out anatomically based on sensillar location compared to ac2.

Revised text: “Different classes of coeloconic sensilla were identified by their known location on the antenna and confirmed with their responses to a small panel of diagnostic odorants: in wildtype flies, ac2 sensilla were identified by their strong responses to 1,4-diaminobutane and 2,3-butanedione. The absence of a strong response to ammonia was used to rule out ac1 sensilla, the absence of a hexanol response was used to rule out ac3 sensilla, and the absence of a phenethylamine response was used to rule out ac4 sensilla. In Ir25a mutant flies in which amine responses were largely abolished, ac2 and ac4 sensilla were distinguished based on anatomical location, as well as the strong response of ac2 to 2,3-butanedione and the moderate response to propanal (both absent in ac4). Ac1 and ac3 sensilla were excluded similarly in the mutant and wildtype flies. No more than 4 sensilla per fly were recorded. Each sensillum was tested with multiple odorants, with a rest time of at least 10s between applications.

Author Response:

Reviewer #1 (Public Review):

1. There was little comment on the strategy/mechanism that enabled subjects to readily attain Target I (MU 1 active alone), and then Target II (MU1 and MU2 active to the same relative degree). To accomplish this, it would seem that the peak firing rate of MU1 during pursuit of Target II could not exceed that during Target I despite an increased neural drive needed to recruit MU2. The most plausible explanation for this absence of additional rate coding in MU1 would be that associated with firing rate saturation (e.g., Fuglevand et al. (2015) Distinguishing intrinsic from extrinsic factors underlying firing rate saturation in human motor units. Journal of Neurophysiology 113, 1310-1322). It would be helpful if the authors might comment on whether firing rate saturation, or other mechanism, seemed to be at play that allowed subjects to attain both targets I and II.

To place the cursor inside TII, both MU1 and MU2 must discharge action potentials at their corresponding average discharge rate during 10% MVC (± 10% due to the target radius and neglecting the additional gain set manually in each direction). Therefore, subjects could simply exert a force of 10% MVC to reach TII and would successfully place the cursor inside TII. However, to get to TI, MU1 must discharge action potentials at the same rate as during TII hits (i.e. average discharge rate at 10% MVC) while keeping MU2 silent. Based on the performance analysis in Fig 3D, subjects had difficulties moving the cursor towards TI when the difference in recruitment threshold between MU1 and MU2 was small (≤ 1% MVC). In this case, the average discharge rate of MU1 during 10% MVC could not be reached without activating MU2. As could be expected, reaching towards TI became more successful when the difference in recruitment threshold between MU1 and MU2 was relatively large (≥3% MVC). In this case, subjects were able to let MU1 discharge action potentials at its average discharge rate at 10% MVC without triggering activation of MU2 (it seems the discharge rate of MU1 saturated before the onset of MU2). Such behaviour can be observed in Fig. 2A. MUs with a lower recruitment threshold saturate their discharge rate before the force reaches 10% MVC. We adapted the Discussion accordingly to describe this behaviour in more detail.

1. Figure 4 (and associated Figure 6) is nice, and the discovery of the strategy used by subjects to attain Target III is very interesting. One mechanism that might partially account for this behavior that was not directly addressed is the role inhibition may have played. The size principle also operates for inhibitory inputs. As such, small, low threshold motor neurons will tend to respond to a given amount of inhibitory synaptic current with a greater hyperpolarization than high threshold units. Consequently, once both units were recruited, subsequent gradual augmentation of synaptic inhibition (concurrent with excitation and broadly distributed) could have led to the situation where the low threshold unit was deactivated (because of the higher magnitude hyperpolarization), leaving MU2 discharging in isolation. This possibility might be discussed.

We agree with the reviewer’s comment that inhibition might have played a critical role in succeeding to reach TIII. Hence, we have added this concept to our discussion.

1. In a similar vein as for point 2 (above), the argument that PICs may have been the key mechanism enabling the attainment of target III, while reasonable, also seems a little hand wavy. The problem with the argument is that it depends on differential influences of PICs on motor neurons that are 1) low threshold, and 2) have similar recruitment thresholds. This seems somewhat unlikely given the broad influence of neuromodulatory inputs across populations of motor neurons.

We agree with the reviewer’s point and reasoning that a mixture of neuromodulation and inhibition likely introduced the variability in MU activity we observed in this study. This comment is addressed in the answer to comment 3.

Reviewer #2 (Public Review):

[...]

1. Some subjects seemed to hit TIII by repeatedly "pumping" the force up and down to increase the excitability of MU2 (this appears to happen in TIII trials 2-6 in Fig. 4 - c.f. p18 l30ff). It would be useful to see single-trial time series plots of MU1, MU2, and force for more example trials and sessions, to get a sense for the diversity of strategies subjects used. The authors might also consider providing additional analyses to test whether multiple "pumps" increased MU2 excitability, and if so, whether this increase was usually larger for MU2 than MU1. For example, they might plot the ratio of MU2 (and MU1) activation to force (or, better, the residual discharge rate after subtracting predicted discharge based on a nonlinear fit to the ramp data) over the course of the trial. Is there a reason to think, based on the data or previous work, that units with comparatively higher thresholds (out of a sample selected in the low range of <10% MVC) would have larger increases in excitability?


We added a supplementary figure (Supplement 4) that visualizes additional trials from different conditions and subjects for TIII-instructed trials and noted this in the text.

MU excitability might indeed be pronounced during repeated activations within a couple of seconds (see, for example, M. Gorassini, J. F. Yang, M. Siu, and D. J. Bennett, “Intrinsic Activation of Human Motoneurons: Reduction of Motor Unit Recruitment Thresholds by Repeated Contractions,” J. Neurophysiol., vol. 87, no. 4, pp. 1859–1866, 2002.). Such an effect, however, seems to be equally distributed to all active MUs. Moreover, we are not aware of any recent studies suggesting that MUs, within the narrow range of 0-10% MVC, may be excited differently by such a mechanism. Supplement 4C and D illustrate trials in which subjects performed multiple “pumps”. Visually, we could not find changes in the excitability specific to any of the two MUs nor that subjects explored repeated activation of MUs as a strategy to reach TIII. It seems subjects instead tried to find the precise force level which would allow them to keep MU2 active after the offset of MU1. We further discussed that PICs act very broadly on all MUs. The observed discharge patterns when successfully reaching TIII may likely be due to an interplay of broadly distributed neuromodulation and locally acting synaptic inhibition.

1. I am somewhat surprised that subjects were able to reach TIII at all when the de-recruitment threshold for MU1 was lower than the de-recruitment threshold for MU2. It would be useful to see (A) performance data, as in Fig. 3D or 5A, conditioned on the difference in de-recruitment thresholds, rather than recruitment thresholds, and (B) a scatterplot of the difference in de-recruitment vs the difference in recruitment thresholds for all pairs.


We agree that comparing the difference in de-recruitment threshold with the performance of reaching each target might provide valuable insights into the strategies used to perform the tasks. Hence, we added this comparison to Figure 4E at p. 16, l. 1. A scatterplot of the difference in de-recruitment threshold and the difference in recruitment threshold has been added to Supplement 3A. The Results section was modified in line with the above changes.

1. Using MU1 / MU2 rates to directly control cursor position makes sense for testing for independent control over the two MUs. However, one might imagine that there could exist a different decoding scheme (using more than two units, nonlinearities, delay coordinates, or control of velocity instead of position) that would allow subjects to generate smooth trajectories towards all three targets. Because the authors set their study in a BCI context, they may wish to comment on whether more complicated decoding schemes might be able to exploit single-unit EMG for BCI control or, alternatively, to argue that a single degree of freedom in input fundamentally limits the utility of such schemes.


This study aimed to assess whether humans can learn to decorrelate the activity between two MUs coming from the same functional MU pool during constraint isometric conditions. The biofeedback was chosen to encourage subjects to perform this non-intuitive and unnatural task. Transferring biofeedback on single MUs into an application, for example, BCI control, could include more advanced pre-processing steps. Not all subjects were able to navigate the cursor along both axes consistently (always hitting TI and TIII). However, the performance metric (Figure 4C) indicated that subjects became better over time in diverging from the diagonal and thus increased their moving range inside the 2D space for various combinations of MU pairs. Hence, a weighted linear combination of the activity of both MUs (for example, along the two principal components based on the cursor distribution) may enable subjects to navigate a cursor from one axis to another. Similarly, coadaptation methods or different types of biofeedback (auditory or haptic) may help subjects. Furthermore, using only two MUs to drive a cursor inside a 2-D space is prone to interference. Including multiple MUs in the control scheme may improve the performance even in the presence of noise. We have shown that the activation of a single MU pool exposed to a common drive does not necessarily obey rigid control. State-dependent flexible control due to variable intrinsic properties of single MUs may be exploited for specific applications, such as BCI. However, further research is necessary to understand the potentials and limits of such a control scheme.

1. The conclusions of the present work contrast somewhat with those of Marshall et al. (ref. 24), who claim (for shoulder and proximal arm muscles in the macaque) that (A) violations of the "common drive" hypothesis were relatively common when force profiles of different frequencies were compared, and that (B) microstimulation of different M1 sites could independently activate either MU in a pair at rest. Here, the authors provide a useful discussion of (A) on p19 l11ff, emphasizing that independent inputs and changes in intrinsic excitability cannot be conclusively distinguished once the MU has been recruited. They may wish to provide additional context for synthesizing their results with Marshall et al., including possible differences between upper / lower limb and proximal / distal muscles, task structure, and species.

The work by Marshall, Churchland and colleagues shows that when stimulating focally in specific sites in M1 single MUs can be activated, which may suggest a direct pathway from cortical neurons to single motor neurons within a pool. However, it remains to be shown if humans can learn to leverage such potential pathways or if the observations are limited to the artificially induced stimulus. The tibialis anterior receives a strong and direct cortical projection. Thus, we think that this muscle may be well suited to study whether subjects can explore such specific pathways to activate single MUs independently. However, it may very well be that the control of upper limbs show more flexibility than lower ones. However, we are not aware of any study that may provide evidence for a critical mismatch in the control of upper and lower limb MU pools. We have added this discussion to the manuscript.

Reviewer #3 (Public Review):

[...]

Even if the online decomposition of motor units were performed perfectly, the visual display provided to subject smooths the extracted motor unit discharge rates over a very wide time window: 1625 msec. This window is significantly larger than the differences in recruitment times in many of the motor unit pairs being used to control the interface. So while it's clear that the subjects are learning to perform the task successfully, it's not clear to me that subjects could have used the provided visual information to receive feedback about or learn to control motor unit recruitment, even if individuated control of motor unit recruitment by the nervous system is possible. I am therefore not convinced that these experiments were a fair test of subjects' ability to control the recruitment of individual motor units.

Regarding the validating of isolating motor units in the conditions analysed in this study, we have added a full new set of measurements with concomitant surface and intramuscular recordings during recruitment/derecruitment of motor units at variable recruitment speed. This provides a strong validation of the approach and of the accuracy of the online decomposition used in this study. Subjects received visual feedback on the activity of the selected MU pair, i.e. discharge behaviour of both MUs and the resulting cursor movement. This information was not clear from the initial submission and hence, we annotated the current version to clarify the biofeedback modalities. To further clarify the decoding of incoming MU1/MU2 discharge rates into cursor movement, we included Supplement 2. We also included a video that shows that the smoothing window on the cursor position does not affect the immediate cursor movement due to incoming spiking activity. For example, as shown in Supplement 2, for the initial offset of 0ms, the cursor starts moving along the axis corresponding to a sole activation of MU1 and immediately diverges from this axis when MU2 starts to discharge action potentials. We, therefore, think that the biofeedback provided to the subjects does allow exploration of single MU control.

Along similar lines, it seems likely to me that subjects are using some other strategy to learn the task, quite possibly one based on control of over overall force at the ankle and/or voluntary recruitment of other leg/foot muscles. Each of these variables will presumably be correlated with the activity of the recorded motor units and the movement of the cursor on the screen. Moreover, because these variables likely change on a similar (or slower) timescale than differences in motor units recruitment or derecruitment, it seems to me that using such strategies, which do not reflect or require individuated motor unit recruitment, is a highly effective way to successfully complete the task given the particular experimental setup.

In addition to being seated and restricted by an ankle dynamometer, subjects were instructed to only perform dorsiflexion of the ankle. Further, none of the subjects reported compensatory movements as a strategy to reach any of the targets. In addition, to be successfully utilised, such compensatory movements would need to influence various combinations of MUs tested in this study equally, even when they differ in size. Nevertheless, we acknowledge, as pointed out by the reviewer, that our setup has limitations. We only measured force in a single direction (i.e. ankle dorsiflexion) and did not track toe, hip or knee movements. Even though an instructor supervised leg movement throughout the experiment, it may be that very subtle and unknowingly compensatory movements have influenced the activity of the selected MUs. Hence, we updated the limitations section in the Discussion.

To summarize my above two points, it seems like the author's argument is that absence of evidence (subjects do not perform individuated MU recruitment in this particular task) constitutes evidence of absence (i.e. is evidence that individuated recruitment is not possible for the nervous system or for the control of brain-machine interfaces). Therefore given the above-described issues regarding real-time feedback provided to subjects in the paper it is not clear to me that any strong conclusions can be drawn about the nervous system's ability or inability to achieve individuated motor unit recruitment.

We hope that the above changes clarify the biofeedback modalities and their potential to provide subjects with the necessary information for exploring independent MU control. Our experiments aimed to investigate whether subjects can learn under constraint isometric conditions to decorrelate the activity between two MUs coming from the same functional pool. While it seemed that MU activity could be decorrelated, this almost exclusively happened (TIII-instructed trials) within a state-dependent framework, i.e. both MUs must be activated first before the lower threshold one is switched off. We did not observe flexible MU control based exclusively on a selective input to individual MUs (MU2 activated before MU1 during initial recruitment). That does not mean that such control is impossible. However, all successful control strategies that were voluntarily explored by the subjects to achieve flexible control were based on a common input and history-dependent activation of MUs. We have added these concepts to the discussion section.

Second, to support the claims based on their data the authors must explain their online spike-sorting method and provide evidence that it can successfully discriminate distinct motor unit onset/offset times at the low latency that would be required to test their claims. In the current manuscript, authors do not address this at all beyond referring to their recent IEEE paper (ref [25]). However, although that earlier paper is exciting and has many strengths (including simultaneous recordings from intramuscular and surface EMGs), the IEEE paper does not attempt to evaluate the performance metrics that are essential to the current project. For example, the key metric in ref 25 is "rate-of-agreement" (RoA), which measures differences in the total number of motor unit action potentials sorted from, for example, surface and intramuscular EMG. However, there is no evaluation of whether there is agreement in recruitment or de-recruitment times (the key variable in the present study) for motor units measured both from the surface and intramuscularly. This important technical point must be addressed if any conclusions are to be drawn from the present data.

We have taken this comment in high consideration, and we have performed a validation based on concomitant intramuscular and surface EMG decomposition in the exact experimental conditions of this study, including variations in the speed of recruitment and de-recruitment. This new validation fully supports the accuracy in of the methods used when detecting recruitment and de-recruitment of motor units.

My final concern is that the authors' key conclusion - that the nervous system cannot or does not control motor units in an individuated fashion - is based on the assumption that the robust differences in de-recruitment time that subjects display cannot be due to differences in descending control, and instead must be due to changes in intrinsic motor unit excitability within the spinal cord. The authors simply assert/assume that "[derecruitment] results from the relative intrinsic excitability of the motor neurons which override the sole impact of the receive synaptic input". This may well be true, but the authors do not provide any evidence for this in the present paper, and to me it seems equally plausible that the reverse is true - that de-recrutiment might influenced by descending control. This line of argumentation therefore seems somewhat circular.

When subjects were asked to reach TIII, which required the sole activation of a higher threshold MU, subjects almost exclusively chose to activate both MUs first before switching off the lower threshold MU. It may be that the lower de-recruitment threshold of MU2 was determined by descending inputs changing the excitability of either MU1 or MU2 (for example, see J. Nielsen, C. Crone, T. Sinkjær, E. Toft, and H. Hultborn, “Central control of reciprocal inhibition during fictive dorsiflexion in man,” Exp. brain Res., vol. 104, no. 1, pp. 99–106, Apr. 1995 or E. Jankowska, “Interneuronal relay in spinal pathways from proprioceptors,” Prog. Neurobiol., vol. 38, no. 4, pp. 335–378, Apr. 1992). Even if that is the case, it remains unknown why such a command channel that potentially changes the excitability of a single MU was not voluntarily utilized at the initial recruitment to allow for direct movement towards TIII (as direct movement was preferred for TI and TII). We cannot rule out that de-recruitment was affected by selective descending commands. However, our results match observations made in previous studies on intrinsic changes of MU excitability after MU recruitment. Therefore, even if descending pathways were utilized throughout the experiment to change, for example, MU excitability, subjects were not able to explore such pathways to change initial recruitment and achieve general flexible control over MUs. The updated discussion explains this line of reasoning.

Reviewer #4 (Public Review):

[...]

1. Figure 6a nicely demonstrates the strategy used by subjects to hit target TIII. In this example, MU2 was both recruited and de-recruited after MU1 (which is the opposite of what one would expect based on the standard textbook description). The authors state (page 17, line 15-17) that even in the reverse case (when MU2 is de-recruited before MU1) the strategy still leads to successful performance. I am not sure how this would be done. For clarity, the authors could add a panel similar to panel A to this figure but for the case where the MU pairs have the opposite order of de-recruitment.

We have added more examples of successful TIII-instructed trials in Supplement 4. Supplement 4C and D illustrate examples of subjects navigating the cursor inside TIII even when MU2 was de-recruited before MU1. As exemplarily shown, subjects also used the three-stage approach discussed in the manuscript. In contrast to successful trials in which MU2 was de-recruited after MU1 (for example, Supplement 4B), subjects required multiple attempts until finding a precise force level that allowed a continuous firing of MU2 while MU1 remained silent. We have added a possible explanation for such behaviour in the Discussion.

1. The authors discuss a possible type of flexible control which is not evident in the recruitment order of MUs (page 19, line 27-28). This reasoning was not entirely clear to me. Specifically, I was not sure which of the results presented here needs to be explained by such mechanism.

We have shown that subjects can decorrelate the discharge activity of MU1 and MU2 once both MUs are active (e.g. reaching TIII). Thus, flexible control of the MU pair was possible after the initial recruitment. Therefore, this kind of control seems strongly linked to a specific activation state of both MUs. We further elaborated on which potential mechanisms may contribute to this state-dependent control.

1. The authors argue that using a well-controlled task is necessary for understanding the ability to control the descending input to MUs. They thus applied a dorsi-flexion paradigm and MU recordings from TA muscles. However, it is not clear to what extent the results obtained in this study can be extrapolated to the upper limb. Controlling the MUs of the upper limb could be more flexible and more accessible to voluntary control than the control of lower limb muscles. This point is crucial since the authors compare their results to other studies (Formento et al., bioRxiv 2021 and Marshall et al., bioRxiv 2021) which concluded in favor of the flexible control of MU recruitment. Since both studies used the MUs of upper limb muscles, a fair comparison would involve using a constrained task design but for upper limb muscles.

We agree with the reviewer that our work differs from previous approaches, which also studied flexible MU control. We, therefore, added a paragraph to the limitation section of the Discussion.

1. The authors devote a long paragraph in the discussion to account for the variability in the de-recruitment order. They mostly rely on PIC, but there is no clear evidence that this is indeed the case. Is it at all possible that the flexibility in control over MUs was over their recruitment threshold? Was there any change in de-recruitment of the MUs during learning (in a given recording session)?

The de-recruitment threshold did not critically change when compared before and after the experiment on each day (difference in de-recruitment threshold before and after the experiment: -0.16 ± 2.28% MVC, we have now added this result to the Results section). Deviations from the classical recruitment order may be achieved by temporal (short-lived) changes in the intrinsic excitability of single MUs. We, therefore, extended our discussion on potential mechanisms that may explain the observed variability given all MUs receive the same common input.

1. The need for a complicated performance measure (define on page 5, line 3-6) is not entirely clear to me. What is the correlation between this parameter and other, more conventional measures such as total-movement time or maximal deviation from the straight trajectory? In addition, the normalization process is difficult to follow. The best performance was measured across subjects. Does this mean that single subject data could be either down or up-regulated based on the relative performance of the specific subject? Why not normalize the single-subject data and then compare these data across subjects?

We employed this performance metric to overcome shortcomings of traditional measures such as target hit count, time-to-target or deviation from the straight trajectory. Such problems are described in the illustration below for TIII-instructed trials (blue target). A: the duration of the trial is the same in both examples (left and right); however, on the left, the subject manages to keep the cursor close to the target-of-interest while on the right, the cursor is far away from the target centre of TIII. B: In both images the cursor has the same distance d to the target centre of TIII. However, on the left, the subject manages to switch off MU1 while keeping MU2 active, while on the right, both MUs are active. C: On the left, the subject manages to move the cursor inside the TIII before the maximum trial time was reached, while on the right, the subject moved the cursor up and down, not diverging from the ideal trajectory to the target centre but fails to place the cursor inside TIII within the duration of the trial. In all examples, using only one conventional measure fails to account for a higher performance value in the left scenario than in the right. Our performance metric combines several performance metrics such as time-to-target, distance from the target centre, and the discharge rate ratio between MU1 and MU2 via the angle 𝜑 and thus allows a more detailed analysis of the performance than conventional measures. The normalisation of the performance value was done to allow for a comparison across subjects. The best and worst performance was estimated using synthetic data mimicking ideal movement towards each target (i.e. immediate start from the target origin to the centre of the target, while the normalised discharge rate of the corresponding MU is set to 1). Since the target space is normalised for all subjects in the same manner (mean discharge rate of the corresponding MUs at 10 %MVC) this allows us to compare the performance between subjects, conditions and targets.

1. Figure 3C appears to indicate that there was only moderate learning across days for target TI and TII. Even for target TIII there was some improvement but the peak performance in later days was quite poor. The fact that the MUs were different each day may have affected the subjects' ability to learn the task efficiently. It would be interesting to measure the learning obtained on single days.

We have added an analysis that estimated the learning within a session per subject and target (Supplement 3C). In order to evaluate the strength of learning within-session, the Spearman correlation coefficient between target-specific performance and consecutive trials was calculated and averaged across conditions and days. The results suggest that there was little learning within sessions and no significant difference between targets. These results have now been added to the manuscript.

1. On page 16 line 12-13, the authors describe the rare cases where subjects moved directly towards TIII. These cases apparently occurred when the recruitment threshold of MU2 was lower. What is the probable source of this lower recruitment level in these specific trials? Was this incidental (i.e., the trial was only successful when the MU threshold randomly decreased) or was there volitional control over the recruitment threshold? Did the authors test how the MU threshold changed (in percentages) over the course of the training day?

We did not track the recruitment threshold throughout the session but only at the beginning and end. We could not identify any critical changes in the recruitment order (see Results section). However, our analysis indicated that during direct movements towards TIII, MU2 (higher threshold MU) was recruited at a lower force level during the initial ramp and thus had a temporary effective recruitment threshold below MU1. It is important to note that these direct movements towards TIII only occurred for pairs of MUs with a similar recruitment threshold (see Figure 6). One possible explanation for this temporal change in recruitment threshold could be altered excitability due to neuromodulatory effects such as PICs (see Discussion). We have added an analysis that shows that direct movements towards TIII occurred in most cases (>90%) after a preceding TII- or TIIIinstructed trial. Both of these targets-of-interest require activation of MU2. Thus, direct movement towards TIII was likely not the result of specific descending control. Instead, this analysis suggests that the PIC effect triggered at the preceding trial was not entirely extinguished when a trial ending in direct movement towards TIII started. Alternatively, the rare scenarios in which direct movements happened could be entirely random. Similar observations were made in previous biofeedback studies [31]. To clarify these points, we altered the manuscript.

Reviewer #2 (Public review):

This study conducted by Lu et al. explores the molecular underpinnings of sexual dimorphism in antiviral immunity in zebrafish, with a particular emphasis on the male-biased gene cyp17a2. The authors demonstrate that male zebrafish exhibit stronger antiviral responses than females, and they identify a teleost-specific gene cyp17a2 as a key regulator of this dimorphism. Utilizing a combination of in vivo and in vitro methodologies, they demonstrate that Cyp17a2 potentiates IFN responses by stabilizing STING via K33-linked polyubiquitination and directly degrades the viral P protein via USP8-mediated deubiquitination. The work challenges conventional views of sex-based immunity and proposes a novel, hormone- and sex chromosome-independent mechanism.

Strengths:

(1) The following constitutes a novel concept, sexual dimorphism in immunity can be driven by an autosomal gene rather than sex chromosomes or hormones represents a significant advance in the field, offering a more comprehensive understanding of immune evolution.

(2) The present study provides a comprehensive molecular pathway, from gene expression to protein-protein interactions and post-translational modifications, thereby establishing a link between Cyp17a2 and both host immune enhancement (via STING) and direct antiviral activity (via viral protein degradation).

(3) In order to substantiate their claims, the authors utilize a wide range of techniques, including transcriptomics, Co-IP, ubiquitination assays, confocal microscopy, and knockout models.

(4) The utilization of a singular model is imperative. Zebrafish, which are characterized by their absence of sex chromosomes, offer a clear genetic background for the dissection of autosomal contributions to sexual dimorphism.

Weaknesses:

(1) Limited discussion on whether this mechanism extends beyond Cyprinidae and its implications for teleost adaptation.

Comments on revisions:

The authors successfully achieved their primary aim, which was to identify and characterize a male-biased gene governing antiviral sexual dimorphism in fish. The data provide robust support for the conclusion that Cyp17a2 enhances antiviral immunity through dual mechanisms, STING stabilization and viral protein degradation, independent of classical sex-determining pathways. The findings are consistent across a range of experimental setups and are statistically robust. The revisions have significantly enhanced the clarity, depth, and overall quality of the manuscript. The authors have addressed each concern meticulously, resulting in a much-improved and robust article. No further suggestions are offered.

Author response:

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

Reviewer #1 (Public review):

Weaknesses:

(1) Figure 10 outlines a mechanistic link between cyp17a2 and the sexual dimorphism the authors report for SVCV infection outcomes. The data presented on increased susceptibility of cyp17a2-/- mutant male zebrafish support this diagram, but this conclusion is fairly weak without additional experimentation in both males and females. The authors justify their decision to focus on males by stating that they wanted to avoid potential androgen-mediated phenotypes in the cpy17a2 mutant background (lines 152156), but this appears to be speculation. It also doesn't preclude the possibility of testing the effects of increased cyp17a2 expression on viral infection in both males and females. This is of critical importance if the authors intend to focus the study on sexual dimorphism, which is how the introduction and discussion are currently structured.

Thank you for your suggestion. We have revised the relevant statements in the introduction and discussion sections accordingly. The cyp17a2 overexpression experiments were not conducted in both male and female individuals was primarily based on two reasons. First, our laboratory currently lacks the technical capability to achieve cyp17a2 overexpression at the organismal level, existing methodologies are limited to gene knockout via CRISPR-Cas9. Second, even if overexpression were feasible, subsequent comparisons would need to be restricted within sexes (i.e., female vs. female controls or male vs. male controls) to eliminate potential confounding effects of sex hormones. Such experimental outcomes would only demonstrate the antiviral function of Cyp17a2 itself rather than directly elucidate mechanisms underlying sexual dimorphism, which diverges from the central objective of this study.

We fully agree with your perspective and have accordingly refined relevant discussions in the revised manuscript. Our conclusions now emphasize that "cyp17a2 is one of the factors contributing to sex-based differences in antiviral immunity" rather than implying that it "solely mediates the entire phenotypic divergence." These modifications have been incorporated into the resubmitted version (Lines 112-115).    

(2) The authors present data indicating an unexpected link between cyp17a2 and ubiquitination pathways. It is unclear how a CYP450 family member would carry out such activities, and this warrants much more attention. One brief paragraph in the discussion (starting at line 448) mentions previous implications of CYP450 proteins in antiviral immunity, but given that most of the data presented in the paper attempt to characterize cyp17a2 as a direct interactor of ubiquitination factors, more discussion in the text should be devoted to this topic. For example, are there any known domains in this protein that make sense in this context? Discussion of this interface is more relevant to the study than the general overview of sexual dimorphism that is currently highlighted in the discussion and throughout the text.

We are grateful to the reviewer for their suggestion to elaborate on this novel finding. The discussion on this point has been expanded significantly (Lines 448-460). It is acknowledged that Cyp17a2 is devoid of the canonical domains that are typically associated with the ubiquitination machinery (e.g., RING, U-box). The present study proposes that the endoplasmic reticulum (ER) localization of Cyp17a2, in conjunction with its capacity to function as a scaffold protein, is of paramount significance. By residing in the ER, Cyp17a2 is strategically positioned to interact with key immune regulators such as STING, which also localizes to the ER. It is hypothesized that Cyp17a2 facilitates the recruitment of E3 ligases (btr32) and deubiquitinates (USP8) to their substrates (STING and SVCV P protein, respectively) by providing a platform for protein-protein interactions, rather than directly catalyzing ubiquitination. This noncanonical, scaffolding role for a cytochrome P450 (CYP450) enzyme represents an exciting evolutionary adaptation in teleost immunity.

(3) Figures 2-9 contain information that could be streamlined to highlight the main points the authors hope to make through a combination of editing, removal, and movement to supplemental materials. There is a consistent lack of clarity in these figures that could be improved by supplementing them with more text to accompany the supplemental figures. Using Figure 2 and an example, panel (A) could be removed as unnecessary, panel (B) could be exchanged for a volcano plot with examples highlighting why cyp17a2 was selected for further study and also the full dataset could be shared in a supplemental table, panel (C) could be modified to indicate why that particular subset was chosen for plotting along with an explanation of the scaling, panel (D) could be moved to supplemental because the point is redundant with panels (A) and (C), panel (E) could be presented as a heatmap, in panels (G) and (H) data from EPC cells could be moved to supplemental because it is not central to the phenotype under investigation, panels (J) to (L) and (N) to (P) could be moved to supplemental because they are redundant with the main points made in panels (M) and (Q). Similar considerations could be made with Figures 3-9.

We thank the reviewer for these excellent suggestions to improve the clarity and focus of our figures. A comprehensive review of all figures has been conducted in accordance with the recommendations made. Figure 2A has been removed. Figure 2B (revised Figure 2A) has been replaced with a volcano plot highlighting cyp17a2 and the full dataset has been provided as supplementary Table S2. Figure 2C (revised Figure 2B) is now a heatmap with eight sex-related genes and an explanation of the scaling has been added to the revised figure legends. Several panels (D, G, H, J-L, N-P) have been moved to the supplementary information (now Figure S1). Figure 2E has been presented as a heatmap. The same approach to streamlining has been applied to Figures 3-9, with confirmatory or secondary data being moved to supplements in order to better emphasize the main conclusions. The figure legends and main text have been updated accordingly.

(4) The data in Figure 3 (A)-(C) do not seem to match the description in the text. That is, the authors state that cyp17a2 overexpression increases interferon signaling activity in cells, but the figure shows higher increases in vector controls. Additionally, the data in panel (H) are not described. What genes were selected and why, and where are the data on the rest of the genes from this analysis? This should be shared in a supplemental table.

We apologize for the lack of clarity. In Figures 3A-C, the vector control shows baseline activation due to the stimulants (poly I:C/SVCV), but the fold-increase is significantly greater in the Cyp17a2-overexpressing groups. We have re-plotted the data to more clearly represent the stimulant-induced activation over baseline and added statistical comparisons between the Vector and Cyp17a2 groups under each condition to highlight the enhancing effect of Cyp17a2. For Figure 3H (revised Figure 3F), the heatmap shows a curated set of IFN-stimulated genes (ISGs) most significantly regulated by Cyp17a2 based on our RNA-seq analysis. We have added a description in the revised figure legend and in the results section (Lines 837-840). The full list of differentially expressed genes from this analysis is now provided in Supplementary Table S3.

(5) Some of the reagents described in the methods do not have cited support for the applications used in the study. For example, the antibody for TRIM11 (line 624, data in Figures 6 & 7) was generated for targeting the human protein. Validation for use of this reagent in zebrafish should be presented or cited. Furthermore, the accepted zebrafish nomenclature for this gene would be preferred throughout the text, which is bloodthirsty-related gene family, member 32.

We thank the reviewer for raising this important point regarding reagent specificity. To address the concern about antibody validation in zebrafish, we performed the following verification steps. First, we aligned the antigenic sequence targeted by the Abclonal btr32 antibody (ABclonal, A13887) with orthologous sequences from zebrafish, which showed 45% protein sequence similarity (Author response image 1). More importantly, we conducted experimental validation by expressing Myc-tagged btr32 in EPC cells. Both the anti-Myc and the anti-btr32 antibodies detected a protein band at the same molecular weight. Furthermore, when a btr32-specific knockdown plasmid was introduced, the band recognized by the anti-btr32 antibody was significantly reduced (Author response image 2). These results support the specificity of the antibody in recognizing fish btr32. In accordance with the reviewer’s suggestion, we have also updated the gene nomenclature to “bloodthirsty-related gene family, member 32 (btr32)” throughout the manuscript.

Author response image 1.

Author response image 2.

Reviewer #2 (Public review):

Weaknesses:

(1) Colocalization analyses (Figures 4G, 6I, 9D) require quantitative metrics (e.g., Pearson's coefficients) rather than representative images alone.

We concur with the reviewer's assessment. We have now performed quantitative colocalization analysis (Pearson's coefficients) for all indicated figures (4G, 6I, 9D). The quantitative results are now presented within the figures themselves and described in the revised figure legends.

(2) Figure 1 survival curves need annotated statistical tests (e.g., "Log-rank test, p=X.XX")

The survival curves have now been annotated with the specific p-values from the Log-rank (Mantel-Cox) test (see revised Figures 1A, 2E).

(3) Figure 2P GSEA should report exact FDR-adjusted *p*-values (not just "*p*<0.05").

Figure 2P (revised Figure S1J) has been updated to include the exact FDR p-values for the presented GSEA plots.

(4) Section 2 overextends on teleost sex-determination diversity, condensing to emphasize relevance to immune dimorphism would strengthen narrative cohesion.

The section on teleost sex-determination diversity in the Discussion (lines 357-365) has been condensed, with a more direct focus on how this diversity provides a unique context for studying immune dimorphism independent of canonical sex chromosomes, as exemplified by the zebrafish model.

(5) Limited discussion on whether this mechanism extends beyond Cyprinidae and its implications for teleost adaptation.

The discussion has been expanded (lines 375-386) to address the potential conservation of this mechanism. It is acknowledged that cyp17a2 is a teleost-specific gene, and it is hypothesized that its function in antiviral immunity may signify an adaptive innovation within this extensively diverse vertebrate group. It is suggested that further research in other teleost families will be essential to ascertain the broader evolutionary significance of the present findings.

Reviewer #2 (Recommendations for the authors):

(1) Expand the Discussion to address why teleosts may have evolved male-biased immunity. Consider: pathogen pressure differentials in aquatic vs. terrestrial environments; trade-offs between immune investment and reproductive strategies (e.g., male-male competition); comparative advantages in external fertilization systems.

We have expanded the discussion on lines 412-430, to address the potential conservation of this mechanism. We note that Cyp17a2 is a teleost-specific gene and speculate that its role in antiviral immunity represents an adaptive innovation within this highly diverse group of vertebrates. We propose that future studies of other teleost families are crucial for determining the broader evolutionary significance of our findings.

Reviewer #1 (Public review):

Summary:

This manuscript reports the discovery and characterization of the first bifunctional degrader of tankyrase. Notably, the tankyrase degrader exhibits stronger β-catenin inhibition and tumor growth suppression compared to conventional tankyrase inhibitors. Mechanistically, while tankyrase inhibitors stabilize tankyrase and promote Axin puncta formation - thereby impairing β-catenin degradation - the degrader avoids this effect, resulting in deeper suppression of β-catenin signaling. These findings suggest that targeted degradation of tankyrase offers a novel therapeutic strategy for β-catenin-driven cancers. Overall, this is a compelling study with significant translational potential.

Strengths:

(1) The manuscript presents a rigorous and well-executed study on a timely and impactful topic.

(2) The biochemical and cellular characterization of the tankyrase degrader is thorough, and the comparative analysis with tankyrase inhibitors is insightful.

(3) The finding that tankyrase stabilization by inhibitors may interfere with Axin function is novel and significant. It aligns with earlier observations (e.g., Huang 2009) that transient tankyrase overexpression can stabilize β-catenin independently of PAR domain activity.

(4) The use of TNKS1/2 knockout cells expressing catalytically inactive tankyrase to demonstrate β-catenin inhibitory activity of the tankyrase degrader is elegant.

(5) The finding that the tankyrase degrader has superior anti-proliferative effects in colorectal cancer models has important therapeutic implications.

Weaknesses:

(1) A key caveat is that the identified tankyrase degrader also targets GSPT1 for degradation. This raises the possibility that GSPT1 degradation may contribute to the observed β-catenin and tumor growth inhibition.

(2) The authors address this concern reasonably by showing that DLD1 cells resistant to GSPT1 degradation remain sensitive to the tankyrase degraded.

(3) To further strengthen this point, the authors might consider generating TNKS1/2 double knockout cells (e.g., in DLD1 or SW480 backgrounds) and demonstrating that the degrader loses its growth-inhibitory effect in these models. However, given the technical challenges of creating double knockouts in cancer cell lines, such experiments could be considered optional.

Reviewer #2 (Public review):

Summary:

The ADP-ribosyltransferase tankyrase controls many biological processes, many of which are relevant to human disease. This includes Wnt/beta-catenin signalling, which is dysregulated in many cancers, most notably colorectal cancer. Tankyrase is a positive regulator of Wnt/beta-catenin signalling in that it counters the activity of the beta-catenin destruction complex (DC). Catalytic inhibition of tankyrase not only blocks PAR-dependent ubiquitylation and degradation of AXIN1/2, the central scaffolding protein in the DC, but also tankyrase itself. As a result, blocking tankyrase gives rise to tankyrase accumulation, which may accentuate its non-catalytic functions, which have been proposed to drive Wnt/beta-catenin signalling. Most tankyrase catalytic inhibitors have shown limited efficacy and substantial toxicity in vivo. By developing tankyrase-directed PROTACs, the authors aim to block both catalytic and non-catalytic functions of tankyrase, aspiring to achieve a more complete inhibition of Wnt/beta-catenin signalling. The successfully developed PROTAC, based on the existing catalytic inhibitor IWR1, IWR1-POMA, induces the degradation of both TNKS and TNKS2, blocks beta-catenin-dependent transcription without stabilising the DC in puncta/degradasomes, and inhibits cancer cell growth in vitro. Mechanistically, this points to a scaffolding role of tankyrase in the DC, at least under conditions of tankyrase catalytic inhibition, in line with previous proposals.

Strengths:

The study clearly illustrates the incentive for developing a tankyrase degrader, namely, to abolish both catalytic and non-catalytic functions of tankyrase. By and large, the study achieves these ambitions, and the findings support the main conclusions, although the statement that a more complete inhibition of the pathway is achieved requires corroboration. The proteomics studies are powerful. IWR1-POMA constitutes a very useful tool to re-evaluate targeting of tankyrase in oncogenic Wnt/beta-catenin signalling. The paired compounds will benefit investigations of tankyrase scaffolding functions across many different biological systems controlled by tankyrase. The findings are exciting.

Weaknesses:

Although the results are promising and mostly compelling, the claim that the PROTACs provide "a deeper suppression of the WNT/β-catenin pathway activity" requires further corroboration, particularly at endogenous tankyrase levels.

There are also some other points that, if considered, would further improve the manuscript, as detailed below.

(1) Abstract and line 62: Many catalytic tankyrase inhibitors tend to display toxicity, which is likely on-target (e.g., 10.1177/0192623315621192; 10.1158/0008-5472). This constitutes the main limiting factor for these compounds. An incomplete inhibition of Wnt/beta-catenin signalling may contribute to the challenges, but this does not appear to be the dominant problem. A more prominent introduction to this important challenge is probably expected by the field.

(2) The authors do a good job in setting the scene for the need for tankyrase degraders. Their observations relating to the formation of puncta (degradasomes) being tankyrase-dependent are compatible with a previous study by Martino-Echarri et al. 2016 (10.1371/journal.pone.0150484): simultaneous silencing of TNKS and TNKS2 by RNAi abolishes degradasome formation. The paper is cited as reference 17, but only in passing, and deserves more prominence. (It includes an entire paragraph titled "Expression of tankyrases 1 and 2 is required for TNKSi-induced formation of axin puncta").

(3) Moreover, the scaffolding concept has been discussed comprehensively in other studies: 10.1111/bph.14038 and more recently 10.1042/BCJ20230230. There are also a few studies that focus on targeting the ankyrin repeat clusters of tankyrase to disengage substrates (10.1038/s41598-020-69229-y; 10.1038/s41598-019-55240-5) that illustrate the concept of blocking the scaffolding function. In that sense, the hypotheses are mature, and it is interesting to see some of them supported in this study. The authors could improve how they set their work into the context of these other efforts and proposals.

(4) In several places in the manuscript, the DC is referred to as "biomolecular condensate", at times even as a "classic example", implying that it operates through phase separation. This has not been demonstrated. In fact, super-resolution microscopy indicates that the puncta are not droplet-like (10.7554/eLife.08022), which would argue against the condensate hypothesis.

(5) It is beautiful to be able to use IWR1 and IWR1-POMA at identical concentrations for direct comparisons. However, this requires the two compounds to bind to tankyrase similarly well and reach the target to a comparable extent. How sure are authors that target engagement is comparable? Has this been evaluated?

(6) Figure 1F: It is not immediately apparent how IWR1-POMA shows more complete containment of Wnt/beta-catenin signalling. Most Wnt/beta-catenin targets lie close to the perfect diagonal, so I do not see how the statement "that IWR1-POMA controlled WNT/β-catenin signaling more effectively than IWR1" (in the legend of Figure 1F) is supported. Minimally, an expanded explanation would benefit the reader. Providing the colour-coding legend directly in the figure would help improve clarity. Also, the panel is very small and may benefit from a different presentation in the figure.

(7) Figure 2: The conclusion of a "deeper suppression" of signalling relies on overexpression of tankyrase in an otherwise tankyrase-null background. Have the authors attempted to measure reporter activity or endogenous gene expression without tankyrase overexpression, in Wnt3a-stimulated cells (in the context of a normal Wnt/beta-catenin pathway) or CRC cells at the basal level? Non-catalytic activity in a similar assay has previously been observed upon tankyrase overexpression (10.1016/j.molcel.2016.06.019). Whether or not there is a substantial scaffolding effect at endogenous tankyrase levels after tankyrase inhibition remains unconfirmed, and the PROTAC is a valuable tool to address this important question. The findings presented in Figure S7C and D go some way towards answering this question - these data could be presented more prominently, and similar assays could be performed in other cell systems.

(8) Line 237/238: "TNKS accumulation negatively impacts the catalytic activity of the DC (Figure 5D)" - the data do not show this. Beta-catenin levels are a surrogate readout for DC function (phosphorylation and ubiquitylation). Minimally, this requires rewording, with reference to beta-catenin levels.

(9) Line 303-304: Beta-catenin is thought to exchange at beta-catenin degradasomes; this is clear from previous FRAP assays and the observation that phospho-beta-catenin accumulates in degradasomes upon proteasome inhibition (10.1158/1541-7786.MCR-15-0125). However, degradasome size hasn't, to my knowledge, been related to activity. Can this be clarified, please?

(10) There are previous hypotheses/proposals that the sensitivity of CRC cells to tankyrase inhibition correlates with APC truncation or PIK3CA status (10.1158/1535-7163.MCT-16-0578; 10.1038/s41416-023-02484-8). Have the authors considered expanding their cell line panel (Figure S7) to sample a wider range of cell lines, including some that are wild-type with regard to APC or Wnt/beta-catenin signalling in general? This would be a valuable addition to the work. Quantitated colony formation data could be moved to the main body of the manuscript.

(11) The manuscript only mentions toxicity (i.e., therapeutic window) in the last sentence of the Discussion section. As this is THE main challenge with tankyrase inhibitors (as mentioned above), can the authors expand their discussion of this aspect? Is there an expectation that PROTACs may be less toxic?

(12) Figures 3, 4, 5A: For fluorescence microscopy experiments, can these be quantified, and can repeat data be included?

(13) Figure 4, S6: An additional channel illustrating the distribution of cells (e.g., nuclei, cytoskeleton, or membrane) would be helpful for orientation and context for the AXIN1 signal.

(14) How were cytosolic fractions of cells prepared to assess cytosolic beta-catenin levels? This detail is missing from the methods.

Reviewer #3 (Public review):

In this manuscript, Wang et al employ a chemical biology approach to investigate the differences between the enzymatic and scaffolding roles of tankyrase during Wnt β-catenin signalling. It was previously established that, in addition to its enzymatic activity, tankyrase 1/2 also plays a scaffolding function within the destruction complex, a property conferred by SAM-domain-dependent polymerization (PMID: 27494558). It is also known that TNKS1/2 is an autoregulated protein and that its enzymatic inhibition leads to accumulation of total TNKS proteins and stabilization of Axin punctae (through the scaffolding function of TNKS1/2), leading to rigidification of the DC and decreased β-catenin turnover. The authors surmised that this could, in part, explain the limited efficacy of TNKS1/2 catalytic inhibition for the treatment of colorectal cancers. To test this hypothesis, they evaluated a series of PROTAC molecules promoting the degradation of TNKS1/2 to block both the catalytic and scaffolding activities. They show that IWR1-POMA (their most active molecule) promotes more efficient suppression of beta-catenin-mediated transcription and is more active in inhibiting colorectal cancer cell and CRC patient-derived organoids growth. Mechanistically, the authors used FRAP to demonstrate that catalytic inhibitors of TNKS led to a reduced dynamic assembly of the DC (rigidification), whereas IWR1-POMA did not affect the dynamics.

Overall, this is an interesting study describing the design and development of a PROTAC for TNKS1/2 that could have increased efficacy where catalytic inhibitors have displayed limited activity. Knowing the importance of the scaffolding role of TNKS1/2 within the destruction complex, targeting both the catalytic and scaffolding roles certainly makes sense. The manuscript contains convincing evidence of the different mechanisms of the PROTAC vs catalytic inhibitors. Some additional efforts to quantify several of the experiments and to indicate the reproducibility and statistical analysis would strengthen the manuscript. Ultimately, it would have been great to evaluate the in vivo efficacy of IWR1-POMA in an in vivo CRC assay (APCmin mice or using PDX models); however, I realize that this is likely beyond the scope of this manuscript.

I have some recommendations listed below for consideration by the authors to strengthen their study:

(1) The title is slightly misleading, as it is already known that the scaffolding function of TNKS is important within the DC. The authors should consider incorporating the PROTAC targeting aspect in the title (e.g., PROTAC-mediated targeting of tankyrase leads to increased inhibition of betacat signaling and CRC growth inhibition).

(2) The authors should comment in the manuscript on the bell-shaped curve obtained with treatment of cells with the PROTACs (Figure S2C). This likely indicates tittering of the targets within a bifunctional molecule with increasing concentration (and likely reveals the auto-inhibition conferred by the catalytic inhibition alone).

(3) The authors comment that using G007-LK as warehead was unsuccessful, but they do not show data. Do the authors know why this was the case?

(4) Throughout the manuscript, the authors need to do a better job at quantifying their results (i.e., the western blots and the IF). For example, the degradation of TNKS1/2 in Figure 1D is not overly convincing. Similarly, the IF data in Figure 3 needs to be quantified in some ways. Along the same lines, the effect of IWR1-POMA treatments on the proliferation of cells and organoids should be quantified using viability assays... There is also no indication of how many times these experiments were performed and whether the blots shown are representative experiments. The quantification should include all experiments.

Author response:

Reviewer #1 (Public Review):

We thank the Reviewer for the favorable feedback. The major concern is the collateral degradation of GSPT1. As the Reviewer noted, IWR1-POMA was able to suppress colony formation in DLD-1 cells resistant to GSPT1/2 degrader, suggesting that TNKS but not GSPT degradation is responsible for growth inhibition.

We also appreciate that the Reviewer brought it to our attention an important early observation of the TNKS scaffolding effects. Cong reported in 2009 that overexpression of TNKS induced AXIN puncta formation in a SAM but not PARP domain-dependent manner (PMID 19759537). We will include this information in the revised manuscript.

Reviewer #2 (Public Review):

We thank the Reviewer for the encouraging and insightful comments. The major critique concerns whether TNKS degraders can suppress WNT/β-catenin signaling more effectively than TNKS inhibitors at endogenous TNKS levels. Fig. 1D shows that IWR1-POMA reduced the level of cytosolic β-catenin more effectively than IWR1 in Wnt3A-stimulated HEK293 cells without protein overexpression, and Fig. S7B shows that IWR1-POMA reduced STF signals more effectively than IWR1 in DLD-1 and SW480 cells with endogenous TNKS expression. We will corroborate these findings with additional cell lines during the revision.

(1) We agree with the Reviewer that on-target toxicities pose challenges to the development of WNT inhibitors. For example, LGK974 that inhibits PORCN to prevent the secretion of all WNT proteins showed significant on-target toxicity in human (PMC10020809), and G007-LK that inhibits TNKS to block canonical WNT signaling selectively exhibited weak efficacy and dose-limiting toxicity at 5‒30 mg/kg BID or 10‒60 mg/kg QD in various mouse xenograft models (PMID: 23539443). Similarly, G-631, another TNKS inhibitor, also showed dose-limiting toxicity without significant efficacy at 25‒100 mg/kg QD in mice (PMID: 26692561). However, G007-LK was well-tolerated at 200 mg/kg QD over 3 weeks in mice in another study (PMC5759193). Treating mice with G007-LK at 10 mg/kg QD over 6 months also improved glucose tolerance without notable toxicity (PMID 26631215). Importantly, constitutive silencing of both TNKS for 150 days in APC-null mice prevented tumorigenesis without damaging the intestines (PMC6774804). Furthermore, basroparib, a selective TNKS inhibitor, was well tolerated in a recent clinical trial (PMC12498271). We are therefore cautiously optimistic that TNKS degraders will have an improved therapeutic index compared with TNKS inhibitors.

(2) We agree with the Reviewer that Henderson's 2016 paper (PMC4773256) shed important light on the role of TNKS scaffolding in the DC. However, whereas this study demonstrated that knocking down both TNKS by siRNA prevented G007-LK to induce AXIN puncta, the function role of TNKS scaffolding in the DC remained unaddressed. We will include a more detailed description on Henderson's discovery.

(3) Indeed, Guettler demonstrated that TNKS scaffolding could promote WNT/β-catenin signaling in 2016, which forms the basis of the current work. Meanwhile, whereas there have been efforts to target the SAM or ARC domain to address TNKS scaffolding, our approach of targeting TNKS for degradation is complementary. We will provide a more detailed discussion of these studies.

(4) Biomolecular condensates are membrane less cellular compartments formed by phase separation of biomolecules, regardless of the physical/material properties (PMID: 28935776 and PMC7434221). Super-resolution microscopy studies by Peifer and Stenmark (PMC4568445 and PMID 26124443) showed that AXIN, APC, TNKS, and β-catenin interacted with each other to assemble into membraneless complexes, wherein AXIN and APC formed filaments throughout the DC. Peifer has also summarized evidence that supports the condensate nature of the DC (PMC6386181). However, we acknowledge that testing the physical properties of reconstituted DC (PMC8403986) will provide a better understanding of the nature, for example liquid vs. gel, of these condensates.

(5) We will evaluate the ability of IWR1 and IWR1-POMA to engage TNKS.

(6) We will modify Fig. 1F to improve clarity and readability.

(7) Fig. S7B shows that IWR1-POMA suppressed WNT/β-catenin signaling more effectively than IWR1 in APC-mut DLD-1 and SW480 CRC cells without TNKS overexpression. Similarly, Fig. S6B shows that IWR1-POMA provided a deeper suppression of STF signals in HeLa cells transfected with AXIN1 and β-catenin while expressing endogenous TNKS. These results provide evidence that inhibitor-induced TNKS scaffolding plays a significant role at endogenous TNKS expression levels. Separately, we will reorganize the figures to better present Fig. 7C and D as suggested by the Reviewer.

(8) We will rephrase "TNKS accumulation negatively impacts the catalytic activity of the DC".

(9) We apologize for confusing β-catenin phosphorylation with β-catenin abundance. Here, we refer the catalytic activity of the DC to as the ability of the DC to promote β-catenin degradation rather than the kinetics of β-catenin phosphorylation and ubiquitination. It is commonly observed that AXIN stabilization by TNKS inhibitors increases the DC size and reduces the β-catenin levels. Peifer has also noted that APC can increase the size and the "effective activity" of the DC (PMC5912785 and PMC4568445). As such, the induction of AXIN puncta by TNKS inhibitors is frequently used as an indicator of WNT/β-catenin pathway inhibition. However, because the DC only primes β-catenin but does not catalyze its degradation, we will revise our manuscript to improve accuracy and clarity.

(10) We will examine the effects of IWR1 and IWR1-POMA in additional cell lines, quantify the colony formation data, and reorganize the figures.

(11) As discussed above, evidence for on-target toxicity of WNT/β-catenin inhibition is mixed. Yet, the observation of no dose-limiting toxicity for basroparib at doses up to 360 mg QD in human (PMC12498271) is encouraging. PROTAC works by catalyzing target degradation, which is different from traditional catalytic inhibitors that require continuous target occupancy at a high level. Because IWR1-POMA has a durable effect on TNKS, we expect that a fully optimized TNKS degrader may allow less frequent dosing than basroparib and consequently an even more favorable therapeutic window.

(12/13) We will include quantification data, replicate information, and nuclei staining or cell outlines for the fluorescence microscopy experiments.

(14) Cytosolic fractions of cells were prepared using a commercial cytoplasmic extraction kit following manufacturer's instructions. We will include detailed information in the revised manuscript.

Reviewer #3 (Public Review):

We thank the Reviewer for the helpful suggestions.

(1) We will modify the title to include the PROTAC aspect.

(2) As the Reviewer suggested, the bell-shaped dose response of the PROTAC originated from the formation of saturated binary complexes. At high PROTAC concentrations, binding of TNKS and CRBN/VHL by separate PROTAC molecules impedes the formation of productive ternary complexes, which results in reduced degradation efficacy and consequently the hook effect.

(3) The structure-activity relationship of PROTACs is often unpredictable, as both the kinetics and thermodynamics of the target and E3 ligase binding play crucial roles. The lack of translation in degradation efficacy from IWR1 to G007-LK derived PROTACs may originate from differences in the binding kinetics or subtle changes in the orientation of the linker exit vector. We will include data on G007-LK in the revised manuscript.

(4) We will quantify the Western blots, immunofluorescence images, colony formation data, and the replicate information.

Author response:

Reviewer #1 (Public Review):

Summary:

The authors aim to demonstrate that GWAS summary statistics, previously considered safe for open sharing, can, under certain conditions, be used to recover individual-level genotypes when combined with large numbers of high-dimensional phenotypes. By reformulating the GWAS linear model as a system of linear programming constraints, they identify a critical phenotypeto-sample size ratio (R/N) above which genotype reconstruction becomes theoretically feasible.

Strengths:

There is conceptual originality and mathematical clarity. The authors establish a fundamental quantitative relationship between data dimensionality and privacy leakage and validate their theory through well-designed simulations and application to the GTEx dataset. The derivation is rigorous, the implementation reproducible, and the work provides a formal framework for assessing privacy risks in genomic research

We thank the reviewer for the positive assessment of our work’s conceptual originality, mathematical rigor, and reproducible implementation.

Weaknesses:

The study simplifies assumptions that phenotypes are independent, which is not the truth, and are measured without noise. Real-world data are highly correlated across different levels, not only genotype but also multi-omics, which may overstate recovery potential. The empirical evidence, while illustrative, is limited to small-scale data and idealized conditions; thus, the full practical impact remains to be demonstrated. GTEx analysis used only whole blood eQTL data from 369 individuals, which cannot capture the complexity, sample heterogeneity, or cross-tissue dependencies typical of biobank-scale studies

We recognize the concern regarding the independence and noiselessness assumptions in our frame work. While assuming independent, noiseless phenotypes represents an idealized scenario, it allows us to clearly demonstrate the conceptual potential of our framework. The GTEx whole blood analysis is intended as a proof-of-concept, illustrating feasibility rather than capturing full biological complexity. In the revised manuscript, we will clarify these assumptions, emphasize that practical reconstruction accuracy maybe lower in correlated and noisy real-world data, and expand empirical validation to multiple GTEx tissue sand independent cohorts to demonstrate robustness under more realistic conditions.

Reviewer #2 (PublicReview):

Summary:

This study focuses on the genomic privacy risks associated with Genome-Wide Association Study (GWAS) summary statistics, employing a three-tiered demonstration framework of” theoretical derivation- simulation experiments- real-data validation”. The research finds that when GWAS summary statistics are combined with high-dimensional phenotypic data, genotype recovery and individual re-identification can be achieved using linear programming methods. It further identifies key influencing factors such as the effective phenotype-to-sample sizeratio(R/N) and minor allele frequency(MAF). These findings provide practical reference for improving data governance policies in genomic research, holding certain real-world significance

Strengths:

This study integrates theoretical analysis, simulation validation, and the application of real world datasets to construct a comprehensive research framework, which is conducive to understanding and mitigating the risk of private information leakage in genomic research

We are glad the reviewer values our integration of theory, simulation, and real data

Weaknesses:

(1) Limited scope of variant types covered:

The analysis is conducted solely on Single Nucleotide Polymorphisms(SNPs), omitting other crucial genomic variant types such as Copy Number Variations(CNVs), Insertions/Deletions (InDels), and chromosomal translocations/inversions. From a genomic structure perspective, variants like CNVs and InDels are also core components of individual genetic characteristics, and in some disease-related studies, association signals for these variants can be even more significant than those for SNPs. From the perspective of privacy risk logic, the genotypes of these variants (e.g., copy number for CNVs, base insertion/deletion status for InDels) can also be quantified and could theoretically be inferred backwards using the combination of ”summary statistics +high-dimensional phenotypes”. Their privacy leakage risks might differ from those of SNPs(for instance, rare CNVs might be more easily re-identified due to higher genetic specificity)

This point raises an important clarification regarding variant types beyond SNPs. We would like to clarify that our mathematical framework is not inherently restricted to SNPs. In fact, it is broadly applicable to any genetic variant that can be represented numerically, e.g., allelic dosage (0/1/2), copy number counts for CNVs, or presence/absence indicators for InDels. Conceptually, CNVs , InDels, and other structural variants can be incorporated in the same way as SNPs.

The main limitation arises from the current availability of GWAS summary statistics for these non-SNP variant types (e.g., CNV dosages≥3), which are still relatively scarce. As a result, empirically evaluating our framework on these variant classes would be challenging. In the revision, we will explicitly emphasize the general applicability of our framework to diverse genetic variants while clearly noting this practical limitation. We also plan to include simulations to investigate the recovery accuracy associated with CNVs and InDels, which will further demonstrate the extensibility of our approach. It should be noted, however, that leaking genotypic data of ordinary SNPs already raises concerns, regardless of other types of genetic variants.

(2) Bias in data applicability scope:

Both the simulation experiments and real-data validation in the study primarily rely on European population samples (e.g.,489 Europe an samples from the 1000 Genomes Project; the genetic background of whole blood tissue samples from the GTEx project is not explicitly mentioned regarding non-European proportions). It only briefly notes a higher risk for African populations in the individual re-identification risk assessment, without conducting systematic analyses for other populations, such as East Asian, South Asian, or admixed American populations. Significant differences in genetic structure (e.g., MAF distribution, linkage disequilibrium patterns) exist across different populations. This may result in the R/N threshold and the relationship between MAF and recovery accuracy identified in the study not being fully applicable to other populations.

Hence, addressing the aforementioned issues through supplementary work would enhance the study’s scientific rigor and application value, potentially providing more comprehensive theoretical and technical support for” privacy protection” in genomic data sharing.

We acknowledge this valid concern regarding the generalizability of our findings. Our analysis already identifies MAF as a key factor influencing recovery accuracy, which begins to address population-specific genetic differences. Importantly, because our reconstruction method treats each variant independently, its success does not rely on population-specific LD patterns. The core determinant of feasibility is the ratio of phenotypic dimensions to sample size(R/N), a relationship we expect to hold a cross populations.

Nevertheless, we agree that further validation across diverse ancestries can be helpful. In the revised manuscript, we will try to include additional cohorts as extended validation analyses

Version 3 of this preprint has been peer-reviewed and recommended by Peer Community in Evolutionary Biology.<br /> See the peer reviews and the recommendation.

Version 3 of this preprint has been peer-reviewed and recommended by Peer Community in Evolutionary Biology.<br /> See the peer reviews and the recommendation.

Version 3 of this preprint has been peer-reviewed and recommended by Peer Community in Evolutionary Biology.<br /> See the peer reviews and the recommendation.

DOI: 10.1093/genetics/iyae053

Resource: Caenorhabditis Genetics Center (RRID:SCR_007341)

Curator: @Apiekniewska

SciCrunch record: RRID:SCR_007341

What is this?

DOI: 10.1155/humu/8719836

Resource: (CLS Cat# 300342/p657_SK-OV-3, RRID:CVCL_0532)

Curator: @scibot

SciCrunch record: RRID:CVCL_0532

What is this?

DOI: 10.1111/jnc.70295

Resource: Nanodrop Qubit 3 Fluorometer (RRID:SCR_020311)

Curator: @scibot

SciCrunch record: RRID:SCR_020311

What is this?

DOI: 10.1038/s41598-025-23545-3

Resource: Texas A and M University Microscopy and Imaging Center Core Facility (RRID:SCR_022128)

Curator: @scibot

SciCrunch record: RRID:SCR_022128

What is this?

DOI: 10.1038/s41467-025-64999-3

Resource: University of Ottawa Louise Pelletier Histology Core Facility (RRID:SCR_021737)

Curator: @scibot

SciCrunch record: RRID:SCR_021737

What is this?

DOI: 10.1038/s41467-025-64999-3

Resource: None

Curator: @scibot

SciCrunch record: RRID:AB_2618166

What is this?

DOI: 10.1038/s41467-025-64999-3

Resource: University of Ottawa Cell Biology and Image Acquisition Core Facility (RRID:SCR_021845)

Curator: @scibot

SciCrunch record: RRID:SCR_021845

What is this?

DOI: 10.1038/s41467-025-64999-3

Resource: University of Ottawa Preclinical Imaging Core Facility (RRID:SCR_021832)

Curator: @scibot

SciCrunch record: RRID:SCR_021832

What is this?

DOI: 10.1038/s41467-025-64999-3

Resource: Ottawa Hospital Research Institute StemCore Laboratories Core Facility (RRID:SCR_012601)

Curator: @scibot

SciCrunch record: RRID:SCR_012601

What is this?

DOI: 10.1007/s00125-025-06603-3

Resource: HIRN Human Pancreas Analysis Program (RRID:SCR_016202)

Curator: @scibot

SciCrunch record: RRID:SCR_016202

What is this?

DOI: 10.1007/s00125-025-06603-3

Resource: Integrated Islet Distribution Program (IIDP) (RRID:SCR_014387)

Curator: @scibot

SciCrunch record: RRID:SCR_014387

What is this?

DOI: 10.1007/s00125-025-06603-3

Resource: Human Islet Research Network (HIRN) (RRID:SCR_014393)

Curator: @scibot

SciCrunch record: RRID:SCR_014393

What is this?

ipfs

https://bafybeid2rtfgfxlqdjo2nedenrk7miue5w6abimo7bz5k5gehglptsc32u.ipfs.dweb.link/?path=/♖/hyperpost/~/indyweb/📓/20/25/11/3

https://bafybeid2rtfgfxlqdjo2nedenrk7miue5w6abimo7bz5k5gehglptsc32u.ipfs.dweb.link/?path=/♖/hyperpost/~/indyweb/📓/20/25/11/3

1.Eu estou dor de cabeça agora porque eu estou com fome. 2.Tânia está com triste porque Luísa e sandra são raiva 3.Quanto somos com sedes, nós não vondade de bebemos café 4.Eu estou morrendo de frio.Será que estou com febre?

Reviewer #3 (Public review):

Summary:

The manuscript by Shukla and colleagues presents a comprehensive study that addresses a central question in kinesin-1 regulation - how cargo binding to the kinesin light chain (KLC) tetratricopeptide repeat (TPR) domains triggers activation of full-length kinesin-1 (KHC). The authors combine AlphaFold3 modeling, biophysical analysis (fluorescence polarization, hydrogen-deuterium exchange), and electron microscopy to derive a mechanistic model in which the KLC-TPR domains dock onto coiled-coil 1 (CC1) of the KHC to form the "TPR shoulder," stabilizing the autoinhibited (λ-particle) conformation. Binding of a W/Y-acidic cargo motif (KinTag) or deletion of the CC1 docking site (TDS) dislocates this shoulder, liberating the motor domains and enhancing accessibility to cofactors such as MAP7. The results link cargo recognition to allosteric structural transitions and present a unified model of kinesin-1 activation.

Strengths:

(1) The study addresses a fundamental and long-standing question in kinesin-1 regulation using a multidisciplinary approach that combines structural modeling, quantitative biophysics, and electron microscopy.

(2) The mechanistic model linking cargo-induced dislocation of the TPR shoulder to activation of the motor complex is well supported by both structural and biochemical evidence.

(3) The authors employ elegant protein-engineering strategies (e.g., ElbowLock and ΔTDS constructs) that enable direct testing of model predictions, providing clear mechanistic insight rather than purely correlative data.

(4) The data are internally consistent and align well with previous studies on kinesin-1 regulation and MAP7-mediated activation, strengthening the overall conclusion.

Weaknesses:

(1) While the EM and HDX-MS analyses are informative, the conformational heterogeneity of the complex limits structural resolution, making some aspects of the model (e.g., stoichiometry or symmetry of TPR docking) indirect rather than directly visualized.

(2) The dynamics of KLC-TPR docking and undocking remain incompletely defined; it is unclear whether both TPR domains engage CC1 simultaneously or in an alternating fashion.

(3) The interplay between cargo adaptors and MAP7 is discussed but not experimentally explored, leaving open questions about the sequence and exclusivity of their interactions with CC1.

Reviewer #2 (Public review):

Summary:

In this study, the authors follow up on a previous suppressor screen of a temperature-sensitive allele of mis4 (mis4-G1487D), the cohesin loading factor in S. pombe, and identify additional suppressor alleles tied to the S. pombe TORC1 complex. Their analysis suggests that these suppressor mutations attenuate TORC1 activity, while enhanced TORC1 activity is deleterious in this context. Suppression of TORC1 activity also ameliorates chromosome segregation and spindle defects observed in the mis4-G1487D strain, although some more subtle effects are not reconstituted. The authors provide evidence that this genetic suppression is also tied to the reconstitution of cohesin loading. Moreover, disrupting TORC1 also enhances Mis4/cohesin association with chromatin (likely reflecting enhanced loading) in WT cells, while rapamycin treatment can enhance the robustness of chromosome transmission. These effects likely arise directly through TORC1 or its downstream effector kinases, as TORC1 co-purifies with Mis4 and Rad21; these factors are also phosphorylated in a TORC1-dependent fashion. Disrupting Sck2, a kinase downstream of TORC1, also suppresses the mis4-G1487D allele while simultaneous disruption of Sck1 and Sck2 enhances cohesin association with chromatin, albeit with differing effects on phosphorylation of Mis4 and Psm1/Scm1. Phosphomutants of Mis4 and Psm1 that mimic observed phosphorylation states identified by mass spectrometry that are TORC1-dependent also suppressed phenotypes observed in the mis4-G1487D background. Last, the authors provide evidence that the mis4-G1487D background and TORC1 mutant backgrounds display an overlap in the dysregulation of genes that respond to environmental conditions, particularly in genes tied to meiosis or other "stress".

Overall, the authors provide compelling evidence from genetics, biochemistry, and cell biology to support a previously unknown mechanism by which nutrient sensing regulates cohesin loading with implications for the stress response. The technical approaches are generally sound, well-controlled, and comprehensive.

Specific Points:

(1) While the authors favor the model that the enhanced cohesin loading upon diminished TORC1 activity helps cells to survive harsh environmental conditions, as starvation of S. pombe also drives commitment to meiosis, it seems as plausible that enhanced cohesin loading is related to preparing the chromosomes to mate.

(2) Related to Point 1, the lab of Sophie Martin previously published that phosphorylation of Mis4 characterizes a cluster of phosphotargets during starvation/meiotic induction (PMID: 39705284). This work should be cited, and the authors should interrogate how their observations do or do not relate to these prior observations (are these the same phosphosites?).

(3) It would be useful for the authors to combine their experimental data sets to interrogate whether there is a relationship between the regions where gene expression is altered in the mis4-G1487D strain and changes in the loading of cohesin in their ChIP experiments.

(4) Given that the genes that are affected are predominantly sub-telomeric while most genes are not affected in the mis4-G1487D strain, one possibility that the authors may wish to consider is that the regions that become dysregulated are tied to heterochromatic regions where Swi6/HP1 has been implicated in cohesin loading.

(5) It would be helpful to show individual data points from replicates in the bar graphs - it is not always clear what comprises the data sets, and superplots would be of great help.

Reviewer #1 (Public review):

Summary:

The authors investigate how UVC-induced DNA damage alters the interaction between the mitochondrial transcription factor TFAM and mtDNA. Using live-cell imaging, qPCR, atomic force microscopy (AFM), fluorescence anisotropy, and high-throughput DNA-chip assays, they show that UVC irradiation reduces TFAM sequence specificity and increases mtDNA compaction without protecting mtDNA from lesion formation. From these findings, the authors suggest that TFAM acts as a "sensor" of damage rather than a protective or repair-promoting factor.

Strengths:

(1) The focus on UVC damage offers a clean system to study mtDNA damage sensing independently of more commonly studied repair pathways, such as oxidative DNA damage. The impact of UVC damage is not well understood in the mitochondria, and this study fills that gap in knowledge.

(2) In particular, the custom mitochondrial genome DNA chip provides high-resolution mapping of TFAM binding and reveals a global loss of sequence specificity following UVC exposure.

(3) The combination of in vitro TFAM DNA biophysical approaches, combined with cellular responses (gene expression, mtDNA turnover), provides a coherent multi-scale view.

(4) The authors demonstrate that TFAM-induced compaction does not protect mtDNA from UVC lesions, an important contribution given assumptions about TFAM providing protection.

Weaknesses:

(1) The authors show a decrease in mtDNA levels and increased lysosomal colocalization but do not define the pathway responsible for degradation. Distinguishing between replication dilution, mitophagy, or targeted degradation would strengthen the interpretation

(2) The sudden induction of mtDNA replication genes and transcription at 24 h suggests that intermediate timepoints (e.g., 12 hours) could clarify the kinetics of the response and avoid the impression that the sampling coincidentally captured the peak.

(3) The authors report no loss of mitochondrial membrane potential, but this single measure is limited. Complementary assays such as Seahorse analysis, ATP quantification, or reactive oxygen species measurement could more fully assess functional integrity.

(4) The manuscript briefly notes enrichment of TFAM at certain regions of the mitochondrial genome but provides little interpretation of why these regions are favored. Discussion of whether high-occupancy sites correspond to regulatory or structural elements would add valuable context.

(5) It remains unclear whether the altered DNA topology promotes TFAM compaction or vice versa. Addressing this directionality, perhaps by including UVC-only controls for plasmid conformation, would help disentangle these effects if UVC is causing compaction alone.

(6) The authors provide a discrepancy between the anisotropy and binding array results. The reason for this is not clear, and one wonders if an orthogonal approach for the binding experiments would elucidate this difference (minor point).

Assessment of conclusions:

The manuscript successfully meets its primary goal of testing whether TFAM protects mtDNA from UVC damage and the impact this has on the mtDNA. While their data points to an intriguing model that TFAM acts as a sensor of damaged mtDNA, the validation of this model requires further investigation to make the model more convincing. This is likely warranted for a follow-up study. Also, the biological impact of this compaction, such as altering transcription levels, is not clear in this study.

Impact and utility of the methods:

This work advances our understanding of how mitochondria manage UVC genome damage and proposes a structural mechanism for damage "sensing" independent of canonical repair. The methodology, including the custom TFAM DNA chip, will be broadly useful to the scientific community.

Context:

The study supports a model in which mitochondrial genome integrity is maintained not only by repair factors, but also by selective sequestration or removal of damaged genomes. The demonstration that TFAM compaction correlates with damage rather than protection reframes an interesting role in mtDNA quality control.

Reviewer #2 (Public review):

Summary:

King et al. present several sets of experiments aimed to address the potential impact of UV irradiation on human mitochondrial DNA as well as the possible role of mitochondrial TFAM protein in handling UV-irradiated mitochondrial genomes. The carefully worded conclusion derived from the results of experiments performed with human HeLa cells, in vitro small plasmid DNA, with PCR-generated human mitochondrial DNA, and with UV-irradiated small oligonucleotides is presented in the title of the manuscript: "UV irradiation alters TFAM binding to mitochondrial DNA". The authors also interpret results of somewhat unconnected experimental approaches to speculate that "TFAM is a potential DNA damage sensing protein in that it promotes UVC-dependent conformational changes in the [mitochondrial] nucleoids, making them more compact." They further propose that such a proposed compaction triggers the removal of UV-damaged mitochondrial genomes as well as facilitates replication of undamaged mitochondrial genomes.

Strengths:

(1) The authors presented convincing evidence that a very high dose (1500 J/m2) of UVC applied to oligonucleotides covering the entire mitochondrial DNA genome alleviates sequence specificity of TFAM binding (Figure 3). This high dose was sufficient to cause UV lesions in a large fraction of individual oligonucleotides. The method was developed in the lab of one of the corresponding authors (reference 74) and is technically well-refined. This result can be published as is or in combination with other data.

(2) The manuscript also presents AFM evidence (Figure 4) that TFAM, which was long known to facilitate compaction of the mitochondrial genome (Alam et al., 2003; PMID 12626705 and follow-up citations), causes in vitro compaction of a small pUC19 plasmid and that approximately 3 UVC lesions per plasmid molecule result in a slight, albeit detectable, increase in TFAM compaction of the plasmid. Both results can be discussed in line with a possible extrapolation to in vivo phenomena, but such a discussion should include a clear statement that no in vivo support was provided within the set of experiments presented in the manuscript.

Weaknesses:

Besides the experiments presented in Figures 3 and 4, other results do not either support or contradict the speculation that TFAM can play a protective role, eliminating mitochondrial genomes with bulky lesions by way of excessive compaction and removing damaged genomes from the in vivo pool.

To specify these weaknesses:

(1) Figure 1 - presents evidence that UVC causes a reduction in the number of mitochondrial spots in cells. The role of TFAM is not assessed.

(2) Figure 2 - presents evidence that UVC causes lesions in mitochondrial genomes in vivo, detectable by qPCR. No direct assessment of TFAM roles in damage repair or mitochondrial DNA turnover is assessed despite the statements in the title of Figure 2 or in associated text. Approximately 2-fold change in gene expression of TFAM and of the three other genes does not provide any reasonable support to suggestion about increased mitochondrial DNA turnover over multiple explanations on related to mitochondrial DNA maintenance.

(3) Figure 5. Shows that TFAM does not protect either mitochondrial nucleoids formed in vitro or mitochondrial DNA in vivo from UVC lesions as well as has no effect on in vivo repair of UV lesions.

(4) Figure 6: Based on the above analysis, the model of the role of TFAM in sensing mtDNA damage and elimination of damaged genomes in vivo appears unsupported.

(5) Additional concern about Figure 3 and relevant discussion: It is not clear if more uniform TFAM binding to UV irradiated oligonucleotides with varying sequence as compared to non-irradiated oligonucleotides can be explained by just overall reduced binding eliminating sequence specific peaks.

Reviewer #3 (Public review):

Summary:

The study is grounded in the observations that mitochondrial DNA (mtDNA) exhibits a degree of resistance to mutagenesis under genotoxic stress. The manuscript focuses on the effects of UVC-induced DNA damage on TFAM-DNA binding in vitro and in cells. The authors demonstrate increased TFAM-DNA compaction following UVC irradiation in vitro based on high-throughput protein-DNA binding and atomic force microscopy (AFM) experiments. They did not observe a similar trend in fluorescence polarization assays. In cells, the authors found that UVC exposure upregulated TFAM, POLG, and POLRMT mRNA levels without affecting the mitochondrial membrane potential. Overexpressing TFAM in cells or varying TFAM concentration in reconstituted nucleoids did not alter the accumulation or disappearance of mtDNA damage. Based on their data, the authors proposed a plausible model that, following UVC-induced DNA damage, TFAM facilitates nucleoid compaction, which may serve to signal damage in the mitochondrial genome.

Strengths:

The presented data are solid, technically rigorous, and consistent with established literature findings. The experiments are well-executed, providing reliable evidence on the change of TFAM-DNA interactions following UVC irradiation. The proposed model may inspire future follow-up studies to further study the role of TFAM in sensing UVC-induced damage.

Weaknesses:

The manuscript could be further improved by refining specific interpretations and ensuring terminology aligns precisely with the data presented.

(1) In line 322, the claim of increased "nucleoid compaction" in cells should be removed, as there is a lack of direct cellular evidence. Given that non-DNA-bound TFAM is subject to protease digestion, it is uncertain to what extent the overexpressed TFAM actually integrates into and compacts mitochondrial nucleoids in the absence of supporting immunofluorescence data.

(2) In lines 405 and 406, the authors should avoid equating TFAM overexpression with compaction in the cellular context unless the compaction is directly visualized or measured.

(3) In lines 304 and 305 (and several other places throughout the manuscript), the authors use the term "removal rates". A "removal rate" requires a direct comparison of accumulated lesion levels over a time course under different conditions. Given the complexity of UV-induced DNA damage-which involves both damage formation and potential removal via multiple pathways-a more accurate term that reflects the net result of these opposing processes is "accumulated DNA damage levels." This terminology better reflects the final state measured and avoids implying a single, active 'removal' pathway without sufficient kinetic data.

(4) In line 357, the authors refer to the decrease in the total DNA damage level as "The removal of damaged mtDNA". The decrease may be simply due to the turnover and resynthesis of non-damaged mtDNA molecules. The term "removal" may mislead the casual reader into interpreting the effect as an active repair/removal process.

Reviewer #2 (Public review):

Summary:

In this manuscript, the authors combined coarse-grained structure-based model simulation, optical tweezer experiments, and AI-based analysis to assess the knotting behavior of the TrmD-Tm1570 protein. Interestingly, they found that while the structure-based model can fold the single knot from TrmD and Tm1570, the double-knot protein TrmD-Tm1570 cannot form a knot itself, suggesting the need for chaperone proteins to facilitate this knotting process. This study has strong potential to understand the molecular mechanism of knotted proteins, supported by many experimental and simulation evidence. However, there are a few places that appear to lack sufficient details, and more clarification in the presentation is needed.

Strengths:

A combination of both experimental and computational studies.

Weaknesses:

There is a lack of detail to support some statements.

(1) The use of the AI-based method, SOM, can be emphasized further, especially in its analysis of the simulated unfolding trajectories and discovery of the four unfolding/folding pathways. This will strengthen the statistical robustness of the discovery.

(2) The manuscript would benefit from a clearer description of the correlation between the simulation and experimental results. The current correlation, presented in the paragraph starting from Line 250, focuses on measured distances. The authors could consider providing additional evidence on the order of events observed experimentally and computationally. More statistical analyses on the experimental curves presented in Figure 4 supplement would be helpful.

(3) How did the authors calibrate the timescale between simulation and experiment? Specifically, what is the value \tau used in Line 270, and how was it calculated? Relevant information would strengthen the connection between simulation and experiment.

(4) In Line 342, the authors comment that whether using native contacts or not, they cannot fold double-knotted TrmD-Tm1570. Could the authors provide more details on how non-native interactions were analyzed?

(5) It appears that the manuscript lacks simulation or experimental evidence to support the statement at Line 343: While each domain can self-tie into its native knot, this process inhibits the knotting of the other domain. Specifically, more clarification on this inhibition is needed.

Author response:

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

Reviewer #1 (Public review): 

Summary: 

The researchers sought to determine whether Ptbp1, an RNA-binding protein formerly thought to be a master regulator of neuronal differentiation, is required for retinal neurogenesis and cell fate specification. They used a conditional knockout mouse line to remove Ptbp1 in retinal progenitors and analyzed the results using bulk RNA-seq, single-cell RNA-seq, immunohistochemistry, and EdU labeling. Their findings show that Ptbp1 deletion has no effect on retinal development, since no defects were found in retinal lamination, progenitor proliferation, or cell type composition. Although bulk RNA-seq indicated changes in RNA splicing and increased expression of late-stage progenitor and photoreceptor genes in the mutants, and single-cell RNA-seq detected relatively minor transcriptional shifts in Müller glia, the overall phenotypic impact was low. As a result, the authors conclude that Ptbp1 is not required for retinal neurogenesis and development, thus contradicting prior statements about its important role as a master regulator of neurogenesis. They argue for a reassessment of this stated role. While the findings are strong in the setting of the retina, the larger implications for other areas of the CNS require more investigation. Furthermore, questions about potential reimbursement from Ptbp2 warrant further research. 

Strengths: 

This study calls into doubt the commonly held belief that Ptbp1 is a critical regulator of neurogenesis in the CNS, particularly in retinal development. The adoption of a conditional knockout mouse model provides a reliable way for eliminating Ptbp1 in retinal progenitors while avoiding the off-target effects often reported in RNAi experiments. The combination of bulk RNA-seq, scRNA-seq, and immunohistochemistry enables a thorough examination of molecular and cellular alterations at both embryonic and postnatal stages, which strengthens the study's findings. Furthermore, using publicly available RNA-Seq datasets for comparison improves the investigation of splicing and expression across tissues and cell types. The work is wellorganized, with informative figure legends and supplemental data that clearly show no substantial phenotypic changes in retinal lamination, proliferation, or cell destiny, despite identified transcriptional and splicing modifications. 

We thank the Reviewer for their evaluation of the strengths of the study.

Weaknesses: 

The retina-specific method raises questions regarding whether Ptbp1 is required in other CNS locations where its neurogenic roles were first proposed. The claim that Ptbp1 is "fully dispensable" for retinal development may be toned down, given the transcriptional and splicing modifications identified. The possibility of subtle or transitory impacts, such as ectopic neuron development followed by cell death, is postulated, but not completely investigated. Furthermore, as the authors point out, the compensating potential of increased Ptbp2 warrants additional exploration. Although the study performs well in transcriptome and histological analyses, it lacks functional assessments (such as electrophysiological or behavioral testing) to determine if small changes in splicing or gene expression affect retinal function. While 864 splicing events have been found, the functional significance of these alterations, notably the 7% that are neuronalenriched and the 35% that are rod-specific, has not been thoroughly investigated. The manuscript might be improved by describing how these splicing changes affect retinal development or function. 

We have revised the text to address these points as requested.

Reviewer #2 (Public review): 

Summary: 

Ptbp1 has been proposed as a key regulator of neuronal fate through its role in repressing neurogenesis. In this study, the authors conditionally inactivated Ptbp1 in mouse retinal progenitor cells using the Chx10-Cre line. While RNA-seq analysis at E16 revealed some changes in gene expression, there were no significant alterations in retinal cell type composition, and only modest transcriptional changes in the mature retina, as assessed by immunofluorescence and scRNAseq. Based on these findings, the authors conclude that Ptbp1 is not essential for cell fate determination during retinal development. 

Strengths: 

Despite some effects of Ptbp1 inactivation (initiated around E11.5 with the onset of Chx10-Cre activity) on gene expression and splicing, the data convincingly demonstrate that retinal cell type composition remains largely unaffected. This study is highly significant since it challenges the prevailing view of Ptbp1 as a central repressor of neurogenesis and highlights the need to further investigate, or re-evaluate, its role in other model systems and regions of the CNS. 

We thank the Reviewer for their evaluation of the strengths of the study.

Weaknesses: 

A limitation of the study is the use of the Chx10-Cre driver, which initiates recombination around E11. This timing does not permit assessment of Ptbp1 function during the earliest phases of retinal development, if expressed at that time.  

We have revised the text to address the potential limitations of the use of the Chx10-Cre driver in this study.

Reviewer #1 (Recommendations for the authors):

(1) The author only selected scRNA-Seq datasets to examine the expression patterns of Ptbp1 in the retina; incorporating immunostaining analysis in the mouse retina is necessary.

Ptbp1 expression patterns in the mouse retina were performed in Fig. 1b-1d, where Ptbp1 expression was analyzed via immunostaining for Ptbp1 protein in Chx10-Cre control and Ptbp1KO retinas at E14, P1, and P30, and are quantified in Fig. 1e. 

(2) In Figure 1, Ptbp1 signals were still detected in the KO mice, with the author suggesting that this may indicate cross-reactivity with an unknown epitope. Why is this unknown epitope only detected in the ganglion cell layer? Additional antibodies are needed to confirm the staining results. Furthermore, it is essential to verify the KO at the mRNA level using PCR. 

We are unsure of the identity of this cross-reacting epitope, although it might be Ptbp2, which is enriched expressed in immature retinal ganglion cells (Fig. S1).  In any case, we do not believe that the identity of this epitope is not relevant to assessing the efficiency of Ptbp1 deletion, as it is not detectably expressed in retinal ganglion cells in any case (Fig. S1).

Although the heatmap in Figure 2B indicates a decrease in Ptbp1 levels in the KO mice, the absence of statistical data makes it difficult to evaluate the KO efficiency. 

Respectfully, we believe that Ptbp1 knockout efficiency is adequately addressed using immunohistochemistry, and that further statistical analysis is not essential here. 

Cre staining of the Chx10-Cre;Ptbp1lox/lox mice or using reporter lines is also suggested to indicate the theoretically knockout cells. Providing high-power images of the Ptbp1 staining would help readers clearly recognize the staining signals.

To clarify the identity of the knockout cells, we have updated Figure 1 to include the Chx10-CreEGFP staining which more clearly delineates the cells in which Ptbp1 is deleted. Regarding verification of the knockout, we believe additional PCR assays are not necessary, as we have already demonstrated efficient loss of Ptbp1 in Chx10-Cre-expressing cells at the RNA level by both single-cell RNA-sequencing and bulk RNA-sequencing, and also at the protein level by immunohistochemistry. Sun1-GFP Cre reporter lines are also used in Figures 1 and S2 to visualize patterns of Cre activity, a point which is now highlighted in the text. Together, these approaches provide sufficient evidence for effective Ptbp1 knockout. 

(3) The possibility of ectopic neuron formation followed by cell death is intriguing but underexplored. Consider adding apoptosis assays (e.g., TUNEL staining) at early developmental stages to test this hypothesis.

While apoptosis assays such as TUNEL staining would be helpful to address this hypothesis, we feel incorporating these additional experiments is currently beyond the scope of this study. We agree the possibility of cell death is intriguing and plan to explore this in future work.

(4) On page 4, the statement "We did not observe any significant differences ... Chx10Cre;Ptbp1lox/lox mice (Fig. 2b,c)" should refer to Fig. 3b,c instead.

We have changed the text to refer to Fig. 3b,c.

(5) The labeling in Figure 3 as "Cre-Ptbp1" is inconsistent with the figure legend "Ptbp1-Ctrl.".

This language was used because the samples for EdU staining in Figure 3 were Chx10-Cre negative Ptbp1^lox/lox mice. We have updated the language in the manuscript and figure to reflect the genotypes more clearly. 

(6) P30 mice are still sexually immature; the term "adolescent" or "juvenile" should be used instead of "adult."

We have updated the language in the text from “adult” to “adolescent” to describe P30 mice, although the retina itself is mature by this age.

Reviewer #2 (Recommendations for the authors):

(1) As mentioned in the public review, a limitation of the study is that Ptbp1 KO is not induced prior to E11. The authors should acknowledge this limitation and include in the Discussion that the use of the Chx10-Cre line does not permit evaluation of a potential role for Ptbp1 during very early stages of retinal development, should it be expressed at that time (an aspect that would be important to determine).

We and have added this limitation to the Discussion in the sentence highlighted below.

Furthermore, the use of the Chx10-Cre transgene in this study does not exclude a potential role for Ptbp1 during very early stages of retinal development prior to E11 (pg. 6).

(2) While the data convincingly show no significant changes in retinal cell type distribution in Ptbp1 mutants, the claims in the abstract and introduction that Ptbp1 is "dispensable for retinal development" or "dispensable for the process of neurogenesis" may be overstated. Indeed, the results indicate that loss of Ptbp1 function influences retinal development by promoting neurogenesis through induction of a neuronal-like splicing program in neural progenitors. Concluding solely that Ptbp1 is dispensable for retinal cell fate specification, rather than for retinal development as a whole, would thus seem more accurate.

We have updated the language in the text to reflect Ptbp1’s role in regulating retinal cell fate specification more clearly.

(3) The authors conclude from Figure 5 that "No changes in the identity or composition of any retinal cell type were observed." Which statistical test was applied to support this conclusion? The figure indicates that Müller cells comprise 10.5% of the total cell population in controls versus 8.2% in Ptbp1-KO retinas. It may be important to consider the overall distribution of glia versus all neurons (rather than each neuron subtype individually). While the observed difference (~2% more glia at the expense of neurons) appears modest, it would be important to determine whether this trend is consistent and statistically significant.

To evaluate cell type composition, we performed differential expression analysis across all major retinal cell types and compared proportional cell type representation between control and Ptbp1 KO retinas. While these analyses did not reveal marked differences in any specific cell type, we acknowledge that the scRNA-Seq dataset includes a single experimental replicate, containing two retinas in each replicate. Therefore, we cannot draw firm statistical conclusions regarding the relative distribution of glia versus neurons, and the modest difference observed in glia cell proportion should be interpreted with caution. We agree that assessing glia-to-neuron ratios across additional replicates will be important in future studies.

(4) Referringx to Figure S1 (scRNA-seq data), the authors state that Ptbp1 mRNA is robustly expressed in retinal progenitors and Müller glia in both mouse and human retina. While the immunostaining in Figure 4 indeed clearly shows strong expression in Müller cells, the scRNAseq data presented in Figure S1 do not support the claim of "robust" expression in Müller glia in the mouse retina. This is even more striking in the human data, where panels F and H show that Ptbp1 is expressed at extremely low, certainly not "robust", levels in Müller cells. The corresponding sentence in the Results section should therefore be revised to more accurately reflect the data presented in Figure S1, or be supported by complementary immunofluorescence evidence.

We thank the reviewer for this comment. We have revised this section of the Results to better reflect Fig S1, as follows:

We observe high expression levels of Ptbp1 mRNA in primary retinal progenitors in both species and Müller glia in mouse retina, with weaker expression in neurogenic progenitors, and little expression detectable in neurons at any developmental age.

(5) When mentioning potential compensation by Ptbp2, the authors may also consider discussing the possibility that compensatory mechanisms can differ between knockdown and knockout approaches. In this context, it is noteworthy that a recent study by Konar et al., Exp Eye Res, 2025 (published after the submission of the present manuscript) reports that Ptbp1 knockdown promotes Müller glia proliferation in zebrafish.

We thank the reviewer for this suggestion. To address this, we have included a section considering this possibility in the discussion section highlighted below.

It is also possible that compensatory mechanisms differ between knockdown and knockout approaches. Notably, a recent study (Konar et al. 2025) reported that Ptbp1 knockdown promotes Müller glia proliferation in zebrafish, suggesting that effects of acute reduction of Ptbp1 may not fully mirror those of complete loss-of-function. 

(6) The statistical analyses were performed using a t-test. However, this parametric test is not appropriate for experiments with low sample sizes. A non-parametric test, such as the MannWhitney test, would be more suitable in this context. Furthermore, performing statistical analysis on n = 2 (Figure 3C) is not statistically valid.

We thank the reviewer for this comment. We agree that with a small n, non-parametric tests are more appropriate. We have added additional retinas (now n=5) for the Ptbp1-KO condition in Figure 3C and reanalyzed with the appropriate non-parametric Mann-Whitney test. For all other datasets with sufficient replicates (n≥ 4/genotype), parametric tests such as unpaired t-tests remain valid, and the results are consistent with non-parametric testing. 

(7) Figure S3 is accompanied by only a brief explanation in the Results section (a single sentence despite the figure containing six panels), which makes it difficult for readers unfamiliar with this type of data to interpret.

We thank the reviewer for the suggestion. To address this, we have included a more detailed explanation of Supplementary Figure S3 to better clarify our analysis of mature neuronal and glial cell types in both Ptbp1-deficient and wild-type animals. The relevant text now reads:

Notably, splicing patterns in Ptbp1-deficient retinas showed stronger correlation with Thy1positive neurons— which exhibit low Ptbp1 expression—and minimal overlap with microglia and auditory hair cells, the adult cell types with the highest Ptbp1 levels (Fig. S3).

Gene expression and splicing changes were compared across several reference tissues: heart tissue and Thy1-positive neurons, mature hair cells, microglia, and astrocytes (Fig. S3a,b). A heatmap of differentially expressed genes showed that while Ptbp1-deficient retinas diverged from WT retinas, their expression profiles did not resemble those of fully differentiated cell types like rods, astrocytes, or adult WT retina (Fig. S3c). Consistently, Pearson correlation analysis revealed that Ptbp1-deficient and WT retinas were more similar to each other than to fully differentiated neuronal or glial populations (Fig. S3d). Splicing profile analysis further revealed that while there was high correlation of PSI between Ptbp1-deficient and WT retinas, Ptbp1deficient retinas more closely resembled Thy1-positive neurons, whereas WT retinas aligned more strongly with mature cells such as astrocytes, microglia, and auditory hair cells (Fig. S3ef). Together, these results suggest that although Ptbp1 loss induces hundreds of alternative splicing events, the magnitude of PSI changes in the KO retinas remains considerably lower than that seen in fully differentiated cell types (Extended Data 3). Thus, while a subset of splicing events overlaps with those characteristic of mature neurons or rods, the overall splicing and expression profiles of KO retinas are more similar to those of developing retinal tissue rather than terminally differentiated neuronal or glial populations.

(8) To assess progenitor proliferation, the authors performed EdU labeling experiments in P0 retinas. Is there a rationale for not examining earlier developmental time points to evaluate potential effects on early RPCs?

We thank the reviewer for this comment. We chose to perform EdU labeling experiments at P0 for several reasons. P0 represents a developmental stage where RPCs are actively proliferating and represent ~35% of all retina cells, and the retina is transitioning to intermediate-late-stage development, providing sufficient time to ensure efficient and widespread disruption of Ptbp1. Earlier embryonic timepoints were not examined here, as addressing all stages of development was beyond the scope of this current study. However, we agree that investigating whether Ptbp1 plays stage-specific roles during development on early RPCs is an important question and potential future direction.

(9) In Figure S2, panel D shows staining in GCL under the Ptbp1 condition that does not make sense and is inconsistent with panel C. If possible, the authors should provide an alternative image to prevent any confusion.

Thank you for bringing this to our attention. The image shown for Ptbp1-KO in Figure 2d shows Sun1-eGFP labeling, which labels every cell affected by the Cre condition. The genotype for this mouse was Chx10-Cre;Ptbp1lox/lox;Sun1-GFP. We apologize for any confusion and have updated the genotype in the figure legend.

(10) The authors should revise the following sentence at the end of the Discussion section, as its meaning is unclear: "...and conditions for in vitro analysis may have accurately replicated conditions in the native CNS."

We thank the reviewer for this comment and have revised this sentence in the discussion for the sentence below.

Previous studies using knockdown may have been complicated by off-target effects (Jackson et al. 2003), and conditions for in vitro analysis may not have accurately replicated conditions in the native CNS.

For the bibliography, I was really interested in [p31], “Disinformation as Collaborative Work: Surfacing the Participatory Nature of Strategic Information Operations.” Just from the title and description, it already changes how I think about fake news. Before, I imagined disinformation like one bad actor or one troll farm pushing lies. This paper instead frames it as a kind of “collaborative work,” where many different people and tools are involved, sometimes even regular users who don’t realize they are part of the campaign. That idea is kind of scary, because it means disinformation is not only top-down, but also bottom-up and participatory. It also connects nicely with the chapter’s point that crowdsourcing can be used both for good (like Foldit or crisis help) and for harmful goals. It makes me feel we really need better education on how to not accidentally help spread these operations.

this is what you should look at for your research

布鲁姆分类法 1. 记忆: 回忆基本事实和概念 2. 理解: 解释概念或者过程 3. 应用: 将只是运用到新的情境中 4. 分析: 将信息分级成各个组成部分 5. 评价: 根据标准和准则做出判断 6. 创造: 制作原创作品或解决方案

theme: Knowledge Serving Commons

from

to

Structuring knowledge commons

Part 2 of an ongoing series on knowledge serving commons

https://bafybeicuznswtf2d3sevop4ripd5hy77nauwbicurnruv4ocf3w32ke7ha.ipfs.dweb.link/?filename=index.html&&urn=🧊/♖/hyperpost/~/indyweb/📓/20/25/11/4/do/🏛%EF%B8%8F/growingcommons.substack.com/p/structuring-knowledge-commons

paraphrase

experiment

added this by hand

```html <link rel="canonical" href="https://hyperpost.peergos.me/~/indyweb/scratch/">

```

4 way to embed hypothesis by the indy0pad

just change the template injected and save to IPFS

Gyuri's Web Snarf Folder - 4th week of October 2025

• Author initially struggled with social skills, feeling awkward, excitable, and bullied due to abrasive behavior and sensitivity.

• Six stages of social learning show a progression from self-focused to deeply embodied connection strategies.

• Stage 1: Tried to be a dazzling, interesting, intellectual person to gain approval.

  • Emulated admired cultural figures, memorized poetry, and read complex literature.
  • Told dramatic personal stories and developed scholarly opinions.
  • This approach earned polarized approval, often distancing others because of its presentational quality.

• Stage 2: Learned to play the social game by adapting to others' social styles, especially in restaurant work.

  • Observed and mimicked the social behaviors that successful servers used.
  • Adopted flexible social roles matching the table's mood (efficient, flirtatious, etc.).
  • Made people feel comfortable by playing their "game," though still somewhat role-driven.

• Stage 3: Loosened grip on social scripts; used quirky, authentic behaviors to relax social interactions.

  • Added benign strangeness and surreal quirkiness to interactions to signal social playfulness.
  • Created moments of unexpected connection by breaking scripts sideways.
  • Learned that how things are said can be more important than what is said.

• Stage 4: Developed bodily awareness and real-time non-verbal communication like dancing with others.

  • Became attuned to subtle body language and unspoken emotional states.
  • Reacted fluidly and spontaneously to ongoing social and emotional cues.
  • This stage deepened presence beyond verbal skill to sensory social attunement.

• Stage 5: Practiced projecting love and acceptance, creating emotional openness and deep connection.

  • Embraced a meditative state of spacious openness inspired by energy healing.
  • Provided a nervous system capacity for emotional distress, enabling catharsis.
  • Found that deep listening and presence facilitated rapid emotional intimacy.

• Stage 6: Learned to moderate emotional connection, balancing openness with boundaries.

  • Recognized that constant emotional openness can be draining and misinterpreted.
  • Managed energetic boundaries and adjusted connection intensity according to context.
  • Balanced desire for connection with comfort in solitude and acceptance of varied social interaction levels.

• Author reflects on effects of spiritual openness in various social roles and the challenge of maintaining boundaries.

• Final insight: Genuine social connection evolves from crafted performance to embodied presence, balancing authenticity, empathy, and emotional limits.