Hello u/sscheper,

Let me start by thanking you for introducing me to Zettelkasten. I have been writing notes for a week now and it's great that I'm able to retain more info and relate pieces of knowledge better through this method.

I recently came to notice that there is redundancy in my index entries.

I have two entries for Number Line. I have two branches in my Math category that deals with arithmetic, and so far I have "Addition" and "Subtraction". In those two branches I talk about visualizing ways of doing that, and both of those make use of and underline the term Number Line. So now the two entries in my index are "Number Line (Under Addition)" and "Number Line (Under Subtraction)". In those notes I elaborate how exactly each operation is done on a number line and the insights that can be derived from it. If this continues, I will have Number Line entries for "Multiplication" and "Division". I will also have to point to these entries if I want to link a main note for "Number Line".

Is this alright? Am I underlining appropriately? When do I not underline keyterms? I know that I do these to increase my chances of relating to those notes when I get to reach the concept of Number Lines as I go through the index but I feel like I'm overdoing it, and it's probably bloating it.

I get "Communication (under Info. Theory): '4212/1'" in the beginning because that is one aspect of Communication itself. But for something like the number line, it's very closely associated with arithmetic operations, and maybe I need to rethink how I populate my index.

Presuming, since you're here, that you're creating a more Luhmann-esque inspired zettelkasten as opposed to the commonplace book (and usually more heavily indexed) inspired version, here are some things to think about:<br /> - Aren't your various versions of number line card behind each other or at least very near each other within your system to begin with? (And if not, why not?) If they are, then you can get away with indexing only one and know that the others will automatically be nearby in the tree. <br /> - Rather than indexing each, why not cross-index the cards themselves (if they happen to be far away from each other) so that the link to Number Line (Subtraction) appears on Number Line (Addition) and vice-versa? As long as you can find one, you'll be able to find them all, if necessary.

If you look at Luhmann's online example index, you'll see that each index term only has one or two cross references, in part because future/new ideas close to the first one will naturally be installed close to the first instance. You won't find thousands of index entries in his system for things like "sociology" or "systems theory" because there would be so many that the index term would be useless. Instead, over time, he built huge blocks of cards on these topics and was thus able to focus more on the narrow/niche topics, which is usually where you're going to be doing most of your direct (and interesting) work.

Your case sounds, and I see it with many, is that your thinking process is going from the bottom up, but that you're attempting to wedge it into a top down process and create an artificial hierarchy based on it. Resist this urge. Approaching things after-the-fact, we might place information theory as a sub-category of mathematics with overlaps in physics, engineering, computer science, and even the humanities in areas like sociology, psychology, and anthropology, but where you put your work on it may depend on your approach. If you're a physicist, you'll center it within your physics work and then branch out from there. You'd then have some of the psychology related parts of information theory and communications branching off of your physics work, but who cares if it's there and not in a dramatically separate section with the top level labeled humanities? It's all interdisciplinary anyway, so don't worry and place things closest in your system to where you think they fit for you and your work. If you had five different people studying information theory who were respectively a physicist, a mathematician, a computer scientist, an engineer, and an anthropologist, they could ostensibly have all the same material on their cards, but the branching structures and locations of them all would be dramatically different and unique, if nothing else based on the time ordered way in which they came across all the distinct pieces. This is fine. You're building this for yourself, not for a mass public that will be using the Dewey Decimal System to track it all down—researchers and librarians can do that on behalf of your estate. (Of course, if you're a musician, it bears noting that you'd be totally fine building your information theory section within the area of "bands" as a subsection on "The Bandwagon". 😁)

If you overthink things and attempt to keep them too separate in their own prefigured categorical bins, you might, for example, have "chocolate" filed historically under the Olmec and might have "peanut butter" filed with Marcellus Gilmore Edson under chemistry or pharmacy. If you're a professional pastry chef this could be devastating as it will be much harder for the true "foodie" in your zettelkasten to creatively and more serendipitously link the two together to make peanut butter cups, something which may have otherwise fallen out much more quickly and easily if you'd taken a multi-disciplinary (bottom up) and certainly more natural approach to begin with. (Apologies for the length and potential overreach on your context here, but my two line response expanded because of other lines of thought I've been working on, and it was just easier for me to continue on writing while I had the "muse". Rather than edit it back down, I'll leave it as it may be of potential use to others coming with no context at all. In other words, consider most of this response a selfish one for me and my own slip box than as responsive to the OP.)

Author Response

Reviewer #1 (Public Review):

Figures 2 through 6. There is no description of the relationship between the findings and the anatomical location of the electrodes (other than distal versus local). Perhaps the non-uniform distribution of electrodes makes these analyses more complicated and such questions might have minimal if any statistical power. But how should we think about the claims in Figures 2-6 in relationship to the hippocampus, amygdala, entorhinal cortex, and parahippocampal gyrus? As one example question out of many, is Figure 2C revealing results for local pairs in all medial temporal lobe areas or any one area in particular? I won't spell out every single anatomical question. But essentially every figure is associated with an anatomical question that is not described in the results.

To address the reviewer’s point we now report the distribution of spike-LFP pairs across anatomical regions for each Figure 2-6. The results split by anatomical regions are reported in Figure 2 – figure supplement 7, Figure 3 – figure supplement 7, Figure 4 – figure supplement 1, Figure 5 – figure supplement 2, and Figure 6 – figure supplement 3. We also calculated a non-parametric Kruskal-Wallis Test to statistically examine the effect of anatomical regions on the results shown in each figure. Generally, these new results show that the effects are similar across regions, apart from two exceptions (i.e. Figure 4 – supplement 1; and Figure 5 – supplement 2). However, we would like to stress that these results should be taken with a huge grain of salt because the electrodes were not evenly distributed across regions (i.e. ~75% of observations pertain to the hippocampus), and patients as the reviewer correctly points out. This leads to sometimes very low numbers of observations per region and it is difficult to disentangle whether any apparent differences are driven by regional differences, or differences between patients. Detailed results are reported below.

Manuscript lines 207-212: “In the above analysis all MTL regions were pooled together to allow for sufficient statistical power. Results separated by anatomical region are reported in Figure 2 – figure supplement 7 for the interested reader. However, these results should be interpreted with caution because electrodes were not evenly distributed across regions and patients making it difficult to disentangle whether any apparent differences are driven by actual anatomical differences, or idiosyncratic differences between patients.”

Manuscript lines 255-258: “Finally, we report the distal spike-LFP results separated by anatomical region in Figure 3 – figure supplement 7, which did not reveal any apparent differences in the memory related modulation of theta spike-LFP coupling between regions.”

Manuscript lines 264-266: “PSI results separated by anatomical regions are reported in Figure 4 – figure supplement 1, which revealed that the PSI results were mostly driven by within regional coupling.”

Manuscript lines 399-303: “We also analyzed whether the memory-dependent effects of cross-frequency coupling differ between anatomical regions (see Figure 5 – figure supplement 2). This analysis revealed that the results were mostly driven by the hippocampus, however we urge caution in interpreting this effect due to the large sampling imbalance across regions.”

Manuscript lines 343-346: “As for the above analysis we also investigated any apparent differences in co-firing between anatomical regions. These results are reported in Figure 6 – figure supplement 3 and show that the earlier co-firing for hits compared to misses was approximately equivalent across regions.”

Figure 1

1A. I assume that image positions are randomized during a cued recall?

Yes, that was the case. We now added that information in the methods section.

Manuscript lines 526: “Image positions on the screen were randomized for each trial.”

What was the correlation between subjects' indication of how many images they thought they remembered and their actual performance?

We did not log how many images the patients thought they remembered. Specifically, if the patients answered that they remembered at least one image, then they were shown the selection screen where they could select the appropriate images. Therefore, we cannot perform this analysis. We report this now in the methods section. However, albeit interesting, the results of such an analysis would not affect the main conclusions of our manuscript.

Manuscript lines 523-524: “The experimental script did not log how many images the patient indicated that they thought to remember.”

1B. Chance is shown for hits but not misses. I assume that hits are defined as both images correct and misses as either 0 or 1 image correct. Then a chance for misses is 1-chance for hits = 5/6. It would be nice to mark this in the figure.

Done as suggested (see Figure 1).

The authors report that both incorrect was 11.9%. By chance, both incorrect should be the same as both correct, hence also 1/6 probability, hence the probability of both incorrect seems quite close to chance levels, right?

Yes, that is correct, however, across sessions the proportion of full misses (i.e. both incorrect) was significantly below chance (t(39)=-1.9214; p<0.05). Nevertheless, the proportion of fully forgotten trials appears to be higher than expected purely by chance. This is likely driven by a tendency of participants to either fully remember an episode, or completely forget it, as demonstrated previously in behavioural work (Joensen et al., 2020; JEP Gen.). We report this now in the manuscript.

Manuscript lines 132-136: “Across sessions the proportion of full misses (i.e. both incorrect) was significantly below chance (t39=-1.92; p<0.05). However, the proportion of fully forgotten trials appears to be higher than expected purely by chance. This is likely driven by a tendency of participants to either fully remember an episode, or completely forget it, as demonstrated previously in behavioral work (25).”

1C. How does the number of electrodes relate to the number of units recorded in each area?

The distribution of neurons per region is shown in the new Figure 1D (see above). It approximately matches the distribution of electrodes per region, except for the Amygdala where slightly more neurons where recorded. This is because of one patient (P08) who had two electrodes in the left and right Amygdala and who contributed at lot of sessions (i.e. 9 sessions, comparing to an average of 4.44 per patient).

Line 152. The authors state that neural firing during encoding was not modulated by memory for the time window of interest. This is slightly surprising given that other studies have shown a correlation between firing rates and memory performance (see Zheng et al Nature Neuroscience 2022 for a recent example). The task here is different from those in other studies, but is there any speculation as to potential differences? What makes firing rates during encoding correlate with subsequent memory in one task and not in another? And why is the interval from 2-3 seconds more interesting than the intervals after 3 seconds where the authors do report changes in firing rates associated with subsequent performance? Is there any reason to think that the interval from 2-3 seconds is where memories are encoded as opposed to the interval after 3 seconds?

Zheng et al. used a movie-based memory paradigm where they manipulated transitions between scenes to identify event cells and boundary cells. They show that boundary cells, which made up 7.24% of all recorded MTL cells, but not event cells (6.2% of all MTL cells) modulate their firing rate around an event depending on later memory. There are quite a few differences between Zheng et al’s study and our study that need to be considered. Most importantly, we did not perform a complex movie-based memory paradigm as in Zheng et al. and therefore cannot identify boundary cells, which would be expected to show the memory dependent firing rate modulation. This alone could contribute to the fact that no significant differences in firing rates in the first second following stimulus onset were observed. Such an absence of a difference of neural firing depending on later memory is not unprecedented. In their seminal paper, Rutishauser et al. (2010; Nature) report no significant differences in firing rates (0-1 seconds after stimulus onset, which is similar to our 2-3 seconds time window) between later remembered or later forgotten images. This finding is also in line to Jutras & Buffalo (2009; J Neurosci) who also show no significant difference in firing rates of hippocampal neurons during encoding of remembered and forgotten images.

The 2-3 seconds time interval, which corresponds to 0-1 seconds after the onset of the two associate images, is special because it marks the earliest time point where memory formation can start, therefore allowing us to investigate these very early neural processes that set the stage for later memory-forming processes. While speculative, these early processes likely capture the initial sweep of information transfer into the MTL memory system which arguably is reflected in the timing of spikes relative to LFPs. It is conceivable that these initial network dynamics reflect attentional processes, which act as a gate keeper to the hippocampus (Moscovitch, 2008; Can J Exp Psychol) and thereby set the stage for later memory forming processes. This interpretation would be consistent with studies in macaques showing that attention increases spike-LFP coupling, whilst not affecting firing rates (Fries et al., 2004; Science). We modified the discussion section to address this issue.

Manuscript lines 468-474: “Interestingly, these early modulations of neural synchronization by memory encoding were observed in the absence of modulations of firing rates, which is consistent with previous results in humans (16) and macaques (12), but contrasts with (43). Studies in macaques showed that attention increases spike-LFP coupling whilst not affecting firing rates (44). It is therefore conceivable that these initial network dynamics reflect attentional processes, which act as a gate keeper to the hippocampus and thereby set the stage for later memory forming processes (45).”

Lines 154-157 and relationship to the subsequent analyses. These lines mention in passing differences in power in low-frequency bands and high-frequency bands. To what extent are subsequent results (especially Figures 3 and 4) related to this observation? That is, are the changes in spike-field coherence, correlated with, or perhaps even dictated by, the changes in power in the corresponding frequency bands?

To address this question we repeated the analysis that we performed for SFC for Power in those channels whose LFP was locally coupled to spikes in gamma, and distally coupled to spikes in theta. Furthermore, we correlated the difference in peak frequency between hits and misses between Power and SFC. If power would dictate the effects seen in SFC then we would expect similar effects of memory in power as in SFC, that is an increase of peak frequency for hits compared to misses for gamma and theta. Furthermore, we would expect to find a correlation between the peak frequency differences in power and SFC. None of these scenarios were confirmed by the data. These results are now reported in Figure 2 – figure supplement 5 for gamma, and Figure 3 – figure supplement 5 for theta.

Manuscript lines 195-199: “We also tested whether a similar shift in peak gamma frequency as observed for spike-LFP coupling is present in LFP power, and whether memory-related differences in peak gamma spike-LFP are correlated with differences in peak gamma power (Figure 2 – figure supplement 5). Both analyses showed no effects, suggesting that the effects in spike-LFP coupling were not coupled to, or driven by similar changes in LFP power.”

Manuscript lines 248-253: “As for gamma, we also tested whether a similar shift in peak theta frequency is present in LFP power, and whether there is a correlation between the memory-related differences in peak theta spike-LFP and peak theta power (Figure 3 – figure supplement 5). Both analyses showed no effects, suggesting that the effects in spike-LFP coupling were not coupled to, or driven by similar changes in LFP power.”

Do local interactions include spike-field coherence measurements from the same microwire (i.e., spikes and LFPs from the same microwire)?

Yes, they do. Out of the 53 local spike-SFC couplings found for the gamma frequency range, 11 (20.75%) were from pairs where the spikes and LFPs were measured on the same microwire. We assume that the reviewer is asking this question because of a concern that spike interpolation may introduce artifacts which may influence the spectrograms and consequently the spike-LFP coupling measures. This was also pointed out by Reviewer #2. To address this concern, we split the data based on whether the spike and LFP providing channels were the same or different. The results show that (i) the spectrogram of SFC is highly similar between the two datasets, with a prominent gamma peak present in both and no significant differences between the two; (ii) restricting the analysis to those data where the LFP and spike providing channels are different replicated the main finding of faster gamma peak frequencies for hits compared to misses; and (iii) limiting the SFC analysis further to only ‘silent’ channels, i.e. channels where no SUA/MUA activity was present at all also replicated the main finding of faster gamma peak frequencies for hits compared to misses.

These analyses suggest that the SFC results were not driven by spike interpolation artefacts.

Manuscript lines 199-203: “To rule out concerns about possible artifacts introduced by spike interpolation we repeated the above analysis for spike-LFP pairs where the spike and LFP providing channels are the same or different, and for ‘silent’ LFP channels (i.e. channels were no SUA/MUA activity was detected (see Figure 2 – figure supplement 6). “

60 Hz. It has always troubled me deeply when results peak at 60 Hz. This is seen in multiple places in the manuscript; e.g., Figures 2B, 2E. What are the odds that engineers choosing the frequency for AC currents would choose the exact same frequency that evolution dictated for interactions of brain signals? This is certainly not the only study that reports interesting observations peaking at 60 Hz. One strong line of argument to suggest that this is not line noise is the difference between conditions. For example, in Figure 2B, there is a difference between local and distal interactions. It is hard for me to imagine why line noise would reveal any such difference. Still ...

The frequency for AC currents in Europe is 50 Hz, not 60 Hz as in the US. Therefore, our SFC effects are well outside the range of the notch.

Figure 6. I was very excited about Figure 6, which is one of the most novel aspects of this study. In addition to the anatomical questions about this figure noted above, I would like to know more. What is the width of the Gaussian envelope?

The width of the Gaussian Window used in the original results was 25ms. We chose this time window because in our view it represents a good balance between integrating over a long-enough time window and thus allowing for some jitter in neural firing between pairs of neurons, whilst still being temporally specific. Finding the right balance here is not trivial because a too short time window underestimates co-firing, and a too long time window may not provide the temporal specificity necessary to detect co-firing lags (Cohen & Kohn, 2011; Nat Neurosci). To test whether this choice critically affected our results, we repeated the analysis for different window sizes, i.e. 15, 35, and 45 ms. The results show that the pattern of results did not change, with hits showing earlier peaks in co-firing compared to misses. Critically, the difference in co-firing peaks was significant for all window sizes, except for the shortest one which presumably is due to the increase in noise because of the smaller time window over which spikes are integrated. These issues are now mentioned in the methods section, and the results for the different window sizes are reported in Figure 6 – figure supplement 4.

Manuscript lines 346-347: “The co-firing analyses were replicated with different smoothing parameters (see Figure 6 – figure supplement 4).”

Manuscript lines 894-898: “We chose this time window because it should represent a good balance between integrating over a long-enough time window and thus allowing for some jitter in neural firing between pairs of neurons, whilst still being temporally specific (57). To test whether this choice critically affected our results, we repeated the analysis for different window sizes, i.e. 15, 35, and 45 ms (see Figure 6 – figure supplement 4).”

Are these units on the same or different microwires?

All units used for the analysis shown in Figure 6 come from different microwires. This was naturally the case because the putative up-stream neuron was distally coupled to the theta LFP, and the putative down-stream neuron was locally coupled to gamma at this same theta LFP electrode. This information is listed in Figure 6 – source data 1 which lists the locations and electrode IDs for all neuron pairs shown in figure 6.

How do the spike latencies reported here depend on the firing rates of the two units?

To address this question we first tested whether firing rates (averaged across the putative up-stream and down-stream neurons) differ between hits and misses. If they do, this would be suggestive of a dependency of the spike latency differences between hits and misses on firing rates. No such difference was observed (p>0.3). Second, we correlated the differences between hits and misses in Co-firing peak latencies with the differences in firing rates. Again, no significant correlation was observed (R=-0.06; p>0.7), suggesting that firing rates had no influence on the observed differences in co-firing latencies. These control analyses are now reported in the main text.

Manuscript lines 347-350: “No significant differences in firing rates between hits and misses were found (p>0.3), and on correlations between firing rates and the co-firing latencies were obtained (R=-0.06; p>0.7), suggesting that firing rates had no influence on the observed co-firing differences between hits and misses.”

What do these results look like for other pairs that are not putative upstream/downstream pairs?

As we reported in the original manuscript in lines 352-355 we did not find a memory dependent effect on co-firing latencies if we select neuron pairs solely on the basis of distal theta SFC. Within this analysis the distally theta coupled neuron would be the up-stream neuron and the neuron recorded at the site where the theta LFP is coupled would be the down-stream neuron. This null-result suggests that in order for the memory dependent difference in co-firing lags to emerge, the down-stream neurons need to be coupled to a local gamma rhythm in order for the memory effect on co-firing latencies to emerge. However, within this previous analysis there is still a notion of up-stream and down-stream neurons because neuron pairs were selected based on distal theta phase coupling. We therefore repeated this analysis for all pairs of neurons in a completely unconstrained fashion such that all possible pairs of neurons that were recorded from different electrodes were entered into the co-firing analysis. This analysis also revealed no difference in co-firing lags, neither for positive lags nor for negative lags. Instead, what this analysis showed is tendency for hits to show a higher occurrence of simultaneous or near simultaneous firing, which is in line with Hebbian learning. These results are now reported in Figure 6 – figure supplement 1.

Manuscript lines 333-335: “In addition, a completely unconstrained co-firing analysis where all pairs possible pairings of units were considered also showed no systematic difference in co-firing lags between hits and misses (Figure 6 – figure supplement 1).”

Reviewer #2 (Public Review):

Roux et al. investigated the temporal relationship between spike field coherence (SFC) of locally and distally coupled units in the hippocampus of epilepsy patients to successful and unsuccessful memory encoding and retrieval. They show that SFC to faster theta and gamma oscillations accompany hits (successful memory encoding and retrieval) and that the timing of the SFC between local and distal units for hits comports well with synaptic plasticity rules. The task and data analyses appear to be rigorously done.

Strengths: The manuscript extends previous work in the human medial temporal lobe which has shown that greater SFC accompanies improved memory strength. The cross-regional analyses are interesting and necessary to invoke plasticity mechanisms. They deploy a number of contemporary analyses to disentangle the question they are addressing. Furthermore, their analyses address limitations or confound that can arise from various sources like sample size, firing rates, and signal processing issues.

Weaknesses:

Methodological:

The SFC coherence measures are dependent in part on extracting LFPs derived from the same or potentially other electrodes that are contaminated by spikes, as well as multiunit activity. In the methods, they cite a spike removal approach. Firstly, the incomplete removal or substitution of a signal with a signal that has a semblance to what might have been there if no spike was present can introduce broadband signal time-locked to the spike and create spurious SFC. Can the authors confirm that such an artifact is not present in their analyses? Secondly, how did they deal with the removal of the multiunit activity? It would be suspected that the removal of such activity in light of refractory period violation might be more difficult than well-isolated units, and introduce artifacts and broadband power, again which would spuriously elevate SFC. Conversely, the lack of removal of multiunit activity would seem to for a surety introduce significant broadband power. One way around this might be that since it is uncommon to have units on all 8 of the BF microwires, to exclude the microwire(s) with the units when extracting the LFP to avoid the need to perform spike removal.

The reviewer raises a valid concern which we address as follows. Firstly, an artefact introduced into SFC by linear interpolation would be a problem for those local SFCs where the spike providing channel and the LFP providing channel are identical. Out of the 53 local spike-SFC couplings found for the gamma frequency range, only 11 (20.75%) were from pairs where the spikes and LFPs come from the identical microwire. It is unlikely that this minority of data would have driven the results. Furthermore, it is unlikely that the interpolation would introduce a frequency shift of SFC that is memory dependent, because the interpolation is more likely to cause a general increase in broadband SFC (as opposed to having a frequency band specific effect). However, to address this concern, we split the data based on whether the spike and LFP providing channels were the same or different. The results show that (i) the spectrogram of SFC is highly similar between the two datasets, with a prominent gamma peak present in both and no significant differences between the two; (ii) restricting the analysis to those data where the LFP and spike providing channels are different replicated the main finding of faster gamma peak frequencies for hits compared to misses.

Secondly, we followed the reviewer’s suggestion and repeated the SFC analysis for ‘silent’ microwires, i.e. microwires where no single or multi-units were detected. This analysis replicated the same memory effects as observed in the analysis with all microwires. Specifically, we found an increase in the local gamma peak SFC frequency for hits compared to misses, as well as an increase in distal theta peak SFC frequency for hits compared to misses. These results are reported in the main manuscript and in Figure 2 – figure supplement 6 for gamma, and figure 3 – figure supplement 6 for theta.

Manuscript lines 199-203: “To rule out concerns about possible artifacts introduced by spike interpolation we repeated the above analysis for spike-LFP pairs where the spike and LFP providing channels are the same or different, and for ‘silent’ LFP channels (i.e. channels were no SUA/MUA activity was detected (see Figure 2 – figure supplement 6).”

Manuscript lines 253-255: “We also repeated the above analysis for spike-LFP pairs by only using ‘silent’ LFP channels (i.e. channels were no SUA/MUA activity was detected (see Figure 3 – figure supplement 6) to address possible concerns about artefacts introduced by spike interpolation.”

In a number of analyses the spike train is convolved with a Gaussian in places with a window length of 250ms and in others 25ms. It is suspected that windows of varying lengths would induce "oscillations" of different frequencies, and would thus generate results biased towards the window length used. Can the authors justify their choices where these values are used, and/or provide some sensitivity analyses to show that the results are somewhat independent of the window length of the Gaussian used to convolve with the times series.

The different choices in window length for the Gaussian convolution reflect the different needs of the two analyses where these convolutions were applied. In one analysis we wanted to get a smooth estimate of spike densities that we can average across trials, similar to a peri-stimulus spike histogram. For this analysis we used a window length of 250 ms which we found appropriate to yield a good balance between retaining smooth time courses whilst still being temporally sensitive. Importantly, for the statistical analysis of the firing rates, spike densities were averaged in much larger time windows than 250 ms (i.e. 1 – 2 seconds) therefore our choice of window length for spike densities would not have any bearing on the averaged firing rate analysis.

In the other analysis, which is more central for our manuscript, we used a cross-correlation between spike trains to estimate co-firing lags in the range of milliseconds. Therefore, this analysis necessitated a much higher temporal precision. We used a Gaussian Window with a width of 25ms because it represents a good balance between integrating over a long-enough time window and thus allowing for some jitter in neural firing between pairs of neurons, whilst still being temporally specific. Finding the right balance here is not trivial because a too short time window would be prone to noise and underestimates co-firing, whereas a too long time window may not provide the temporal specificity necessary to detect co-firing lags (Cohen and Kohn, 2013; Nat Neurosci). To test whether this choice critically affected our results, we repeated the analysis for different window sizes, i.e. 15, 35, and 45 ms. The results show that the basic pattern of results did not change, with hits showing earlier peaks in co-firing compared to misses. Critically, the difference in co-firing peaks was significant for all window sizes, except for the shortest one which is likely due to the increase in noise because of the smaller time window over which spikes are integrated. These issues are now mentioned in the methods section, and the results for the different window sizes are reported in Figure 6 – figure supplement 4.

Manuscript lines 346-347: “The co-firing analyses were replicated with different smoothing parameters (see Figure 6 – figure supplement 4).”

Manuscript lines 894-898: “We chose this time window because it should represent a good balance between integrating over a long-enough time window and thus allowing for some jitter in neural firing between pairs of neurons, whilst still being temporally specific (57). To test whether this choice critically affected our results, we repeated the analysis for different window sizes, i.e. 15, 35, and 45 ms (see Figure 6 – figure supplement 4).”

Conceptual:

The co-firing analyses are very interesting and novel. In table S1 are listed locally and distally coupled neurons. There are some pairs for example where the distally coupled neuron is in EC and the downstream one in the hippo, and then there is a pair that is the opposite of this (dist: hippo, local EC). There appear to be a number of such "reversal", despite the delay between these two regions one would assume them to be similar in sign and magnitude given the units are in the same two regions. It seems surprising that in two identical regions of the hippo the flow of information or "causality", could be reversed, when/if one assumes information flows through the system from EC to hippo. This seems unusual and hard to reconcile given what is known about how information flows through the MTL system.

The reviewer is correct that the spike co-firing analysis suggests a bi-directional flow of information between the hippocampus and surrounding MTL regions (e.g. entorhinal cortex; see Figure 6 – figure supplement 3). However, this bi-directional flow of information is not incompatible with neuroanatomy and the memory literature. The entorhinal cortex serves as an interface between the hippocampus and the neocortex with superficial layers providing input into the hippocampus (via the perforant pathway), and the deeper layers receiving output from the hippocampus (van Strien et al., 2009; Nat Rev Neurosci). Therefore, on a purely anatomical basis we can expect to see a bi-directional flow of information between the hippocampus and the entorhinal cortex, albeit in different layers. Importantly, reversals as shown in our Figure 6 – source data 1 involved different microwires and therefore different neurons (i.e. the entorhinal unit in row 1 was recorded from microwire 3, whereas the entorhinal unit in row 2 was recorded from microwire 8). It is conceivable that these two neurons correspond to different layers of the entorhinal cortex and therefore reflect input vs. output paths. Moreover, studies in humans demonstrated that successful encoding of memories depends not only on the input from the entorhinal cortex into the hippocampus, but also on the output of the hippocampal system into the entorhinal cortex, and indeed on the dynamic recurrent interaction between these input and output paths (Maass et al. 2014; Nat Comms; Koster et al., 2018; Neuron). Our bi-directional couplings between hippocampal and surrounding MTL regions (such as the EC) are in line with these findings. We have added a discussion of this issue in the discussion section.

Manuscript lines 447-452: “Notably, the neural co-firing analysis indicates a bidirectional flow of information between the hippocampus and surrounding MTL areas, such as the entorhinal cortex (see Figure 6 – figure supplement 3; Figure 6 – source data 1). This result parallels other studies in humans showing that successful encoding of memories depends not only on the input from surrounding MTL areas into the hippocampus, but also on the output of the hippocampal system into those areas, and indeed on the dynamic recurrent interaction between these input and output paths (43, 44).”

Author Response

Reviewer #3 (Public Review):

This paper is based on digital reconstruction of a serial EM stack of a larva of the annelid Platynereis and presents a complete 3D map of all desmosomes between somatic muscle cells and their attachment partners, including muscle cells, glia, ciliary band cells, epidermal cells and specialized epidermal cells that anchor cuticular chaetae (chaetal follicle cells) and aciculae (acicular follicle cells). The rationale is that the spatial patterning of desmosomes determines the direction of forces exerted by muscular contraction on the body wall and its appendages will determine movement of these structures, which in turn results in propulsion of the body as part of specific behaviors.

To go a step further, if connecting this desmosome connectome with the (previously published) synaptic connectome, one may gain insight into how a specific spatio-temporal pattern of motor neuron activity will lead, via a resulting pattern of forces caused by muscles, to a specific behavior. In the authors' words: "By combining desmosomal and synaptic connectomes we can infer the impact of motoneuron activation on tissue movements".This is an interesting idea which has the potential to make progress towards understanding in a "holistic" way how a complex neural circuitry controls an equally complex behavior. The analysis of the EM data appears solid; the authors can show convincingly that desmosomes can be resolved in their EM dataset; and the technology used to plot and analyze the data is clearly up to the task. My main concern is with the way in which the desmosome pattern is entered in the analysis, which I think makes it very difficult to extract enough relevant information from the analysis that would reach the stated goal.

1) The context of how different structures of the Platynereis larval body, by changing their position, move the body needs much more introduction than the short paragraph given at the end of the Introduction.

-My understanding is that the larval body is segmented, and contraction of the segments can cause a certain type crawling or swimming: does it? Do the longitudinal muscles, for example, insert at segment boundaries, and alternating contraction left-right cause some sort of "wiggling" or peristalsis?

Longitudinal muscles do not insert only at segment boundaries, but have desmosomal connections along the entire length of the cell. Individual longitudinal muscle cells can span up to 3 segments. However the cells are staggered in such a way that all longitudinal muscle cells with somas in one segment can collectively cover up to 4 segments. Longitudinal muscles are involved in turning when swimming (Randel et al., 2014). The undulatory trunk movements and parapodial walking movements are due to the contraction of oblique and parapodial muscles. The longitudinal muscles provide support during crawling (via desmosomal links) but it is unlikely that these muscles contract segmentally. Disentangling the distinct contributions of 53 types of muscles during crawling will require further studies.

-In addition, there are segmental processes (parapodia, neuropodia), and embedded in them are long chitinous hairs (Chaetae, Acicula). Do certain types of the muscles described in the study insert at the base of the parapodia/neuropodia (coming from different angles), such that contraction would move the entire process, including the chaetae/acicula embedded in their tips?

Yes, acicular muscles insert at the proximal base of the acicula, and by moving the acicula they move the entire noto-/neuropodia. We have presented the anatomy of all acicular and chaetal muscles types in the figures and videos.

-Or is it that only these chaetae/acicula move, by means of muscles inserting at their base (the latter is clearly part of the story)? Or does both happen at the same time: parapodium moves relative to the trunk, and chaeta/acicula moves relative to the parapodium? How would these movements lead to different kind of behaviors?

-Diagrams should be provided that shed light on these issues.

We have extended Video2 to show individual muscles and their relation to the aciculae in one of the parapodia. We also clarified this in the text:

“Several acicular muscles attach on one end to the proximal base of the aciculae and on the other end to the paratrochs and epidermal cells. Oblique muscles attach to the basal lamina, epidermal and midline cells at their proximal end, run along the anterior edge of parapodia and attach to epidermal and chaetal follicle cells at their distal tips. Both of these muscle groups are involved in moving the entire parapodium. Acicular muscles move the proximal tips of the aciculae, while oblique muscles move the parapodium by moving the tissue around the chaetae and the aciculae. All acicular movements also correspond to parapodial movements. Chaetae are embedded in the parapodium and therefore move with it, but the chaetal sac muscles can also independently retract the chaetae into the parapodium or protract them and make them fan out.”

2) The main problem I have with the analysis is the way a muscle cell is treated, namely as a "one dimensional" node, rather than a vector.

-In the current state of the analysis, the authors have mapped all desmosomes of a given muscle cell to its attached "target" cell. But how is that helpful? The principal way a muscle cell acts is by contracting, thereby pulling the cells it attaches to at its two end closer together. As the authors state (p.4) "...desmosomes..are enriched at the ends of muscle cells indicating that these adhesive structures transmit force upon muscle-cell contraction."

At the level of the current analysis our data reveal which cells may be moved by the contractions of the individual muscle cells. The reviewer is right that treating a muscle as a vector (or set of vectors) would be a more accurate description, which would potentially also open up the possibility of computational modelling. We have provided such a vectorised dataset in the revised version, where each muscle-cell skeleton is subdivided into short linear segments (Figure2–source–data 2). This dataset may be useful to approach the problem with a three dimensional approach, which is beyond the scope of the current analysis. We also included an additional video (Video 7) showing examples of muscles and their partners where the cells and the desmosomes connecting them are highlighted. This reveals that the desmosomes connecting two cells are often at the very end of the muscle cell.

-for that reason, the desmosomes at the muscle tips have to be treated as (2) special sets. Aside from these tip desmosomes there are other desmosomes (inbetween muscles, for example), but they (I would presume) have a very different function; maybe to coordinate muscle fiber contraction? Augment the force caused by contraction?

Desmosomes between muscles only occur between muscles of different types, not for homotypic connections. There are other types of junctions (adhaerens-like junctions) that connect individual cells of a muscle bundle together (not analysed here). We clarified this in the text.

• As far as I understand for (all of) the desmosome connectome plots, there is no differentiation made between desmosome subsets located at different positions within the muscle fiber. I therefore don't see how the plots are helpful to shed light on how the multiplicity of muscles represented in the graphs cause specific types of neurons.

We would like to point out that the cells and structures that muscles connect to via desmosomes are very likely the parts of the body that will move during the contraction of the muscle or will provide structural support (e.g. basal lamina) for the muscle cell to contract. This is most evident in the parapodial complex. The majority of muscles in the body connect to the aciuclar folliclecells and the aciculae are the most actively moving parts in the body during crawling (see Video 4). In any case, since we provide all skeleton reconstructions and the xyz coordinates of all desmosomes, the data could be further analysed following these suggestions by the reviewer.

• As it stands these plots "merely" help to classify muscles, based on their position and what cell type they target: but that (certainly useful) map could have probably also be achieved by light microscopic analysis.

This has never been achieved by light microscopy analysis in the hundreds of papers on invertebrate muscle anatomy (e.g. by phalloidin staining). For an LM analysis, it would not be sufficient to label the muscle fibres, but one would also need to label the desmosomes and a multitude of non-muscle cell types including the extent of their cytoplasm. This is technically very challenging (we would nevertheless be happy to hear specific suggestions for markers etc. from the Reviewer). Currently, only EM provides the required depth of structural information and resolution. This is why we believe that our dataset and analysis is unique, despite over a century of research in invertebrate anatomy.

3) Section "Local connectivity and modular structure of the desmosomal connectome" p.4-7" undertakes an analysis of the structure of the desmosome network, comparing it with other networks.

-What is the rationale here? How do the conclusions help to understand how the spatial pattern of muscles and their contraction move the body?

We hope that our analysis may also be of interest to the community of network scientists and we believe that the reconstruction of a quite large and novel type of biological network warrants a more quantitative network analysis, using the standard methods and measures of network science – as we presented e.g. in Figure 4 – even if these mathematical analyses may not directly reveal how muscles move the body. We hope that some readers with an interest in quantitative analyses will also appreciate the broader picture here.

-Isn't, on the one hand (given that position of the desmosome was apparently not considered), the finding that desmosome networks stand out (from random networks) by their high level of connectivity ("with all cells only connecting to cells in their immediate neighbourhood forming local cliques") completely expected?

We disagree that the result was completely expected. Even if this was the case, we think it is quite different to say that a result is expected or to thoroughly quantify certain parameters and mathematically characterise key properties of the desmosomal graph (as we have done). These network analyses help to conceptualise our findings and to think about the muscle system in more global, whole-body terms.

-On the other hand, does this reflect the reality, given that (many?) muscle cells are quite long, connecting for example the anterior border of a segment with the posterior border.

Indeed, a quantitative analysis helped us to identify cases where the reality deviated somewhat from what was completely expected, and we thank the reviewer for these comments. As we explain in the revised version, some longitudinal muscles show an unexpected position in the force-field layout of the graph, due to their long-range connections. We have added extra clarifications to the text: “To analyse how closely the force-field-based layout of the desmosomal connectome reflects anatomy, we coloured the nodes in the graph based on body regions (Figure 5). In the force-field layout, nodes are segregated by body side and body segment. Exceptions include the dorsolateral longitudinal muscles (MUSlongD) in segment-0. These cells connect to dorsal epidermal cells that also form desmosomes with segment-1 and segment-2 MUSlongD cells. These connections pull the MUSlongD_sg0 cells down to segment-2 in the force-field layout (Figure 5D).”

1. In the section "Acicular movements and the unit muscle contractions that drive them" the authors record movement of the acicula and correlate it with activity (Ca imaging) of specific muscle types. This study gives insightful data, and could be extended to all movements of the larva.

-The fact that a certain muscle is active when the acicula moves in a certain direction can be explained (in part) by the "connectivity": as shown in Fig.7L, the muscle inserts at a acicular follicle cell on the one side, and to an epithelial (epidermal?) cell and the basal lamina on the other side. But how meaningful is a description at this "cell type level" of resolution? The direction of acicula deflection depends on where (relative to the acicula base) the epithelial cell (or point in the basal lamina) is located. This information is not given in the part of the connectome network shown in Fig.7L, or any of the other graphs.

This information is indeed not shown in the graphs, where each cell is treated as a node. However, we provide this information in the detailed anatomical figures in Figure 6 – figure supplement 1-3 and Video 7, where the individual acicular and oblique muscle types are visualised. In principle, one could subdivide aciculae into e.g. proximal and distal halves and derive a more detailed network. We have not done this but since all the EM, anatomical rendering and connectivity data are available in our public CATMAID server (https://catmaid.jekelylab.ex.ac.uk/), we hope that the interested readers will be able to further analyse the data.

We renamed ‘epithelial’ cells to ‘epidermal’ cells.

Because seeing a play is such a fleeting event, writing a play review may be a thrilling, though tough, effort. You must be both a spectator watching and appreciating the performance as well as a critical analyst of the production itself. You must be able to offer a quick overview of the play, as well as a close objective analysis of the performance you attended, as well as an interpretation and review of the full ensemble of staging, acting, directing, and so on. Couldn't the same be said about a file that is rendered on a computer screen, it has the ability to disappear at any point, so should we not read it carefully and think of why this specific work was digitized. What does this work really look like in its original form? How was this artifact written?

I think this approach to the matter is a fantastic step forward to such a sensitive issue. The items being archived are no more the archivist's property than they are the institutions property. These records belong to a culture that we should aim to preserve independently of our own, and we cannot truly attempt such a feat if we try to claim ownership over every piece we host. After all, in essence, these records are knowledge that local indigenous communities are offering to preserve for us as opposed to it being lost to time. Some of it may never be shared with outsiders of that community, yet some of it may be shared, and surely that value alone would be worth the cost of the preservation programs.

To give an analogy to this idea, if you could prevent the library of Alexandria from burning, even though you may never personally access it, but others might, would you? or would you let it burn and lose an unknown amount of knowledge and history in the process.

I think that it is important to keep these records of our nation's past as a reminder of where we came from and what horrid mistakes we made. Without these records, future generations may be doomed to repeat the same atrocities committed at residential schools, as well as other places. I also think that the ability to digitize and spread these documents allows the families of those affected to finally find closure in what has happened to their families.

I feel like if I was a part of this experiment that this would effect my decisions. If I knew that someone was watching and judging my decisions I would subconsciously change my original answers to answers that I think the people watching would approve of. It may just be something that is wired into our minds, that we have to accommodate our answers/actions based upon who is watching.

I never really thought of it this way, the fact that adolescents may be picking up these bad behaviors because they're told to do the opposite. I believe that our hearts are not as pure as we think they are, when Adam and Eve ate of the forbidden fruit and brought sin into this world, we have all now been born with sin, therefore I believe that there is something within us that desires to go against good... there is evil within us that wants to succeed, and maybe that's where this rebellion comes from that we see being mentioned here.

Author Response

Reviewer #1 (Public Review):

Using a large neonatal dataset from the developmental Human Connectome project, Li and colleagues find that cortical morphological measurements including cortical thickness are affected by postnatal experience whereas cortical myelination and overall functional connectivity of ventral cortex developed significantly were not influenced by postnatal time. The authors suggest that early postnatal experience and time spent inside the womb differentially shape the structural and functional development of the visual cortex.

The use of large data set is a major strength of this study, furthermore an attempt to examine both structural and functional measures, and connectivity analysis and separating these analyses based on the pre-and full-term infants is impressive and strengthens the claims made in the paper. While I find this work theoretically well-motivated and the use of the large dHCP dataset very exciting, there are some concerns, that need to be addressed.

There is a bit of confusion if the authors really compared the structural-functional measures in the final analysis. If the authors wish to make claims about the relationship, then there must be a compelling analysis detailing these findings.

Thanks for the suggestions. We have added analysis to directly investigate the relationship between the development of homotopic connection and corresponding structural measurements in the area V1 (Page 13 Line 5-16):

“The above results revealed that structural and functional properties of the ventral visual cortex both developed with PMA, but were differently influenced by the in-utero and external environment (Table 1). We further investigated the relationship between structural and functional development based on area V1, which showed a strong developmental effect in both structural and functional analyses. Mediation analysis was employed to see whether the development (GA or PT) of the homotopic connection between bilateral V1 was mediated by the structural properties (CT or CM). We found that the PT had a significant direct effect on the homotopic function that was not mediated by CT or CM (Fig 6a-b). In contrast, the direct effect of GA on the homotopic connection was not significant but the indirect effect of GA through CM on the connection was significant (Fig 6c-d).”

There is also a bit of confusion in the terminology used in the study regarding ages; the gestational age, premenstrual age, and postnatal time. I think clarifying and simplifying it down to GA and postnatal time will help the reader and avoid confusion.

Thank you for the suggestion. We have made extensive revision regarding the terminology throughout the paper and simplified it down to GA and PT. Please see the response to the 1st major concern in the Essential Revisions (for the authors) section above.

*Reviewer #2 (Public Review):

The authors utilize the publicly available dHCP dataset to ask an interesting question: how does postnatal experience and prenatal maturation influence the development of the visual system. The authors report that experience and prenatal maturation differentially contribute to different aspects of development. Namely, the authors quantify cortical thickness, myelination, and lateral symmetry of function as three different metrics of development. The homotopy and preterm infant analyses are strengths that, on their own, could have justified reporting. However, I have concerns about the analytic approaches that were used and the conclusions that were drawn. Below I list my major concerns with the manuscript.

PMA vs. GA vs. PT

The authors seek to understand the contribution of experience and prenatal development, yet I am unsure why the authors focused on the variables they did. There are three variables of interest used throughout this study: Gestational age at birth (GA), postnatal time (PT), and postmenstrual age at the time of scan (PMA). The last metric, PMA, is straightforwardly related to GA and PT since PMA = GA + PT. In most (but not all) of the manuscript, the authors use PMA and PT, with GA used without justification in some cases but not in others.

It is unclear why PMA is used at all: PMA is necessarily related to PT and GA, making these variables non-independent. Indeed, the authors show that PMA and PT are highly correlated. The authors even say that "the contribution of postnatal experience to the development was not clarified because PMA reflects both prenatal endogenous effect and postnatal experience." So, why not use GA at birth instead of PMA? Clearly, GA is appropriate in some cases (e.g., Figure S4 or in some of the ANOVA applications), and to me, it seems to isolate the effect the authors care about (i.e., duration of prenatal development). Perhaps there is some theoretical justification for using PMA, but if so, I am unaware.

That said, I expect that replacing all analyses involving PMA with GA will substantially change the results. I do not see this as a bad thing as I think it will make the conclusions stronger. As is, I am left unsure about what the key takeaways of this paper are.

We appreciate the suggestions, and we have replaced the related analyses involving PMA with GA in the manuscript. Please see the Response to the 1st major concern in the Essential Revisions (for the authors) section above for more detail.

Using GA instead of PMA will have several benefits: 1) It will be much simpler to think of these two variables since they contrast the duration of fetal maturation and time postnatally. 2) This will help the partial correlation analyses performed since the variance between the variables is more independent. It will also mean that the negative relationships observed between PT and cortical thickness when controlling for PMA (e.g., Figure 2h) might disappear (reversed signs for partial correlations are common when two covariates are correlated). 3) this will allow the authors to replace Figure 1a with a more informative plot. Namely, they could use a scatter of GA and PT, giving insight into the descriptive statistics of both dimensions.

We have revised the manuscript throughoutly following the reviewer’s suggestion. However, we thought it would be necessary to show the overall development of CT and CM across the general age (PMA) in Figure 1. Therefore, we didn’t replace the figure 1a but added a scatter figure between GA and PT in Figure 2-figure supplement 1 and added descriptive statistics of them in the manuscripts: “The mean GA of the neonates was 39.93 weeks (SD = 1.26) and the mean PT was 1.21 weeks (SD = 1.25), the correlation between them was not significant (r = - 0.08, p > 0.1; Figure 2-figure supplement 1).” Moreover, the negative relationships between PT and CT when controlling for PMA disappeared in the revised results as the reviewer’s predicted.

I suspect that one motivation for the use of PMA over GA is for the analysis in Figure 6. In this analysis, the authors pick a group of term infants with a PMA equal to the preterm infants. Since PMA is the same, the only difference between the groups (according to the authors) is the amount of postnatal experience. However, this is not the only difference between the groups since they also vary in GA (and now PT and GA are negatively correlated almost perfectly). I don't know how to interpret this analysis since both the amount of prenatal maturation and postnatal experience vary between the groups.

We appreciate the reviewer’s opinion that both GA and PT were different between preterm and term-born neonates. Then any of the differences between the two groups might came from the combined effect of GA and PT in our results, and unfortunately, we might not able to separate them in this analysis. However, the preceding results indicated that the CT was significantly influenced by PT and GA while CM was significantly influenced by GA, which So we discuss the preterm and term-born comparison in the context of these findings (Page 19 Line 26-29 and Page 20 Line 1-5): “We found CT in the ventral cortex was generally lower in the term-born than preterm-born infants, while the CM showed the opposite trend in the two groups. Since the preterm babies have longer PT but shorter GA compared to full-term infants at the same PMA, this result supported the above analysis that CT was preferably influenced by PT while CM was largely dependent on GA during the neonatal period”. Furthermore, we added a description in the limitation section to stress the caveat (Page 20 Line17-19): “Meantime, both GA and PT were different between preterm and term-born neonates. Then any of the differences between the two groups might came from the combined effect of GA and PT, and unfortunately, we were not able to separate them in this study.”

Justification of conclusions and statistical considerations

I had concerns about some of the statistical tests and conclusions that the authors made. I refer to some of these in other sections (e.g., the homotopy analyses), but I raise several here.

I am not sure what evidence the authors are using to make this claim: "we found that the cortical myelination and overall functional connectivity of ventral cortex developed significantly with the PMA but was not directly influenced by postnatal time." Postnatal time is significantly correlated with cortical myelination, as shown in Figures 2g, 2h, 3b, 3c, and postnatal time is significantly correlated with functional connectivity, as shown in Figures 4h, 5c, 5d, and 5e. Hence, this general claim that "the development of CT was considerably modulated by the postnatal experience while the CM was heavily influenced by prenatal duration" doesn't seem to be supported: both myelination and thickness are affected by postnatal experience and prenatal duration (as measured by PMA). A similar sentiment is expressed in the abstract. Perhaps the authors suggest different patterns in the strength of change for PMA vs. PT across these metrics, but if so, then statistical tests need to support that conclusion, and the claims need to reflect that sentiment.

Interestingly, Figure S4 presents a compelling ANOVA that does support this conclusion. Still, this result is relegated to the supplement, and it also uses GA, rather than PMA, making it hard to reconcile with the other claims made in the main text. Moreover, it uses ANOVAs, which dichotomizes a continuous variable. Here and elsewhere in the manuscript (e.g., Figures 3d, 3e), the authors split the infants into quartiles and compare them with ANOVAs. Their use for visualization is helpful, but it is unclear what the statistical motivation for this is rather than treating these as continuous variables like is possible with linear mixed-effects models. Moreover, it is unclear why the authors excluded half the data from the study (i.e., quartiles 2 and 3) in this ANOVA when all four quartiles could be used as factors.

We appreciate the reviewer’s comments. We have clarified our results and conclusion in the revised manuscript based on the new analyses that replaced PMA with PT and GA (See the response to the 1st major concern in the Essential Revisions). The previous claims have been changed as following:” the postnatal time could modulate the cortical thickness in ventral visual cortex and the functional circuit between bilateral primary visual cortices. But the cortical myelination, particularly that of the high-order visual cortex, developed without significant influence of postnatal time in such early period” (Page 2, Lines 8-12). This claims could be supported by the results in figure 2. Moreover, to support the claims about the comparison of the influence between GA and PT on structural development, we replaced the ANOVA analysis with a linear mixed-effect model as the reviewer mentioned.

1) To compare the influence of GA against PT on the structural development in the whole ventral visual cortex (Page 7 Line 15-19), “We applied a linear mixed-effect model to test whether the CT (or CM) of the whole ventral cortex were differently influenced by the GA vs. PT, and found that the GA had a significantly stronger effect on the CM than PT (interaction between GA and PT, p < 0.05) but no significant difference was found of the effect on the CT between the ages (p > 0.6).”

2) To compare the influence of GA against PT on the structural development in the area V1 and VOTC, we applied a similar linear mixed-effect model analysis for the two ROIs (Page 8 Line 17-18 and Page 9 Line 1-4): “Moreover, we applied a linear mixed-effect model to test the developmental influence of GA vs. PT on the cortical structure , and the results showed that the CT in two ROIs showed non-significantly different influences from GA against PT (p > 0.3), but CM showed at least marginally significant results in both two ROIs (V1: p < 0.01 and VOTC: p < 0.09).”

It is unclear what the evidence is to support the following claim: "Both CT and CM show higher correlation with PMA in the posterior than anterior region, and higher correlation in the medial than lateral part within the anatomical mask (Figure 2a and Figure S2b-c [sic])" From Figure 2 or Figure S2, I don't see a gradient. From Figure S3, there might be a trend in some plots, but it is hard to interpret since it is non-monotonic. More generally, is there a statistical test to support this claim?

We added a correlation analysis between the diction (x: lateral to medial; y: posterior to anterior) and measurements (CT and CM) in the ventral visual cortex, and the resulting coefficient was all significant (r = 0.7/-0.8 for CT along x/y axis, and r = 0.91/-0.83 for CM along x/y axis; p < 0.001). See Figure 1-figure supplement 2. However, the consideration provided by the reviewer still exists that such significance was driven by part of the areas and the gradient was non-monotonic. Therefore, we replaced the original claim with the following sentence (Page 6 Line 3-8): “In addition, we found distinct spatial variation along ventral cortex, e.g. posterior-anterior and medial-lateral directions (Figure 1-figure supplement 2a-b). Generally, both CT and CM showed higher correlation with PMA in the posterior than anterior region (r = -0.8 and -0.83; p < 0.001), and higher correlation in the medial than lateral part within the ventral visual cortex (r = 0.7 and 0.91; p < 0.001; Figure 1-figure supplement 2c-d).”.

"and the interaction [sic] was more prominent in CM (simple effect: t = 10.98, p < 10-9) that in than CT (t = 2.07, p < 0.05)." Does 'more prominent' mean it is 'significantly stronger'? If not, then the authors should adjust this claim

The claim ‘more prominent’ did express ‘significantly stronger’ since we found that the interaction between CM and CT along PMA or PT was significant in the ANOVA analysis. This analysis has been removed because we thought that the comparison between two structural measurements is not very relevant to the conclusion of the paper. We now applied a linear mixed-effect model to compare the influence of GA against PT on specific structural development. So this result and claim have been removed from the new manuscript.

Are the authors Fisher Z transforming their correlations? In numerous places, correlation values seem to be added together or used as the input to other correlation analyses. It is unclear from the methods whether the authors are transforming their correlation values to make that use appropriate.

We are sorry for the confusion. All the statistical analyses involving correlation coefficients were Fisher-Z transformed. We have added a clear description in the manuscripts involving the Fisher-Z transformation (Page 25 Line 16-18).

Homotopy analyses

The homotopy section is a strength of the paper, but I have doubts about the approach taken to analyze this data and some of the conclusions drawn. I don't expect any of my suggestions to change the takeaway of this section, but I do think they are essential criticisms to address.

I do not think that the non-homotopic control condition is appropriate. In Arcaro & Livingstone (2017), the authors had 3 categories for this analysis: homotopic pairs (e.g., left V1 vs. right V1), adjacent pairs (e.g., left V1 vs. right V2), and distal pairs (e.g., left V1 vs. right PHA1). In the homotopy analysis performed by Li and colleagues, they compare homotopic pairs with all other pairs. I don't think that is generous to the test since non-homotopic pairs include adjacent pairs that should be similar and distal pairs that shouldn't be similar. This may explain why some non-homotopic distribution overlaps with the homotopic distribution in Figure 4c.

Thanks for these suggestions. In the revised manuscript, we reanalyzed the data by dividing the connections into three groups for each subject. See Page 26 Line 24-29: “For each subject, Pearson correlations were carried out on the ROI-averaged time series within and across the left and right ventral cortex. The resulting connections were divided into three groups, namely the homotopic connection (the connection between two paired areas in two hemispheres. e.g. right and left V1), adjacent connection (e.g., right V1 and left V2 since V1 and V2 are adjacent) and distant connections (two areas that were not the paired or adjacent)”.

Regardless of this decision, I think the authors should reconsider their statistical test. I think the authors are using a between samples t-test to compare the 34 homotopic pairs with the hundreds of non-homotopic pairs. This is statistically inappropriate since the items are not independent (i.e., left V1 vs. right V1 is not independent of left V1 vs. right V2, which is also not independent of left V3 vs. right V2). This means the actual degrees of freedom are much lower than what is used. Moreover, I am unsure how the authors do this analysis across participants since this test can be done within participants. The authors should clarify what they did for this analysis and justify its appropriateness.

Thank you for the suggestion. In the previous manuscript, we first averaged the connection matrix across subjects and then calculated the homotopic (or non-homotopic) connections between areas, and therefore, statistical analysis could not be performed. In the revised paper, we calculated the three groups of connections for each subject before the average. We applied a non-parameter statistical analysis (Wilcoxon signed-rank) to address the issue of the independent comparison among the connections, and found the homotopic connections were significantly stronger than the adjacent or distant connections.

See (Page 26 Line 29 and Page 27 Line 1-3): “Independent-sample T-test was used to test whether the homotopic correlation was significantly greater than zero across subjects. To compare the correlation among the three types of connections, we applied a non-parameter statistical analysis (Wilcoxon signed-rank) across subjects”.

The results showed that (Page 9 Line 17-21) “the homotopic connections in all ROIs of ventral cortex were significant (mean r = 0.13– 0.43, t > 12.87, s < 10-9; Fig 4a-b), and were significantly higher than adjacent connections (0.29 ± 0.12 vs. 0.19 ± 0.10, Wilcoxon signed rank test on the Fisher-Z transformed r value: z = 16.32, p < 10-9) and distal connections (0.04 ± 0.06, z = 16.32, p < 10-9; Fig. 4c)”.

Could the authors speculate on why the correlations in homotopic regions are so much lower than what Arcaro and Livingstone (2017) found. I can think of a few possibilities: higher motion in infants, less rfMRI data per participant, different sleep/wake states, and different parcellation strategies. Regarding the last explanation, I think this is a real possibility: the bilateral correlation may be reduced if the Glasser atlas combines functionally heterogeneous patches of the cortex. Hence, the authors should consider this and other possible explanations.

Thank you for the suggestion. The neonates included in this study were all under natural sleep during the scan, so sleep/wake states would not be one of the causes. We added some possible reasons for this difference following the related results (Page 19 Line 9-13): “However, the present homotopic connections in the human neonates were lower than those in neonate macaca mulattas (Arcaro and Livingstone, 2017). This difference might relate to the higher motion in human infants, less r-fMRI data in the present study, coarser parcellation in the visual cortex used in this work, and the developmental difference between primates and humans in the neonatal period.”

The authors assume that the homotopic analyses mean that there are lateral connections between hemispheres (e.g., "Furthermore, the connections among the ventral visual cortex have developed during this early stage. Specifically, the homotopic connections between bilateral V1 and between bilateral VOTC both increased with GA, indicating an increased degree of functional distinction"). While this might be true, it doesn't need to be. Functional connectivity can be observed between regions that lack anatomical connectivity. Instead, two regions could both be driven by another region. In this case, the thalamus might drive symmetrical activity in the visual cortex.

We agree with the reviewer’s view that the development of functional connectivity might be driven by other regions like thalamus. So we added this interpretation in the discussion section (Page 19 Line 23-25): “It is worth noting that the increased homotopic connection can be direct or indirect, e.g., the effect might be driven external regions with enhanced connection to both of the areas (e.g. thalamus)”.

Miscellaneous

I am not sure what the motivation of this line is: "Moreover, those studies did not fully control the visual experience in the first few weeks of the subjects, thus cannot give a clear conclusion whether the innate functional connectivity is unrelated to postnatal visual experience." Arcaro, Schade, Vincent, Ponce, & Livingstone (2017) did control the visual experience of subjects. Moreover, the research here doesn't control infant experience in the way this sentence implies: it implies an experiment manipulation (i.e., fully control) rather than a statistical control that is done here. Consider rephrasing

We have rephrased this sentence in the introduction section (Page 5 Line 2-5): “Moreover, the human infants participating in a previous study (Kamps et al., 2020) were around one month old (mean age: 27 d; range from 6 to 57 d), who might already acquire some visual experience, and thus this study could not exclude postnatal visual experience on the innate functional connectivity”.

I am not sure why this claim is made: "Area V1 was selected because this region is the most basic region for visual processing and probably is the most experience-dependent area during early development". Is there evidence supporting this claim? Plasticity is found throughout the visual cortex, and I think which region is most plastic depends on the definition of plasticity. For instance, most people have the same tuning properties to gabor gratings (e.g., a cardinality bias), but there is enormous variability in face tuning across cultures.

We have removed this claim in the manuscript.

The abstract says 783 infants were included in this study, but far fewer are actually used. The authors should report the 407 number in the abstract if any number at all.

We have revised the number accordingly.

Any comparisons of preterms and terms ought to be given the caveat that the preterm environment can be very different than the term environment: whereas a term infant goes home and sees friends and family without restriction, the preterm environment can be heavily regulated if they are in a NICU. Authors should either provide details about the environments of the preterms in their study, or they should consider how differences in the richness of visual experience - regardless of quantity - may affect visual development.

We agree with the reviewer’s concern, and added a paragraph in the limitation section to stress the caveat (Page 20 Line 12-16): “One limitation of this study is the comparison between preterm and term-born infants did not consider the different visual experience in these infants. The preterm-born neonates may experience very different environment than those of the term-born, e.g. the preterm environment can be heavily regulated if they were in a NICU, but we didn’t have detailed information about the postnatal environment to control for it.”

Reviewer #3 (Public Review):

The authors use a large neonatal dataset to examine how development may occur differently based on whether on not the neonate spent that time in gestation or out of the womb accruing potentially accruing visual experience. In this manner, the authors hope to tease apart those aspects of development that are biologically programmed versus those that occur in response to experience within the visual cortex. They show structurally that cortical thickness is affected by postnatal experience while cortical myelination is not, and functionally they find regional differentiation present between visual areas at birth and that their connectivity changes with development and postnatal experience. The conclusions seem well supported by the data and analyses and provide some insight into which aspects of brain structure at birth are sculpted more by postnatal experience and which are more determined by endogenous developmental timelines.

The analyses are based on a large sample of infants, and the authors were careful to statistically separate which aspects of an infant's age, gestational or postnatal, are driving brain development, providing a deeper picture of infant brain development than previous publications. Overall, the findings seem well supported by the data as the analyses are relatively straightforward.

Visualization of the data and findings could be improved, as a few figures are difficult to interpret without having to read the methods.

We have extensively revised the figures in the manuscript to improve the readability. See updated Figures 2-7.

The acronyms regarding gestation, postnatal, and post-menstrual time are a little distracting. Please consider explicitly writing "gestational time" etc when referring to these numbers to improve readability.

We have replaced the analyses involving PMA with gestational age (GA) or postnatal time (PT) in the revised manuscript to simplify the terminology. Please see the Response to the 1st major concern in the Essential Revisions (for the authors) section above. We believe this change makes the paper easier to follow even with the abbreviations.

Because the cortical ribbon of infants is so thin at birth, there seems to be a possibility that partial-volume effects could be more prevalent in less-developed infants and impact myelin metrics. If not modeled or estimated, it should at least be discussed.

In fact, the cortical thickness of the neonatal brain is not thinner than that of the adult. Particularly, the average cortical thickness of infants aged 0-5 months is around 2-2.5 mm (Wang et al., 2019), which is similar to adults (Fjell et al., 2015). Therefore, the partial-volume effect for cortical gray matter is not a special concern for infants.

Nevertheless, we agree that the partial-volume effects might have different influences on infants of different ages. We added this consideration in the limitation section (Page 20 Line 20-24). “Another concern was about the partial-volume effect on the cortical measurements. The changing thickness of cortical ribbon during development may changes the degree of partial-volume effect, and thus may affect the cortical myelination measurement and may contribute to the myelination difference observed between preterm and term-born groups.”

Structural and functional development could be more formally compared using quantitative models if the authors want those points more strongly related; the two are only qualitatively discussed at present.

We have added a formal analysis to investigate the relationship between structural and functional development. Please see the Response to the 1st concern of Reviewer 1 (public review).

Author Response

Reviewer 2 (Public Review):

1) The hypothesis that the genes responsible for the Mendelian traits are also the causal genes for the cognate complex traits does not seem to hold, given the prior work and the data shown in the study. For example, if this hypothesis is true, it is unexplained why the candidate genes were not even enriched in the GWAS regions for height and breast cancer.

Following the removal of a data artifact from our breast cancer analysis and the inclusion of Backman et al.’s larger list of genes implicated in height, every phenotype in our analysis displays enrichment in proximity to GWAS peaks. Enrichment is present not only in genes selected based on cognate Mendelian phenotypes, but also on those from Backman et al., which examined the same complex trait phenotypes that were used for GWAS. In that work, the enrichment GWAS signal near of genes selected on coding variants was as high as 59.3-fold.

Our use of Mendelian-trait-causing genes is not dependent on GWAS. Short of large-scale experimental work, we do not know any better way to confirm the genes’ broad relevance to GWAS phenotypes than their enrichment near peaks. This enrichment has been persuasively demonstrated by previous research. Freund et al. (2019) tested the enrichment of 20 Mendelian disorder gene sets against 62 complex phenotypes. Though there was no statistically significant overlap of phenotypically non-matched Mendelian genes and GWAS peaks (2% matched), the overlap of matched Mendelian genes and GWAS peaks was significant (54% matched).

We have included additional evidence and references for this relationship in Supp. Note 1.

2) The only evidence supporting their hypothesis appears to be the enrichment of the candidate genes in the GWAS regions for seven out of the nine traits. However, significant enrichment of the candidate genes in the GWAS regions does not necessarily mean that a large proportion of the candidate genes are the causal genes responsible for the GWAS signals. Analogously, we cannot use the strong enrichment of eQTLs in GWAS regions as evidence to claim that a large proportion of the GWAS signals are driven by eQTLs.

Our gene sets were selected by considering two criteria: whether they are relevant to each complex trait, and whether they are biologically interpretable.

The genes identified in Backman et al. have a strong case for relevance. They are evaluated for association, not with cognate Mendelian phenotypes, but with the exact same complex traits used for GWAS.

Our genes, selected based on cognate Mendelian traits, are less obviously relevant, but have advantages for interpretation. Many have well-understood biological roles and are part of pathways that have been studied in great detail. Because most of these genes can cause dramatic phenotypic changes with one variant, the direction of effect is easier to understand than genes identified through burden testing. In fact, loss-of-function coding variants that cause autosomal dominant traits can be thought of as large-effect, context-independent eQTLs—they cause phenotypic change by decreasing gene expression roughly 50% across cell types, developmental stages, etc.

Ideal genes for our analysis would combine the advantages of both sets. They would have individual coding variants that could be tied to complex traits using exome sequences. However, natural selection creates tradeoffs between variant frequencies and variant effect sizes. Large-effect variants (such as those responsible for Mendelian traits) are generally too rare to be detected in population sequencing. Coding variants that reach frequencies detectable in databases such as UK Biobank typically have smaller effect sizes, requiring them to be aggregated in order to implicate genes.

We believe that our original gene set is plausible both because of its collective enrichment in GWAS signal and because each gene is individually known to cause cognate phenotypes. Enrichment is not proof, but can serve as strong evidence when backed up by known biology. Though selection precludes a perfect gene set, the enrichment in both our Mendelian gene set and the set from Backman et al. addresses each criterion—interpretability and relevance—individually, and, taken together, provides an argument for the relevance of genes selected based on coding variants.

3) Considering the large numbers of GWAS signals, we would expect a substantial number of genes in the GWAS regions by chance. It would be interesting to quantify the number of genes in the GWAS regions if the 143 genes are randomly selected. Correcting the observed number of genes for that expected by chance (e.g., subtracting the observed number by that expected by chance), the proportion of the candidate genes in the GWAS regions would be small.

The proportion of the candidate genes whose eQTL signals were colocalized with the GWAS signals or in close physical proximity with the fine-mapped GWAS hits was small. However, I would not be surprised if they are significantly enriched, compared with that expected by chance (e.g., quantified by repeated sampling of the 143 genes at random).

Taking random sets of genes, or the entire set of non-putatively-causative genes shows that, given the size of our gene set, we would expect 43 randomly selected genes to fall within 1 Mb of a peak (95% confidence interval: 31.5-54.5). Instead, we find 147 peak-adjacent genes. When looking closer to genes, the enrichment increases. At a distance of 100 kb, we find 104 putatively causative genes, but the null model predicts only 11 (95% CI 4.5-17.0), a roughly ten-fold difference.

Enrichment remains significant even when using a more conservative null. It may be that genes like ours, with importance to phenotype, are more likely than random genes to fall near GWAS peaks, even if their phenotype does not correspond to the GWAS phenotype. In this case, we might see enrichment even in the absence of a relationship between our Mendelian and complex traits. To account for this, we also tested significance by testing genes sets against different phenotypes (e.g. testing our LDL genes with a UC GWAS, and our height genes with a T2D GWAS). The results of this permutation are visible in Supp. Fig. 1, and further confirm the enrichment.

Finally, non-expression based analysis found that Mendelian genes had large enrichments in heritability. As in our study, they included Mendelian genes for diabetes and LDL—the Mendelian diabetes genes were enriched 65-fold for common-variant heritability and the Mendelian LDL genes were enriched 212-fold (Weiner et al. 2022).

Though it is true that the number of colocalizations and TWAS hits likely represents a statistically significant enrichment over all genes, we feel that this does not affect the conclusions of the paper. The model that noncoding variants identified by GWAS act as eQTLs certainly has some truth—colocalization and TWAS studies have found, in total, many associations. But the model’s success has not lived up to its expectations. This has been suggested, albeit inconclusively, by the failure of most GWAS peaks to colocalize. By evaluating, not the portion of loci that can be tied to a gene, but the portion of already-implicated genes that can be tied to a locus, we believe the model’s deficiencies are both more clear and more puzzling.

4) It is unclear how the authors selected the breast cancer genes. If the genes were selected based on tumor somatic mutations, it is a problem because there is no evidence supporting that somatic mutation target genes are also cancer germline risk genes.

Genes for breast cancer were selected using the MutPanning method (Dietlein et al. 2020), which takes somatic mutations found in tumors, and evaluates them in the context of known mutation patterns. The relationship between somatic and germline variants in cancer is little studied. We believe it is meaningful that, as explained in our response to overall comment 2ii, we do now find an enrichment of our breast cancer genes near GWAS peaks. Though these genes are very unlikely to be a perfect set, the conclusions of our paper remain true with or without the inclusion of this phenotype.

5) The authors observed no enrichment of the candidate genes in height and breast cancer GWAS regions. In this case, should these traits and the corresponding genes be removed from the subsequent analyses?

The reviewers’ notes about enrichment—and its absence in height and BC—prompted us to review our analysis of it. The enrichment for five of our phenotypes remained significant, and the lack of enrichment for breast cancer genes proved artifactual. After accounting for the artifact, the enrichment of breast cancer genes displays the same pattern as most other phenotypes, displaying highly significant enrichment as compared to the genomic background and a permutation analysis. Supplementary figure 1 has been updated to reflect this change, and to add the enrichments found in Backman et al.

Because our original analysis of height has nominal, but not corrected, significance for enrichment, the problem may be one of power. The set of height genes identified by Backman et al. is larger than our original set and displays a significant enrichment in proximity to GWAS signal. This enrichment is also present when the two gene sets are combined, as shown in the updated Supp. Figure 1.

Reviewer 3 (Public Review):

1) The positive results are substantially reduced when restricting the analyses to a set of selected tissues of relevance to the trait. Isn't it implicated that the selection of relevant tissues in this study is not comprehensive, and further, tissue specificity is common in mediating genetic effects by gene expression? First, it seems some apparently relevant tissues are not selected (Table 2), such as bone for height (Finucane et al. 2015 NG). One approach to assess the relevant tissues for the predefined set of putatively causative genes is to see if these genes are enriched in the differentially expressed gene sets for those tissues. Second, among 84 putatively causative genes overlapped with GWAS signals, they identified 39 genes by TWAS, 11 genes by fine mapping with linear distance to chromatin modification features, and 41 genes by fine mapping with ChromHMM enhancer annotations, but these numbers reduced substantially to 9, 5 and 27 when restricting the same analysis to the selected tissues for each trait. If genes function only in the relevant tissues, I think using bulk expression data would lose power but is unlikely to give false positives. Thus, it is possible that for the traits analysed, not all relevant tissues are selected so that only a fraction of genes identified in bulk expression analysis can be replicated in the tissue-specific analysis. This appears to me a notable piece of evidence to support the hypothesis of biological context that the authors tend to have reservations in discussion.

Testing for colocalizations or TWAS hits in all tissues may increase power for several reasons. First, it is possible that some GTEx tissues have unrealized relevance to our phenotypes. Secondly, in the event that a tissue is not present in GTEx, we may still detect relevant eQTLs in a tissue that is not itself involved in the trait, but which has similar patterns of expression. Finally, some tissues may be correct, but underpowered due to their small sample size. In this case, we may better detect the colocalization in tissues that are “irrelevant,” but are well-powered and have correlated expression.

However, this creates problems of interpretation. Say we find, for example, a colocalization of an APOE eQTL with an LDL GWAS peak in skin tissue. Does this mean that skin tissue contributes to LDL levels? Is it simply because skin tissue has more samples than liver? Are we uncovering a strange, unexpected pleiotropy?

We believe we can achieve both objectives—power and interpretability—with our use of MASH (Urbut et al. 2019) as described in response 3 of the first section. Briefly, MASH is a Bayesian tool that we use to update the estimates of eQTLs in GTEx data. Each tissue is adjusted to incorporate signals detected in other tissues with similar expression. This mitigates the danger of ignoring the correct tissue, and increases the power of tissues with small sample sizes. Its benefit is demonstrated by the substantial increase in the number of expression-GWAS colocalizations identified by coloc—however, the number of genes identified that fall within our putatively causative gene sets remains strikingly small.

2) How much do both LD differences between GWAS and eQTL samples and the presence of allelic heterogeneity contribute to the observed low colocalization rate? One of their main findings is the low colocalization between trait-associated variants and eQTL in non-coding regions, which accounts for only 7% of the putatively causative genes. In discussion, the authors believe that this finding cannot be explained by lack of statistical power and is directly supported by a Bayesian analysis which reported high posterior probabilities of distinct signals for GWAS and eQTL. I agree that power is probably not a big issue. However, my concern is that given the large difference in sample size between GWAS and GTEx datasets, any small differences in LD between the two samples might cause a statistical separation of the signals even when trait phenotype and gene expression truly share a causal variant. Moreover, the presence of more than one causal variant with allelic heterogeneity in the locus may also play a part in the failure of colocalization. Consider two causal variants for the complex trait, one regulating the target gene and the other regulating another gene in co-expression. Potentially, the presence of the second causal variant would diminish the colocalization probability at the target gene.

The ability of our statistical tools to actually find colocalizations is a critical one in this project. Small sample size increases the variance of the LD matrix, but is one of only many factors that influence power, which include LD differences between study populations and eQTL effect sizes.

Though we restricted both GWAS and GTEx samples to subjects with European ancestry and used PCs as covariates, reviewers are correct that there are likely to be LD differences between samples, due to both slight variations in populations and the smaller sample sizes of GTEx. Analysis of colocalization tools in cases of mismatched LD have shown that decreases in power are small. Chun et al. (2017) tested JLIM in simulated conditions of modest population mismatch, using CEU haplotypes to create the GWAS, and haplotypes from all non-Finnish Europeans for eQTL associations. They then attempted to distinguish shared vs. distinct causative variants for GWAS and eQTL, finding no decrease in sensitivity or specificity (Supp. Fig. 6 of Chun et al. 2017).

The case in which two genes are co-regulated by nearby variants, both causative for the GWAS trait, creates a condition of allelic heterogeneity for the GWAS trait (as opposed to the expression trait). Chun et al. evaluated JLIM’s loss of power as a result of AH, and found that the power loss is small, except in cases in which the two variants have equal effects (Supp. Fig. 10). Testing cases in which the AH occurs for the expression trait returned a similar result (Supp. Fig. 9).

Hukku et al. (2021) performed similar analyses on coloc, eCAVIAR, and fastENLOC. Allelic heterogeneity was found to damage the power of coloc (by about a factor of 2). Testing on different pairs of populations, they conclude that extreme LD mismatches (e.g. Finnish vs. Yoruban samples) can lead to substantial power loss, but moderate LD mismatches (e.g. Finnish vs. British samples) do not. Though a factor of two is substantial, it would not change the qualitative conclusions of this paper. Overall, given the variety of methods we employ (including those, such as JLIM, more robust to AH), we are confident that they have, when taken together, been shown to be robust to the concerns raised.

Finally, TWAS should, by design, be less vulnerable to LD differences and allelic heterogeneity. This can result in false positives, when genes with correlated expression are identified together, despite only one being causative. It can also result in non-causative genes being prioritized over causative ones, however, generally both genes will be identified (Wainberg et al. 2019).

3) Perhaps the authors can perform some simulations to quantify the influence of tissue-specific expression effects, LD differences between eQTL and well-powered GWAS, and allelic heterogeneity, as discussed above, on their analyses. I understand that the authors may not be willing to do as it would involve a lot of work. But I'd like to see at least some discussion on how these questions can be better addressed in the future research.

These are nuanced technical questions, and to address them by simulation in our paper would, as noted, involve a lot of work. We have summarized previous work that evaluated the effects of LD differences and AH in our response to essential revision 4. We discuss our concerns about the possibility of an overly broad tissue search in essential revisions 3 and 5, and our decision to address this question using MASH in essential revision 3.

4) It looks quite striking that only 6% of the putatively causative genes are identified by TWAS with the correct effect direction. But I think this number is slightly misleading as one may interpret it as only 6% of the functionally relevant genes are regulated by trait-associated variants. In fact, 46% of the genes are detected by TWAS but only 11% are confirmed in their selected tissues, among which about half (5/9) have correct effect direction. First, the result could be limited by the selection of relevant tissues, as discussed above. Second, the fact that half of the genes do not show correct effect direction may reflect a nonlinear relationship between expression and trait, or the presence of cell-type heterogeneity within a tissue. These may not necessarily overturn the assumption that these genes are regulated by trait-associated variants in the causal tissues or cell types.

In our initial submission, we had been reluctant to expand the list of tissues for two reasons. First, increasing from the small number of tissues with known biological relevance to all tissues (or all non-brain tissues) increases the multiple-testing correction burden. Second, and, in our eyes, more important, colocalizations in tissues without clear biological relevance are not biologically interprable. Such hits can be results of complicated genetic architecture (e.g. shared eQTLs), power differences in tissues with correlated expression, or biology not directly related to the trait in question.

That said, the tissue data we have access to are incomplete, and we are without question missing some relevant tissues. Additionally, some relevant tissues have lower sample sizes, and thus lower power, than tissues that are not relevant but may still share eQTLs. To overcome these problems, we applied Multivariate Adaptive Shrinkage (MASH), a Bayesian method that detects correlations between different (in this case tissues) and uses them to produce posterior estimates of summary statistics in each tissue (Urbut et al. 2019). Unlike meta-analysis, which produces one result, the effect size estimates for each tissue are distinct, though informed by one another.

Using MASH has a pronounced effect on colocalization results. The number of non-putatively causative genes colocalizing increases from 389 to 489, while the number of putatively causative genes in our Mendelian set is unchanged, remaining at 2. The number of genes from the Backman et al. set increases from 2 to 5. Though this is a proportionally large increase, it still represents a small fraction of genes. We have updated our paper to use these results—which should be less dependent on the tissues we selected—but the message has not changed.

5) While they highlight the roles of alternative regulatory mechanisms, few testable hypotheses are put forward for the field, which is somewhat disappointing but understandable given how little we know about the human genome at the mechanistic level.

We have added a set of models that may explain the “missing heritability” to Table 4 in the discussion. Though we do not propose experiments, we have included citations for research relevant to confirming or disproving these models.

Reviewer #3 (Public Review):

Connally et al investigated a central question in complex trait genomics - what's the main mechanism that mediates the effects of trait-associated variants in non-coding regions, which harbour most of the signals identified by genome-wide association studies (GWAS). It is widely perceived that these variants affect trait phenotypes by regulating expression of genes in cis that are functionally relevant to the trait. The authors argue that this is not true because they find limited evidence of linking the trait-associated non-coding variants to a set of putatively causative genes that are known to cause the severe form of the complex trait. The authors discussed four possible explanations to their observations. They argue that incorrect assumptions and lack of statistical power are not likely to be critical, withhold their judgment on the biological context, and claim that the most convincible explanation is the existence of alternative regulatory mechanisms. This conclusion is very important and sobering if it is true because it will inform where to invest the most efforts in the future GWAS.

It is an interesting idea of using genes of known roles in the "Mendelian forms" of the cognate complex traits as true positives to investigate the biology of non-coding variants. The analyses are done carefully. The discussion of the results is sharp, stands high, and provides lots of food for thought. My major comments lie in the strength of support of their results for the conclusion of "missing regulation" likely attributed to alternative regulatory mechanisms. The results presented seem to also support the biological context hypothesis that non-coding variants regulate gene expression in a tissue or cell type-specific manner.

Major comments:

The positive results are substantially reduced when restricting the analyses to a set of selected tissues of relevance to the trait. Isn't it implicated that the selection of relevant tissues in this study is not comprehensive, and further, tissue specificity is common in mediating genetic effects by gene expression?<br /> First, it seems some apparently relevant tissues are not selected (Table 2), such as bone for height (Finucane et al. 2015 NG). One approach to assess the relevant tissues for the predefined set of putatively causative genes is to see if these genes are enriched in the differentially expressed gene sets for those tissues. Second, among 84 putatively causative genes overlapped with GWAS signals, they identified 39 genes by TWAS, 11 genes by fine mapping with linear distance to chromatin modification features, and 41 genes by fine mapping with ChromHMM enhancer annotations, but these numbers reduced substantially to 9, 5 and 27 when restricting the same analysis to the selected tissues for each trait. If genes function only in the relevant tissues, I think using bulk expression data would lose power but is unlikely to give false positives. Thus, it is possible that for the traits analysed, not all relevant tissues are selected so that only a fraction of genes identified in bulk expression analysis can be replicated in the tissue-specific analysis. This appears to me a notable piece of evidence to support the hypothesis of biological context that the authors tend to have reservations in discussion.

How much do both LD differences between GWAS and eQTL samples and the presence of allelic heterogeneity contribute to the observed low colocalization rate?<br /> One of their main findings is the low colocalization between trait-associated variants and eQTL in non-coding regions, which accounts for only 7% of the putatively causative genes. In discussion, the authors believe that this finding cannot be explained by lack of statistical power and is directly supported by a Bayesian analysis which reported high posterior probabilities of distinct signals for GWAS and eQTL. I agree that power is probably not a big issue. However, my concern is that given the large difference in sample size between GWAS and GTEx datasets, any small differences in LD between the two samples might cause a statistical separation of the signals even when trait phenotype and gene expression truly share a causal variant. Moreover, the presence of more than one causal variant with allelic heterogeneity in the locus may also play a part in the failure of colocalization. Consider two causal variants for the complex trait, one regulating the target gene and the other regulating another gene in co-expression. Potentially, the presence of the second causal variant would diminish the colocalization probability at the target gene.

Perhaps the authors can perform some simulations to quantify the influence of tissue-specific expression effects, LD differences between eQTL and well-powered GWAS, and allelic heterogeneity, as discussed above, on their analyses. I understand that the authors may not be willing to do as it would involve a lot of work. But I'd like to see at least some discussion on how these questions can be better addressed in the future research.

It looks quite striking that only 6% of the putatively causative genes are identified by TWAS with the correct effect direction. But I think this number is slightly misleading as one may interpret it as only 6% of the functionally relevant genes are regulated by trait-associated variants. In fact, 46% of the genes are detected by TWAS but only 11% are confirmed in their selected tissues, among which about half (5/9) have correct effect direction. First, the result could be limited by the selection of relevant tissues, as discussed above. Second, the fact that half of the genes do not show correct effect direction may reflect a nonlinear relationship between expression and trait, or the presence of cell-type heterogeneity within a tissue. These may not necessarily overturn the assumption that these genes are regulated by trait-associated variants in the causal tissues or cell types.

While they highlight the roles of alternative regulatory mechanisms, few testable hypotheses are put forward for the field, which is somewhat disappointing but understandable given how little we know about the human genome at the mechanistic level.

Reviewer #1 (Public Review):

As an m6A reader, YTHDC1 is known to affect the processing of RNA post-transcriptionally and this article attempted to relate this function in splicing and nuclear export to defects in muscle regeneration after acute injury using LACE-seq. Mechanistically, they provided evidence on m6A-YTHDC1 participation in modulating splicing and target export in myoblast. Additionally, the authors preliminarily confirmed the interaction of YTHDC1 with several key RNA processing factors such as hnRNPG1 to suggest a possible mechanism for m6A-YTHDC1 regulating splicing. Overall it provides new insight into YTHDC1 function in regulating SC activation/proliferation, although some of the data could be improved to fully support the conclusions.

1. The title "Nuclear m6A Reader YTHDC1 Promotes Muscle Stem Cell Activation/Proliferation by Regulating mRNA Splicing and Nuclear Export" seems a bit overstated. Their data are not sufficient to show YTHDC1 regulating nuclear export. From figure 6 we could see some mRNAs export was inhibited upon YTHDC1 loss but intron retention also occurs on these mRNAs, for example, Dnajc14. Since intron retention could lead to mRNA nuclear retention, the mRNA export inhibition may be caused by splicing deficiency. From the data they provided we could not draw the conclusion that YTHDC1 directly affects mRNA export. I think they should not emphasize this point in the title.

2. The mechanism of YTHDC1 promoting muscle stem cell activation/proliferation is not solidified. The authors could strengthen their evidence through bioinformatics analysis or give more discussion. Besides, the previous work done by Zhao and colleagues (Zhao et al., Nature 542, 475-478 (2017).) reported another m6A reader Ythdf2 promotes m6A-dependent maternal mRNA clearance to facilitate zebrafish maternal-to-zygotic transition. Does YTHDC1 regulate mRNA clearance during SC activation/proliferation? The authors should explore this possibility by deep-seq data analysis and provide some discussion.

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Reply to the reviewers

Reviewer #1

Major #1

This study primarily uses the genetic mouse model in which LSD1 gene is inactivated after tamoxifen injection in 8 weeks old mice, as shown in supplemental figure 1 B and C. 8 weeks after birth postnatal growth of muscle is not complete and the contribution of satellite cells to muscle growth is still significant. Therefore the timing of tamoxifen injection used cannot discriminate if the observed phenotype involves the function of LSD1 during the post-natal growth of the muscle or in the muscle fibers or both. One way to demonstrate the real contribution of LSD1 in the maintenance of muscle fibers plasticity under environmental stress would be to inject Tamoxifen later (around 10-12 weeks of age), in order to remove a possible bias caused by the contribution of satellite cells during the post-natal growth. At least key findings should be confirmed at this later stage.

In this study, we used ACTA1-CreERT mice to conditionally knockout LSD1 in the skeletal muscle. The ACTA1 promoter is derived from human muscle actin gene, which is not expressed in the satellite cells, and has been widely used for the transgene expression in myofibers (Stantzou et al. Development 2017). Thus, the inactivation of LSD1 occurs in the existing myofibers, and alterations in satellite cell function, if any, would be indirect effects of the loss of LSD1 in mature myocytes or differentiating myoblasts.

To test whether postnatal muscle growth was affected in our LSD1-mKO mice, we administrated tamoxifen (4OHT) to pre-weaning mice (11 days old). LSD1 depletion did not affect the expression of muscle fiber genes, when muscle tissues were isolated from mice 11 days after the start of 4OHT (Additional Data).

These evidences exclude the contribution of satellite cells in the phenotypes observed in the LSD1-mKO mice. __Additional Data __will be added in the revised manuscript.

Major #2

LSD1 m-KO muscles seem to have more type I and IIA fibers than WT, even without DEX treatment. Is it possible to quantify the results in supplemental figure 4C?

As suggested, we quantitatively analyzed the fiber type compositions in Supplemental Fig. 4C using the data from WT (n=4) and LSD1-mKO (n=5) mice (Additional Data). We did not find a significant difference between these mice, confirming our finding that the loss of LSD1 accelerates the Dex-driven phenotypic changes. __Additional Data__will be added in the revised manuscript.

Major #3

The effect on fiber type is convincing, while variations in gene expression are of quite low amplitude. However, the atrophy should be induced by other means to ensure that the effects are specific to GC/nuclear receptors pathways; Denervation? Starvation? Not all the experiments need to be repeated, just key results such as, for example, exacerbation of atrophy in LSD1 m-KO, Foxk1 increase.

We agree that testing alternative atrophy models is important for generalizing our findings. For this, we employed a model for diabetes-related muscle atrophy. A pro-diabetic agent streptozotocin (STZ) disturbs the function of pancreatic islet leading to fast-fiber atrophy (O’Neill et al. Diabetes 2019). LSD1-mKO did not affect the muscle weight in STZ-treated mice (Additional Data). Consistently, there were no major difference in the expression of atrophy genes in STZ-treated WT and LSD1-mKO mice (Additional Data). These results suggest that the LSD1 function depends on the source of atrophy-inducing stress, and that the loss of LSD1 sensitized the muscle to GC-mediate signaling. Additional Data will be added in the revised manuscript.

Major #4

Autophagy data: the effect on the LC3I/LC3II ratio are modest. The autophagy part should be removed or completed with additional data to convincingly show that autophagy is affected. Links between LSD1 and mTOR have been published, so the mTOR pathway could be investigated in the model (S6k, S6 and 4EBP1 phosphorylation). Given AKT levels and phosphorylation are affected by the absence of LSD1 + DEX, it can be predicted that mTOR activity will change.

We have analyzed the expression of additional autophagy markers p62 and phosphorylated 4EBP1. Consistent with the upregulated expression of atrophy genes and increased LC3I/II ratio, LSD1-mKO mice had elevated levels of p62 and phosphorylated 4EBP1 (Additional Data). Altogether, the data suggest that Dex-induced muscle atrophy was exacerbated by the loss of LSD1. Additional Data will be added in the revised manuscript.

Major #5 The ability of LSD1 to retain FOXK1 in the nucleus is an important information that should be better supported experimentally. In the absence of such information, no mechanism can be proposed for the effect of LSD1 of FOXK1. The immunofluorescence images provided are not convincing and moreover they could be interpreted by a reduction in the level of FOXK1 protein (degradation?) rather than by a nuclear exclusion in the presence of DEX. This point should be addressed, authors could include western blot of nuclear and cytoplasmic fractions to better quantify the nuclear level of FOXK1 in absence of LSD1.

We agree that performing the suggested experiment would further enhance the quality of our study.

Major #6 The absence of centralized nuclei indicates that there is no fiber regeneration but it does not exclude the possibility that satellite cells were recruited to existing fibers and thus participated to hypertrophy. To eliminate this possibility, the average nuclei/cytoplasm volume should decrease if hypertrophy results from increased protein synthesis and not myonuclei accretion. This should be checked.

We histologically analyzed the sections of Gas muscles after Dex treatment and found that there is no evidence of central nuclei in either WT or KO mice (Supplemental Fig. 4D).

As mentioned above (Major #1), it is unlikely that the satellite cell function was responsible for the enhanced atrophic phenotype.

Major #7 The upregulation of ERR____g in the absence of LSD1 is convincing in the VWR conditions. ERR____g level should be evaluated in the sedentary LSD1 KO mice.

We have analyzed the expression of ERRg in sedentary mice, and found no significant difference between WT and KO mice (Additional Data). This suggests that the loss of LSD1 in combination with VWR training led to the increased expression of ERRg. Additional Data will be added in the revised manuscript.

Minor #1

There is a clear difference in the number of mouse replicates between treated (Dex or VWR) and non-treated mice, regardless the genotype. Experiments with non-treated mice lack adequate numbers to make a definitive conclusion. For example, there is a huge spread in the data in Figure 1 B and 4 D. If the number of animals would have been increased, would the absence of difference hold up?

We increased the number of non-treated animals in Figures 1B and 4B as suggested. Nonetheless, we did not find any significant differences in the muscle weight (Additional Data). These changes will be reflected on original Figures 1B and 4B.

Minor #2 The authors claim that: "Consistent with the results of the augmented endurance capacity, the Sol muscle in the KO mice showed enhanced succinate dehydrogenase (SDH) staining, indicating that the number of oxidative fibers increased (Figure 4F and Supplemental Figure 8F)". However, supplemental figure 8 D indicates that the number of type I fibers does not change compared to WT. Authors should clarify this statement.

Indeed, we found that the area of type I fiber but not the number was increased in the LSD1-KO Sol (Fig. 4D and Supplemental Fig. 8D). Because SDH staining reflects the OXPHOS capacity in all fiber types, it is possible that the OXPHOS capacity in the fibers other than type I had been augmented by LSD1-KO. Thus, for clarification, we will change the statement as follows: OXPHOS capacity of Sol was enhanced by the loss of LSD1.

Reviewer #2

__Methods

1__

The authors used the Cre-lox system with tamoxifen to generate skeletal muscle-specific LSD1 KO mice. It is clear that both the mRNA and protein levels of LSD1 in various muscles were dramatically reduced, but there is still some LSD1 expressed in skeletal muscle, especially in Sol muscle (Supplemental Figure 1C). The author needs to think about whether it is appropriate to use the term "LSD1 knockout" or "LSD1 deficiency".

We thank the reviewer for this comment. In this study, we crossed LSD1-floxed mice with ACTA1-creERT mice. This enables the deletion of critical exons of LSD1 in mature myocytes and myogenic precursors that have initiated the differentiation program. LSD1 is a ubiquitously expressed gene, and it is known that immature myogenic cells (e.g., satellite cells, Tosic et al. Nat Commun. 2018) and other non-myogenic cells such as hematopoietic and vascular cells abundantly express LSD1 (Kerenyi et al. Elife 2013, Yuan et al. Biochem Pharmacol. 2022). Thus, it is likely that LSD1 expression by these cell types were detected in our whole muscle western blots. We will add these statements in the text for clarification.

__Results

2__

To identify the transcriptional regulators that mediate the regulation of atrophy-associated genes by LSD1, the authors performed motif analyses on the promotor regions of upregulated genes in LSD1-mKO Gas. Based on the results and other reports, they focused on Foxk1 and proved LSD1 and Foxk1 cooperatively regulate the atrophy transcriptome in the presence of Dex. However, Figure 3C showed that a transcription factor Nfatc1 is also reduced in Sol muscle similar to Foxk1. Also, other studies demonstrated that the transcription factor NFATc1 controls fiber type composition and is required for fast-to-slow fiber type switching in response to exercise in vivo. More specifically, NFATc1 inhibits MyoD-dependent fast fiber gene promoters by physically interacting with the N-terminal activation domain of MyoD and blocking recruitment of the essential transcriptional coactivator p300 (Cell Rep. 2014 Sep 25; 8(6): 1639-1648). Furthermore, it has been reported that LSD1 Controls Timely MyoD Expression via MyoD Core Enhancer Transcription (Cell Rep. 2017 Feb 21;18(8):1996-2006. doi: 10.1016/j.celrep.2017.01.078). It is unclear how the authors exclude Nfatc1 for the LSD1-mediated effects in different muscle fibers. Further experiments may be necessary to exclude Nfatc1.

We thank the reviewer for an insightful comment. In addition to Foxk1, we tested the involvement of NFATc1 in the gene regulation under LSD1-depleted state. We treated C2C12 with an LSD1 inhibitor S2101 in combination with a calcium ionophore that promotes the transcriptional function of NFATc1 by inducing its nuclear localization (Meissner et al. J Cell Physiol. 2007). While LSD1 inhibition promoted the expression of Pgc1a and Myh7, ionophore treatment had no additive effects (Additional Data). Because we found a physical association of Foxk1 with LSD1, we focused on the functional involvement of Foxk1 in LSD1-mediated repression of atrophy genes. We recently performed an ATAC-seq analysis in Dex-treated muscle, and found that the Foxk1 motif but not the NFATc1 motif was enriched in the LSD1-KO-specific open chromatin regions. This data further suggests the significant contribution of Foxk1 in the transcriptional regulation under LSD1 depletion.

#3

In figure 3D, only merged images were colored. It would be better to show colored images for Foxk1 and DAPI.

We will replace the images with the colored ones.

#4

Immunofluorescence analysis in C2C12 myotubes showed that Dex exposure reduced the nuclear retention of Foxk1, which was further promoted by the addition of T-3775440, an LSD1 inhibitor (Figure 3D). The author also used Foxk1-KO C2C12 myotubes to prove LSD1 and Foxk1 cooperation to regulate the expression of type I /IIA fiber and atrophy genes in Foxk1-KO cells. Are the effects of LSD1 dependent on Foxk1 or synergistically acting with Foxk1? The treatment of LSD1 inhibitor in Foxk1-KO C2C12 may be helpful to answer this question.

As suggested, we will examine the combination effect of LSD1 inhibition and Foxk1-KO. In addition, we will analyze chromatin association of LSD1 in Foxk1-KO cells by ChIP experiments, to test whether the function of LSD1 depends on Foxk1.

#5

In supplementary figure 2, body weight in the mKO+Dex group was reduced in comparison to that of WT+Dex. How about the body weight of mKO mice without Dex injection compared to that of WT? This data will be helpful to understand the effect of muscle-specific LSD1 deficiency on whole-body energy balance.

We measured the body weight of untreated mice, and found that there is no genotype effect (Additional Data). Thus, we think that LSD1-mKO alone does not influence the whole-body energy balance. We will include this data in the revised version.

#6

The authors analyzed the size distribution of myofibers and mentioned that large type I and type IIA fibers preferentially increased in the LSD1-mKO muscle, whereas large type IIB + IIX fibers decreased (Supplemental Figure 4, B, E, and F). It is better to show the results of statistics. If no significance were found, it should be mentioned in the result section.

We have performed statistical analyses on Supplemental Fig. 4E and F, and found that a fraction of large type I fibers was significantly larger in KO mice. This result will be added in the next version.

#7

Page 11, To reveal the genes regulated by LSD1 under the VWR condition, the authors performed additional RNA-seq analysis using Sol muscle. The non-hierarchical clustering analysis was informative and showed signaling pathways related to ‘mitochondrion’, ‘mitochondrion organization’, and ‘oxidative phosphorylation’ were altered in the Sol muscle deficient in LSD1 under the VWR condition (Figure 5B). However, it is unclear why they focus on Err-gamma to explain LSD1-KO phenotypes in Sol muscle. Is this gene also derived from RNA seq? It is better to show whether Err-gamma expression is also significantly altered based on RNA seq data.

Indeed, ERRg was upregulated by LSD1-KO+VWR and was included in the Cluster 6 genes together with the OXPHOS and mitochondria-related genes (Additional Data and Fig. 5A). These data prompted us to focus on ERRg as a potential factor that explains the LSD1-KO phenotype. Additional Data will be included in the revised version.

#8

The authors claim that LSD1 serves as an "epigenetic barrier" that optimizes fiber type-specific responses and muscle mass under stress conditions. This claim is derived from the loss of function studies. To generalize the functions of LSD1, the gain of function studies will be also necessary. Adding the characteristics of LSD1 overexpression in C2C12 cells will further improve the quality of the manuscript.

We agree that the gain of function studies will further strengthen the quality of our manuscript. As suggested, we will perform an LSD1 overexpression experiment using C2C12 cells and analyze the expression of atrophy and fast fiber related genes. Because Esrrg is completely silenced in C2C12 cells, it is difficult to monitor ERRg-mediated gene regulation in these cells. To overcome this, we will use a cardiomyocyte cell line, in which ERRg is functionally involved in differentiation (Sakamoto et al. Nat Commun 2022). We will overexpress LSD1 in these cells and examine whether the expression of ERRg and its downstream targets are altered.

__Discussion

9__

The authors mentioned supplementary figure 10 only at the end of the manuscript of the discussion section (page 15) without a specific explanation of the figures in the result section. The data are important in that LSD1 expression in human muscles declined with age and showed a negative correlation with the expression of the atrophy gene. It should be presented in the result section with a more detailed description.

We agree that these data are important and need further explanations. We will describe the details in the Results section and move the entire figure to the main figure.

#10

There are other studies to examine LSD1 and muscle regeneration or functions (e.g. Nat Commun 9, 366 (2018). ____https://doi.org/10.1038/s41467-017-02740-5____). More discussion to compare the current study and other studies will be necessary.

We thank the reviewer for this comment. We will add the discussion accordingly.

Humans are social animals for a reason. It is due to others that we have a sense of ourselves. We compare ourselves to others, and by knowing the differences between us and others, we gradually gain a sense of identity. If we are just alone, without any comparison between ourselves and other people, we can never know the defining characteristics of ourselves. There will be no sun without shadows. And by forming a prison around ourselves, we create a sense of “otherness” that forms a wall between a body of our own and our surroundings. However, as the objective world holds unlimited amounts of truths for us to perceive, Bradley argued that there is fundamentally “no difference between the inner and the outer”. All humans have access to the same amount of information, but what makes us distinct is not “any difference of kind, but only of degree”. In other words, there is an extent to which we perceive the surrounding world. There may be overlaps between the perceptions of mine and that of others, but ultimately, it is the chain of every single person that makes up the whole world. Eliot may have mentioned Bradley’s argument about self-identity to further his opinion on the continued existence of the self after death. The world is made up of a chain of identities, where as one goes away, another one spawns and fills up the spot. There may be millions and millions of overlapping areas, but they are not necessarily the same due to tiny nuances. There is that sense of continuity that transcends bodily boundaries as well.

This section of the Waste Land may be read as a commentary on religion that works on multiple levels, created via allusion to the books of the Bible. The two key lines of this section are ‘He who was living is now dead/ We who were living are now dying’. The first line has much Biblical precedent. In the Book of Revelations, Jesus says ‘I am the first and the last. I am he that liveth, and was dead; and behold, I am alive for evermore, Amen; and have the keys of hell and of death’. The reason that Jesus used to be dead but is now living is because of the Reïncarnation. Eliot’s ‘He’ – an obvious nod to God – is a reversal. It can be read as a Jesus that was never reïncarnated, as he went simply from being alive to being dead, without the final stage, or as having been reïncarnated – so ‘living’ again – but then somehow expired following that. Whichever way we read it, however, present an anti-Biblical view, one in which Jesus is no longer ‘living’. The second line – ‘we who were living are now dying’ – casts ‘us’ (the question arising: is the reader included amongst the ‘we’?) as being in the long, drawn-out, almost timeless process of ‘dying’, but still somehow alive. Therefore: ‘we’ have outlived Jesus. This may be a commentary on religion itself – that is, Jesus does not exist in the modern world – or on failing attitudes towards religion, with the question on the minds of many at the time being ‘how is it possible for both Christian love to exist in the world and such deep suffering?’. Additionally, John 11.25 states ‘he that believeth in me, though he were dead, yet shall he live.’ According to Eliot, however, ‘we’ are dying, perhaps representing the decline of belief. To take this yet further, Psalm 63 begins ‘O God, thou art my God; early will I seek thee: my soul thirsteth for thee’. Therefore, God is here presented as a life-nourishing water. In Eliot’s Waste Land, ‘there is no water’. By contrast, to take the other interpretation: if we, however, have outlived Jesus, then what happens now with the ‘keys to hell and to death’. Are Hell and Death flung open? Is ‘Hell empty, and all the Devils here’ (another potential reference to the Tempest…)? Is tha perhaps why such suffering permeates the world?

An additional note: few of Eliot’s contemporary readers could have read the line ‘we who were living are now dying’ without thinking of John McCrae’s famous ‘In Flanders Fields, especially those most poignant and emotive of lines: ‘We are the Dead. Short days ago/ We lived, felt dawn, saw sunset glow,/ Loved and were loved, and now we lie,/ In Flanders fields.’ To think of these lines allows us to perhaps transcend the meta-religious interpretation of this passage, to forget God, salvation, belief and faith, but merely to focus on the human: the suffering, the pain, the love lost – the Dead.

I strongly agree. Often, especially in the industries where art is concerned, what is actually made is just a "watered down" version of many desires. What people may perceive that piece of work as is not all there is to it. For example, persons may look at a painting and say "It's very pretty" while the artist himself viewed it as an entire storyline while trying to put it into object form.

This is very true noting that our imaginations can often run wild and create the literal most. However, creating these wishes into an object may prove to be challenging because of many factors such as processes that need to be done

This statement was particularly interesting because it makes one think what the true motive of creating false news truly is? As stated, for some it may be a paycheck but there must be a deeper reason and unfortunately we may never know that answer.

General comments:

This study carefully delineates the role of magnesium in cell division versus cell elongation. The results are really important specifically for rod-shaped bacteria and also an important contribution to the broader field of understanding cell shape. Specifically, I love that they are distinguishing between labile and non-labile intracellular magnesium pools, as well as extracellular magnesium! These three pools are really challenging to separate but I commend them on engaging with this topic and using it to provide alternative explanations for their observations!

A major contribution to prior findings on the effects of magnesium is the author’s ability to visualize the number of septa in the elongating cells in the absence of magnesium. This is novel information and I think the field will benefit from the microscopy data shown here.

I completely agree with the authors that we need to be more careful when using rich media such as LB. It is particularly sad that we may be missing really interesting biology because of that! It’s worth moving away from such media or at least being more careful about batch to batch variability. Batch to batch variability is not as well appreciated in microbiology as it is for growing other cell types (for example, mammalian cells and insect cells).

For me, the most exciting finding was that a large part of the cell length changes within the first 10min after adding magnesium. The authors do speculate in the discussion that this is likely happening because of biophysical or enzymatic effects, and I hope they explore this further in the future!

I love how the paper reads like a novel! Congratulations on a very well-written paper!

Kudos to the authors for providing many alternative explanations for their results. It demonstrates critical thinking and an open-mind to finding the truth.

Specific comments:

Figure 2C → please include indication of statistical significance

Figure 3C → please include indication of statistical significance

Figure 6A → please include indication of statistical significance

Figure 8B → please include indication of statistical significance

Figure S1B → please include indication of statistical significance

Figure S3B → please include indication of statistical significance

For your overexpression experiments, do the overexpressed proteins have a tag? It would be helpful to have Western blot data showing that the particular proteins are actually being overexpressed. I think the phenotypes that you observe are very compelling so I don’t doubt the conclusions. Western blot data would just provide some additional confirmation that you are actually achieving overexpression of UppS, MraY, and BcrC.

Questions:

Based on your data, there are definitely differences in gene expression when you compare cells grown in media with and without magnesium. Because the majority in cell length increase occurs in such a short time though (the first 10min), I was wondering if you think that some or most of it is not due to gene expression? Do you have any hypotheses what is most likely to be affected by magnesium? Do you think if the membrane may be affected?

Why do you think less magnesium activates this program of less division and more elongation? Additionally why is abundant magnesium activating a program of increased cell division and less elongation? Do you think there is some evolutionary advantage, especially considering how important magnesium is for ATP production?

Related to this previous question, I also wonder if this magnesium-dependent phenotype would extend to other unicellular organisms, may be protists or algae? That would be a really exciting direction to explore!

Regarding the zinc and manganese experiments, why do you think they lead to additional phenotypes compared to magnesium? Do you have any hypotheses?

Regarding your results that Lipid I availability may be a major a problem for the cell division in the absence of magnesium, do you think that is due to effects magnesium has on the enzymes directly, or do you think magnesium affects the substrate availability/conformation by coordinating the phosphate groups? Or something else, may be membrane conformation?

Author Response

Reviewer #1 (Public Review):

The authors took advantage of an existing protein-trap resource in zebrafish to identify genes important for normal pacemaker function in adults. They generated a collection of lines with mutation in genes that expressed at reasonably high levels in the heart and assess their ECG. They identified 3 candidates with increased incidence of sinus arrest and focused on validation of dnajb6b. The dnjb6b mutant fish display other defects including enhanced response to atropine and carbacol and bradycardia. They show that dnajb6b is expressed in a subset of cells in the sinus node in zebrafish. In mouse sinus node, DNAJB6 expressing cells have low expression of TBX3 and its target HCN4. In addition, Dnajb6b+/- mice also display similar phenotypes. Analysis of pacemaker function in ex vivo mouse hearts by high-resolution fluorescent optical mapping of action potentials revealed that the number of leading pacemakers in Dnajb6b+/- hearts is decreased in the sinus node, with a concomitant increase in the auxiliary pacemakers. RNAseq analysis of the right atrial tissues detected expression changes in ion channels and genes involved in Ca2+ handling and Wnt signaling. Overall, the results support the conclusion that DNAJB6 is important for proper sinus node function, thus adding it to the short list of sick sinus syndrome genes. However, the manuscript has several weaknesses.

Weakness:

The manuscript does not address the mechanism by which decreased DNAJB6B causes sick sinus syndrome. For example, it is unknown if DNAJB6B functions cell autonomously or non-cell autonomously in the sinus node. The RNAseq analysis identified changes in ion channels in the right atrial tissues of 1-year old mice, cellular electrophysiology of the sinus node cells was not assessed.

The main goal of this research is to prove the feasibility of discovering novel SSS genes in adults via a forward genetic approach in zebrafish. Thus, the major hallmark would be to prove causality and specificity of the candidate genes identified from this screen, such as Dnajb6. Comprehensive mechanistic study would be a focus for future studies.

Nevertheless, we carried out the following experiments to address the mechanisms. Based on these data, a new section was added to the discussion section (Lines 424-465).

(1) In mice, we did more antibody immunostaining and confirmed a negative correlation in terms of expression intensity between the Dnajb6 and Tbx3 proteins. We further detected a significantly increased Tbx3 immunostaining signal in the SAN tissues of Dnajb6 heterozygous mice compared to WT controls (new Figure 3D-F).

(2) In zebrafish, we compared expression patterns of the sqET33-mi59B conduction system reporter line between the GBT411/dnajb6b heterozygous and homozygous mutants. We found the atrio-ventricular canal (AVC) signal became diffused in GBT411/dnajb6b homozygous adult hearts. In addition, the ring-like structure usually seen in the SAN region of WT controls and in the GBT411/dnajb6 heterozygous was largely lost in 3 out of 9 GBT411/dnajb6b homozygous adult hearts examined (new Figure 2).

Together with the ectopic pacemaker activity detected in the Dnajb6 heterozygous mice (new Figure 5A and 5B), we speculate that Dnajb6 might act as a suppressor of Tbx3 transcription factor in defining cell fate specification into SAN pacemaker myocytes. Since Tbx3 was reported to suppress chamber myocardial differentiation (Mommersteeg et al., Circ Res. 2007;100(3):354-62), upregulation of Tbx3 may thus contribute to enhanced atrial ectopic activity in Dnajb6 heterozygous mice.

Furthermore, TBX3 has been recently identified as a component of the Wnt/β-catenin-dependent transcriptional complex (Zimmerli et al., eLife. 2020;9:e58123), which is significantly affected in Dnajb6 heterozygous mice (see new Figure 7B-C). This further supports a possible role of TBX3 in both SAN and atrial remodeling.

(3) Finally, in collaboration with Drs. Grandi, Morotti, and Ni from University of California Davis, we utilized a population-based computational modeling approach to determine the cellular/ionic mechanisms that could underlie the ex vivo observed SSS phenotype in the Dnajb6 heterozygous mice (new Figure 6). We used our previously published model of the mouse SAN myocyte (Morotti et al. Int J Mol Sci. 2021; 22(11):5645) and enhanced it with addition of both sympathetic and parasympathetic stimulations to model the effects of isoproterenol- and carbachol-induced changes in pacemaker activity (i.e., firing rate), respectively. We generated a population of 10,000 mouse SAN myocyte models by random modification of selected model parameters describing maximum ion channel conductances and ion transport rates from the baseline model and assessed isoproterenol- and carbachol-induced effects on each model variant. We then separated this population of models in two subpopulations representing the WT and Dnajb6+/- mice phenotypes: namely, we extracted the model variants that recapitulate changes observed in Dnajb6+/- vs. WT mice, including a reduced firing rate at baseline, an increased response to isoproterenol, and a decreased response to carbachol administration (new Figure 6). This filtering process resulted in n=438 models that correspond to the Dnajb6+/- mice phenotype and n=6,995 models that correspond to the WT phenotype. We analyzed the parameter value differences in these two subgroups to revealed several crucial parameters that are significantly correlated with the observed electrophysiological changes. The analysis revealed a significant decrease in the maximal conductances of the fast (Nav1.5) sodium current, the L-type Ca2+ current (ICa,L), the transient outward, sustained, and acetylcholine-activated K+ currents, the background Na+ and Ca2+ currents, as well as the ryanodine receptor maximal release flux of the Dnajb6+/- vs. WT model variants. We also found a significant increase in the Na+/Ca2+ exchanger (NCX) maximal transport rate, and conductances of the T-type Ca2+ current and the slowly-activating delayed rectifier K+ current. These new studies provide some novel mechanistic insights into the observed SSS phenotype in Dnajb6+/- mice. Importantly, these new in silico experiments add another conceptual level to the phenotype-based screening approach introduced in the current study to identify new genetic factors associated with SAN dysfunction. Direct testing of these mechanisms would require a substantial amount of single SAN cell patch clamp and confocal microscopy experiments which are out of scope of the current manuscript and will be pursued in a follow-up study.

The manuscript does not address why the zebrafish homozygous mutants are adult viable while the mouse homozygotes are embryonic lethal. The insertion of the GBT411 disrupt dnajb6b(L) but not dnajb6b(S), while the mouse mutation deletes the entire gene. Does this difference partially explain the difference?

Indeed, the difference between zebrafish and mouse can be partially explained by the fact that only the long isoform of dnajb6b gene, dnajb6b(L), was disrupted in the GBT411 mutant, while both the long-Dnajb6(L) and short-Dnajb6(S) isoforms of Dnajb6 gene was largely deleted in the Dnajb6 knockout mice. However, we think the main reason is probably that functional redundancy in zebrafish but not mouse: zebrafish has two dnajb6 homologues, dnajb6b and dnajb6a, while mouse has only one Dnajb6 homologue. We added these points to the paper (Lines 377-379).

Reviewer #2 (Public Review):

In this manuscript, the authors expand upon previous work describing development of a protein trap library made with the gene-break transposon. This library was screened to identify lines displaying gene trap expression in the heart (zebrafish insertional cardiac mutant collection). A pilot screen of these lines using adult ECG phenotypes identifies dnajb6b as a new gene important for cardiac rhythm. Using the GBT/dnajb6b zebrafish line, Ding et al. find a proportion of aged homozygous mutant fish (1.5-2 years) present sinus arrest episodes and reduced heart rate. Treating GBT411/dnajb6b mutant adults with compounds revealed aberrant responses to autonomic stimuli, and sinus arrest episodes were induced following verapamil exposure, providing evidence that GBT411/dnajb6b as an arrhythmia mutant. This conclusion could be better supported by presenting specific ECG parameters to characterize the conduction defect more thoroughly. The authors then report that Dnajb6+/- adult mice recapitulate some of the phenotypes observed in zebrafish, including sinus arrest and AV blocks, as well as impaired (although different) responses to autonomic stimuli. The authors describe that these are features of sick sinus syndrome in the absence of cardiomyopathy phenotypes in either the zebrafish or mouse lines. However, overall cardiac morphology is not well described for either the GBT411/dnajb6b or Dnajb6+/- models.

We carried out more experiments to examine left ventricular (LV) structure in Dnajb6 heterozygous mice at 1 year of age, using H&E staining, Masson’s trichrome staining, and transmission electron microscopy (TEM) analysis. We now show clearly that there are no significant myocardium structural changes in the LV as well as atrial and SAN tissues of Dnajb6 heterozygous mice (new Supplemental Figures 3 and 5), when the SSS phenotype was already noticeable. However, in the GBT411/dnajb6b heterozygous mutant at ~2 years of age, we detected severe sarcomere structural abnormality in 1 out of 3 fish hearts examined (see Response-only Figure 1). In addition, in a previous publication (Ding et al., Circ Res, 2013:112(40:606-17), we reported evident cardiac remodeling phenotypes in the GBT411/dnajb6b homozygous fish at 12 months of age.

Together, we have obtained more experimental evidence to strengthen the claim that arrhythmia is not due to cardiomyopathy/structural remodeling in the Dnajb6+/- mice. However, the evidence from fish remains weak. Therefore, we removed the claim that “when structural remodeling/cardiac dysfunction have not yet occurred” in fish and modified our statement in mice accordingly (Lines 372-377, 385-386).

To further support a role for Dnajb6 in sinoatrial node dysfunction, the authors performed optical mapping of action potentials from isolated mouse atrial tissue. These data reveal that Dnajb6+/- cultures exhibit ectopic pacemakers outside of the sinoatrial node, including within the atrial wall and inter-atrial septum. These data also show prolongation of SAN recovery time at baseline and following autonomic stimulation, further suggesting SAN dysfunction. RNA-sequencing experiments of DNAjb6+/- adult right atrial tissue showed differentially expressed genes encoding Ca2+ handling related proteins, ion channels, and WNT pathway related proteins. As these genes are involved in the cardiac conduction system, the authors suggest these pathways as molecular mechanisms underlying SSS phenotypes in Dnajb6 models.

Sick sinus syndrome is a relatively rare arrhythmia most commonly found in older populations. Therefore, it has been challenging to establish clinically relevant models and there is a limited understanding of mechanisms of SSS pathogenesis. One particular strength of this manuscript is the ECG phenotype-based forward screen of the gene-breaking transposon (GBT)-based gene trap library in aged animals. This pilot study provides proof-of-concept that this screening approach is well suited to identify regulators of cardiac function in adults and genes linked to adult diseases like SSS.

Thank you very much for recognizing the major strength of our manuscript!

აღნიშნული ბლოგი არის იმის შესახებ, თუ რატომ არიან ქართველები ნოსტალგიურად განწყობილნი საბჭოთა კავშირის მიმართ. ავტორი გვთავაზობს რამდენიმე მიზეზს სსრკსადმი ქართველი ერის დადებითი დამოკიდებულების საილუსტრაციოდ. უპირველესი მიზეზი ამ კეთილგანწყობის არის ის, თუ როგორ გამოსცადა ქართველმა ხალხმა სოციალისტური  წყობილებიდან კაპიტალისტურში გადასვლა. ბლოგში აღნიშნულია, რომ ქართველებმა ძალზედ განიცადეს საბჭოთა კავშირის დაშლა, ვინაიდან მათი ეკონომიკური მდგომარეობა გაუარესდა. 
  მეორე მიზეზი კი არის ის, რომ საბჭოთა კავშირის დაშლამ უარყოფითად იმოქმედა საქართველოს შიდაპოლიტიკურ ცხოვრებაზე. ბლოგში ნახსენებია ის კონფლიქტები, რაც მოჰყვა დამოუკიდებლობის მოპოვებასა და სახელმწიფო ტერიტორიების დაკარგვას. საყურადღებოა ისიც, რომ სსრკ-ს მონატრებას გრძნობს ძირითადად ძველი თაობა. ახალი თაობა კი განათლების ძალით ხვდება, თუ რატომ არ არის საბჭოთა კავშირში ნოსტალგიის სამართლიანი საფუძველი. 
   ჩემი დამოკიდებულება ამ საკითხის მიმართ აშკარაა. ვინაიდან და რადგანაც, მე მივეკუთვნები საქართველოს იმ ახალგაზრდა თაობას, რომელიც დაიბადა და ცხოვრობს დამოუკიდებელ საქართველოში, იოტისოდენა სურვილიც არ მაქვს მენატრებოდეს საბჭოთა კავშირი. ის ბოროტების იმპერია, რომელიც ტოტალიტარულად მართავდა მცირე ერებს, ქვეყნებს. მიკვირს, როგორ შეიძლება მისტიროდე იმ დესპოტურ რეჟიმს, რომელიც ხალხს მუდმივ ტერორში ამყოფებდა და თავისუფალი აზრის ნებისმიერ გამოვლინებას სასტიკად უდგებოდა? ვფიქეობ, განათლების როლი ამ საკითხში ყველაზე დიდია. ერუდირებული ადამიანი მოვლენებს სწორად აფასებს და შესაბამისად, ნაკლები შანსია მისი მხრიდან სსრკ-სადმი ნოსტალგიის.

Jay emphasizes the compelling sense of this allure of immediacy. We believe that our perceptual and our introspective faculties give us an infallible representation of reality, and never question that it could be fallible.

This is very much aligned with the research on Umwelt by Jakob Von Uexkull.

Aperception, the introspection and awareness of our inner space is just as alluring.

So in summary: perception gives us the feeling that we are sensing the way the external world actually is and aperception gives us the feeling that we are aware of the inner world as it is. However, both are relative, the first to our peculiar sense faculties and the second to our linguistic and conceptual modeling of reality. Both are specific filters that create the specific situated interpretation of reality as a human being.

I quite like the wording of this sentence.

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Reply to the reviewers

Manuscript number: RC-2022-01536

Corresponding author(s): Michael Glotzer

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1. General Statements We thank the reviewers for their thoughtful and helpful comments. In general, the reviews were highly positive, although their reviews indicated parts of the manuscript that needed further clarification. We have made extensive changes that improve the clarity and rigor of this submission. We have performed several additional experiments which have extended our analysis in several ways detailed below. None of the conclusions have changed.

The following is a list of eight major changes implemented during the revisions. Point-by-point responses to the reviewers comments follow on subsequent pages.

1. The reviews made clear that we needed to more explicitly discuss the AIR-1 depletion phenotype. This phenotype is complex, it does not result in a complete loss of asymmetry, unlike, for example, depletion of the centrosome component SPD-5. This is because, in AIR-1 depleted embryos, a PAR-2 and cortical flow-dependent pathway induces PAR-2 accumulation at both anterior and posterior poles that induces flows from each pole to the lateral region (Reich 2019, Kapoor 2019, Zhao 2019, Klinkert 2019; PMIDs 31155349, 31636075, 30861375, 30801250). These flows also modulate ECT-2 localization. To clarify this point which came up in multiple reviews, we now include an explanation of the complexity of the AIR-1 phenotype and we present an analysis of ECT-2 localization in embryos depleted of both AIR-1 and PAR-2.

In addition to the 95% confidence intervals that were present on our graphs, we now include indications of the results of statistical tests of significance to the results of different treatments.

We have revised the analysis ECT-2 accumulation in two ways. First, in the previous draft, we assessed the anterior accumulation over the anterior 40% and the posterior 15% of the embryo. We have revised this analysis comparing the anterior and posterior 20% of the cortex, respectively. This is simpler and more logical in contexts where embryos are symmetric. In addition, we altered the measurements of the length of the posterior boundary. Previously we used a common threshold value, below which we counted pixels to assess boundary length. During the revisions, we noticed that this value was not appropriate for our mutant transgenes which accumulated to higher levels. Therefore, we revised our analysis pipeline such that, for each embryo, we measure the average intensity of the cortex in the anterior 60% of the embryo. We set a threshold of 0.85* this average anterior intensity value. As before, cortical positions below this threshold contribute to the boundary length. This is a more robust and simpler means of evaluating the size of the posterior domain. Neither of these changes affect any of our conclusions, but they are simpler and more rigorous.

Most of our figures include quantification of the degree of ECT-2 asymmetry as well as the average anterior and posterior accumulation of ECT-2 as a function of time. While the images show the intensity profiles across the embryo, previously, we did not explicitly show a quantification of the average intensity of ECT-2 as a function of position along the embryo. A new graph, Figure 2Bv, shows this for control embryos and embryos in which tubulin is depleted and depolymerized. This shows that the MT depolymerization results in lower accumulation at the posterior of the embryo and higher accumulation at the anterior.

We provide documentary and quantitative evidence that ZYG-9 depletion induces potent cortical flows (Figure 3c and Figure 3, supplement 3), further bolstering the central role of cortical flows in inducing ECT-2 asymmetry.

As requested by reviewer 2 (R2b), we have included the analysis of ECT-2 distribution in Gα depleted embryos. As expected due to the lack of spindle elongation, the displacement of ECT-2 from the posterior cortex is greatly attenuated.

As requested by reviewer 2 (R2d), we now show that ECT-2C fragments accumulate on the cortex in embryos depleted of ECT-2.

One other important point raised by several reviewers concerns the behavior of the ECT-2 T634E allele. This allele, due to the substitution of a phosphomimetic residue, accumulates on the cortex at about 50% the level of the wild-type version. To investigate the possibility that this quantitative difference was the cause of the phenotype, we depleted both the wild-type and mutant ECT-2 constructs by RNAi (these are the sole sources of ECT-2 in the animals). First, we find that wild-type ECT-2 can be depleted to 20% of wild type levels with only a 13% rate of cytokinesis failure (when T634E is depleted to 20%, embryos fail more than 50% of the time). Thus the two-fold reduction in cortical ECT-2 seen in T634E not likely highly significant (ECT-2 is not haploinsufficient). In addition, embryos with ECT-2 T634E initiate ingression in a timely manner, but the furrows ingress more slowly than wild-type. In contrast, depletion of ECT-2 to 20% results in a delay in furrow initiation, but once these furrows form, they ingress at rates similar rates to wild-type. Thus, the T634E variant exhibits a behavior that is quite distinct from that resulting from a (strong) reduction in the levels of wild-type ECT-2.

Point-by-point description of the revisions

This section is mandatory. Please insert a point-by-point reply describing the revisions that were already carried out and included* in the transferred manuscript. *

(Reviewer comments: italicized 9 pt font, author response: plain text 10 pt font. Numbers have been added to the reviewer comments e.g. R2c=Reviewer 2, third comment)

Reviewer #1 (Evidence, reproducibility and clarity (Required)):

Summary

R1a* In this study the authors addressed how Ect2 localization is controlled during polarization and cytokinesis in the one-cell C. elegans embryo. Ect2 is a central regulator of cortical contractility and its spatial and temporal regulation is of uttermost importance. After fertilization, the centrosome induces removal of Ect2 from the posterior plasma membrane. During cytokinesis Ect2 activity is expected to be high at the cell equator and low at the cell poles. Similarly to polarization, the centrosome provides an inhibitory signal during cytokinesis that clears contractile ring components from the cell poles. Whether and how the centrosomes regulate Ect2 localization is not know and investigated in the study. *

This is an accurate summary of the goals of this study.

R1b *The authors start by filming endogenously-tagged Ect2 and find that Ect2 localizes asymmetrically, with high anterior and low posterior membrane levels during polarization and cytokinesis. They reveal that the centrosome together with myosin-dependent flows results in asymmetric Ect2 localization. Previous studies had suggested that Air1, clears Ect2 from the posterior during polarization and the authors expand those finding by showing that Air1 function is also required to displace Ect2 from the posterior membrane during cytokinesis. *

*To elucidate if Ect2 displacement is induced by phosphorylation of Ect2 by Air1, the authors investigate the localization of a C-terminal Ect2 fragment containing the membrane binding PH domain. When the predicted Air1 phosphorylation sites are mutated to alanine, the Ect2 fragment still localizes asymmetrically but exhibits increased membrane accumulation. *

*Finally, they investigate the functional role of Air-1 during furrow ingression. They demonstrate that embryos deficient of Air1 and NOP1 have impaired furrow ingression. Lastly, the authors sought to confirm that there is a direct effect of Air1 on Ect2 function by generating a phosphomimetic point mutation of Ect2 using Crispr. They find that the membrane localization of phosphomimetic Ect2 is reduced and consequently furrow ingression is impaired. *

This is an accurate summary of our results.

Major comments

R1c *It is not convincing that the six putative phosphorylation sites are targeted by the Air1. If Air1 phosphorylation displaces Ect2 from the membrane, a reduction in Ant/Post Ect2 ratio is expected in the phosphodeficient mutants, like after air1 RNAi. However this is not observed for cytokinesis or polarization (Fig. 5D(i); E). This suggests that phosphorylation of those sites is not essential for the asymmetric Ect2 localization. *

In otherwise wild-type embryos, phosphorylation of these sites is not required for asymmetric ECT-2 localization. Non-phosphorylatable ECT-2 variants exhibit asymmetric localization because these proteins relocalize due to myosin-directed flows. To test the role of phosphorylation, we examine the distribution of ECT-2 and ECT-2C fragments in myosin-depleted embryos in which the flows are blocked, under these conditions, transient local depletion is observed with the phosphorylatable variants, Fig 5E.

While AIR-1 promotes normal polarity establishment, as shown in several recent papers, cortical changes nevertheless occur in the absence of AIR-1. Specifically, a parallel PAR-2 dependent pathway induces weaker flows from both poles toward the equator. To further substantiate the effect of PAR-2 accumulation on ECT-2 accumulation in AIR-1 depleted embryos, we assayed ECT-2 accumulation in air-1(RNAi); par-2(RNAi) embryos (Figure 4, supplement 2). These results show that ECT-2 is nearly symmetric in these double depleted embryos. In addition we have edited the text to describe the unusual bi-polar PAR-2 accumulation that occurs in AIR-1 depleted embryos.

R1d *The authors aim to demonstrate that phosphorylation of the identified sites is important for cytokinesis. For this they investigate contractile ring ingression in the phosphomimetic point mutation. Since ring ingression is slower and fails in nop1 mutant they authors conclude that this demonstrates a functional importance of this site. I am not surprised that embryos ingress slower in this mutant since Ect2 localization to the membrane is reduced. This however does not show that this phosphorylation site is the target of the centrosome signal. Importantly, authors would need to demonstrate that Rho signaling and thus Ect2 activity, is increased at the poles, when phosphodeficient Ect2 is the only Ect2 in the embryo. *

The fact that a phosphomimetic residue at this site leads to reduced membrane localization is highly relevant, as we suggest that phosphorylation of this site contributes to the mechanism by which AIR-1 generates asymmetric ECT-2. Given the role of AIR-1 in regulating polarity, a version of ECT-2 that can not be phosphorylated would be predicted to be dominant lethal, necessitating a conditional expression strategy which does not currently exist in the early C. elegans embryo system (indeed we were unable to recover a T-> A allele at this site, despite extensive efforts). To avoid this issue, we used a viable, fertile, hypomorphic allele that is predicted to be less responsive to AIR-1 activity. The goal of this experiment was to evaluate whether the putative AIR-1 sites affect not only the NOP-1 pathway for furrow ingression, but also impact furrowing that is centralspindlin-dependent.

To complement this finding have performed experiments in which ECT-2 was partially depleted We used RNAi to partially deplete ECT-2 and ECT-2 T634E and measured the total embryo fluorescence of each ECT-2 variant and the kinetics of furrow ingression. Partial depletion of wt ECT-2, to ~ 20% of control levels leads to delay in furrow formation and all but 2/18 (11%) of embryos complete cell division. In contrast, a similar depletion of ECT-2T634E depletion results in a failure of furrow ingression in ~52 % of embryos. Furthermore, while ECT-2T634E embryos initiate furrowing with normal kinetics, they exhibit a slower rate of furrow ingression, in contrast, partial depletion of WT ECT-2 results in a delay in furrow initiation, but once initiated, the rate of furrow ingression is not significantly affected. These results demonstrate that ECT-2T634E behavior can not simply be explained by a modest reduction in membrane binding.

R1e *The authors use the Aurora A inhibitor MLN8237: It was shown prior (De Groot et al., 2015) that this inhibitor is not highly specific for Aurora A, and that it also inhibits Aurora B. Thus experiments need to be repeated with MK5108 or MK8745. They should also be conducted during polarization. Why does Aurora A inhibition not abolish asymmetry? That would be expected? *

The role of AIR-1 in symmetry breaking during polarization is previously published, including with chemical inhibitors (Reich 2019, Kapoor 2019, Zhao 2019, Klinkert 2019, PMID 31155349, 31636075, 30861375, 30801250). ECT-2 localization depends on both the spatial regulation of AIR-1 activity and the distribution of cortical factors that contribute to ECT-2 cortical association, as a result of cortical flows. During acute, chemical perturbation of AIR-1 it is likely that these factors, which were polarized prior to drug treatment, remain polarized, allowing the residual cortical ECT-2 to remain asymmetric. The reviewer is correct about the specificity of MLN8237 and we do not rely on it alone to demonstrate the role of AIR-1. Rather this experiment is a complement to our AIR-1 depletion studies, which are sufficient to establish specificity. We present this experiment merely to show that AIR-1 acutely regulates ECT-2 during cytokinesis in embryos that were entirely unperturbed during polarization.

R1f *There is no statistical analysis of the results in the entire study. For all claims stating a change in Ant/Post Ect2 ratio or Ect2 membrane localization selected time points should be statistically compared: for example the main point of Fig.1 is that Ect2 becomes more asymmetric during anaphase. Thus a statistical analysis of the Ect2 ratio at anaphase onset (t=0s) and eg. t=90 s after anaphase onset should be performed; or Fig. 3A nop-1 mutant Ant/Post Ect2 ratio during polarization: again statistical analysis of control and nop-1 mutant embryos is needed at a particular time point. *

All of the graphs were presented with the mean of ~10 embryos per condition and included the 95% confidence intervals. In the revised manuscript, we have included tests of statistical significance, at each time point. While non-overlapping confidence intervals generally suggest statistical significance, we include these analyses on the graphs as it can be difficult to assess statistical significance when the confidence intervals overlap.

R1g *The aim of Fig. 2B is to demonstrate that Ect2 localization is independent of microtubules, however they still observe some microtubules with the Cherry-tubulin marker and those are even very close to the membrane and therefore could very well influence Ect2 on the membrane. Therefore I am not convinced that this experiment rules out that microtubules have no role in regulating Ect2 localization. *

We do not exclude that microtubules play a contributing role in ECT-2 phosphoregulation, but rather we conclude that the primary cue is the centrosome. Indeed, microtubules can play an important role in controlling spindle positioning which affects the proximity of the centrosome to the cortex.

The manuscript states, “Despite significant depletion of tubulin and near complete depolymerization of microtubules (Figure 2B, insets), we observed strong displacement of ECT-2 from a broad region of the posterior cortex during anaphase (Figure 2B).” Thus, despite dramatic reductions in microtubules, not only does ECT-2 become polarized, it becomes hyperpolarized. In contrast, were microtubules directly involved in ECT-2 displacement, one would expect a reduction in polarization as a result microtubule depolymerization. Conversely, though SPD-5 depleted embryos contain far more microtubules than embryos in which microtubule assembly is suppressed, ECT-2 is not polarized in SPD-5 depleted embryos. Thus in the manuscript, we conclude, “Collectively, these studies suggest that ECT-2 asymmetry during anaphase is centrosome-directed.” This conclusion is well supported by the results shown.

R1h *Throughout the paper the authors should tone down their statement that Air1 breaks symmetry by phosphorylating Ect2, since phosphorylation of Ect2 by Air2 is not shown. *

We agree with this comment and will make the necessary edits to the text. Indeed, this is the reason why we had included the final section in our original draft, “Limitations of this study” which makes this point explicitly.

R1i *I understand that the establishment of Ect2 asymmetry is important for polarization. However, how does asymmetric Ect2 localization result in more active Ect2 at the cell equator, which is required for the formation of the active RhoA zone? Would we not expect an accumulation of Ect2 at the cell equator, or if that is not the case more active Ect2 at the equator versus the poles? *

The pseudocleavage furrow forms as a result of the anterior enrichment of active RHO-1 and its downstream effectors. There is no evidence for a local accumulation of active RHO-1 specifically at the site of the pseudocleavage furrow. Rather, this furrow forms at the boundary between the portion of the embryo where RHO-1 is active and the posterior of the embryo where RHO-1 is far less active (Figure 1 Supplement 2). We suggest that aster-directed furrowing during cytokinesis likewise results from asymmetric accumulation of the same components, without them necessarily being specifically enriched solely at the furrow.

While cytokinesis generally involves an equatorial contractile ring, furrow formation can be driven by an asymmetric - i.e. non-equatorial - accumulation of actomyosin. This behavior is exemplified during pseudocleavage during which the entire anterior cortex is enriched for actomyosin and the posterior is depleted of myosin (Figure 1 Supplement 2). Several published studies provide evidence that the asymmetric pattern of myosin accumulation contributes to cytokinesis (PMID 22918944, 17669650).

Minor comments

R1j *Can the authors explain why the quantification of Ant/Post Ect2 ratio in control embryos differs in different figures? For example: in Fig. 1D i) a slight increase of Ect2 asymmetry ratio is seen at around 80 s after anaphase onset. In comparison, in Fig. 2C (i) this increase is not obvious. Are those different genetic backgrounds? *

In figure 1 D, time 0 begins at anaphase onset, whereas in 2C, time 0 is specified at the time of nuclear envelope breakdown (NEBD). The duration between NEBD and anaphase onset is ~130 sec and an increase in ECT-2 polarization is observed at 220 s post NEBD, ie 90 sec post anaphase onset comparable to that seen in Fig 1D.

R1k *One key point of the paper is that myosin-dependent cortical flows amplify Ect2 asymmetry during polarization and cytokinesis. During polarization the data is convincing, however during cytokinesis Ect2 ratio is only slightly decreased after nmy-2 depletion, again is this decrease even significant? *

Figure 3 supplement 1 shows a significant difference in ECT-2 asymmetry between control and myosin-depleted embryos.

R1l *In the introduction: "Centralspindlin both induces relief of ECT-2 auto-inhibition and promotes Ect2 recruitment to the plasma membrane" it should be added 'Equatorial' membrane, since Ect2 membrane binding is, to my knowledge, not compromised in centralspindlin mutants or in Ect2 mutants that cannot bind centralspindlin. *

Generally speaking, the reviewer is correct that cortical accumulation of ECT-2 globally is centralspindlin independent. However, as seen in e.g. ZYG-9 depleted embryos, ECT-2 is recruited to the posterior cortex in a centralspindlin-dependent manner. Thus centralspindlin can promote ECT-2 accumulation to the cortex and the site of that accumulation will be dictated by the position of the spindle midzone.

R1m *Labels in the figures are often very small eg Fig. 1 ii-v) and difficult to read. In addition it is easier for the reader if the proteins shown in the fluorescent images is also labeled in the figure (eg Fig. 2B add NG-Ect2). *

These useful suggestions have been incorporated.

R1n *Material and methods it should be mentioned which IPTG concentration was used. *

The IPTG concentration (1 mM) has been added to the revised text.

R1o *The authors speculate that the Air1 phosphorylation sites in Ect2 PH domain prevent binding to phospholipid due the negative charge. At the same time, the authors propose that the PH domain binds to a more stable protein on the membrane, which is swept along with the cortical flows and they propose anillin could be that additional binding partner. I might miss something, but do the authors suggest Ect2 has two binding partners: anillin and the phospholipids? It would be necessary to explain this better. *

*The authors should test if anillin represents the suggested myosin II dependent Ect2 anchor. For this they should check if Ect2 localization to the membrane is altered upon on anillin RNAi. *

This summary of our model is largely correct, though we do not know the identity of the more stable cortical anchor(s). While we suspect the PH domain binds to a phospholipid, ECT-2 cortical localization also requires ~100 residues C-terminal to the PH domain. It is likely that this domain interacts with a cortical component.

In preliminary experiments, ECT-2 accumulation is not strictly anillin-dependent. However, functional redundancy may obscure a contribution of anillin. Anillin was mentioned simply because of the evidence for a physical interaction between ECT-2 and anillin (Frenete PMID 22514687). In the revised manuscript we also include the possibility that ECT-2 accumulations involves one or more anterior PAR proteins. The identity of the cortical anchor(s) is an interesting question for future studies. We consider this question beyond the scope of the current manuscript.

R1p *The title of fig. 3 does not fit the statement the authors want to make, since the key point is how Ect2 polarization is affected and not membrane localization in general. *

Thank you for this suggestion. The title has been changed to “Cortical flows contribute to asymmetric cortical accumulation of ECT-2”

R1q *In Fig 4A/C. After air1 depletion the authors observe a reduction in Ect2 asymmetry. Why are the centrosomes not marked in the figures? Because they cannot be detected? The authors would also need to show that the mitotic spindle and centrosomes are no altered by air1 RNAi in the zyg9 mutant. Otherwise the observed effect might be indirect. *

Centrosomes are perturbed by depletion of AIR-1 (Hannak, PMID 11748251), but they are still detectable and their positions will be added to figure 4. As has been extensively demonstrated, AIR-1 depletion does lead to attenuated spindles and defects in spindle assembly, some of which are also seen TPXL-1 depleted embryos. These consequences of AIR-1 depletion does complicate the analysis, but this is typical of factors that regulate many processes. This is one of the key reasons why we used ZYG-9 depletion in combination with AIR-1 depletion to overcome these indirect effects.

R1r *The authors state that tpxl-1 depletion attenuates Ect2 asymmetry, this is not seen in the quantification ((Fig. 4B(i)). The main phenotype they observe is that Ect2 levels on the membrane increase (Fig. 4 (ii) and (iii). They go on testing the function of tpxl1 by depleting tpxl1 in the zyg9 mutant, where the centrosomes are close to the posterior cortex. Here they see no effect on Ect2 asymmetry. Based on that they conclude that tpxl1 has no role in this process. To me this finding is not surprising since the centrosome is close the cortex in zyg9 mutant embryos. Therefore sufficient amounts of active Air1 could reach the membrane and displace Ect2. Thus an amplification of the inhibitory signal by tpxl1 on astral microtubules might not be required. The authors need to mention this possibility and tone down their statment (also in the discussion) that tpxl1 is not required for this process. *

In the text, we state, “Cortical ECT-2 accumulation is enhanced by TPXL-1 depletion, though the degree of ECT-2 asymmetry is unaffected (Figure 4B).… we observed robust depletion of ECT-2 at the posterior pole in zyg-9 embryos depleted of TPXL-1, but not AIR-1 (Figure 4C). We conclude that while AIR-1 is a major regulator of the asymmetric accumulation of ECT-2, the TPXL-1/AIR-1 complex does not play a central role in this process.” We consider this to be an accurate description of the results. In sum, we have found no evidence that TPXL-1 contributes to generating ECT-2 asymmetry, beyond its well established role in regulating spindle length and position. The are several other processes that are known to be AIR-1 dependent and TPXL-1 independent; these primarily involve the centrosome (Ozlu, PMID 16054030). Given that TPXL-1 associates with astral microtubules, the fact that microtubule depletion can enhance ECT-2 asymmetry also argues against a requirement for TPXL-1.

R1s *It was shown that the C-terminus of Ect2 is sufficient and the PH domain is required for Ect2 membrane localization in C. elegans (Chan and Nance, 2013; Gomez-Cavazos et al., 2020). Papers should be cited. *

Thank you for this helpful comment. Chan and Nance 2013 indeed shows that the ECT-2 C-term is sufficient to localize to the cell cortex. In contrast, the Gomez-Cavasos paper (PMID 32619481) shows in figure S2 that the PH domain is required for cortical localization of ECT-2; this paper does not focus extensively on cortical accumulation of ECT-2. We have cited Chan and Nance in the revised manuscript.

R1t *The authors find that nmy-2 depletion results in loss of asymmetry for the Ect2 C-term and Ect2 3A fragment during polarization. Why is the same experiment not shown for cytokinesis? *

Strong depletion of NMY-2 prevents polarity establishment, resulting in symmetric spindles, which in turn results in symmetric ECT-2 accumulation. Thus, the requested experiment would not provide significant additional information.

R1u *Air1 is targeted to GFP-C-term Ect2 fragment via GFP-binding to determine the influence on GFP-C-term Ect2 localization (Fig. 5F). They state that they see a reduction of Ect2 C-term but not of C-term 3A after targeting. The reader has to compare Fig. 5D with F. Since the differences are not big, they need to compare the Ect2 C-term and Ect2 C-term 3A with and without Air1 targeting in the same graph (plus statistics). Otherwise this statement is not convincing. *

It is not straightforward to directly compare ECT-2C in the presence and absence of GBP-mCherry-AIR-1, because the GBP:AIR-1 fusion protein recruits a large fraction of ECT-2C to the centrosome. For this reason we think it is best to compare the behavior over time of ECT-2C and ECT-2C3A in the presence of GBP-mCherry-AIR-1. At the onset of anaphase, these two fragments localize similarly, but they then diverge over time.

R1v *In Fig. 6A the authors determine the contribution of air1 to furrowing. For this they deplete air1 in the nop1 mutant. According to previous studies, air1 mutants have a monopolar spindle. How can the authors analyze the function of air1 in cytokinesis when the spindle is monopolar? Did the authors do partial air1 depletion? They authors need to show that there is not major effect on the spindle and centrosome for their conditions. For comparison air1(RNAi) alone has to be included, otherwise the experiment is not conclusive. *

AIR-1 depletion does not result in a monopolar spindle in C. elegans embryos, though the spindle is attenuated and disorganized (PMID 9778499). TPXL-1 depletion also results in short, well organized spindles (PMID 19889842). The concerns are the reason we performed the ZYG-9 depletion experiments in Figure 4C to ensure the centrosomes are proximal to the cortex.

R1w *Upon air1(RNAi) in the nop1 mutant NMY2 intensity seems decreased and not increased. Can the authors comment on that, since that is opposite of what is expected. *

This is expected as previous studies have shown that NOP-1 contributes to RHO-1 activation during polarization and cytokinesis (Tse, PMID 22918944). (NOP stands for No Pseudocleavage).

R1x *In Fig 6B they introduce a phosphomimetic point mutation in S634 [sic, T634] in the endogenous Ect2 locus. It not clear why the authors chose this site out of the six putative sites and why they only chose one and not 3 or 6 sites? This needs some explanation. *

In our early work with ECT-2 transgenes, we found that a T634E mutation strongly affected cortical ECT-2C, so we decided to assess its affect on the function and localization of endogenous ECT-2. While we were able to recover a T634E variant, we were not able to recover a T634A variant, despite considerable effort. Based on these experiences, we anticipated that we would be unable to recover a mutant version of ECT-2 in which all sites were changed to phosphomimetic.

R1y *In the model (fig. 7) no astral microtubules are shown during pronuclear meeting and metaphase. Astral microtubules are present at this stage and should be added to the schematic. *

MTs will be added to the figure.

Reviewer #1 (Significance (Required)):

R1z *The centrosomes inhibit cortical contractility during polarization and cytokinesis in the one-cell C. elegans embryo. Centrosome localized Air1 was proposed to be part of this inhibitory signal, however the phosphorylation target of Air1 is not known. The identification of Ect2 as a phosphorylation target of Air1 would be a great advancement in the field. However, the presented manuscript lacks convincing data that Ect2 is the phosphorylation target of Air1 during polarization and cytokinesis. *

We explicitly acknowledge that we have not directly shown that AIR-1 phosphorylates ECT-2. However, we have shown that (i) AIR-1 inhibits cortical ECT-2 localization, (ii) the negative regulator of AIR-1, SAPS-1, promotes AIR-1 cortical accumulation, (iii) that the cortical localization domain of ECT-2 has putative AIR-1 sites, which, when mutated to non-phosphorylatable residues leads to increased cortical accumulation of ECT-2 (and (iv) phosphomimetic residues reduce its cortical accumulation), and (v) that these AIR-1 sites are required to render GFP-ECT-2C responsive to GBP-AIR-1. For these reasons we feel that our data makes a strong, albeit indirect, case that AIR-1 regulates ECT-2, even though we clearly acknowledge that we do not directly show that AIR-1 directly phosphorylates ECT-2.

Direct proof would require the demonstration that AIR-1 phosphorylates ECT-2 in vivo. This would be difficult to show as ECT-2 phosphorylation is likely transient, it likely affects only a subset of the total ECT-2 pool, and it likely results in loss of membrane association of ECT-2. As it it not possible to synchronize C. elegans embryos, biochemical analysis would be very difficult. Even a phosphospecific antibody for the putative ECT-2 phosphosites might not be particularly informative, as it would be predicted to give a diffuse cytoplasmic signal.

Reviewer #2 (Evidence, reproducibility and clarity (Required)):

R2a* In this work, Longhini and Glotzer investigate the localization of an essential regulator of polarity and cytokinesis, RhoGEF ECT-2, in the one-cell C. elegans embryo. The authors show that centrosome localized Aurora A kinase (AIR-1 in C. elegans) and myosin-dependent cortical flows are critical in asymmetric ECT-2 accumulation at the membrane. Since membrane interaction of ECT-2 is dependent on the Pleckstrin homology domain present at the C-terminus of ECT-2, they further analyzed the importance of putative AIR-1 consensus sites present in this domain. The authors linked the relevance of these sites in controlling ECT-2 localization and its significance on cytokinesis. The manuscript is well written, the work is interesting, and the data quality is high. *

We thank the reviewer for their critique.

Major comments:

R2b - In Fig. 2, the authors claim that the centrosomes and the position of the mitotic spindle are critical in regulating the asymmetric enrichment of ECT-2 at the membrane. To test the relevance of spindle positioning on ECT-2 localization, the authors depleted PAR-3 and PAR-2. The authors observed that the ECT-2 asymmetry is affected in these settings. However, PAR-3 or PAR-2 depletion impacts polarity, which is critical for many cellular processes, including spindle positioning. Can the authors try to specifically misposition the spindle without affecting polarity? For instance, by depleting Galpha/GPR-1/2 and assessing the impact of such depletion on ECT-2 localization.

Thank reviewer for good suggestion. We have performed the suggested experiment (presented in Figure 2, supplement 2). As one might predict, ECT-2 starts out polarized as Gα is not required for polarity establishment. During anaphase, ECT-2 becomes more symmetric in Gα depleted embryos as compared to wild-type.

R2c *-I wonder why the intensity of ECT-2 at the anterior and posterior membrane decreases in air-1(RNAi) post anaphase onset (Fig. 4A)? Moreover, I fail to observe a significant asymmetric distribution of ECT-2 in embryos depleted for PERM-1. Therefore it appears that the difference between DMSO and MLN8237-treated embryos is not substantial (at least in the images)? *

We do not have a complete or rigorous explanation for all the changes in cortical ECT-2, but they are highly reproducible. We speculate that there are cell cycle regulated changes in ECT-2 accumulation, in addition to its regulation by AIR-1. For example, in figure 1, a strong reduction in both anterior and posterior cortical ECT-2 is evident beginning at approximately -350 sec, which may reflect the initial stages of Cdk1 activation. This may result from cell cycle regulated modulation of ECT-2, as there is evidence that mammalian ECT-2 is subject to a very potent inhibition membrane association by Cdk1 (PMID 22172673). Alternatively, there could be cell cycle modulation of the cortical factor that serves as the “co-anchor” of ECT-2. The ability of GBP-AIR-1 to induce GFP-ECT-2C dissociation also appears cell cycle regulated.

Consistent with a cell cycle regulated component, note that NEBD is delayed in AIR-1 depleted embryos (PMID 17669650, 17419991, 30861375). This delay results in a shorter interval between NEBD and e.g. the peak in Cdk1 activation, explaining the earlier decrease in AIR-1(RNAi) embryos vs. control, relative to NEBD.

Our quantitative analysis indicates a significant increase of cortical ECT-2 upon treatment with MLN8237. In addition, the quantitation in the previous version did show a significant polarization of ECT-2 in PERM-1-depleted embryos prior to treatment. We have revised this figure to simply show an acute increase in cortical ECT-2 upon drug treatment, as the focus of this experiment was solely to show that ECT-2 cortical accumulation is acutely responsive to chemical inhibition during cytokinesis in otherwise normal embryos.

*-The data in Fig. 5 and 6 are exciting but raise a few concerns: *

R2d *a). The authors show that ECT-2C localization mimics the localization of endogenous tagged ECT-2. However, all these analyses with ECT-2C and various mutants are performed in the presence of endogenous ECT-2. Can the author check the localization of these mutant strains in conditions where the endogenous proteins are depleted? I understand that the cortical flow would be perturbed in conditions where endogenous ECT-2 is depleted. However, I suspect that one can analyze the anaphase-specific distribution. *

We have examined ECT-2C localization in embryos depleted of ECT-2. Cortical localization of ECT-2C is not dependent upon endogenous ECT-2. This result is now shown in figure 5 supplement 1. However, as the reviewer suggested, embryos depleted of ECT-2 do not show a high degree of ECT-2C asymmetry as ECT-2 is required for the cortical flows that amplify the symmetry breaking during polarization. During cytokinesis, ECT-2C does show a modest change in localization at the poles; the extent of the polar reduction is limited and the changes are symmetric as ECT-2 displacement causes spindles to be symmetrically positioned and limits their elongation during anaphase.

R2e *b). Can the author comment on why ECT-2C does not accumulate at a similar level as ECT-2C(3A or 6A) at the cell membrane when AIR-1 is depleted (compare Fig. 5D with Supplemental Fig. 5)? *

When ECT-2C(3A or 6A) are expressed in otherwise wild-type embryos, embryo polarization occurs, resulting in anterior-directed flows that concentrate the factor(s) that enables the anterior enrichment of ECT-2 (and ECT-2C 3A/6A). By contrast, when AIR-1 is depleted, most embryos exhibit a “bipolar” phenotype in which PAR-2 is recruited to both anterior and posterior poles, and the actomyosin network becomes somewhat concentrated laterally (PMID 30801250, 30861375, 31636075). The differential positioning of the actomyosin network in AIR-1 depleted embryos is likely responsible for the interesting difference that the reviewer points out. This section of the results states. “Nevertheless, these variants accumulated in an asymmetric manner. ECT-2C asymmetry temporally correlated with anteriorly-directed cortical flows (Figure 5 D,E), raising the possibility that asymmetric accumulation of endogenous ECT-2 drives flows that cause asymmetry of the transgene, irrespective of its phosphorylation status.”

R2f *c). Does the cortical localization of the ECT-2C(6A) mutant become symmetric upon further depletion of AIR-1? Of course, if the asymmetric distribution of ECT-2C(6A) is dependent on the presence of endogenous protein in the cellular milieu, the point raised earlier will help address this concern. *

We have not performed this exact experiment with ECT-2C-3A though we have performed it with a longer ECT-2 C-terminal fragment (aa 559-924). As expected, due to the considerations described above, the asymmetry of ECT-2C-3A is reduced when AIR-1 is depleted. Likewise, ECT-2C-6A is becomes symmetric when endogenous ECT-2 is depleted due to the dependence of its asymmetry on cortical flows, as discussed above.

In the revised manuscript, we provide additional explanation of the AIR-1 depletion phenotype which will explain the origin of the asymmetric distribution of ECT-2.

R2g *d). The authors predict that the AIR-1 mediated phosphorylation delocalizes ECT-2 from the polar region of the cell cortex. Since the posterior spindle pole is much closer to the posterior cortical region, the delocalization is much more robust at the posterior cell membrane. I wonder why targetting AIR-1 at the membrane (GBP-mCherry-AIR-1) does not entirely abolish GFP-ECT-2C membrane localization? Can the author include the localization of GBP-mCherry-AIR-1 in the data? Also, do we know for sure if GBP-mCherry-AIR-1 is kinase active? *

The GBP-mCherry-AIR-1 transgene was obtained from the Gönczy lab which demonstrated that it has some activity (PMID 30801250). Given that centrosomal AIR-1 (as compared to astral AIR-1) is the primary pool of AIR-1 responsible for modulating cortical ECT-2 levels, it is a not clear that the GBP-fused form of AIR-1 is as active as the centrosomal pool of AIR-1; indeed we suspect it is significantly less active, similar to the manner in which TPXL-1/AIR-1 appears less active towards ECT-2 than centrosomal AIR-1. Indeed as the reviewer suggests, were this pool of AIR-1 highly active, we would expect that its cortical recruitment would preclude embryo polarization, and this transgene would cause lethality when expressed with a GFP-tagged cortical protein. These concerns notwithstanding, we do observe a specific reduction in the anterior accumulation of ECT-2C as compared to ECT-2C3A, suggesting that this form of the kinase has some ability to modulate ECT-2C.

Co-expression of GFP-ECT-2C with GBP-mCherry-AIR-1 induces the centrosomal/astral accumulation of GFP-ECT-2C, which is highly visible in the figure and not seen in the absence of GBP-mCherry-AIR-1. Not surprisingly, the co-expression also induces a cortical pool of GBP-mCherry-AIR-1 that is not seen in the absence of GFP-ECT-2C. These redistributions indicate formation of the complex between GFP-ECT-2C and GBP-mCherry-AIR-1. The mCherry-AIR-1 images could be added as insets to the figure, but in our opinion, they would not make a substantive contribution, given the dramatic accumulation of centrosomal GFP-ECT-2C.

R2h *e). The authors show that centrosomal enriched AIR-1 [spd-5(RNAi)], but not the astral microtubules localized AIR-1 [tpxl-1(RNAi)], is vital for ECT-2 membrane localization. Interestingly, the authors showed that AIR-1 acts in the centralspindlin-directed furrowing pathway (Fig. 6A). I wonder if the authors can combine NOP-1 depletion with TPXL-1 depletion? I guess this will further help to exclude the function of TPXL-1 in the centralspindlin-directed furrowing pathway. *

We would like to clarify that our data indicates that AIR-1 acts on both the centralspindlin-independent furrowing (e.g. the anterior furrow in 4C), as well as centralspindlin-dependent furrowing (Figure 6).

While the experiment the reviewer proposes appears simple in theory, the interpretation is potentially a bit more complex, due to the role of TPXL-1 in spindle elongation, which can affect centralspindlin-directed furrowing. That said, there are two published experiments and one experiment in the manuscript that indicate that centralspindlin dependent furrowing can occur in TPXL-1 depleted embryos. First, Lewellyn et. al. showed that while tpxl-1(RNAi) embryos furrow, tpxl-1(RNAi); zen-4(RNAi) embryos do not, suggesting centralspindlin can function in the absence of TPXL-1. Second, the same paper shows that embryos doubly depleted of TPXL-1 and GPR-1/2 exhibit multiple furrows. Our previous work has shown that furrowing in Galpha-depleted embryos is centralspindlin dependent (Dechant and Glotzer). Furthermore, in the current manuscript we found that embryos depleted of both TPXL-1 and ZYG-9 form posterior furrows (8/8 embryos, 6/8 furrows were strong furrows) although the appearance of these furrows is delayed, presumably due to the reduction in spindle elongation due to TPXL-1-depletion. As described in the manuscript, these posterior furrows have been previously shown to be centralspindlin dependent and NOP-1 independent.

In accordance with these results, and in direct response to the reviewer’s specific suggestion, we do observe furrowing in nop-1(it142); TPXL-1(RNAi) embryos (10/10 embryos furrow, 9/10 complete cytokinesis) . Thus, all of the available results indicate that TPXL-1 is largely dispensable for centralspindlin dependent furrowing. However, the role of TPXL-1 in centralspindlin-dependent furrowing is not a focus of the manuscript, thus we do not favor including this result, as it distracts from the primary focus of the study.

R2i *f). Why do NMY-2-GFP cortical levels appear lower in 30% of the embryos that show various degrees of cytokinesis defects (Fig. 6A)? *

There are a number of possible origins of the variability. As shown in (Reich 2019, Kapoor 2019, Zhao 2019, Klinkert 2019, PMID 31155349, 31636075, 30861375, 30801250), AIR-1 depletion results in variable polarization (unpolarized PAR-2, bipolarized PAR-2, anterior PAR-2, posterior PAR-2). Furthermore, spindles in AIR-1 depleted embryos exhibit somewhat variable positioning. While we were unable to correlate these sources of variability with furrow formation, these results demonstrate that AIR-1 depletion impairs furrowing directed by centralspindlin, which was not entirely expected, given that (i) AIR-1 depletion potently suppresses NOP-1 dependent flows of cortical myosin, as evidenced by the loss of an anterior furrow in AIR-1(RNAi); nop-1(it142) embryos and (ii) centralspindlin directed furrowing can occur in the posterior in ZYG-9 depleted embryos both in the presence or absence of AIR-1 (Figure 4C).

R2j *g). The authors report that phosphomimetic mutation at the phospho-acceptor residue in ECT-2 impacts its cortical accumulation. This strain, together with NOP-1 depletion, affects furrow ingression. One explanation for this phenotype is that phosphomimetic mutant weakly accumulates at the membrane. However, one interesting observation is that ECT-2T634E enriches at the central spindle (Fig. 6B, panel 120 sec), which somehow I could not find in the text. Could this additional localization of ECT2 at the central spindle contribute to the cytokinesis defects that the authors have observed? The microscopy images the authors have included show that ECT-2T634E significantly localizes at the equator at the time of furrow initiation. Can the authors add the localization of ECT2 wild-type and ECT-2T634E in NOP-1 depleted conditions where they see an apparent impact on the cytokinesis? Similarly, if the authors include the localization of NMY-2 in these conditions-it will further add more weightage to the data. *

We regularly detect trace amounts of ECT-2 on the central spindle and this is slightly enhanced at in the ECT-2T634E mutant. However, given the large cytoplasmic pool of ECT-2, it seems unlikely that the slight enrichment of ECT-2 on the central spindle significantly affects the cortical pool of ECT-2, though the reduction in cortical ECT-2 may facilitate its enrichment on the central spindle.

As shown in figure 3B, depletion of NOP-1 does not dramatically affect cortical ECT-2 levels in wild-type embryos. Likewise, we did not observe a significant effect of NOP-1 depletion in ECT-2 T634E, thus we decided not to include this negative result.

As discussed in general point 8, we suggest the modest reduction in the membrane pool of ECT-2 is unlikely to be the primary cause of the T634E, but rather the ability of AIR-1 to modulate induce its relocalization. Consistent with this interpretation, the embryos that failed ingression tended to have more symmetric spindles, which could limit the residual cortical flows that facilitate furrow ingression.

Minor comments:

R2k -An explanation of how the timing of NEBD was analyzed in multiple settings would be helpful.

Depending on the experiment, we used either ECT-2:mNG fluorescence (it is excluded from the nucleus until NEBD) and/or the Nomarski images to score NEBD.

R2l ____-*The authors mentioned on p. 6-'Despite significant depletion of tubulin.....during anaphase'. These experiments are performed in the near complete depolymerization of microtubules; thus, regular anaphase will not establish. I understand that the authors are monitoring localization wrt the timing similar to anaphase in the non-perturbed condition, and thus a bit of change in the sentence is required. *

Thank you for highlighting this point. We have substituted “following mitotic exit” for “anaphase”. In these images, mitotic exit can be scored by the emergence of contractility.

R2m*-After testing the relevance of SPD-5 (that primarily acts on PCM and not on centrioles)-the authors write on p. 6 that 'two classes of explanation...early embryo'. I did not understand the importance of this sentence here. *

To clarify, we deleted the words “classes of” from the sentence in question and following that sentence we added the word, “first” indicating that we were explaining the first of the two possible explanations

R2n*-The observed impact of spd-5 (RNAi) on ECT-2 localization could be because of the effects of SPD-5 depletion on centrosomal AIR-1? The authors can link the impact of SPD-5 depletion not only with the centrosome but also with AIR-1 in the discussion. *

Indeed, it is well established that SPD-5 is required for centrosomal AIR-1 (Hamill DR, et. Al Dev Cell (2002). The revised discussion now states, “Specifically, during both processes, ECT-2 displacement requires the core centrosomal component SPD-5, which is required to recruit AIR-1 to centrosomes{Hamill et al., 2002, #1201}, but ECT-2 displacement is not inhibited by depolymerization of microtubules and it does not require the AIR-1 activator TPXL-1 (see below).”

R2o-In the various Figure legends, sometimes the authors mention time '0' as anaphase, and other time as anaphase onset.

In all cases, anaphase onset was intended and the legends will be corrected.

Reviewer #2 (Significance (Required)):

R2p *The manuscript is well written, the work is interesting, and the data quality is of good quality. *

We thank the reviewer for their encouragement as well as for their thoughtful critique!

Reviewer #3 (Evidence, reproducibility and clarity (Required)):

R3a* Symmetry breaking is the process by which uniformity of the system is broken. Many biological systems, such as the body axes establishment and cell divisions in embryos, undergo symmetry breaking to pattern cellular interior design. C. elegans zygote has been a classic model system to study the molecular mechanism of symmetry breaking. Previous studies demonstrated critical roles of centrosomes and microtubules in breaking symmetry in the actin cytoskeleton during anterior-posterior polarization and cytokinesis. It, however, remains elusive how centrosomes and/or microtubules regulate the assembly and contractility of the actin cytoskeleton. Recent reports identified Aurora-A AIR-1 as the key centrosomal kinase that suppresses the function of the actin cytoskeleton, but little is known about a substrate of the kinase during symmetry breaking events. *

Longhini and Glotzer proposed in this manuscript that RhoGEF ECT-2 plays a critical role in symmetry breaking of the actin cytoskeleton under the control of AIR-1 kinase. Kapoor and Kotak (2019) previously proposed the same GEF as a downstream effector of centrosomes, but this work did not provide direct evidence for ECT-2 as the AIR-1 effector. This manuscript identified three putative phospho-acceptor sites in the PH domain of ECT-2 that render ECT-2 responsive to inhibition by AIR-1. Although this manuscript lacks direct in vivo and in vitro evidence for phosphorylation of ECT-2 by AIR-1 kinase, the above findings reasonably support a model where in AIR-1 promotes the local inhibition of ECT-2 on the cortex. Design of the experiments, the quality of images, and data analysis are reasonable, and the main text was written very well. The main conclusion of this work will attract many readers in cell and developmental biology fields. I basically support its publication in the journals supported by Review Commons with minor revisions (see below).

We thank the reviewer for their encouraging remarks and helpful comments.

Minor comments

R3b 1) In Figures 2A and 2B, the authors claimed apparent correlation between spindle rocking and ECT-2 displacement. However, because both MTs and ECT-2 in Fig2AB images are blur, I cannot convince myself whether ECT-2 intensities on the cortex showed negative correlation with the distance between the posterior centrosome and the cortex. The authors may want to provide quantitative data set and use a statistical test to support this conclusion.

Only figure 2A focuses on the rocking. The important structure to assess is the position of the centrosome, as the astral arrays of microtubules are largely radially symmetric (except towards the spindle midzone). As this point in the manuscript were were not discriminating between the astral microtubules and the centrosomes, rather focusing on the overall position of the aster as a whole. Figures 2B, 2D, Fig 2 Supplements 1 and 2, Fig 3C, and Fig 4B, summarized in figure 7A provide quantitive evidence that the centrosome-cortex distance is an important determinant of ECT-2 cortical accumulation.

R3c *2) Figure 2D would [sic; presumably should] show a ratio between the anterior/posterior pole and the lateral cortex. *

The reviewer is presumably noticing that the lateral cortex is brighter than the poles when PAR-3 is depleted. While we agree with this assessment, the point of this experiment was to evaluate whether both centrosomes are equally capable of regulating cortical ECT-2 at the respective poles. It appears to us that comparing the anterior and posterior poles is the appropriate measurement to make to address this point and comparison of the poles to the lateral cortex in par-3(RNAi) vs control would be confusing to readers.

R3d *3) In Figure 3D, the authors need to clarify why they measured ECT-2 dynamics only within the "anterior pole". It would be reasonable to measure ECT-2 dynamics by FRAP and cortical high-speed live imaging on the posterior and the lateral cortex during symmetry breaking. *

We measured ECT-2 recovery at a variety of sites with similar recovery kinetics. The comparison of ECT-2 dynamics on anterior and posterior furrows were shown in order to compare ECT-2 dynamics on centralspindlin-dependent and -independent furrows.

We now provide additional supplemental data on ECT-2 dynamics during symmetry breaking. When ECT-2 is polarized, the residual signal is too low to obtain a measure of its recovery.

R3e 4) In Figure 4 supplement, a difference between with or without ML8237 seems marginal. The authors need to show a statistical test to claim "rapid enhancement of cortical ECT-2 after ML8237 treatment".

We will provide a statistical analysis. As the inhibitor affects ECT-2 globally, the anterior/posterior ratio doesn’t change significantly. To avoid confusion, we now present total cortical ECT-2 levels upon anaphase onset in this experiment as this is the most relevant parameter.

R3f *5) I would strongly suggest the authors to clearly state in the first paragraph of discussion that "this working hypothesis is not supported by direct evidence for phosphorylation of ECT-2 by AIR-1 kinase in vitro and in vivo." It should be reasonable to weaken the statement "by Aurora A-dependent phosphorylation of the ECT-2 PH domain" in p13. *

We agree with the underlying sentiment (as indicated by the “limitations” section that was present in the original version) and we have revised these sentences accordingly: “Our studies suggest that asymmetric, posteriorly-shifted, spindle triggers an initial focal displacement of ECT-2 from the posterior cortex by Aurora A-dependent phosphorylation of the ECT-2 PH domain, though the evidence for this phosphorylation event is indirect.”

Reviewer #3 (Significance (Required)):

*See the second paragraph of the Evidence, Reproducibility, and Clarity section. *

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

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Referee #3

Evidence, reproducibility and clarity

This paper presents an investigation of the mechanisms of how chitin is synthesized in Drosophila by investigating the chitin synthetase Kkv and two proteins related/redundant proteins that are required for chitin production Exp and Reb.

The authors show that synthesis of nascent chitin polymers is separable from the secretion of chitin and that Ex/Reb is specifically required for chitin translocation/secretion. To understand the functions of Exp/Reb, the authors perform structure/function analyses and examine the localization of the proteins. They find that Na-MH2 domain in Exp/Reb is required for chitin translocation, and that a motif the authors name CM2 is required for Exp localization. For Kkv, they show the WGTRE domain is required for ER exit and that a coiled-coiled domain is required for KKV localization and full Kkv activity. By using live imaging and mutations that disrupt membrane trafficking, the authors show that Kkv, which is a transmembrane protein, cycles to the membrane, and like most membrane proteins, is endocytosed and transits through the endocytic system and is returned to the apical surface. Interestingly, despite being dynamically moved around the cell, chitin synthesis produces highly organized extracellular matrixes. Considering that constitutive production of chitin by Kkv everywhere in the cell would create a mess, these results underscore that regulated organized secretion/translocation of chitin is central to generating patterned extracellular matrixes (as the saying goes, "location, location, location"). Consistent with Exp/Reb being important regulators in extracellular matrix patterning, Exp/Reb not only are required for export of chitin, in the absence of Exp/Reb, the pattern of Kkv localization at the apical surface is altered. Unexpectedly however, by using super resolution microscopy the authors show that Kkv and Exp/Reb have complementary rather than matching localizations. Thus, while it is not clear exactly how Exp/Reb are regulating Kkv, they are doing something very interesting.<br /> Overall, this paper will be of broad interest to the cell biology and developmental biology communities, and to the translational community working to develop chitin as a commercial biopolymer. It is also generally clearly written, although I think there are some inaccuracies in the how some points are phrased. The experiments are well done, and subject to the revisions out lined below.

Major concerns:

• A major conclusion of the paper is that Exp/Reb are not required for chitin synthesis. On the most basic level this statement is well supported, because chitin grains are made in the cytoplasm in the Exp/Reb mutants. However, I think the field would be better served with a more nuanced consideration or the role of Reb/Exp. From the data presented, it seems that in the absence of Reb/Exp, the total amount of chitin produced is greatly reduced. I think it would be worth considering Exp/Reb, or the synthesis process in general, as having processivity or duty cycle or quality control such that in the absence of Exp/Reb while Kkv may make short chitin polymers, or occasional long polymers, the major production of chitin doesn't get going without Exp/Reb. Thinking of Reb/Exp as processivity factors in addition to export factors dramatically changes how one thinks of the proteins and the process of chitin synthesis. While these considerations can be handed with some discussion, it would be very interesting to look at the length of the chitin polymers in the Reb/Exp mutants and see if the average chain length is much reduced. This would help distinguish between Exp/Reb reving up the total number of Kkv molecules that produce chitin and Exp/Reb allowing the same number of Kkv molecules to stay active and produce much longer chitin chains. A caveat here is that I have no idea how hard this is to do, so I won't put this at the level of a required revision, but this result would significantly deepen the analysis in the paper.
• In looking that the subcellular localization of the Kkv and Reb in regular and super resolution, the authors I think the authors missed an important, but straight forward way to gain insight into the apparent complementary distribution of Kkv and Exp/Reb. In stage 16 WT embryos, Kkv has a distinct ringed pattern that corresponds to the tanedial ridges (e.g. clearly visible in Fig. 6A and 6G). How those ridges are set up is unclear, although there are some interesting Turing-pattern models out there. One prediction might be that Exp/Reb should be in between the Kkv rings. If so, maybe Exp/Reb are key components of patterning chitin secretion to make this 3D patterned matrix? Alternatively, maybe Exp/Reb act on a smaller length scale and will match the Kkv ring pattern, just not overlapping with Kkv at the very fine scale. These are straightforward experiments and again could provide key insights into the function of Exp/Reb.
• In general, most of the figures do not include WT or a control for comparison. This makes it hard for non-experts to assess what the effect of a mutation or condition is. For example, there are no examples of WT or Df(exp reb) in Figures 1-4. I realize this would increase the number of panels, but the paper would be more accessible if comparisons were within figures instead of comparing between main and supplementary figures and other papers.
• To bolster the case the Exp/Reb directly regulate Kkv distribution, the authors should examine the distribution of Kkv in a catalytically null Kkv mutant, or drugs that block Kkv, or mutations in other genes required for Kkv activity to show that the altered distribution of Kkv in Exp/Reb mutants is a direct consequence of the lack of Exp/Reb rather than in indirect consequence of lack of extracellular chitin, which causes gross perturbations in the trachea. Also, are there differences in the distributions of Kkv in salivary glands with or without the presence of Exp/Reb? If Exp/Reb change the distribution of Kkv in the salivary glands, which normally do not express Kkv and presumably many other components of the chitin ECM system, this would be a powerful argument that there is a direct effect.

Minor concerns.

• Page 5 "These intracellular chitin punctae disappeared from stage 14, when chitin is then deposited extracellularly (Fig 1B')." Fig. 1B' is stage 15 embryos.
• Page 5 "lead to tracheal morphogenetic defects". It would be helpful to the reader if the text or legend told the reader what they were looking for? Broken tubes? Inflated tubes? Variable tubes?
• Fig. 1H. Main text says "co-expression of Kkv and expMH2/rebMH2 did not lead to tracheal morphogenetic defects (Fig 1H, ...". The tracheal dorsal trunk in Fig. 1H does not look WT. The legend does not state the stage, but the DT looks to have an enlarged diameter and it might be too long. Please present measurements on stage 16 trachea to confirm that there is no effect on tracheal morphology.
• Fig. 3E there is a lot of GFP-Kkv that is not in co-localized with the KDEL marker. Can the authors clarify what compartment all the other staining is? ER?
• Section 3.1. The authors imply that the WGTRE domain is specifically required for ER exit. However, an alternative is that absent the WGTRE domain, the protein just does not fold correctly, which would also preclude ER exit, but would be a different problem for the protein to make chitin if it isn't folded.
• Page 15. I disagree with statement "At stage 16, control embryos showed a highly homogeneous apical distribution of Kkv in stripes, corresponding to the taenidial folds, and Kkv vesicles were largely absent (Fig 6G)." In Fig. 6G, the tandeal ring pattern is clearly visible, as are the fusion cells. If Kkv distribution were "highly homogeneous" these structures/pattern would not be visible.
• Page 15. I also disagree with the characterization of the apical Kkv distribution in st 15 embryos. "In control embryos we detected a very uniform and homogenous pattern of apical Kkv (Fig 6I).". To my eye, the pattern is punctate and random for the clumps of stain, with the underlying beginnings of the tanidial pattern starting to be visible. The pattern appears neither uniform nor homogenous.
• P16. The degree of order in the distribution of Kkv is overstated. The authors state that "The results of this analysis, showed that Kkv on the apical membrane, is evenly distributed following a regular pattern (Fig. 6L,L',L',M)." However, given that there is barely a visibly perceptible difference between the actual distribution of Kkv in 6L' and a calculated random distribution in 6L", and that the pattern is neither visibly even or regular, it would be more representative to say something to the effect that the analysis shows there is "underlying order" or "some degree of order" or a "non-random pattern". Visually, the key difference between 6L ' and L" is that there are fewer closely clustered Kkv dots. You could still have an uneven distribution of Kkv that maintains minimum spacing, which is a kind of ordered organization, but not one that would be assumed from the description. It would be helpful if the authors instead of just saying a "regular pattern" also stated the nature of the pattern they observe, i.e. Grid? Stripes? Minimum spacing?
• Discussion. Another model for the role of Exp/Reb could be to bind and neutralize an inhibitor of Kkv activity. This would account complementary distribution of Kkv and Exp/Reb.
• Fig. 6L. what tissue is being analyzed? Presumably trachea, but this should be specified as salivary glands are also mentioned in the legend.
• Fig. 7 C models. I believe that the super resolution data is not accurately accounted for in the models. In both model 1 and model 2, Kkv and Exp/Reb are shown to be in close proximity, but the super resolution data suggests that most Kkv and Exp/Reb are separated hundreds of nanometers. Further, showing Kkv and Exp/Reb as touching was not supported by the coIP experiments, which failed to detect an interaction. It is possible that only a small fraction of Exp/Reb that is in close proximity to Kkv is active, but if so, this should be explicitly mentioned in the models to reconcile the data showing that Kkv and Exp/Reb are mostly not anywhere near each other.
• -Image analysis. Please detail the criteria for "apical" and "basal" regions were the basis for freehand segmentation. What was counted as apical and what was basal?
• Abstract and Introduction: The authors state that "We find that Kkv activity in chitin translocation, but not in polymerization, requires the activity of Exp/Reb, and in particular of its conserved Na-MH2 domain.", but then follow that with the statement that "Furthermore, we find that Kkv and Exp/Reb display a largely complementary pattern at the apical domain, and that Exp/Reb activity regulates the topological distribution of Kkv at the apical membrane." Many readers, will find the use of "furthermore" confusing because they will take furthermore as the about to be described data logically following the previous data, but then run headlong into the fact the Kkv and Exp/Reb show a complementary distribution, which does not obviously follow from Kkv activity requiring Exp/Reb. The authors could clarify this and highlight the interesting, unexpected and exciting nature of their results by replacing "Furthermore" with "Unexpectedly" or "Surprisingly", and emphasizing the important role of Exp/Reb in Kkv organization. Maybe something like: Unexpectedly, we find that although Kkv and Exp/Reb display largely complementary patterns at the apical domain, Exp/Reb activity nonetheless regulates the topological distribution of Kkv at the apical membrane.

Significance

The topic is interesting from the aspect of cell biology in terms of how a long polymer is created intracellularly, secreted and spatially organized to create a sophisticated extracellular matrix. The topic is also of general interest because chitin is central to the body plan of all insects, crustaceans and many other species, and chitin is of increasing interest as a biopolymer that could have extensive commercial uses.

In addition to an informative structure/function analysis of the Kvv and Exp/Reb, the results identify what is, to my knowledge, the first regulator of the spatial organization of chitin sythase in insects and it unexpectedly shows a complementary pattern to the the synthase. This highlights just how little we understand about how complex extracellular matrixes are synthesized.

I appreciate the author bringing up this point to acknowledge that there may be other sources which lead to the extreme weather that occurs today. It is effective in avoiding biases and also raising the question to the readers of whether there may be more than one factor which goes into the extreme weather we see today (i'm not saying I think global warming is not the cause).

As talked about in Duncan and Murname's article, there is a significant difference in academic success based on the children's socioeconomic status. Another factor, such as the structure of the school may have an impact on the student's academic success as well. In high school, I remember it was pretty diverse, but the students still separated among themselves into racial groups. I think it is inevitable for these groups to not be created based on specific traits since we are naturally attracted to others that have similar traits.

Author Response

Reviewer #1 (Public Review):

This report describes evidence that the main driving force for stimulation of glycolysis in cultured DGC neurons by electrical activity comes from influx of Na+ including Na+ exchanging into the cell for Ca2+. The findings are presented very clearly and the authors' interpretations seem reasonable. This is important and impactful because it identifies the major energy demand in excited neurons that stimulates glycolysis to supply more ATP.

Strengths are the highly rigorous use of fluorescent probes to directly monitor the concentrations of NADH/NAD+, Ca2+ and Na+. The strategies directly test the roles of Na+ and Ca2+.

A weakness is an ambiguity about the effects of ouabain to inhibit the Na+/K+ ATPase directly and the absence of biochemical controls to validate the interpretation of the ouabain experiment.

We appreciate the reviewer's comments about the work. While we can not rule out non-specific effects of ouabain at the concentrations needed to block Na+/K+ ATPase in these experiments, we do think that we can rely on the prior biochemical work characterizing the multiple components of ouabain binding in fresh mouse brain tissue, which is a close match to the acute mouse brain slice tissue used here.

Reviewer #2 (Public Review):

This study seeks to determine how neuronal glycolysis is coupled to electrical activity. Previous studies had found that glycolytic enzymes cluster within nerve terminals (in C. elegans) during activity. Furthermore, the glucose transporter GLUT4 is recruited to synaptic surface during activity. The authors previously showed that Ca2+ does not stimulate glycolysis in active neurons. Here, the authors show that the cytosolic Na+, not Ca2+, and the activity of the Na+/K+ pump drive glycolysis. However, it is important to note that in this study, glycolysis was examined in the soma, not nerve terminals, where some of the previous studies were conducted. A few other caveats in the interpretation of the findings are listed below:

1) The NADH/NAD+ ratio is used throughout as the only measurement reflecting glycolytic flux.

In this and previous work, we have validated that increased cytosolic NADH production (whose major sources are related to glycolysis), rather than altered NADH reoxidation, produces the changes in NADH/NAD+ ratio.

2) It has been hypothesized that the close association of glycolytic enzymes with ion transporters (such as the Na+/K+ pump) is meant to provide localized ATP to power these pumps. How does bulk glycolysis (monitored with NADH/NAD+ ratio) relate to localized/compartmentalized glycolysis?

Even if glycolysis is indeed localized to the plasma membrane (an interesting and difficult-to-address hypothesis), we believe that because the mitochondrial shuttles are the main pathway for NADH re-oxidation, and most mitochondria are not localized to the plasma membrane, changes in glycolytic NADH production are likely to be reflected in changes of the bulk cytosolic NADH/NAD+.

3) Related to point 2, most of the Peredox measurements in the paper have been made at baseline, in the absence of electrical activity. Therefore, it is not clear how the findings relate to activity-driven glycolysis.

The ion exchange experiments and even the faster Ca2+ puff experiments can mimic but indeed cannot match the speed of activity-driven changes in ion concentrations. Unfortunately, it is impossible to induce normal electrical activity in neurons in the absence of extracellular Na+. We believe that the complete inability of Ca2+ elevation alone (without Na+-Ca2+ exchange) to stimulate glycolysis, combined with the substantial Ca2+ contribution to activity-driven glycolysis, makes a good argument that Ca2+ entering during activity is likely to stimulate glycolysis via Na+ entry and the Na+/K+ ATPase.

4) The finding that inhibition of SERCA during stimulation actually elevates cytosolic NADH level argues against Na+ being the only ion that regulates glycolysis.

The ability of SERCA inhibition to produce a small increase in activity-driven glycolysis is consistent with the simple argument that reduced SERCA-driven uptake of Ca2+ into ER results in additional Ca+ removal via Na+/Ca2+ exchange (which can then affect glycolysis via Na+ levels).

5) The finding that "SBFI ΔF/F transients were longer in duration than the RCaMP LT transient" does not necessarily mean that Na+ elevation lasts longer than Ca2+ in the cell. This could be an artefact of the SBFI on/off rate relative to RCaMP. In fact, prolonged elevation of cytosolic Na+ would make neurons refractive to depolarization in AP trains.

The rates of Na+ binding and unbinding to SBFI are likely to occur on the microsecond timescale (based on the known properties of crown ether molecules), much faster than the observed transient duration of approximately one minute. Prolonged elevation of cytosolic Na+ alone (to the levels seen here) should not cause neurons to be refractory to firing; refractoriness typically occurs in the setting of prolonged depolarization and consequent inactivation of NaV channels.

Reviewer #3 (Public Review):

Meyer et al have studied the mechanisms of glycolysis activation in the hippocampus during neuronal activity. The study is logically laid out, uses sophisticated fluorescence lifetime imaging technology and smart experimental designs. The support for intracellular [Na+] vs [Ca2+] rise driving glycolysis is strong. The evidence for the direct involvement of the Na+/K+ pump is based only on pharmacology using ouabain but the Na+/K+ pump is admittedly not an easy subject for specific perturbations. I still think that the Authors should strengthen the support for the pathway.

We are happy that the reviewer feels that the evidence for Na+ rather than Ca2+ as the effector of glycolysis is strong. The tools for investigating the role of the Na+/K+ pump (NKA) are indeed limited to pharmacology, because (as the reviewer says) there are not many other options. The requirement for Na+ elevation (which stimulates NKA activity) to trigger glycolysis and the ability of ouabain, a specific NKA inhibitor, to prevent this seem like strong implication of NKA in the mechanism of glycolysis activation. Genetic manipulation of the NKA may be unable to change the level of pump activity, because of compensation by altered expression of other subunits (PMID 17234593); it also is unclear how any chronic manipulation would shed light on the role of NKA in triggering glycolysis. But perhaps future studies of knock-in mice in which the α1 isoform of NKA has made more sensitive to ouabain (PMIDs 15485817; 34129092) might allow the identification of the NKA as the target of ouabain in this situation to be made even more secure.

Also, there is a long list of publications on the connection between the Na+/K+ pump and glycolysis. It might be useful to highlight the role of the NCX- Na+/K+ pump coupling in the activation of glycolysis in the title.

Author Response

Reviewer #1 (Public Review):

Dotov et al. took joint drumming as a model of human collective dynamics. They tested interpersonal synchronization across progressively larger groups composed of 1, 2, 4 and 8 individuals. They conducted several analyses, generally showing that the stability of group coordination increases with group numerosity. They also propose a model that nicely mirrors some of the results.

The manuscript is very clear and very well written. The introduction covers a lot of relevant literature, including animal models that are very relevant in this field but often ignored by human studies. The methods cover a wide range of distinct analyses, including modelling, giving a comprehensive overview of the data. There are a few small technical differences across the experiments conducted with small vs. large groups, but I think this is to some extent unavoidable (yet, future studies might attempt to improve this). Furthermore, the currently adopted model accounts well for behaviors where all individuals produce a similar output and therefore are "equally important". However, it might be interesting to test to what extent this can be generalized to situations where each individual produces a distinct sound (as in a small orchestra) and therefore might selectively adapt to (more clearly) distinguishable individuals.

We agree that this is important. We discuss this in a new section (4.1) at the end of the discussion. We suggest that heterogeneity makes it possible for other modes of organization to compete with the attractive tendency towards the global average. We also point out that factors such as individual skill, task difficulty, delays, and selective attention enable such heterogeneity in the ensemble.

Similarly, it would be interesting to test to what extent the current results (and model) can be generalized to interactions that more strongly rely on predictive behavior (as there is not much to predict here given that all participants have to drum at a stable, non-changing tempo).

We can only speculate that the present results are less relevant to interactions that rely strongly on predicitive behavior, as behaviour in our simple task could be modeled well by our hybrid single oscillator Kuromoto model. We inserted the idea that the presence of a group rhythm can diminish the demands for individuals to predict each other’s notes, the end of paragraph 1, page 27.

An important implication of this study is that some well-known behaviors typically studied in dyadic interaction might be less prominent when group numerosity increases. I am specifically referring to "speeding up" (also termed "joint rushing") and "tap-by-tap error correction" (Wolf et al., 2019 and Konvalinka et al., 2010, also cited in the manuscript, are two recent examples). I am not sure whether this depends on how the data is analyzed (e.g. averaging the behavior of multiple drummers), yet this might be an important take-home message.

Thank you for the suggestion. We edited to emphasize that the relevant part of the analysis of the drumming data was performed at the individual level and using the same methods as typically done in dyadic tapping (first sentences of Section 2.7.2). Speeding up was the only variable where we used group-averages. For consistency, and to avoid confusion, in the present version we re-did the stats (the changed statistical parameters are highlighted) and figures using the individual data points and we did not observe major changes.

I am confident that this study will have a significant impact on the field, bringing more researchers close to the study of large groups, and generally bridging the gap between human and animal studies of collective behavior.

Reviewer #2 (Public Review):

In this manuscript Dotov et al. study how individuals in a group adjust their rhythms and maintain synchrony while drumming. The authors recognize correctly that most investigation of rhythm interaction examines pairs (dyads) rather than larger groups despite the ubiquity of group situations and interactions in human as well as non-human animals. Their study is both empirical, using human drummers, and modeling, evaluating how well variations of the Kuramoto coupled-oscillator describe timing of grouped drummers. Based on temporal analyses of drumming in groups of different sizes, it is concluded that this coupled oscillator model provides a 'good fit' to the data and that each individual in a group responds to the collective stimulus generated by all neighbors, the 'mean field'.

I have concerns about 1) the overall analysis and testing in the study and about 2) specific aspects of the model and how it relates to human cognition. Because the study is largely empirical, it would be most critical for the authors to propose two - or more - alternative hypotheses for achieving and maintaining synchrony in a group. Ideally, these alternatives would have different predictions, which could be tested by appropriate analyses of drummer timing. For example, in non-human animals, where the problem of rhythm interaction in groups has been examined more thoroughly than in humans, many acoustic species organize their timing by attending largely to a few nearby neighbors and ignoring the rest. Such 'selective attention' is known to occur in species where dyads (and triads) keep time with a Kuramoto oscillator, but the overall timing of the group does not arise from individual responses to the mean field. Can this alternative be evaluated in the drumming data ? Would this alternative fit the drumming data as well as, or better than , the mean field, 'wisdom of the crowd' model ?

These are very important points. The present paper is restricted to a simple task where participants are instructed to synchronize with each other. However, we now more explicitly acknowledge the limitations of our study and include a new section, “Beyond the group average” at the end of the Discussion that is dedicated to this issue and discussed other organizing tendencies that are particularly relevant in larger and more diverse ensembles. In the context of the present task, the relative difference between local and global interactions was likely negligible because of the small differences in timing, from 4 to 16 ms, between the closest and most distant pairs.

It will be interesting in future studies to introduce acoustic heterogeneity by varying the timbre of the instruments, for example. In the present study, the instruments had the same timbre with narrowly varying fundamental frequencies (117-129 Hz in the duets/quartets and 249-284 Hz in the octets), a situation that encourages integration of all the acoustic information. We do point out that the present approach needs to be expanded to be able to account for competitive pressure and selective attention.

The well-known Vicsek model (discussed briefly in paragraph 2, page 15), related to the Kuramoto under certain assumptions, can account for a variety of dynamic behaviors in flocking animals. The ability for selective attention in the form of a heterogeneous coupling matrix, combined with the existence of competitive pressure in the form of negative coupling terms can result in spontaneous formation of clusters and spatiotemporal patterns of movement. This is consistent with prior research in chorusing animals (insects and anurans). Large musical ensembles also involve groupings of instruments such as separate sections that change their relative loudness across time. Typically these are not spontaneous but composed and conducted, yet they may satisfy the same constraints.

We also pointed out that we see these as complementary organizing principles. Even in the Vicsek model, there is a notion of a ‘local order parameter’ whereby individuals are coupled to a group average within a narrow interaction radius. The relative importance of other organization tendencies depends on the layout of the acoustic environment and the competitive and collaborative aspects of the task. Hence, parameters such as delay and individual heterogeneity could act as symmetry breaking terms that enable different stabilities from the basic global group synchrony.

A second concern arises from relying on a hybrid, continuous - pulsed version of the Kuramoto coupled oscillator. If the human drummers in the test could only hear but not see their neighbors, this hybrid model would seem appropriate: Each drummer only receives sensory input at the exact moment when a neighbor's drumstick strikes the drum. But the drummers see as well as hear their neighbors, and they may be receiving a considerable amount of information on their neighbors' rhythms throughout the drum cycle. Can this potential problem be addressed? In general, more attention should be paid to the cognitive aspects of the experiment: What exactly do the individual drummers perceive, and how might they perceive the 'mean field' ?

This is all very relevant. We instructed participants to focus on X’s in the centers of their drums and not look at their peers (edited to mention that in at the end of Section 2.4, page 9). Additionally, the pattern of results for tempo change, cross-correlations, and variability in the dyadic condition was consistent with previous studies that involved purely auditory tapping tasks (emphasized in the begging of paragraph 2, page 26). The best way to address this limitation would be to repeat the study and block the visual contact among participants, as well as include a condition emphasizing visual contact.

It is beyond the scope of the present paper to make model-based predictions of effects of coupling and information availability, but this should be done in future work. For the present paper, we now include a simulation involving continuous coupling (end of section 2.9.2, page 16) and Supplementary Figure 8A) which fails to reproduce the results for variability, results that are well captured by the hybrid continuous-pulsed model we developed, see the Supplementary Materials.

Reviewer #3 (Public Review):

The contribution provides approaches to understanding group behaviour using drumming as a case of collective dynamics. The experimental design is interestingly complemented with the novel application of several methods established in different disciplines. The key strengths of the contribution seem to be concentrated in 1) the combination of theoretical and methodological elements brought from the application of methods from neurosciences and psychology and 2) the methodological diversity and creative debate brought to the study of musical performance, including here the object of study, which looks at group drumming as a cultural trait in many societies.

Even though the experimental design and object of study do not represent an original approach, the proposed procedures and the analytical approaches shed light on elements poorly addressed in music studies. The performers' relationships, feedbacks, differences between solo and ensemble performance and interpersonal organization convey novel ideas to the field and most probably new insights to the methodological part.

It must be mentioned that the authors accepted the challenge of leaving the nauseatic no-frills dyadic tests and tapping experiments in the direction of more culturally comprehensive (and complex) setups. This represents a very important strength of the paper and greatly improves the communication with performers and music studies, which have been affected by the poor impact of predictable non-musical experimental tasks (that can easily generate statistical significant measurements). More specifically, the originality of the experiment-analysis approach provided a novel framework to observe how the axis from individual to collective unfolds in interaction patterns. In special, the emergence of mutual prediction in large groups is quite interesting, although similar results might be found elsewhere.

Thank you for these comments.

On another side, important issues regarding the literature review, experimental design and assumptions should be addressed.

I miss an important part of the literature that reports similar experiments under the thematic framework of musical expressivity/expression, groove, microtiming and timing studies. From the participatory discrepancies proposed in 1980's Keil (1987) to the work of Benadon et al (2018), Guy Madison, colleagues and others, this literature presents formidable studies that could help understand how timing and interactions are structured and conceptualized in the music studies and by musicians and experts. (I declare that I have no recent collaborations with the authors I mentioned throughout the text and that I don't feel comfortable suggesting my own contributions to the field). This is important because there are important ontological concerns in applying methods from sciences to cultural performances.

Thank you for the suggestions. We included a brief discussion in the newly added “Beyond the group average” section at the end of the Discussion, specifically the first paragraph, pages 27-8. We think that expressive timing naturally fits in continuation with the other reviewers’ concerns about how much the idea of the group average generalizes to real musical situations. By design and instruction, we stripped individual expression from the present task. Specific cultural contexts and performance styles may want to escape or at least expressively tackle this constraint of our task, and we believe that now that we have established the mean field as one factor affecting group behaviour, further studies can take on the challenge of developing models that make predictions in more complex situations closer to real musical interactions – and testing those models empirically.

One ontological issue that different cultural phenomena differ from, for example, animal behaviour. For example, the authors consider timing and synchrony in a way that does not comply with cultural concepts: p.4 "Here we consider a musical task in which timing consistency and synchrony is crucial". A large part of the literature mentioned above and evidence found in ethnographic literature indicate that the ability to modulate timing and synchrony-asynchrony elements are part of explicit cultural processes of meaning formation (see, for example, Lucas, Glaura and Clayton, Martin and Leante, Laura (2011) 'Inter-group entrainment in Afro-Brazilian Congado ritual.', Empirical musicology review., 6 (2). pp. 75-102.). Without these idiosyncrasies, what you listen to can't be considered a musical task in context and lacks basic expressivity elements that represent musical meaning on different levels (see, for example, the Swanwick's work about layers/levels of musical discourse formation).

Indeed, this is an important issue. We often use cultural phenomena merely as a motivation but do not dive in the relevant details. Here, in addition to the previous discussion, we now reiterate that the tendency towards the group average is one organizing tendency but there are additional ones, enabled by individual heterogeneity and context. For example, marching bands and chanting crowds probably impose different constraints than individual artistic expression by skillful musicians.

Such plain ideas about the ontology of musical activities (e.g. that musical practice is oriented by precision or synchrony) generate superficial constructs such as precision priority, dance synchrony, imaginary internal oscillators, strict predictive motor planning that are not present in cultural reports, excepting some cultures of classical European music based on notation and shaped by industrial models. The lack of proper cultural framing of the drumming task might also have induced the authors to instruct the participants to minimize "temporal variability" (musical timing) and maintain the rate of the stimulus (musical tempo), even though these limiting tasks mostly take part of musical training in some societies (examples of social drumming in non-western societies barely represent isochronous tempo or timing in any linguistic or conceptual way). The authors should examine how this instruction impacts the validity of results that describe the variability since it was affected by imposed conditions and might have limited the observed behaviour. The reporting of the results in the graphs must also allow the diagnosis of the effect of timing in such small time frame windows of action.

We agree totally. We made changes and tried to be more specific about the cultural framing, delineating contexts where the present ideas are more relevant and where they are less relevant, or at least incomplete (the bottom of page 3, and pages 27-8).

Author Response

Reviewer #1 (Public Review):

This paper primarily assessed the host/phage interactions for bacteria in the order of Cornyebacteriales to identify novel host factors necessary for phage infection, in regards to genes responsible for bacterial envelope assembly. Bacteria in this order, such as Mycobacterium tuberculosis and Corynebacterium diphtheriae have unique, complex envelopes composed of peptidoglycan, arabinogalactan, and mycolic acids. This barrier is a potent protector against the therapeutic effects of antibiotics. Phages can be used to discover novel aspects of this bacterial envelope assembly because they engage with cell surface receptors. To uncover new factors, the researchers challenged a high-density transposon library of Corynebacterium glutamicum (called Cglu in the paper) with phages, Cog, and CL31. Results by transposon sequencing identified loci that were interrupted, leading to phage resistance. This study implicated the importance of Cglu genes, ppgS, cgp_0658, cgp_0391, and cgp_0393. They also identified a new gene called cgp_0396 necessary for arabinogalactan modification and recognized a conserved host factor called Ahfa (Cpg_0475) that plays a crucial role in Cglu mycolic acid synthesis. Ultimately, this work implicated the importance of mycomembrane porins, arabinogalactan, and mycolic acid synthesis pathways in the assembly of the Cornyebacteriales envelope.

Strengths of the research:

• Language choice: A major strength of the paper is that this could easily be given to an undergraduate student with introductory knowledge of biology and they would still be able to get the gist of this paper. The language is written in a clear, concise fashion with explanations of terms not everyone would immediately know unless they worked in the field specifically.

• These figures are generally explained in a direct manner, clearly stating the major conclusions the reader should get after carefully analyzing the presented data

We thank the reviewer for the enthusiasm for our work and our description of it.

How the research could be strengthened:

• It could be worthwhile to describe some of your results mathematically. For example, the differences you see in your phage infections relating to the differences in logs, etc. Bar graphs also should be described in mathematical terms, when "something is lower compared to the WT," how much is lower, etc?

To keep the text streamlined, we refrained from adding descriptions of the results mathematically in the text. The reader can refer to the figures to get the magnitudes of any changes observed.

• There were no p values relating to the statistical significance of any of the data presented, which should be changed for the final manuscript implicating the importance of this work.

We added the p-values as requested.

• Figure 8 was not entirely supported by the data, especially Figure 8A which either could be improved with better images that support the author's claims, etc.

We do not understand why the reviewer believes that Figure 8A does not support our conclusions. The mutant cells do not label with the 6-TMR-Tre dye whereas the WT control does. The dye labels mycolic acid such that our conclusion that AhfA is involved in mycolic acid synthesis is valid. In any case, we have included an additional supplementary source data file of the uncropped image of the 6-TMR-Tre treated cells to show a larger number of mutant cells that fail to stain, further supporting our conclusion.

Reviewer #2 (Public Review):

In this manuscript, McKitterick and Bernhardt use genetic approaches to investigate genes in Corynebacterium glutamicum that are required for efficient phage infection. They make use of a high-density transposon library that was generated in the Bernhardt lab recently. They challenged the library with two phages, CL31 and Cog. Importantly, they elegantly adapted the phages to the laboratory strain MB001 before. The MB001 strain is ideal for genetic experiments since all prophage elements were removed in this strain. The evolved phages are likely a very useful tool for further investigations aiming to understand host/virus interactions in this model. The phage-infected libraries were plated and the collected colonies were sequenced. Genes involved in efficient phage infection had multiple transposon insertions. Using this method the authors identified specific genes required for infection with Cog and CL31. The Cog phage needs apparently the porin proteins in the mycolic acid membrane for efficient infection and the authors speculate that the porins may act as auxiliary receptors for phage adsorption. Furthermore, genes involved in putative arabinogalactan modification were found to be important. Mutants in these genes did not abolish phage adsorption and thus play a role in viral genome injection. For phage CL31 the authors show that in particular genes involved in mycolic acid synthesis are essential. The genes identified include one coding for a protein involved in protein mycoloylation. A candidate for such a lipidation is the porin protein complex PorAH. The trehalose-6-phosphate synthase OtsA was also identified as important for phage infection. Also strictly required for the establishment of the myco membrane, otsA deletions are viable in C. glutamicum. As part of their analysis, they also identified an unknown factor in mycolic acid synthesis in C. glutamicum. Analysis of a spontaneous resistant mutant to CL31 revealed a mutation in cg_0475 (renamed ahfA). Deletion of ahfA drastically reduced mycolic acid production. This was proven by thin layer chromatography and fluorescent staining. Interestingly, deletion of ahfA also results in a cell morphology defect, indicating the importance of a correct mycolic acid layer for cell shape.

In summary, the authors provide an excellent paper that is clearly written and experiments are conducted nicely.

We thank the reviewer for their kind words and enthusiasm for the work.

Reviewer #3 (Public Review):

In their manuscript, McKitterick and Bernhardt perform a screen to determine host factors, such as receptors, which are important for bacterial viruses (phages) to infect Corynebacterium glutamicum., an organism that shares the unique membrane of mycobacteria (mycomembrane), with M. tuberculosis. To do so, they challenge a previously described Tn-seq library with a high MOI of 2 phages - Cgl and Cog. The surviving strains are those in which genes important for phage infection (such as receptors) are disrupted. The authors' screen is successful, and the authors identify and validate several factors important for the infection of each phage, providing the first such screen in Corynebacterium. Moreover, the authors perform a suppressor screen to identify additional factors and experimentally follow up several genes of interest. Finally, the authors use the newly determined host specificity of te phages to implicate new genes in mycolic acid synthesis. As a whole, this is a strong work that paves the way to a deeper understanding of Corynebacterial and (by extension) Mycobacterial phages and should be of broad interest.

Below, we suggest additional analyses, context, and elaboration that will help the ms. elaboration to fully realize its impact.

Major points:

1. Although the authors' experimental design is fundamentally sound, I am worried about the possibility of "jackpotting" in shaping their results, particularly in the uninfected control experiment. If the authors' Tn-seq library is ~200,000 strains, and they don't plate at least 10-100x times that many colonies then any given strain (regardless of its phenotype) may or may not be represented in the output of the experiment, causing false phenotypes to be ascribed to genes based on chance. This is particularly a problem for the uninfected control, where the authors choose to dilute the culture 1000fold to mimic the number of colonies that survive infection. They may be better served by plating the whole culture on the plates, to ensure adequate representation of the library. Part of the reason for this concern is that an overwhelming majority of statistically significant hits (something like 80-90%) appear to confer susceptibility rather than resistance (source data Fig 2) - something the authors' experimental design should not be able to measure. The lack of accurate representation of distributions of strains in the starting culture also calls into question the quantitative differences they present in the results

We thank the reviewer for their thorough analysis of our experimental design. The Tn-Seq experiments were repeated with the uninfected controls plated at a density that maintains the representation of the original library. The overall results are largely unchanged because we maintain our focus on hits that become greatly enriched following phage infection not those that become depleted. The vast majority of these hits were validated for their involvement by constructing mutant strains, indicating the robustness of the current and previous analyses. With respect to the depletion of insertion mutants, we mentioned in the original submission that they are unlikely to be biologically meaningful.

a. L138. Where the authors describe their initial experimental design it would be helpful to add more details. What is the size of the Tn library? What is the coverage in their experiment? Approximately how many colonies are recovered on the plates after phage infection and in the uninfected control?

This information has been added (Fig. 2 table supplement 1).

b. it is important to know how the number of colonies on the plates compares to the number of reads in the experiment. In the analysis of most HT screens, one implicitly assumes that each read corresponds to 1 cell, hence each read can be treated as statistically independent. This assumption is critical to the statistical methods used to analyze this data. By scraping a plate of colonies (which may be required for efficient phage infection), the authors potentially violate this assumption (since the number of cells → number of colonies, which are the actual statistically independent entities in the experiment). Does this assumption hold (or approximately hold) for the screen? If not, a different statistical method should be used to determine p-values.

We respectfully disagree with the reviewer on this point. In our view, a slurry of colonies from a plate is no different than a culture. Both contain a mixture of cells containing an array of different transposon mutants each represented multiple times in the population due to replication of the original mutant. We do not think there is any meaningful difference to the analysis whether this replication occurs in liquid or on a plate. In both cases, a read corresponds to a single cell/molecule of purified genomic DNA from the population.

1. The authors' Tn-seq methodology is different from previously published HT-phage screens (e.g. Mutalik et al., 2020 and Rousset et al., 2018). Based on my knowledge of classical phage biology, I agree that plating the infected cells has advantages. However, the rationale will not be clear for most people performing such experiments. Please explain the rationale for the experimental protocol.

Although the authors in the Mutalik et al paper did do competition experiments in liquid over several infection cycles, they also made use of a solid platebased assay in which they adsorbed their phages to the library cells for 15 minutes before plating. These plates were incubated overnight and resistant colonies were scraped, pelleted, and DNA prepped in a similar manner to the approach we took.

We prefer plating over liquid growth because colony formation is an easy way to ensure that the mutant population has undergone numerous rounds of doubling under a given condition before the analysis is performed.

a. Why did the authors plate the cultures after initial phage absorption instead of remaining in liquid?

We were concerned that some potential receptor-related mutants would be less fit and would therefore be lost in a competition experiment. As such, plating after phage adsorption would decrease the competition between phage survivors. Furthermore, we thought that plating would additionally ensure that the bacteria that are sequenced are true survivors and not just reflect remnant DNA in the culture.

b. How reproducible are the authors' Tn-seq results? The SRA ascension shows multiple replicates but this is not described in the manuscript nor reflected in the supplementary data. Given the potential for bottleneck and jackpotting effects in this assay, some measure of reproducibility is important for interpreting the results (see point 1).

We performed completely new Tn-seq experiments for each phage in duplicate. The hit lists remained largely unchanged from our initial analysis and those that were investigated further were enriched for insertions in both new data sets. Thus, the results are highly reproducible.

c. L587 "Significant hits with fewer than 10 insertions on each strand were manually removed." Why did the authors choose this criterion? Almost all of the genes they removed have very asymmetric distributions (e.g. in the Cog experiment, cgp3051 has 47853 fwd reads and 6 rev reads. Asymmetric distribution of insertions suggests that overexpression of downstream genes has an important (positive or negative) effect. This is a worthwhile pursuit, and many automated analysis pipelines can disambiguate these effects, including those developed in the Walker Lab (e.g. doi: 10.1038/s41589018-0041-4). These genes shouldn't be thrown away when they are arguably some of the most informative hits!

We have updated the criteria we used for selecting the most impactful insertion enrichments. Our concern in this report was to investigate mutants that affect phage infection when inactivated. We will pursue genes that affect phage infection when overexpressed (as indicated by asymmetric insertion orientation distributions) in a follow-on study. We think such a study would best be carried out with a different transposon containing a strong outward facing promoter.

1. There is a somewhat extensive phylogeny of M. smegmatis phages (phagesdb.org). Are the phages that the authors work on related to any of these phages? If so, what cluster do they map to? What is the host range of other phages in that cluster? If not, may be worthwhile to mention that these are quite distinct from other studied phages.

We agree that the phylogenetic history of corynephages is quite interesting. Very few phages that infect Cglu have been isolated and sequenced, let alone studied. Neither Cog nor CL31 share significant nucleotide identity with other sequenced phages, thus they do not have assigned clusters at the moment.

1. Given that cgp_0475 was a strong hit in the Tn-seq, why was it not identified in the previous chemical genomics experiments from the lab (https://doi.org/10.7554/eLife.54761) ?

We appreciate the reviewer’s interest in previous work from the lab. In the prior phenotypic analysis, cgp_0475 was identified as having severe fitness defects across many conditions. However, it was not possible to correlate its phenotype with other genes involved in mycolic acid synthesis like pks and fadD2 because they were found to be so sick in the phenotypic outgrowth that they were classified as essential.

1. Is there any relationship between the growth-rate of the mutants and their phage susceptibility? This can be analyzed using the authors' previous studies of this library.

While some of the phage resistant mutants are associated with poor fitness (namely those involved in mycolic acid synthesis), not all were associated with decreased growth. For example, there were minimal fitness defects associated with deletions of either porAH or the genes involved GalN decoration. However, loss of these genes greatly inhibited the ability of Cog to infect.

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Referee #3

Evidence, reproducibility and clarity

In the manuscript entitled "Long-term mitotic DNA damage promotes chromokinesin-mediated missegregation of polar chromosomes in cancer cells," the authors propose that DNA damage on mitotic chromosomes causes chromokinesin-mediated polar chromosomes, which eventually results in missegregation and micronuclei formation. They first performed screening of compounds that cause DNA damage on mitotic chromosomes and found that DNA damage delayed mitosis in the nocodazole wash-out experiment. The authors found that several DNA damage-inducing compounds all caused an increase of asymmetric Mad1 localization on polar chromosomes. Using photoactivatable GFP-a-tubulin, the authors showed that a-tubulin stabilizes after Etoposide treatment. They finally showed that chromokinesin Kid and Kif4a knockdown rescues the asymmetric Mad1 localization.

Major comments:

1. Page 6, line 155: the authors claim that "In contrast, among other defects, treatment with any of the DNA-damaging compounds caused a significant mitotic delay due to the presence of misaligned chromosomes near the spindle poles." Although Figure 2A shows a representative image of polar chromosomes, I do not find quantitative data that analyze %polar chromosomes in mitosis treated with DNA-damaging compounds. I also do not find the data supporting the claim that polar chromosomes caused a mitotic delay. Because most subsequent analyses were performed based on this result, the quantitative data should be provided here. For the latter, I suggest showing "time in mitosis (Fig 2B)" separately with or without polar chromosomes.
2. According to Figure 2C, the ratio of "Exit with micronuclei (from misaligned chromosome(s))" is relatively low compared to other phenotypes such as "Mitotic arrest" or "Cell death." I wonder if polar chromosome phenotype is also correlated with these other cell fates. Please clarify which fate is correlated with polar chromosome formation after DNA damage.
3. In Figure 3, the authors used Nocodazole-treated background to assess the involvement of SAC in DNA-damaging compound-induced mitotic delay. However, as shown in Figure 2B, DNA-damaging compounds cause a minor delay in mitosis, which might be challenging to analyze in the presence of Nocodazole. There is also a possibility that DNA damage response (DDR) works independently and adjunctly to delay mitosis. Because one of the major claims of the authors is that "the SAC is the only mechanism that is required to delay mitosis in the presence of long-term mitotic DNA damage (page 10, line278)", I recommend Nocodazole wash-out (as in Figure 2B) to examine the effect of MPS1-IN-1 (and ideally an inhibitor of the DDR pathway, such as ATMi) on mitotic delay induced by DNA-damaging compounds.
4. Line 226, (our unpublished observations): because the authors claim that "the formation of polar chromosomes due to the stabilization of kinetochore-microtubule attachments upon long-term mitotic DNA damage is likely exclusive to cancer cells," the authors should present data on RPE-1 cells at least for %polar chromosome formation (as suggested in comment 1) and Mad1 localization. Plus, even though the data is provided, the statement "exclusive to cancer cells (page 8, line 230)" is speculative and should be toned down. Mad1 localization data is also important because the authors claim that "long-term mitotic NA damage specifically stabilized kinetochore-microtubule attachments in cancer cells (page 10, line 288)" in the discussion.
5. For the Mad1 assay, such as in Fig. 4A, the authors analyzed the CENP-C pair with two or one Mad1 foci formation. However, in some representative pictures, for example, Fig S4A-Etoposide, I found pairs of CENP-C signals on the polar chromosome without any Mad1 foci (the one next to the pairs shown in the square). As the authors argue, these kinetochores may represent polar chromosomes that eventually satisfy SAC and may be important. I, therefore, wonder why those kinetochores are omitted from the assay. Please explain this point in the manuscript if there is any reason.

Minor comments:

1. Page 7, line 168: the authors claim that "regardless of the type of DNA lesion, long-term mitotic DNA damage persists throughout mitosis and promotes micronuclei formation from polar chromosomes." However, the former claim is not fully supported by Figure S3, which addressed the effect of Etoposide only; the latter claim is not fully supported by Figure 2C, which lacks clarity (as pointed out in comment 2) and statistical analysis. Please revise this sentence.
2. Line 182: it would be helpful for readers to explain why MG132 was used.
3. Line 210: it would be helpful for readers to explain briefly what PA-GFP means and how the assay works.
4. Figure 6E-G: I wonder whether siKid+siKif4a affected %polar chromosomes or not.
5. Page 10, line 287: the authors claim that "we show that long-term mitotic DNA damage..., causing the missegregation of polar chromosomes due to the action of arm-ejection forces by chromokinesisns,...." However, only Mad1 localization data is provided in Figure 6E-G, and whether siKid + siKif4a rescues the missegregation of polar chromosomes is not clear. The authors should either provide supporting evidence or revise this sentence for clarity.
6. Figure 1E: some color codes for each compound are difficult to distinguish. I also found it challenging to locate some lines on the graph. I recommend separating this graph, for example, by types of DNA lesions caused by compounds, and color codes that are easy to distinguish should be used.

Referees cross-commenting

I generally agree with other reviewers' comments and confirmed that they raised similar concerns.

Significance

It has been described previously that mitotic arrest induces DNA damage and that the DDR pathway during mitosis is attenuated. The data presented in this manuscript provide a potentially novel cellular response against DNA damage during mitosis. The manuscript will be of interest to those in the field of the cell cycle (especially mitosis), the DDR, and tumor chemotherapies. While the finding that DNA damage during mitosis causes polar chromosomes is potentially interesting, the manuscript is still rather descriptive, and data that address the molecular mechanism is insufficient for the level that the authors conclude. Although the data quality is high, I think some essential data supporting their conclusion and clarity of the description are missing from the manuscript, which can be addressed before publication.

j’adore.

I wonder when the last time was that I read a book written by a man. Maybe the Adventure Zone comics? Ah, and Yeats. But I feel a bit as though… I am willing to humble myself before some authors to see what I must expand my view to understand. But for white dudes I do not extend very much Benefit Of Doubt unless I have a lot of social context telling me they are worth it. This has been working out pretty okay. I still read about cis dudes and they aren’t always boring, so I think it’s possible to keep tuning an approach until you’re finding stuff that works along multiple dimensions; it isn’t entirely zero-sum.

Some clients will actually fight you to put you in a power position over them. There is something which they may find relieving or conforting in surrendering that empowerment, or perhaps they just believe they don't have the right to expect a non-heirarchical relationship with their counselor. Often when we are asked for advise as counselors it is actually a bid for the permission for the client to surrender their will, intentions and decisiveness to you. That may have characterised familiar abusive relationships for them in the past. Or it may be a way for them to avoid looking inside or taking up their power. But if you give advice too readilly - you might end up responsible if things don't turn out right. What if things don't turn out right for them because it wasn't what they wanted and they get hurt- even if they don't blame you for this- you might actually be to blame. Cultural humility is partly having the ability to recognize that we can accompany and help people quite a lot, but we can not rescue them or think for them, or want for them or know better for them. We are actually fighting for a capacity within the therapeutic relationship- to build connections of equality and empowerment.

Again, I think McCosh is highlighting that all of these scientific men in varying fields have found answers to certain questions we have about the universe and ourselves. They may have uncovered some truths through the practice of research, experimentation, and other scientific tools, but they still cannot account for the larger aspect of their existence, who created them and how were they made from the start? Only a supreme being could be responsible. So, he's saying, yes some of what you're saying/ found, identified, uncovered is true, but it's not the entire truth. You still can't account for the innate, and there is where we find God.

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Reply to the reviewers

1. General Statements [optional]

See cover letter for more details.

Summary of response to reviewers:

We were immensely pleased that the reviewers considered our conclusions “well supported” and our study “beautifully executed”. Reviewers also recognized the significance of our work. Reviewer 1 stated that “building a model that describes one of these pathways will allow us to begin to test therapies to treat or prevent scoliosis” then noted that we “help to build a larger model of normal spine morphogenesis” and that this is “important”. Reviewer 2 called our work an “exciting advance in our understanding of one of the essential signaling pathways that help regulate body axis straightening and spine morphogenesis in zebrafish” and mentioned that our work “may also help to further our understanding of the etiology and pathophysiology of multiple forms of neuromuscular scoliosis in humans”. Reviewer 3 agreed, stating that our work “adds important information on the role of urotensin signaling in spine formation” and noted that it is timely: “findings are of special significance in the light of recent reports that mutations in UTS2R3 show association with spinal curvature in patients with adolescent idiopathic scoliosis”.

We thank the three reviewers for reading our research and providing feedback. In all cases, we have incorporated (or plan to incorporate) their suggestions, and we believe this has (will) make our manuscript much stronger. Indeed, reviewers had only a small number of “major points”, and all are easily addressed as summarized below. We have already addressed some of those “major points”, as well as the majority of “minor points” raised by reviewers, in our current draft. We expect that all comments can be fully addressed within around 1 month.

2. Description of the planned revisions

Insert here a point-by-point reply that explains what revisions, additional experimentations and analyses are plannedto address the points raised by the referees.

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We have divided our responses by whether the reviewers considered their points major or minor. All points have already been, or will soon be, fully addressed.

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Major points

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Reviewer 1

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The key conclusions are well supported, see below for my two major issues.

Please don't call this lordosis. Lordosis or hyperlordosis effects lumbar vertebra. The curve in the lumbar region shifts body weight so that human gait is more efficient that that in the great apes, or so the story goes. Zebrafish do not have lumbar vertebra equivalents or a natural curve in the caudal region. Similarly, fish do not have the equivalent vertebra to generate kyphosis, which is again a hyper flexion of a normal human spinal curve. Instead zebrafish have Weberian, precaudal and caudal vertebra. It would be so much more useful for the field if the authors used these terms and specified ranges, i.e. numbered vertebrae, that are effected so we can directly and accurately compare regions of defects between zebrafish mutants. It would help to make the point that the uts2r3 mutant has more caudally located curves than urp1/2 double mutants. We appreciate this point and agree with the reviewer. Lordosis (or hyperlordosis) is indeed the accentuation of a curve which naturally exists in humans but not zebrafish. We called the phenotype of Urotensin pathway mutants ‘lordosis’ or ‘lordosis-like’ because of the position of the curves — in caudal vertebrae, which are evolutionarily and positionally equivalent to lumbar vertebrae, though they are structurally different to human lumbar vertebrae. To address this comment, we will no longer refer to the phenotype as lordosis in our Introduction or Results sections and we will expand our Discussion to include this point raised by the reviewer.

1. The observation that urp1/2 double mutants have curves only in the D/V plane and almost completely lack side-to-side curves is noteworthy. Does the urp1-/-urp2-/- mutant uncouple two systems for posture? If this separate a DV from side-to-side postural control system, that would be very interesting. It is particularly important to describe how penetrant the phenotype is and how many times it was observed. See 9 minor comments. It would help the reader if the authors explicitly described the features that they see in the cfap298 mutant that constitute lateral curves and that are lacking in urp1/2 (e.g. in figure 4E).

We plan to expand the figure and analysis describing D/V curves and M/L curves. While our first draft included only cfap298 and urp1-∆P;urp2-∆P mutants, our next draft will also incorporate uts2r3 and pkd2l1 mutants. We have already scanned cohorts of all mutant fish, and so the remaining work to render and quantify the degree of lateral curvature will not take long. This will allow us to conclusively determine whether these different mutations indeed uncouple two systems controlling posture in different directions. As the reviewer requests, we will include all fish analyzed in either main or supplementary figures, include numbers in figure legends, and quantify the penetrance of M/L and D/V curves.

We have also generated cfap298;urp1-∆P;urp2-∆P triple mutants and are currently scanning them to reveal skeletal form. Preliminary data suggests triple mutants have three-dimensional curves but D/V curves are more severe in triple mutants than in cfap298 mutants alone. This makes sense if Urp1/Urp2 are important for controlling D/V spinal shape and, as our qPCR shows, Urp1/Urp2 are downregulated but not lost completely in cfap298 mutants. It also furthers the notion that cilia motility controls D/V and M/L curves by separable mechanisms. * *

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Reviewer 2

Need to show that the CRISPANT targeting was effective for mutagenesis at each loci screened in the work presented in Figure 1E. In Figure 1E, we presented the phenotypes of crispant embryos (i.e. embryos injected with four gRNAs targeting a specific gene alongside relatively high doses of Cas9 protein; see schematic in Figure 1G). In positive controls (cfap298 and sspo), crispants showed the expected phenotype in all cases (Figure 1E and see Figure 1H for quantitation). As for germline mutants, urp1 and urp2 crispants showed no early axial phenotypes (Figure 1E and 1H). As such, the reviewer requests that we perform molecular assays to determine whether mutagenesis was successful in these embryos. To do so, we will perform either T7 assays or next-generation/Sanger sequencing of mutated loci. This will allow us to determine and quantify the effectiveness of our mutagenesis. Results will be shared in a new supplementary figure. These assays are straightforward and we expect they will not take very long to complete. Indeed, we have performed these assays previously for other genes (e.g. Grimes et al., 2019 and several unpublished genes). We have achieved high levels of mutagenesis in all cases, making us very confident that we will achieve similarly high levels of mutagenesis in this case.

Reviewer 3

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The addition of the F0 crispant experiment to show that the pro-peptide of urp1/2 does not have a function and is responsible for the difference between the observed morpholino and the crispr phenotype was important. However, since no phenotype was observed in crispants it is important to add evidence of induced cuts for all guide RNAs used in the crispant experiment. These control experiments might have been done already. If not, they can easily be done in a short period of time by performance of T7 assays on injected fish and would not require additional reagents. This is the same point raised by reviewer 2 and so we refer to the response above. In summary, we agree with the reviewer and we are currently performing these suggested experiments which are straightforward and working well.

The authors claim that there were no structural defects observed in urp1/2 double mutants. However, the hemal arch in figure 3 E seems to be deformed. This could be normal variance or a phenotype. This can be addressed by simple reinspection of the scans.

We believe there are no major vertebral structural defects that could be attributed to causing the spinal curves because vertebrae are well-formed in mutants and we see no defects in the initial patterning of vertebrae in our calcein experiments. However, since urp1-∆P;urp2-∆P and uts2r3 mutant spines are curved, the vertebrae are a little misshapen. We plan two revisions, one textual and one analytical.

First, we will make clear in our textual edits that some vertebrae are slightly misshapen, as occurs in non-congenital forms of human spinal curve disease (in congenital forms, the shape defects are more striking and likely causative in the curvature). We agree with the reviewer that stating that there is a lack of vertebral structural defects lacked nuance, so we will expand on this in our next draft.

Second, we will quantify vertebral shapes in spinal curve mutants and report these data in our next draft. After reinspection of the scans, as the reviewer suggested, we believe it would be informative for our readers to see quantitation of vertebral shape. We expect these data to more rigorously back up our statements about ‘minor structural differences’ of vertebrae between uncurved and curved individuals. We have already begun this work, and completing it should only take a few more weeks. As an example, we have measured the shape of centra by calculating aspect ratios in wild-type and urp1-∆P;urp2-∆P double mutants in curved regions of the spine:

These preliminary data already make clear that there are indeed subtle morphological differences between vertebrae in mutants and wild-type, as occurs in human spinal curve deformities. We will present completed versions of these data (several parameters that describe vertebral shape) in our next draft and provide comments about whether such changes could be causative in spinal curve etiology as occurs in congenital-type scoliosis.

Minor points

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Reviewer 1

Supplementary FigS3B How to measure the Cobb Angle is unclear. Why is the first curve not counted? I count 3 curves. First a ventral displacement, then a dorsal to ventral return, then a sharp flex before the tail. How to measure Cobb angle might be easier to explain if the figure is expanded into steps. Identify the apical vertebra, then showing how the lines are drawn parallel to those vertebrae, then where the measured angle forms between the lines perpendicular to the drawn parallel lines.

We will more thoroughly explain how Cobb angle is measured in our next draft.

5a. I think we (zebrafish biologists) need be explicit about what we mean with "without vertebral defects." What do we count as defects? Vertebrae can be fused, bent, shortened or the growing edges can be slanted. In Figure 3E, and movie7, it is clear that the highlighted mutant vertebrae are shorter than WT. The growing ends of normal vertebra are perpendicular to the long axis of the vertebra. In the mutants the ends are slanted. Please define in the text what you consider a relevant vertebral defect, because these vertebrae have defects. Or are you only considering the calcein stained centra at 10dpf?

We strongly agree with the reviewer. As described more thoroughly above in response to Major Comment – Reviewer 3, we plan both textual edits and new quantitation of vertebral shape to address this comment. Our quantitation indeed shows some vertebrae are shorter in mutants as the reviewer noticed. We also plan a new paragraph in the Discussion section which will speak about the issue of what zebrafish biologists might mean by “without vertebral defects”.

5b. Do you want to base your patterning conclusion on primarily the calcein data as these are closer to the notochord patterning time window. Please anchor this conclusion to a specific time or standard length e.g. 10dpf/5.6mm.

When we edit our descriptions of vertebral defects, and include new quantitative data on the shape of vertebrae, we will be clear that the vertebrae are slightly structurally malformed. In addition, when we speak of the calcein data, we will anchor those conclusions to the specific timepoint best studied by this method, as the reviewer suggests.

"At 30 dpf... several mutants exhibited a significant curve in the pre-caudal vertebrae, in addition to a caudal curve (Fig. 3D and S3C). Since pre-caudal curves were rare in mutants at 3-months, this suggested that curve location is dynamic".The frequency of this observation is important. Does it effect all or a fraction of mutants? Can you provide some numbers to anchor these observations? Maybe fractions e.g.. 3 of 4 fish had precaudal curves at 30pdf, and 0 of 10 fish had precaudal curves by 3 mpf?

In our next draft, we will provide numbers of fish examined at 30 dpf and also show graphical summaries of curve position (as we did for younger fish). Last, all scans will be included in a new supplementary figure.

The description of the pkd2l1 mutant, instead of terming it kyphosis can you tell the reader the vertebra number at the peak of the curve. The authors say the pkd2l1 mutant is highly distinct from urp1/urp2-/-, but the reader needs to hear exactly what is distinct. For example, does this mutant have both lateral and D/V curves?

We have now scanned several pkd2l1 mutant fish and we will include images of pkd2l1 mutants at two different timepoints together with quantitation of curve position. Our results agreed with those previously published for this mutant line (Sternberg et al., 2018) but we believe it is important for our readers to see side-by-side images and quantitation so they can see the distinctions.

At 3-months of age, pkd2l1 mutants essentially appear wild-type but by around 12-months they have developed a D/V curve in the pre-caudal vertebrae. They do not exhibit M/L curves; we will quantify this and include these data in our Figure about M/L deviation.

We called the phenotype displayed by pkd2l1 mutants “kyphosis” to be in line with a previous publication describing these mutants (Sternberg et al., 2018). We will add new wording in the Discussion about whether or not zebrafish can truly model kyphosis and lordosis (see response to Reviewer 1 major comment above), and we make clear in our Results that the phenotype has “been argued to model kyphosis (Sternberg et al., 2018)” rather than “is kyphosis”.

It is intriguing that pkd2l1 mutants do not exhibit any curves until much later in life than urp1-∆P;urp2-∆P and uts2r3mutants. Inspired by this finding, we aged urp1-∆P and urp2-∆P single mutants and found that they go on to develop D/V curves by 12-months i.e.

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• *3-months 12-months Position of curve

urp1-∆P no curves mild D/V curves Mostly caudal

urp2-∆P mild D/V curves intermediate D/V curves Mostly caudal

urp1-∆P;urp2-∆P severe D/V curves severe D/V curves Mostly caudal

uts2r3 severe D/V curves severe D/V curves Mostly caudal

cfap298 severe 3D curves severe 3D curves Caudal and pre-caudal

pkd2l1 no curves mild D/V curves Mostly pre-caudal

Phenotypes in urp1-∆P and urp2-∆P single mutants upon aging shows: 1) Urp1 and Urp2 are not entirely redundant in long-term spine maintenance and 2) proper Urp1/Urp2 dose is essential. We will include these new data in our next draft.

Does uts2r3-/- have no /minimal side-to-side curves like urp1/urp2-/-?

This is an interesting question. To address it, we will add images of uts2r3 mutant spines from the dorsal aspect and include them with our new quantitation of lateral curvature. To sum, the reviewer’s suggestion is correct – there are minimal side-to-side curves in uts2r3 mutants.

One finding that deserves more discussion is the observation that urp1/urp2 double mutants have almost no side-to-side defects and all the obvious bends are in the D/V plane. Does this uncouple two systems for posture? Please consider the following paper. It shows a proprioception system that maintains normal side-to-side posture. A spinal organ of proprioception for integrated motor action feedback. Picton LD, Bertuzzi M, Pallucchi I, Fontanel P, Dahlberg E, Björnfors ER, Iacoviello F, Shearing PR, El Manira A. Neuron. 2021 Apr 7;109(7):1188-1201.e7. doi: 10.1016/j.neuron.2021.01.018. Epub 2021 Feb 11. PMID: 33577748

Thank you for pointing out this manuscript. We will include it in our expanded Discussion.

Reviewer 2

Fig 3F: might be improved by making the images black and white and possibly inverted. It is not easy to clearly see the vertebrae as is. * *

Thanks for the suggestion, we will make this change.

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

Minor points

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Reviewer 1

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Figure 1D legend says urp1 is expressed in dorsal while urp2 is express in all CSF-cNeurons, but the image for urp1 shows only ventral cells in WT, while the image for urp2 shows the same cells ...and more dorsal cells. Please replace image with one that matches the text. Apologies for this, we have now corrected it. The image was correct but we accidentally wrote “dorsal” instead of “ventral” when describing the CSF-cN sub-population harboring urp1 transcripts.

In Figure 2H, the position of curve apex graphic, how many fish were examined? In 2f it looks like n=8 and n=9. Can this info be added to the figure?

We have now included the number examined in the legend.

I did not find legends for the movies. The first call to the movies calls movies 1-3 without explaining what each shows. The labels on the downloaded files are not informative.

Apologies for forgetting to submit these. We have now added informative Movie legends.

Reviewer 3

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It would be helpful to the reader to add a little more information on urp1 and upr2 proteins and their processing to make it clear while only the 3' region of the protein was targeted to induce mutations. We have incorporated textual edits to make this more clear. We now state in the second sentence of the Results section:

Urp1 and Urp2 are encoded by 5-exon genes with the final exon coding for the 10-amino acid peptides that are released by cleavage from the pro-domain (Fig. 1A).

Together with Fig. 1A and Supplementary Fig. 1, we hope it is now clear to readers how Urp1 and Urp2 are generated from a 5-exon gene encoding the pro-domain and the peptide, which are separated by cleavage.

It would also be helpful to the reader to have a schematic indicating the guide target sites (they could be added to figure S1 C + D) in the protein to be able to interpret the result more easily.

Done!

Figure 5: Addition of a square to H would help understand were the pictures in D-F were taken.

Done!

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

N/A. We are performing all experiments/analyses requested by reviewers.

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Reply to the reviewers

Manuscript number: RC-2022-01574

Corresponding author(s): Casey, Greene

1. General Statements [optional] We thank the reviewers for their thorough feedback. We have addressed all the points raised, revised the manuscript accordingly, and explained our changes below. To aid readability, the reviewers’ comments have been converted to italics, and our responses have been bolded.

Point-by-point description of the revisions

Reviewer #1 (Evidence, reproducibility and clarity (Required)):

The authors systematically evaluate the performance of linear and non-linear ML methods for making predictions from gene expression data. The results are interesting and timely, and the experiments are well designed.

I have a few minor comments:

- It was hard for me to understand Figure 1B. I think a figure like this would be very helpful however. What do the numbers represent? If sample ID, then I am not sure why x-axis label is also "samples"

- For analysis of GTEx data, not sure what "studywise splitting" would mean, since the GTEx dataset is one study? Do you leave out the same individuals from all tissues for evaluation?

We thank the reviewer for their input on these two points. To make Figure 1B clearer and to elaborate on our stratified splitting methods, we have amended its description to “We stratify the samples into cross-validation folds based on their study (in Recount3) or donor (in GTEx). We also evaluate the effects of sample-wise splitting and pretraining (B).”

- I found the sample size on x-axis of Fig 2a confusing. If I understand correctly, GTEx has a total of ~1000 subjects. So in some sense, effective sample size can not be bigger than 1000. If you are counting subjects x tissue as sample, then it can be misleading in terms of the effective sample size.

We thank the reviewer for this point. To incorporate it into the manuscript, we’ve added the following text to the description of Fig. 2: “It is worth noting that "Sample Count" in these figures refers to the total number of RNA-seq samples, some of which share donors. As a result, the effective sample size may be lower than the sample count. “

- Would be interesting to assess out-of-sample generalizability of linear and non-linear models. Have you tried training on GTEx and predicting on Recount3 or vice versa?

This question intrigued us. We reran the tissue prediction experiments from the manuscript on a subset of the GTEx and Recount3 datasets in which we performed an intersection over tissues and genes. We found that in the out-of-sample domain the logistic regression model and the three layer neural network performed similarly, while the five layer net generally had a lower accuracy despite having similar accuracy in the training domain. We also found (consistent with our results in the paper) that GTEx predictions are an easier task than their Recount counterparts. Below are plots demonstrating these findings:

[These plots appear in the PDF but do not appear to work in the ReviewCommons Form].

Reviewer #1 (Significance (Required)):

Important and timely study, evaluating linear vs non-linear methods for predicting phenotype from gene expression datasets.

We appreciate the reviewer’s positive comments on the timeliness of our manuscript.

Reviewer #2 (Evidence, reproducibility and clarity (Required)):

Summary

The authors want to assess the presence of non-linear signal in gene expression values in the task of tissue and sex classification. They use logisitic regression classifiers and two types of neural networks, with 3 and 5 layers, and assess classification performance on two large expression datasets from Recount3 and GTEX and three simulated datasets.

The authors carefully construct their learning setup in such a way that one can reason about the removal of linear signal from the expression features. The interesting conclusion is, that although the linear approach works well on both datasets, and sometimes even better than the more complex models. The authors convingly show, that there is a significant non-linearity in the gene expression data. However, just because it is "there" does not imply that any non-linear methods performs better.

Major comments:

- Are the key conclusions convincing?

The authors did a good job in showing, that there is non-linear signal in gene expression features for the classification problems studied.

We thank the reviewer for their positive feedback.

- Should the authors qualify some of their claims as preliminary or speculative, or

remove them altogether?

The overall claims of the authors are justified, the discussion may be improved.

We appreciate the reviewer’s support for our overall claims and we have adjusted the manuscript as noted point by point below.

- Would additional experiments be essential to support the claims of the paper?

No, additional experiments are not essential. But the authors did not compare to other non-linear methods such as SVM or knn-classifiers in the resulst or conclusion section. It is unlikely that the main conclusion would change if those methods were tried. But it is possible that other "simpler" non-linear methods, such as knn for example, are able to outperform the logistic regression classifier on the GTEX and Recount3 data set. Thus, the authors should at least mention this as part of the conclusion and could extend their discussion on the implications of their study concerning other tasks or models.

We agree that there should be more discussion of other models in the conclusion section. We have updated the fifth paragraph of the conclusion accordingly:

“We are also unable to make claims about all problem domains or model classes. There are many potential transcriptomic prediction tasks and many datasets to perform them on. While we show that non-linear signal is not always helpful in tissue or sex prediction, and others have shown the same for various disease prediction tasks, there may be problems where non-linear signal is more important. It is also possible that other classes of models, be they simpler nonlinear models or different neural network topologies are more capable of taking advantage of the nonlinear signal present in the data.”

- Are the suggested experiments realistic in terms of time and resources?

Not applicable.

- Are the data and the methods presented in such a way that they can be reproduced?

There is a separate github repo which has the code to reproduce the analyses. This is good. However, would be nice to explain in more detail in the manuscript how the limma function was used for removing the linear signal, as they mention the "removeBatchEffect" function was used, but it would be good to tell the reader how that works, as this is their way for assessing the effect of linear-signal removal. Are there any limitations for the assessment of signal removal in this way?

We thank the reviewer for their input, and have updated the model training section on signal removal to read: “We also used Limma[24] to remove linear signal associated with tissues in the data. We ran the ‘removeBatchEffect’ function on the training and validation sets separately, using the tissue labels as batch labels. This function fits a linear model that learns to predict the training data from the batch labels, and uses that model to regress out the linear signal within the training data that is predictive of the batch labels.”

We have also elaborated on the limitations of signal removal by updating the sentence “This experiment supported our decision to perform signal removal on the training and validation sets separately, as removing the linear signal in the full dataset induced predictive signal (supp. fig. 6)” to read “This experiment supported our decision to perform signal removal on the training and validation sets separately. One potential failure state when using the signal removal method would be if it induced new signal as it removed the old. This state can be seen when removing the linear signal in the full dataset(supp. fig. 6).”

- Are the experiments adequately replicated and statistical analysis adequate?

Yes

Minor comments:

- Specific experimental issues that are easily addressable.

no

- Are prior studies referenced appropriately?

Yes

- Are the text and figures clear and accurate?

*Also, they conducted 3 different experiments in Figure 3. It would be useful to separate the figure into 3) A, 3) B, and 3) C and link that specifically in the text. Figure 4 is an extended version of Figure 2, just with the additional results of the signal removed performances. *

We appreciate the feedback. To make the figure and the text more clear, we have added A, B, and C subheadings to figure 3, and updated the subfigure’s references within the text accordingly.

First, the pairwise results in 4B are hard to read as the differences in colors and line type are difficult to see as some lines are short. Second, we did not find it helpful to reproduce the full signal approach in Figure 4. We would suggest to make Figure 4 as Figure 2, and simply only talk about the Full signal mode in the beginning, how it is in the text.

We agree. We have made Figure 4 our new Figure 2 and updated the references in the text.

Further, it would be nice to give better names in the legends of these plots. Pytorch_lr is not a nice name.

We thank the reviewer for pointing this out. We have updated the names in the legends to be “Five Layer Network”, “Three Layer Network”, and “Logistic Regression”

- Do you have suggestions that would help the authors improve the presentation of

their data and conclusions?

As the Recount3 dataset is different in quality and complexity it would be reasonable to show the results of the binary classifcation also in the main paper. In particular, as this behaves different to the GTEX binary classification.

We have now moved the Recount binary classification figure from the supplement to join the GTEx binary classification data as the new figure 4.

-The title is somewhat unprecise. It may induce the impression that the paper is about expression-prediction, although that is not the case. Further, in the abstract they don't mention what prediction problem they solve and that these are classification problems. After reading the paper it is clear why the authors choose that, but we are suggesting an alternative title that the authors may consider:

The effect of nonlinear signal in classification problems using gene expression values

We agree with the reviewer’s comment and have updated our title to “The effect of non-linear signal in classification problems using gene expression”

Further, they should give more details on the problem learned in the abstract.

We thank the reviewer for their feedback, and have added details to the abstract about the problem domains. The relevant sentence now reads “We verified the presence of non-linear signal when predicting tissue and metadata sex labels from expression data by removing the predictive linear signal with Limma, and showed the removal ablated the performance of linear methods but not non-linear ones.”

*-In addition, the conclusion section, which may be title as Disucssion and Conclusion, could contain additional points concerning the topology and training of the neural networks. *

We have updated the heading of the final section to Discussion and Conclusion. To expand on the potential drawbacks of our neural network topologies, we have also updated the limitation portion of Discussion and Conclusion to read “We are also unable to make claims about all problem domains or model classes. There are many potential transcriptomic prediction tasks and many datasets to perform them on. While we show that non-linear signal is not always helpful in tissue or sex prediction, and others have shown the same for various disease prediction tasks, there may be problems where non-linear signal is more important. It is also possible that other classes of models, be they simpler nonlinear models or different neural network topologies are more capable of taking advantage of the nonlinear signal present in the data.”

Obviously, it is possible that other simpler or more complex neural networks have a better performance on the GTEX and Recount3 data sets compared to logistic regression. In fact, the results from Figure4 suggest that, as there is clearly useful non-linear signal in those datasets for the classification problems studied. However, optimizing a non-linear model is inherently more complex and time-consuming, and thus may not be done thoroughly in previously published papers. Compared to a linear model that is easier and faster to optimize, this may be one reason why studies find that, despite non-linear signal, the linear model performs better. Other factors such as the samples size, which the authors already mention, of course also plays a big role, and if hundreds of thousands of datasets would be there , e.g. from single cell measurements, non-linear methods may have a better chance of outcompeting linear models.

We agree, which is why we consider the signal removal experiment to be so important. By demonstrating that the non-linear methods we used were in fact learning non-linear signal we were able to show that there was something that non-linear models were able to learn that logistic regression was unable to. That is to say that while the presence of non-linearity in the decision boundary is necessary for non-linear models to outperform linear ones, it is not by itself sufficient. Perhaps with more data or a different model non-linear methods would perform better, but there is certainly a class of models and problems where logistic regression is preferable.

Reviewer #2 (Significance (Required)):

The submitted manuscript adds to the discussion of the necessity of non-linear models when solving classification problems using gene expression data. The significance is mostly technically, as a comparison of logistic regression and two neural network topologies that are being compared on two large expression datasets. However, there is also a conceptual part of the contribution, which is with regards to the implications of their experiments.

Interested audience would be computer scientists and bioinformaticians or others, that are involved in creating or interpreting these or similar prediction models.

Our field of expertise is in the creation of machine learning models using different types of OMICs data. All aspects of the work could be assessed.

Reviewer #3 (Evidence, reproducibility and clarity (Required)):

In this manuscript, the authors discuss an interesting problem regarding the comparative performance of linear and non-linear machine learning models. The main conclusion is that logistic regression (linear model) and neural networks (non-linear model) have comparable performance if the data contain both linear and non-linear relations between the features (X) and the prediction target (Y), however, if the linear component in the X-Y relation is removed (e.g. regressed out) the neural networks will outperform logistic regression. This conclusion implies that linear models such as logistic regression mainly relies on the linearity in the X-Y relation.

However, whether X-Y relation has a linear component and whether the data (e.g. for different Y classes) are linearly separable are two different questions. For example, consider a data generating mechanism, y=x^2+x and label the data points using two classes (y1). Clearly, the data is linearly separable, and any machine learning algorithm should perform very well on this problem. Now remove the linear component form the X-Y relation and use y=x^2 to generate the data. The data is still linearly separable, and the performance of logistic regression should not be affected.

We agree that there is a difference between optimal linear decision boundaries and linear relationships between elements in the training data. Our use of the term “relationship” in place of “decision boundary” was imprecise. To make this more clear, we have made the following changes:

Introduction:

“Unlike purely linear models such as logistic regression, non-linear models should learn more sophisticated representations of the relationships between expression and phenotype.” -> “Unlike purely linear models such as logistic regression, non-linear models can learn non-linear decision boundaries to differentiate phenotypes.”

“However, upon removing the linear signals relating the phenotype to gene expression we find non-linear signal in the data even when the linear models outperform the non-linear ones.” -> “However, when we remove any linear separability from the data, we find non-linear models are still able to make useful predictions even when the linear models previously outperformed the nonlinear ones.”

Discussion and conclusion:

We removed the following paragraph: “Given that non-linear signal is present in our problem domains, why doesn’t that signal allow non-linear models to make better predictions? Perhaps the signal is simply drowned out. Recent work has shown that only a fraction of a percent of gene-gene relationships have strong non-linear correlation despite a weak linear one [23].”

The point is that the performance of linear models is mainly dependent on whether the data are linearly separable instead of the linearity in X-Y relation as the manuscript suggests.

We agree that this is the key point and appreciate the reviewer for helping us to more carefully hone the language to convey this point.

Reviewer #3 (Significance (Required)):

The performance comparison between linear and non-linear machine learning models is important.

We appreciate the reviewer’s recognition of the significance of the work.

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Reply to the reviewers

Reply to the Reviewers

We thank the reviewers for their excellent suggestions and constructive comments. We now added new data on PE15/PPE20 binding to Ca2+, the PDIM status of mutant strains, additional controls, added to the discussion, added detail to the Methods, and provide all RNA-seq data. Please see replies to the comments in detail below:

Reviewer 1:

Major points

1. Cellular localization:
2. “The authors do not describe the cellular fractionation method…”, “The authors show some Western blot data in Fig. S3, though the legend is superficial (abbreviations not explained) and the controls with markers for cellular localization appear to be lacking”. “Further, the authors do not prove that FLAG-tagged PE20 is functional.”

We included a description of the fractionation method in Materials and Methods (lines 475-485). We also added detail to the legend of Fig. 4A to explain the abbreviations and controls used. The same cell fractions were used in Fig. 4A and Fig. S3A, as mentioned in the Figure S3 legend (“The same cell fractions as in Fig. 4A were used, see controls therein”). We know that the FLAG-tagged PPE20 is functional because the strain used in this experiment is the same we used for genetic complementation experiments in which FLAG-tagged PPE20 functionally complements ppe20 deletion in all three assays (ATP consumption, biofilm, Ca2+ influx, Fig.4 B,C,D,G).

• “The authors should extend discussion part of the manuscript. Several proteomic studies.” “Did authors analyze culture filtrate fraction by Western?

We thank the reviewer for the references and extended the Discussion to include results from existing proteomic studies on PE15/PPE20 (lines 229-234). We did not test for PE15/PPE20 in culture filtrate, and previous proteomic results are contradictory. Several PE/PPE proteins, including PE15/PPE20 have been detected in the cell wall and in the CFP, but not consistently. The functional significance of this dual localization is unclear.

1. Is PE15/PPE20 a channel?

2. “PPE20 purified alone from the cytosol of E. coli?”

We did not purify either protein by itself. As the reviewer correctly notes, PE/PPE proteins are refractory to individual purification. We clarified that we purified and used the complex for experiments even if only PPE20 is shown, as in Figure 3C,D, and E (Lines 124-127). See also Methods line 382 ff.

• “…a positive control of a mutant that is indeed deficient in Mg2+ import (and thus showing a phenotype) is lacking.”

Lacking a specific Mg2+ import mutant, and because it is a relatively minor point, we removed the statements about selectivity.

1. Thermal melting assay

2. It is surprising to see that the thermal melting assays was done for PPE20 and PE15 as separately purified proteins.

We co-purified PE15 and PPE20 for all biochemical experiments. We clarified that point (see also point 2 above).

• “the thermal melting assay only seemed to give some results for PPE20 alone, and not for PE15”

PE15 did not produce interpretable results in this assay, as mentioned in line 144. We clarified in the Fig. 3 legend that the complex was used although only PPE20 is detected by Western blot and shown in Figure 3C.

• “…the results are counter-intuitive… How can the authors be sure that the presence of Ca2+ does not simply lead to more protein precipitation (via rather unspecific interactions) at elevated temperatures? Some positive controls with bona fide calcium binding protein in the same thermal melting setup would have helped to clarify this.”

The effect of Ca2+ on PPE20 is somewhat counterintuitive, although not unprecedented. Proteins can be stabilized or destabilized by ligand binding, and a recent proteome-wide study on the basis of thermal shift analysis showed that ~17% of proteins were destabilized by ligand (ATP). For a channel in particular, ligand binding might be expected to be coupled to protein relaxation in the process of channel opening, which could well translate to lower thermal stability. We added the positive control showing the behavior of a known Ca2+ binding protein (new Fig. S2A). In addition, we included a negative control showing that Ca2+ does not generally increase protein denaturation (Fig. S2B). We think that this control addresses the reviewer’s concern more directly.

• If the authors want to stick to their claims regarding Ca2+ binding to PE15/PPE20, they have to perform additional assays (e.g. equilibrium dialysis or ITC) with the entire PE15/PPE20 complex. Further, they have to show that PE15/PPE20 forms a proper oligomeric protein that is membrane bound and reasonably behaved on size exclusion chromatography, when expressed in and purified from E. coli.

Detecting Ca2+ binding to proteins is not trivial, and we thank the reviewer for suggesting equilibrium dialysis as another, orthogonal assay. We now show an equilibrium dialysis experiment that confirms Ca2+ binding by the PE15/PPE20 complex. Please see the new Fig. 3F. and G. and lines 146-152 (Results) and 429-443 (Methods).

The PE/PPE proteins are generally difficult to express and purify recombinantly, likely due to the typically large unstructured regions. Also, the yield of PE15/PPE20 when expressed in E. coli was very low so that we were not able to detect the complex by SEC. However, data in Fig. 3 conclusively show that PE15 and PPE20 bind.

1. RNA-seq data

2. The authors should include a table with all other identified genes that are potentially involved in calcium homeostasis

We provided all other significant differentially expressed genes in the new Table S1.

Minor points:

1. “what is the binding affinity of the Ca sensor?”

We added the Ca2+ binding affinity of Twitch-2B (KD: 200nM) in line 176.

1. Figure 4D: “one would expect a drop in FRET signal after EGTA addition… Can the authors explain?”

We do see a clear drop in FRET signal after EGTA addition, in particular in 7H9 medium (black versus red line, Fig. 5B). Given the high affinity of Twitch-2B for Ca2+ (200nM), however, it is not surprising that the drop is not more pronounced, as intracellular Ca2+ is expected to be tightly bound to Twitch.

1. The experiments showing outer membrane localization of PE15/PPE20 are very important, but results of these experiments (western-blot and FRET) are shown in supplementary figures. They should be transferred/integrated into the main Figures.

We agree and moved Figure S3A to the main Figures as Figure 4A.

1. Line 166: the authors claim that the assay did not work in 7H9 due to low Ca2+ concentration in this medium. Why did the authors not just add a bit more calcium to show whether this claim holds true?

7H9 is not a suitable medium for these experiments because the baseline Ca2+ concentration is too high, not too low (see Fig. 5B, grey versus black line). Adding more Ca2+ to 7H9 medium resulted in precipitation, probably due to its interaction with phosphates. Our use of “low” in this context was confusing, we changed the wording of this sentence (line 180-181).

1. Line 183: more detailed description on cellular fractionation and subsequent anti-FLAG Western needed here.

We added more detail in the Materials section (lines 475 ff).

Reviewer 2:

• A major concern regarding the importance of the data: there are considerable technical challenges in generating Ca2+ depleted media. This is clear in that M. tuberculosis seems to be unaffected by Ca2+ in the medium - similar growth seems in Ca2+-free media to media with up to 10mM Ca2+ (Fig. S1). This raises a concern about the physiological relevance of the data (mammalian cells have intracellular Ca2+ of 0.01-0.1mM, extracellular free Ca2+ is around 1mM).

If we correctly understand this comment, the reviewer is unconvinced that we fully and reproducibly depleted Ca2+ from medium based on a lack of an effect of Ca2+ on in vitro growth. We tested for baseline Ca2+ levels and depletion in media by inductively coupled plasma optical emission spectrometry and added these data showing precise quantitation of Ca2+ in medium (see new Fig. S1B).

• The role of PE15/PPE20 in Ca2+ acquisition may be clearer if the authors ensure that the PDIM layer is intact. Specifically, there is a technical issue: The authors use Tween80 as a detergent. Tween-80 partially strips the outer cell wall of M. tuberculosis resulting in shedding of PDIM and PE/PPE proteins. Tyloxapol is a somewhat milder detergent. Some of the experiments would possibly show clearer phenotypes by use of Tyloxapol.

We share the concern about PDIM, as PDIM loss is common in in vitro culture. We analyzed the total lipids by thin layer chromatography and confirmed the presence of PDIM in all three strains (Fig S3C, lines 198-201). We repeated experiments with Tyloxapol and did not see differences to Tween-80. We nonetheless now show the Tyloxapol data (Fig 5D).

• The authors could increase the impact of their work be exploring the role of PE15/PPE20 during pathogenesis of resting versus activated bone marrow macrophages where Ca2+ fluxes of the host cell play a role in host responses.

We agree. In vivo or macrophage experiments are a logical next step to fully characterize the function of PE15/PPE20, but we think it is beyond the scope of this manuscript. The main contribution of this paper is the identification of channel function of a PE/PPE protein pair that extends the novel channel paradigm for these proteins. These data support that transport might indeed be a shared function of the entire PE/PPE family with 169 members.

Minor:

• The authors should consider citing Sharma et al (2021)

We cited the paper.

• Are there Ca2+ binding motifs in PPE20?

We did not detect canonical Ca2+ binding motifs in PPE20.

• RNAseq data may need to be deposited in a public database.

RNA-seq data have been deposited to NCBI - GEO accession GSE214266

Link: https://urldefense.com/v3/https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE214266;!!NuzbfyPwt6ZyPHQ!tCf4MS_HRKJFn6qV2orkDAkXTWvx9IIU11fAV7TguYE2ietoMBpBgRC7rvfnM9bsoiVdIvDBUHdPmHZliDP2o5sRZR2ziK4$

Token: cvmhakcgbpmbfuz

• In its current state, the work is somewhat incremental

The function of the large PE/PPE protein family of Mtb has been one of the most longstanding and perplexing puzzles in Mtb biology. For more than 20 years, speculation about their potential role, for example in antigenic variation, abounded but no conclusive evidence for this or another shared function emerged. A recent landmark paper then conclusively showed that a subset of the PE/PPE proteins function as nutrient channels (Wang et al., Science 2020). However, whether transporter function is a general function of the family of 169 PE/PPE proteins remains untested. Our PE/PPE pair is associated with a different type VII secretion system (Esx-3) and belongs to a different subfamily than the previous examples, suggesting a shared function across families and perhaps even all of these proteins. Given the intense interest and many false leads that have plagued the identification of PE/PPE function in the last 20 years, the difficulty of working with them biochemically, as well as the almost complete absence of understanding of Ca2+ homeostasis in Mtb, we do not consider our work incremental.

Reviewer 3

• My only slight concern is the meaning attached to the "biofilm" assays. It is never very clear to me that this is anything more than formation of a surface pellicle and general hydrophobicity of the mycobacterial cells.

We fully agree that Mtb biofilms remain poorly defined. However, the term biofilm as used in our study has already found its way into the literature and we would rather not cause confusion by calling the same phenomenon by a different name. Whatever the term used, we do not suggest any other relevance other than it being a Ca2+-dependent phenotype that serves as one of several tests to parse PE15/PPE20’s role in Ca2+ homeostasis.

Cross-consultation comments:

• We agree with the concerns of reviewer#2 that the role of PDIM and use of detergent should be looked at more closely.

We tested the roles of PDIM and detergent, see reviewer 2.

• Likewise, the paper would strongly benefit from some further insights into the potential physiological role of PPE20/PE15 in calcium homeostasis.

We show PE15/PPE20 function in the transport of Ca2+ and the first Ca2+-related cellular phenotypes in Mtb. Testing the role of the complex in an infection model is outside of the scope of this manuscript and mouse infection experiments would take many months and would likely be intractable because of the expected extensive redundancy among the 169 PE/PPE proteins.

we may not hope to find its solution within the range of opportunities open to any one individual. The very structure of opportunities has collapsed. Both the correct statement of the problem and the range of possible solutions require us to consider the economic and political institutions of the society, and not merely the personal situation and character of a scatter of individuals.16

1. C. Wright Mills, The Sociological Imagination (New York: Oxford University Press, 1959), p. 9.

I love this quote and it's interesting food for thought.

Framing problems from the perspectives of a single individual versus a majority of people can be a powerful tool.

The idea of the "welfare queen" was possibly too powerful because it singled out an imaginary individual rather than focusing on millions of people with a variety of backgrounds and diversity. Compare this with the fundraisers for impoverished children in Sally Stuther's Christian Children's Fund (aka ChildFund) which, while they show thousands of people in trouble, quite often focus on one individual child. This helps to personalize the plea and the charity actually assigned each donor a particular child they were helping out.

How might this set up be used in reverse to change the perspective and opinions of those who think the "welfare queen" is a real thing instead of a problematic trope?

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Reply to the reviewers

Reviewer #1 (Evidence, reproducibility and clarity (Required)):

This paper demonstrates a link between oxidative stress, lipid biosynthesis, and targeted histone acetylation in fission yeast. In mutant cells with defects in lipid synthesis (cbf11, mga2 lacking transcription factors, and cut6 lacking acetyl-CoA carboxylase), transcripts of a number of genes implicated in resistance to oxidative stress are increased. This is associated with higher levels of H3K9 acetylation and increased tolerance to oxidative stress. These effects are mediated through Sty1, a stress-activated MAP kinase and the transcription factor Atf1.

It is also shown that H3K9 acetylation levels in the promoter region and just downstream of the transcriptional start site are increased in cbf11 mutants (Fig. 5A).

By mutational analysis, the authors implicate the acetyl transferases Mst1 and Gcn5 in this transcriptional effect. Other related acetyl transferases, Hat1, Elp3, Mst2, Rtt109 have been ruled out as main contributors to the dysregulation in unstressed cbf11 mutants. That specific acetyl transferases have been shown to be required is a strength of the investigation.

Major comments:

The hypothesis is put forward in the manuscript that altered acetyl-CoA levels in cbf1 mutants would underlie the dysregulation of genes induced by oxidative stress. Histone acetyl transferases compete for acetyl-CoA with lipid biosynthesis, and so with increased demand for acetyl-CoA underacetylation in the concerned promoters would result - specifically at H3K9. These results do not directly support the hypothesis, on the other hand they are not sufficient to rule it out.

Actually, we view this phenomenon the other way round: We primarily focus on exponentially growing cells, which have substantial demand for fatty acid (FA) production (= high acetyl-CoA consumption). So the level of promoter histone acetylation under these conditions is our baseline, or “normal” state. When FA production is decreased (cbf11 or cut6 mutants; inhibition of FA synthase by cerulenin…), stress gene promoters get *hyper*acetylated. We do not have any data on (or claims about) histone underacetylation compared to the baseline. Nevertheless, we now show that overexpression of Cut6/ACC results in decreased resistance to oxidative stress (Fig. 5C), which is compatible with the notion that increased acetyl-CoA consumption would result in insufficient histone acetylation at stress gene promoters during stress.

Acetyl-CoA levels were measured only in undisturbed cells, and the possibility remains that under oxidative stress there would be changes in acetyl-CoA pools that could explain this apparent contradiction - why did not the authors examine that?

Under oxidative stress, the Sty1 stress MAPK is activated, leading to a massive Atf1-dependent transcription wave, which is also associated with increased SAGA-dependent H3K9 acetylation (PMID: 21515633). This well-studied cellular response, however, is not the main focus of our study. Rather, we found a novel connection between perturbed lipid metabolism and increased expression of stress genes in cells *not challenged* by oxidative stress (i.e. Sty1-Atf1 are not hyperactivated). This is why we only measured acetyl-CoA concentrations in untreated cells.

The authors argue that although the global acetyl-CoA levels are not increased, local concentrations might be altered in a way to permit higher H3K9 acetylation levels at selected promoters. Although a formal possibility, this is rather far-fetched as a small and freely diffusible molecule like acetyl-CoA should quickly equilibrate within one cellular compartment. I think that although the overall relationships that the authors have established between oxidative stress, H3K9 acetylation levels with increased expression, and lipid biosynthesis, are compelling, the role of acetyl-CoA concentrations is not clear and should be de-emphasized.

Interestingly, acetyl-CoA production in the nucleus has been published by several studies (reviewed in PMID: 29174173), suggesting that local acetyl-CoA concentrations (microgradients) within the cell are functionally relevant. We agree that acetyl-CoA is a small molecule which, in theory, should diffuse quickly throughout the nucleocytoplasmic space. However, empirical evidence shows that the lipid synthesis in the cytosol and histone acetylation in the nucleus may not access a uniform nuclear-cytosolic pool of acetyl-CoA (PMID: 28099844, PMID: 28552616). This is related to the fact that the acetyl-CoA sink is large and acetyl-CoA may react with many proteins (i.e. any extra amounts will be consumed rapidly).

Even though we provide strong evidence that HAT activity is critical for the crosstalk between FA synthesis and stress gene expression, we do agree that we have not conclusively established the role of acetyl-CoA in the process. However, we still feel that it is justified to point out acetyl-CoA is a “possible” mediator molecule for the crosstalk in the Results and Discussion sections.

Minor comments:

In many of the bar diagrams, only a borderline statistical significance is indicated (p ~ 0.05) despite seemingly large numerical differences between the means. In the legends it is stated that one-sided Mann-Whitney U tests were used. This is a non-parametric test with low power - would it not have been better to use a t test?

We do agree that the non-parametric Mann-Whitney U test is rather conservative and, therefore, less sensitive for small sample sizes, such as n = 3. Our reason for using this particular test instead of the parametric t-test is that qPCR fold-change values come from a log-normal distribution, which is incompatible with t-test (requires normal distribution of data). Importantly, using conservative statistical testing does not invalidate our conclusions.

What do the error bars in the diagram show, SEM? If a non-parametric test is used, a parametric measure of variability is irrelevant.

The error bars represent standard deviation (SD). We do not see an issue here as, in our opinion, the visual style of numeric data presentation is independent from any chosen statistical testing methods.

It would be helpful to the reader to indicate directly in the diagram panels what is actually shown, not just "fold change vs ..." In Fig. 1, 2, 4 D and 5 we see mRNA levels, in Fig. 3 chromatin IP.

Done

Reviewer #1 (Significance (Required)):

The paper represents conceptual advances for our understanding of how stress responses, metabolism and transcriptional regulation are linked, although one of the links (acetyl-CoA levels in this case) is tenuous.

This manuscript belongs in a rich literature on stress responses on the gene expression level, mostly from studies in yeast. Potentially, it adds entirely new information on how cellular stress may be mechanistially linked to stress responses.

These results are potentially general and of broad interest to the biological community.

This reviewer is familiar with yeast genetics, stress responses, and quantification of gene expression.

Reviewer #2 (Evidence, reproducibility and clarity (Required)):

As more and more metabolic intermediates are found to also serve as co-factors for epigenetic modifications, it has been widely accepted that regulating the levels of these key metabolites can be an effective way to control nutrient related gene expression. Acetyl-CoA is one of those early examples. Increased acetyl-CoA was shown to promote local acetylation at growth genes (Mol Cell 2011 PMID: 21596309), and ACC deletion funnels more Acetyl-CoA towards histone acetylation reactions and causes global hyperacetylation (Ref 17). However, whether those increased metabolite/co-factor can exert signal-specific effects remains elusive. For instance, although increased acetyl-CoA stimulates the SAGA complex enzymatic activity, it is not clear whether it also causes SAGA to be targeted to new sites without external cues to induce new transcription factor binding. Does increased acetyl-CoA cause broad hyperacetylation at all inducible genes which are the primary targets for those HAT complexes?

In this manuscript, Princová et al. found that deletion of fatty acid synthesis transcriptional factors Cbf11 and Mga2 increases cell survival under H2O2 induced oxidative stress in S. pombe. They further showed that several stress-related genes increased upon Cbf11 deletion, and H3K9 acetylation at their promotor regions were elevated. They argued that FA-TF deletion may indirectly regulate stress-related genes potentially through influencing Acetyl-CoA level, although they failed to detect significant changes of global Acetyl-CoA levels. While it's interesting to see yet another example of metabolite-mediated gene expression regulation, the current manuscript only made incremental advance towards mechanistic principles of how these co-factors finetune specific gene expression program.

Specific comments:

1. This work showed convincingly that deletion of CBF11 or MGA2 leads to resistance to oxidative stress. However, it provides little mechanistic insight into how deletion of Cbf11 increased the expression of stress response genes and why some HATs are involved but others not (Figure EV5).

We respectfully disagree with the notion that we only provide “little mechanistic insight” into the process whereby FA metabolism affects stress gene expression.

• First, we show that not only deletion of cbf11, but also a very specific manipulation of the rate-limiting FA-producing enzyme (Cut6/ACC; Fig. 4D), or chemical inhibition of FA synthase by cerulenin (new Fig. 4F) all lead to increased stress gene expression. On the other hand, overproduction of Cut6/ACC results in decreased stress gene expression and lower resistance to ox. stress (new Fig. 5B-C). These findings clearly show the specific and tight mutual relationship between FA synthesis and expression of stress genes.

• Second, we show that the DNA-binding activity of Cbf11 is critical for affecting stress gene expression levels, yet Cbf11 does not act as a stress gene repressor.

• Third, we show that, compared to e.g. peroxide treatment, stress gene mRNA levels are only moderately increased upon downregulation of FA synthesis. So the situation can be called stress gene “derepression”. At the same time the major stress-response regulators (Sty1-Atf1, Fig. 2A-C; Pap1, new Fig. 2D-E) are required for the derepression, but, importantly, neither of them shows increased activation compared to unstressed WT cells (Fig. 3A-C). These data suggest a qualitative difference between the two phenomena (canonical stress response vs dysregulation of FA synthesis). Furthermore, they hint at an important role of the chromatin environment.

• Fourth, we show that Gcn5/SAGA and Mst1, but not 4 other HATs, mediate the connection between FA metabolism and stress gene expression (Fig. 5D-E), and we show clear and specific H3K9 hyperacetylation of stress gene promoters in FA metabolism mutants (Fig. 5A), arguing that this is not a general acetylome issue.

• Fifth, we show that the stress genes affected by changes in FA metabolism show unusually high nucleosome (H3) occupancy in their transcribed regions (even in unperturbed WT cells; Fig. 5A bottom panels), which could dictate the observed specificity in regulation.

While we agree that our understanding is not yet complete, we have already described many mechanistic aspects of the link between FA metabolism and stress gene expression.

1. Although in Cbf11 deletion cells, increased resistance to H2O2 is relied upon the Sty1/Atf1 pathway, the authors did not establish a link between lipid synthesis and Atf1 activity because Cbf11 deletion does not affect the phosphorylation of Atf1.

Sty1 and/or Atf1 show non-zero activity even in normal, healthy, unstressed cells. Importantly, Atf1 is bound to many target promoters even in the absence of stress (Fig. 3B; PMID: 20661279, PMID: 28652406). Moreover, Sty1 is actually needed for orderly cell cycle progression (sty1KO cells are elongated, a result of postponed mitotic entry; e.g. PMID:7501024), which we now mention in the Introduction and Discussion. Our point is that Sty1-Atf1 are not hyperactivated under normal conditions - this only happens during major stress insults. Thus, in unstressed cbf11KO cells, stress gene promoters are hyperacetylated, which may facilitate their (Sty1-Atf1 and Pap1-dependent) transcription, without the need for hyperactivation of the stress response regulators. Such increased transcriptional competence of stress promoters is consistent with our findings that upon peroxide treatment stress gene mRNA levels in cbf11KO exceed those in WT (Fig. 1B). We have amended the corresponding section of the Discussion to more clearly explain our conclusions and hypotheses.

1. Cbf11 deletion causes elevated H3K9 acetylation at the promotor regions of a number of stress respond genes, the author did not mention whether demonstrate how lipid synthesis defect causes the hyperacetylation at these promoters.

As discussed in our manuscript, we suggest that following downregulation of FA synthesis, the surplus acetyl-CoA is used by Gcn5 and Mst1 HATs to hyperacetylate stress gene promoters.

1. As all lipid-metabolism mutants show increased stress response, it would helpful to examine whether H2O2 induction of WT cells influence lipid synthesis, thus establish physiological links between FA synthesis and stress response.

We now mention in the Discussion section that, curiously, cut6/ACC mRNA levels are downregulated upon peroxide treatment. However, the significance of this finding is unclear as FA metabolism is strongly regulated at the post-translational level (PMID: 12529438). Unfortunately, we are not in a position to measure changes in metabolic fluxes upon stress. In any case, we believe that such experiments would be outside the scope of the current study.

Beside, fatty acid may be beneficial to fight oxidative stress because they maintain the integrity of cell membrane. What is the potential effect of CBF11 deletion in this aspect? The author may want to discuss it.

The reviewer suggests that higher production of FA would result in higher resistance to oxidative stress. However, our data do not indicate this - we show that under low FA synthesis the stress resistance is actually higher. Nevertheless, we acknowledge in the Discussion that the scenario suggested by the reviewer can occur, for example, in cancer cells which become more resistant to oxidative stress following increased lipid biosynthesis/storage.

1. Since H2O2 treatment also causes change in glucose metabolism including upregulation of glucose transporter Ght5 (PMID: 30782292), it would be enlightening to see if there is a crosstalk between the lipid and glucose metabolisms. Does Ght5 expression increase upon H2O2 treatment in CBF11 deletion strain?

While the topic is interesting, we strongly believe that the relationship between glucose metabolism and stress gene expression is outside the scope of this study.

According to our data used in Fig. 4A, ght5 expression in cbf11KO at 60 min after 0.74 mM H2O2 treatment is downregulated 3-fold.

5 Different H2O2 concentration causes different stress response in pombe: Pap1 and Sty1 mediate responses for low and high H2O2, respectively. For fully activated Sty1 response, the concentration of H2O2, needs to reach 1mM (PMID: 17043891). In this study, the H2O2 concentration ranges from 0.5-1.5mM and Pap1 regulated Ctt1 does show increase upon H2O2 treatment. To test if suppressed lipid synthesis facilitates Sty1 dependent activation, it would be helpful to examine the activation of Pap1 (its nuclear translocation) to eliminate other influences.

We agree with the reviewer. We have now included data on the role of Pap1 in the crosstalk between lipid metabolism and stress gene expression. We show that Pap1 is required for increased expression of gst2 and ctt1 in untreated cbf11KO cells (Fig. 2D). We note that ctt1 is coregulated by both Pap1 and Atf1 (Fig. 2B, D). Also, Pap1 is partially required for H2O2 resistance of cbf11KO cells (Fig. 2E). Importantly, similar to Sty1-Atf, Pap1 is not hyperactivated (no nuclear accumulation) by 10 or 60 min of cerulenin treatment (Fig. 3C), while stress gene expression is upregulated at 60 min in cerulenin (Fig. 4F) and keeps increasing after 120 min (data not shown). These data collectively support our hypothesis that upon decreased FA synthesis, stress gene promoters become more transcription-competent without the requirement for hyperactivation of the corresponding stress gene regulators.

Reviewer #2 (Significance (Required)):

see above

Reviewer #3 (Evidence, reproducibility and clarity (Required)):

This study examines the intriguing phenomenon that perturbation of fatty acid biosynthesis induces expression of stress-response genes by increased intracellular levels of acetyl-CoA and hyperacetylation of histones at the promoters of these genes. Loss of the CSL transcription factor Cbf11 results in induced expression of a subset of stress-response genes in unperturbed conditions and resistance to H2O2. These stress-response genes are not direct targets of Cbf11, but their upregulation is dependent on the Sty1-Atf1 pathway. Similar effects in upregulation of stress-response genes were observed in the cut6 hypomorph and mga2 deletion strain, however no change in global levels of acetyl-Co-A in the former as well as in the cbf11 deletion was detected. The upregulated stress-response genes appear to be linked to increased H3K9 acetylation in their promoters and dependent on the Gcn5 and Mst1 HATs.

The authors present good supportive evidence linking fatty acid biosynthesis to epigenetic regulation of stress response genes potentially mediated by intracellular levels of acetyl-CoA. This is an exciting area and the fission yeast model system is ideal to elucidate the molecular mechanisms behind this process. This is a substantial body of work with state-of-the art functional genomics approaches and LC-MS analysis. The data is of high quality and the manuscript is well written and relatively easy to read. Below are my comments for the manuscript.

It was determined that increased expression of stress-response genes in the cbf11 deletion is dependent on the presence of Sty1, and partially dependent on Atf1. How about Pap1 (or Prr1) - would this transcription factor that is also regulated by Sty1 be involved in the upregulation of the stress-response genes in the cbf11 deletion? Activation of Sty1 and Atf1 by phosphorylation was not observed in unperturbed cbf11 deletion cells which would be expected in the proposed model. This discrepancy was not well explained. Could activation of Sty1/Atf1/Pap1 in unperturbed cbf11 cells be assayed in a different way such as nuclear localization?

As these concerns were also raised by Reviewer 2, to avoid duplication, we kindly ask you to read our detailed responses above. Briefly, we have now included new data clarifying the role of Pap1 in the increased expression of selected stress genes in cbf11KO cells (or when FA synthesis is chemically inhibited) - comment #5 of Reviewer 2 above. Also, we explain why Sty1-Atf1 and/or Pap1 hyperactivation (i.e. above their activity level in untreated WT) is actually not needed in order for decreased FA synthesis to trigger a mild/moderate increase in stress gene expression - comment #2 of Reviewer 2 above. We have now also clarified this issue in the Discussion section.

As for the use of alternative methods for measuring the activation status of Sty1-Atf, we have already provided data from multiple independent and very sensitive methods (western blot, ChIP-qPCR; Fig. 3A-B). Also, it is questionable whether microscopy would be more sensitive than our current methods. Moreover, our H2O2-sensitive reporter does not indicate an increasingly oxidative environment inside cbf11KO cells, quite on the contrary (Fig. 1D).

It would strengthen the model that perturbation of fatty biosynthesis induces expression of stress-response genes and H2O2 resistance if more mutant strains other than cut6 and two of its known regulators were tested. Does the proposed model apply to any deficiency in fatty acid synthesis in general or only those that result in increased levels of acetyl-CoA? For example, would deletion strains of fas1, fas2, lsd90, lcf1, lcf2 or the4 show the same stress response as cut6, mga2, and cbf11 mutants?

The roles of lsd90, lcf1, lcf2 and the4 have been only poorly characterized so far, making it potentially difficult to interpret any stress-related phenotypes of these mutants. However, the role of the fatty acid synthase Fas1/Fas2 complex in FA production is well established. We have therefore inhibited FAS using cerulenin and found that this treatment also leads to increased stress gene expression (Fig. 5F), without causing Pap1 hyperactivation (Fig. 3C). Importantly, fas1/fas2 are not Cbf11 target genes, and FAS inhibition by cerulenin represents an acute intervention, very different from the long-term effects in cbf11/mga2/cut6 mutants.

Also, does overexpression of cut6+ confer sensitivity to H2O2?

Yes, our new data show that ~2-fold overexpression of cut6 both partially abolished the derepression of stress genes in cbf11KO cells (Fig. 5B), and increased sensitivity to H2O2 of WT cells (new Fig. 5C).

The authors hypothesize that induced expression of stress-response genes in the cbf11 deletion and cut6 hypomorph is due to H3K9 hyperacetylation because of increased acetyl-CoA abundance in the cell. However, LC-MS analysis showed no change in global abundance of acetyl-CoA in the cbf11 deletion and cut6 hypomorph although differential levels of acetyl-CoA in the nucleus relative to the rest of the cell cannot be ruled out. The authors mentioned that ppc1-537 and ssp2 null are known to have lower abundance of acetyl-CoA and the latter could suppress the cbf11 deletion-induced gene expression for two of three genes tested by qPCR. Can ppc1-537 also suppress the cbf11 deletion-induced gene expression? Are ppc1-537 and the ssp2 null sensitive to H2O2?

The ppc1-537 mutant is sick and has a growth defect, making it difficult to interpret any findings regarding its survival/resistance phenotype (see a similar issue with the cut6-621 mutant in Fig. 4E). Ssp2/AMPK has a pleiotropic role in the cell and its activity is actually controlled by Sty1-Atf1 under some stress conditions (PMID: 28515144) and the ssp2KO is resistant to osmotic stress (PMID: 28600551). All this makes it potentially difficult to derive reliable conclusions about ppc1 and ssp2. However, our current data on cut6 (ts hypomorph, Pcut6MUT, overexpression) and FAS/cerulenin are derived from precisely targeted and specific interventions, and support the proposed connection between FA synthesis and stress gene expression, and are consistent with the suggested role of acetyl-CoA (and its microgradients) in mediating the connection.

I think Rtt109 is H3K56 specific.

Indeed, H3K56 is the characterized specificity of Rtt109, and we indicate this explicitly in the manuscript. We wanted to make our HAT screen comprehensive since we could not presume which histone or even non-histone acetylation target(s) is involved in lipid metabolism-mediated stress gene expression. Even though we have observed increased H3K9ac (Gcn5/SAGA target), other modifications are likely involved since Mst1 affects stress gene expression in lipid mutants, but Mst1 is not known to target H3K9.

Reviewer #3 (Significance (Required)):

The authors present good supportive evidence linking fatty acid biosynthesis to epigenetic regulation of stress response genes potentially mediated by intracellular levels of acetyl-CoA. This is an exciting area and not all the molecular details have been elucidated in this process. S. pombe is ideal to study this fundamental process and discoveries would be applicable to other eukaryotic study organisms.

My expertise is in eukaryotic gene regulation, molecular genetics and functional genomics, so I am quite qualified to critically review this paper.

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Referee #1

Evidence, reproducibility and clarity

This paper demonstrates a link between oxidative stress, lipid biosynthesis, and targeted histone acetylation in fission yeast. In mutant cells with defects in lipid synthesis (cbf11, mga2 lacking transcription factors, and cut6 lacking acetyl-CoA carboxylase), transcripts of a number of genes implicated in resistance to oxidative stress are increased. This is associated with higher levels of H3K9 acetylation and increased tolerance to oxidative stress. These effects are mediated through Sty1, a stress-activated MAP kinase and the transcription factor Atf1.

It is also shown that H3K9 acetylation levels in the promoter region and just downstream of the transcriptional start site are increased in cbf11 mutants (Fig. 5A).

By mutational analysis, the authors implicate the acetyl transferases Mst1 and Gcn5 in this transcriptional effect. Other related acetyl transferases, Hat1, Elp3, Mst2, Rtt109 have been ruled out as main contributors to the dysregulation in unstressed cbf11 mutants. That specific acetyl transferases have been shown to be required is a strength of the investigation.

Major comments:

The hypothesis is put forward in the manuscript that altered acetyl-CoA levels in cbf1 mutants would underlie the dysregulation of genes induced by oxidative stress. Histone acetyl transferases compete for acetyl-CoA with lipid biosynthesis, and so with increased demand for acetyl-CoA underacetylation in the concerned promoters would result - specifically at H3K9.

These results do not directly support the hypothesis, on the other hand they are not sufficient to rule it out. Acetyl-CoA levels were measured only in undisturbed cells, and the possibility remains that under oxidative stress there would be changes in acetyl-CoA pools that could explain this apparent contradiction - why did not the authors examine that?

The authors argue that although the global acetyl-CoA levels are not increased, local concentrations might be altered in a way to permit higher H3K9 acetylation levels at selected promoters. Although a formal possibility, this is rather far-fetched as a small and freely diffusible molecule like acetyl-CoA should quickly equilibrate within one cellular compartment. I think that although the overall relationships that the authors have established between oxidative stress, H3K9 acetylation levels with increased expression, and lipid biosynthesis, are compelling, the role of acetyl-CoA concentrations is not clear and should be de-emphasized.

Minor comments:

In many of the bar diagrams, only a borderline statistical significance is indicated (p ~ 0.05) despite seemingly large numerical differences between the means. In the legends it is stated that one-sided Mann-Whitney U tests were used. This is a non-parametric test with low power - would it not have been better to use a t test? What do the error bars in the diagram show, SEM? If a non-parametric test is used, a parametric measure of variability is irrelevant.

It would be helpful to the reader to indicate directly in the diagram panels what is actually shown, not just "fold change vs ..." In Fig. 1, 2, 4 D and 5 we see mRNA levels, in Fig. 3 chromatin IP.

Significance

The paper represents conceptual advances for our understanding of how stress responses, metabolism and transcriptional regulation are linked, although one of the links (acetyl-CoA levels in this case) is tenuous.

This manuscript belongs in a rich literature on stress responses on the gene expression level, mostly from studies in yeast. Potentially, it adds entirely new information on how cellular stress may be mechanistially linked to stress responses.

These results are potentially general and of broad interest to the biological community.

This reviewer is familiar with yeast genetics, stress responses, and quantification of gene expression.

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Reply to the reviewers

Reviewer #1 (Evidence, reproducibility and clarity (Required)):

This study presents a first structural insight on formin mDia bound to actin filaments in physiological conditions. Based mainly negative stain EM, the authors use 2D and 3D class averaging to describe two main configuration of the formin at the filament barbed end. The two configurations support the previously proposed stair-stepping model, which was based on crystal structures, with an open state where the formin binds two actin monomers and a closed state where three monomers are bound. Because the majority of the structures fall in the first, open state, this supports the existence of this intermediate. The authors also show that the orientation of the free FH2 in this open state is somewhat flexible, as several sub-classes with different angles can be distinguished. Finally, they identify, for the first time, formin densities bound along the length of the filament.

The data is well presented and I don't have any major issue. The only point is that the information that all the initial structural data comes from negative stain EM comes should be put upfront. One gets the feeling that cryoEM is used throughout until one reads the section on cryoEM. Given that the methodology is now also established for cryoEM, it is regrettable that data was not collected with a 300kV microscope, which may have revealed more details of the conformations, but I understand microscope time is hard to come by, and the authors did a remarkable job from negative-stain EM.

The finding of formin densities binding along the length of the actin filament is very interesting. Besides the previous cited finding, it also reminds of the observations made in yeast where Bni1 (in S. cerevisiae; PMID 17344480) and For3 (in S. pombe; PMID 16782006) where shown to exhibit retrograde movement with polymerizing actin cables in vivo. This would be interesting to consider in the discussion.

Reviewer #1 (Significance (Required)):

This study extends our understanding of the mechanism of formin-mediated actin assembly, by providing a first structural observation in physiological conditions. While confirmatory of previously proposed model, but also excludes an alternative model, and offers novel observations of flexibility and binding along the actin filament length. It will be of great interest to researchers on the actin cytoskeleton.

My expertise is in the actin cytoskeleton and formins, but I am no expert in EM structural analysis.

We thank reviewer 1 for the very positive comments and for pointing out the relevance of our study for the actin cytoskeleton field. As advised, we now specify upfront in the abstract and in the introduction that most of the presented results were obtained from negative stain electron microscopy. Following the reviewer’s advice, we have enriched the discussion to highlight the retrograde movements of formins in actin cables observed in vivo.

Reviewer #2 (Evidence, reproducibility and clarity (Required)):

Maufront et al. have used EM to study the conformation of mDia1 at the barbed end and the core of actin filaments to explain the molecular mechanism of the FH2 dimer processivity at these sites. Based on modelled structural data they tried to describe how the conformational changes in FH2 dimer lead to its partial dissociation, and then association with filaments during the process of translocation coupled to subunit addition at actin filaments barbed ends. This supports a previous study (Otomo et al. 2005, Nature), in which using X-ray crystallography structural data were used to propose a stair-stepping model for Bni1p translocation at the barbed ends during actin polymerization. The model for mDia1 binding to core filaments is also given. Moreover, using EM structure and the previously reported structures of actin (PDB: 5OOE), and actin with formin FH2 dimer (PDB: 1Y64), authors explained the dynamic nature of FH2 dimer at barbed ends of the filaments using the flapping model. But due to the low resolution of their structures ~ 26-29A0, the finer details of actin and the FH2 dimer structure at barbed ends could not be resolved, leaving open questions about the orientation of actin helical twist at this end during elongation. The authors tried several conditions to get high density barbed-end filaments, but that did not collect adequate number of particles, resulting in low number of particles selected for structure modelling purposes. However, to attain more physiologically relevant structure they used cryo-EM, but were successful in capturing only the open conformation structure of FH2 dimer (at low resolution). Thus, due to low resolution of structures the key findings have not added much to what we already know about the mechanism of FH2 dimer translocation during actin polymerization, except that their studies support the stair-stepping model (Otomo et al. 2005, Nature) and not that of "stepping second" model ( Paul and Pollard. 2008, Curr. Bio.). Thus, this manuscript does not merit publication in this journal.

We thank reviewer 2 for taking the time to read and review our study. However, we respectfully disagree with the statement that our findings “have not added much to what we already know about the mechanism of FH2 dimer translocation during actin polymerization”. As mentioned in our report, collecting EM data for formins in physiological conditions (at the barbed ends of growing filaments), as we do here for the first time, entails limitations on the number of particles one can observe and on the resulting resolution. Despite this rather low resolution, our data allow us to discriminate between two proposed models accounting for the processivity of formin FH2 domains at filament barbed ends. Being able to determine which of two competing models is valid (as the reviewer says we do) does add a lot to what we already know.

Major comments:

1. Present study does not provide any new insight about the conformation of the actin dimer at the barbed ends of actin filaments when FH2 domains of formin are bound. This study appears to be more like an extension of previous research (Otomo et al. 2005, Nature), in which the authors used X-ray crystallography data to propose a model for actin filaments elongation by formin bound at the barbed ends.

As mentioned above, we respectfully disagree with this remark. First, in Otomo et al. 2005, formins are arranged in a crystal into a non-physiological “daisy chain” arrangement around a non-canonical tetramethyl rhodamine-actin filament. Our observations were made in physiological conditions displaying a single formin dimer at the barbed end of a polymerizing filament. Second, the stair stepping model originating from Otomo et al. was only inferred and extrapolated from the crystal structure and not directly observed. Both the open and the closed conformations were speculations, that had never been observed up to now. In our current report we directly visualize these two conformations. Third, the observations of Otomo et al. were obtained using formin Bni1p from yeast, not the mammalian formin mDia1, for which there is little (PDB 1V9B) structural data available describing the structure of a truncated mDia1 in the absence of actin. Finally, in addition to validating the stair-stepping model experimentally, we make unexpected observations that are totally absent from the model derived from Otomo et al. and subsequent studies.

The low resolution of structures is a major concern.

As mentioned above, the limited resolution is the price we had to pay for being in physiological conditions, with formins interacting with the barbed ends of growing actin filaments. Nonetheless, this resolution is sufficient to discriminate between the two previously existing models, and to make new observations, beyond these models.

Given the low resolution of data, how can the authors decide on the number (4) of classes of FH2 domain (in open state) and present them as "continuum of conformations". They stated "details featured in class 4 do not appear as sharp as in class 2". What was the basis of deciding on the sharpness level?

We agree that this point was unclear, and we thank the reviewer for pointing it out. The choice of the number of sub-classes for the open state is a trade-off between the sharpness (ie signal-to-noise ratio) of the resulting image, which is a direct consequence of the number of particles within each sub-class, and the internal variability within each sub-class. Class 4 might appear more “blurry” because it gathers particles displaying a range of angles. When increasing the number of generated classes in the 2D processing, we observe angular variations of the FH2 domains intermediate to the ones displayed in Figure 3. However, because increasing the number of classes results in averaging less particles per class, the generated classes appeared more noisy or “blurry” and not as “sharp”, as mentioned in the manuscript. Hence, we chose the number of displayed classes so that the signal-to-noise would remain satisfactory and sufficient to be able to determine the relative angle between the two FH2 domains. To make things clearer, “do not appear as sharp” was replaced by “displayed a lower signal-to-noise ratio and thus looked noisier”. The expression “sharp” was replaced by “enough contrast”.

The authors showed 30Å structure of FH2 domain encircling actin filaments towards their pointed ends, but said nothing about the kind of decoration it could be, a "daisy-chain" or "concentric circle"? Also, they did not mention anything about the orientation of actin helical twist and specific sites of binding. These information would provide new in-depth understanding of how formins binds while diffusing along the filaments.

The quality is sufficient to distinguish isolated FH2 dimers along the core of actin.

Accordingly, the FH2 dimers we observed along the core of our actin filaments adopt a conformation similar to that observed at the barbed end, as mentioned in the text (‘concentric circle’). This observation differs from the reported for INF2 which accumulated along filaments and may interact in a ‘daisy-chain‘ manner (Gurel et al, 2014 ; Sharma et al, 2014). From our data, we can thus assume that formins interact with F-actin along the core of filaments similarly to the way they do at the barbed ends, and might translocate in a two-step manner alongside the actin filament. As stated in the manuscript, the actin helical twist could not be deciphered. For docking the crystal structures within our EM envelope, we used the formin-actin contacts described previously in Otomo et al.

The author stated - "The leading FH2 domain likely provides a first docking intermediate for actin monomers that would help their orientation relative to the barbed end, resulting in a higher actin monomer on-rate". This statement was made on the basis of observing 79% times FH2 in the open state in their data set. This seems like an overstatement because they don't have any direct structural data to support such claim.

We agree with the reviewer that our statement, taken from the discussion section, is speculative, and we apologize if this was unclear. Our purpose was to propose a plausible mechanism, based on our structural data, since the FH2 domain stands in front of the barbed end in the “open conformation” and since it likely interacts with actin monomers. We have now rephrased our sentence to state more clearly that is a hypothetical mechanism : “We propose that… could provide…”.

In the Discussion they mentioned "the FH2 dimer would then be "lagging" behind the elongating barbed end if actin twisting back to 180{degree sign} occurs before the addition of actin monomer and this explains the diffusing along the actin filaments". Did authors encounter filaments with two formins bounds to them in their negative stain images? What is their view on this? In current data, they showed structure in which only one FH2 dimer is bound to the pointed ends of actin filaments. Have they tried increasing the concentration of formins to obtain structures with more than one formin is bound towards the pointed ends of actin filaments?

Following the recommendations from reviewer 2, we have performed an additional analysis and we now show typical examples of filaments observed with a formin along their core, including cases where two formins are observed on the same filament (Supplementary Figure 12). As we now explain in the discussion section, five different mechanisms (including lagging) can be invoked to explain how a formin can be located along the core of the filament. These five mechanisms can all account for the possibility to have more than one formin on the same filament.

The lagging mechanism, however, is the only one where we would expect that the filaments with a formin along their core are less likely to also have a formin at their barbed end (because the formin at the core spontaneously departed the bare barbed, that was left bare and with a shorter time to load another formin before fixation of the sample). A simple statistical analysis of our data leads to the estimation that 48 ± 7% (n=50) of actin filaments with a formin within their core also display a formin at their barbed ends. This is significantly less than for the global filament population, where 77 ±0.4% (n=10,461) of barbed ends are decorated with formins. This supports the lagging scenario as a likely mechanism putting formins along the core of the filament.

Regarding the specific suggestion to increase the formin concentration: We did screen different formin concentrations, but with higher concentrations the level of noise due to unbound formins was significantly increased in the image background and impeded a proper analysis. This is why we consistently used 100 nM formins.

To increase the density of short filaments for sample preparation, the authors used additional actin binding proteins "shown in supplementary Figure 2.C". There is no supplementary Figure 2.C. Moreover, it would be nice if the concentrations of these proteins are mentioned in the text.

We apologize for this mistake. Supplementary Figure 2.C has now been added and the protein concentrations have been added in the main text.

Minor comments:

1. Figure 1 legend needs editing. E is missing in the legend.

Thanks for noticing this. We have added the missing legend for 1.E. 2. There is no supplementary Figure 2.C.

We apologize for this mistake. We have now added supplementary Figure 2.C.

It is recommended that the authors report the number of particle used during 2D and RELION 3D classifications in the figures. This would help in better understanding of the probability of the conformations mentioned in the text.

It was mentioned in the text. We have now made this information clearer to the reader.

Reviewer #2 (Significance (Required)):

This is the first direct study showing the two (open and closed) conformations of mDia1 FH2 domain at the barbed ends of actin filaments using EM and cryoEM. The study supports the proposed molecular mechanism of FH2 processivity at the barbed ends during filaments elongation using stair-stepping model reported earlier (Otomo et al. 2005, Nature). For the first time, FH2 has been shown to fluctuate between various angles with respect to static actin filaments, and on this basis they propose a flapping model (Fig 5). They explained the whole mechanism using structural proof, but the low resolution of data raises a question about their quality sufficiency to propose this mechanism. The overall novelty of this manuscripts is insufficient for the publication in this journal. Audience having understanding of the actin and actin binding proteins will be interested in this study. Additionally, researcher from the field of structural biology (EM and CryoEM) will be interested. I have been working in the field of actin and actin binding proteins for past 4 years. Over 10 years' experience in protein biochemistry, structural biology and molecular biology.

We do not fully understand why, on one hand, reviewer 2 indicates that “for the first time, FH2 has been shown to fluctuate between various angles…” and that “Audience having understanding of the actin and actin binding proteins will be interested in this study. Additionally, researcher from the field of structural biology (EM and CryoEM) will be interested.”. On another hand, reviewer 2 states that “The overall novelty of this manuscripts is insufficient for the publication in this journal.”, which seems contradictory with the above statements and comments.

Regarding novelty, we insist on the fact that we have achieved for the first time the direct observation of FH2 formin domains at a resolution sufficient to discriminate between two distinct models at the barbed ends, as well as to observe the presence of formin mDia1 along the core of actin filaments in conditions where nobody has proposed that this could happen.

In addition, we have not specified any specific journal within the possible ones from “review commons”, up to now.

Reviewer #3 (Evidence, reproducibility and clarity (Required)):

Summary:

In this manuscript, Julien et al. use negative stain electron microscopy and cryo-EM to show two conformations of the FH2 domain for the formin mDia1 bound to the barbed end of an actin filament. These conformations support the "stair-stepping" model of FH2 domain movement with an elongating actin filament, as previously postulated by Otomo et al. (reference 1). The two states observe correspond to the "open" (~79%) and closed (~21%). The authors also show the conformational variability of the open state suggesting flexibility in this state. Finally, the authors observe FH2 domains encircling the actin filament at a distance from the barbed end, and suggest that the FH2 can diffuse from the barbed end down the filament.

Major comments:

1) Novel insights into formin function derived from this structure would raise impact. Issues that could be addressed include the following. Simply adding some lines to the discussion would not really add impact, but additional experimental/modeling work would.

We agree that comparing the binding mode of different formins on actin filaments, testing the impact of profilin, and assaying FH2 domains in the absence of FH1, as proposed below, would provide a broad set of interesting additional data. However, without claiming that our results can be generalized to all formins in all conditions, we believe that our findings are novel and should be of interest to a large community. The proposed additional experiments/modeling represent an impressive amount of work, and will be carried out in future investigations. We answer these comments in more details below.

1. Whether this model really holds true for all FH2 domains. Formin FH2 dimerization and processive filament barbed end elongation are widespread features of formins, which have been evidenced for many organisms from metazoan to plants. Since we could dock the FH2 from yeast formin Bni1p to account for mammalian mDia1, we think the FH2 domain conformations may be conserved enough among species to display similar translocation mechanisms at the barbed ends of actin filaments, using a two-state mechanism. We chose to use the crystal structure from Bni1 formin (PDB 1Y64) because this structure was obtained in the presence of an actin filaments and brings some insights about the formin-actin contacts.

In order to convince reviewer 3, we superimposed the existing crystal structure of the FH2 mDia1 domain (PDB: 1V9D) with our model and reconstruction and show (Supplementary Figure 12) that the differences are minor. The mDia1 FH2 domains (atomic structures in red, PDB : 1V9D) are aligned with Bni1p FH2 domains (atomic structures in green and blue, PDB : 1Y64) previously fitted into the electron microscopy envelope of a barbed end capped by a formin in the « open state ». The FH2 domains are well aligned with a slight discrepancy in the knob/actin contact regions (blue arrows). This discrepancy most likely results from the absence of actin partners in the crystals obtained with mDia1 FH2 domains. The Bni1p structure thereby most accurately represents the knob/actin contact region. In addition, the folding of the lasso domain around the post domain is resolved in the Bni1p structure. Note here that the Bni1p lasso domains wrap equally well around the Bni1p post domain and the mDia1 post domain (green arrows).

1. Whether the % time spent in the open and closed states might dictate the vastly different elongation rates mediated by different formins. For example, mDia1 is considered one of the 'faster' elongators (equivalent to actin alone in the absence of profilin), while fission yeast Cdc12 essentially caps filaments in the absence of profilin. We have discussed this aspect thoroughly in the discussion section to conclude that:” Our direct assessment of the open state occupancy rate thus provides important information on the molecular nature of the formin-barbed end conformations which could not be directly inferred from kinetic measurements, with or without mechanical tension, so far. Considering a gating factor of 0.9 and considering that formin mDia1 spends 79% of the time in the open state, we can compute that the on-rate for monomers would be slightly higher (14% higher) for an mDia1-bearing barbed end in the open state, than for a bare barbed end.”

We agree that repeating our set of EM experiments and analysis with other formins, like fission yeast Cdc12, would be interesting. However, this would take a long time, and falls out of the scope of our paper.

1. Whether the % time spent in the open and closed states varies if filaments are actively elongating in the presence or absence of profilin. We have chosen not to include profilin in our experiments, and to limit the concentration of G-actin, in order to reduce the background in our EM micrographs. Also, a rapid filament elongation would increase the amount of F-actin per barbed end, while a dense population of short filaments is key to obtain accurate data (as we explain in the discussion, paragraph 1, p9).

We speculate that, by providing a link between the FH1 domains and the filament barbed end, profilin might very well alter the percentage of time spent in the open state, and mitigate lagging as mentioned in the discussion section. Properly addressing the impact of profilin with our EM experiments is very challenging, for the reasons we have explained. It would require further investigations, beyond the scope of this study.

1. How this model impacts the interactions of formins with other proteins at the barbed end. For example, capping proteins. We did not include capping proteins (or other additional proteins) because we wanted to avoid increasing the number of particles from diverse nature per field of view, as they constitute a background that is detrimental for the analysis of EM micrographs. We would have add to sort out additional populations in the course of image analysis. We thus only mixed actin and formin in our assays.

2. Do these results relate to formin function in disease? Because formins regulate actin polymerization, their malfunction is linked to a variety of diseases. We therefore expect our findings to be useful to researchers in the medical field. However, our study remains in the scope of basic research and primarily aims at understanding the mechanisms of formin-assisted actin polymerization.

2) The observation that formin FH2 domains can bind filament sides has been made several times. In particular, a structural model of the FH2 domain of the INF2 formin along the side of an actin filament (Gurel et al 2014, PMID 24915113). This publication also references other papers showing other formins binding to filament sides. There are two points to this comment:

1. The model in Gurel et al is that the FH2 domain does not slide down the filament from the barbed end. Rather, the FH2 dimer has an appreciable dissociation rate, enabling it to encircle the filament without having to slide. This FH2 dissociation has been observed for another formin that has been shown to bind filament sides, FMNL1 (called FRL1 in the listed publication), in Harris et al 2006 (PMID 16556604). The authors must explain their reasoning for thinking that mDia1's FH2 can slide down the filament from the barbed end. One possibility is to make observations of this FH2 population in filaments that were not sonicated. What is the average distance of FH2s from the barbed end? We thank the reviewer for pointing our attention to this report from Gurel et al. which we now cite. Following this comment, as well as point 6 of reviewer 2, we now discuss the different mechanisms that could lead to our observation of mDia1 along the core of the filament. We provide a new analysis of our data (discussion section), arguing in favor of the lagging mechanism (i.e. ‘sliding down’ from the barbed end), without excluding the competing scenarios. Briefly, we compute that 48 ± 7% (n=50) of actin filaments with a formin within their core also display a formin at their barbed ends. This is significantly less than for the global filament population, where 77 ±0.4% (n=10,461) of barbed ends are decorated with formins. This supports the lagging scenario, which is the only one where a filament with a formin along its core should be less likely to also have a formin at its barbed end.

The distance of FH2s from the barbed end would provide additional information. However, it is difficult to estimate, since we often to not see the entire filament, and since we do not know which end is the barbed end.

1. Interestingly, in some of the works studying formin binding to filament sides, mDia1 was shown to be rather poor in this property. It would be useful to get an idea of what % of the observed FH2s are in the filament core, as opposed to at the barbed end. Along with the additional analysis mentioned in the previous point, we have now estimated that about 8% of actin filaments display a formin within their core. We have added this number in the manuscript (end of the Results section). As a comparison, in our assays, 77% of filament barbed ends bear a formin.

2. The authors must reference the past works showing FH2 binding to filament sides, particularly the structural work. At present, no mention of prior work on FH2 side binding is mentioned. As advised, we have now added additional references and more particularly Gurel et al, 2014.

3) My major technical concern in this manuscript is that the authors use the FH1-FH2-DAD domain of mDia1 for the imaging, but use FH2 structure of Bni1p for 3D characterization (Otomo et al.). Even though Bni1p has been used for functional and structural analysis, mDia1 and Bni1p FH2 domains share low sequence homology. In addition, mDia1 only partially complements loss of Bni1 function in vivo (Moseley et al., 2004 PMID 14657240). Can the authors use the partial structural information of the mDia1 FH2 from Shimada et al 2004 (PDB 1V9D, PMID 14992721)? Alternately, the authors could have used FH2 domain of Bni1p for imaging. At the very least, the authors should explain clearly why they used different proteins for imaging and modeling.

As mentioned above (please see our response to point 1.a), we chose to use the crystal structure from formin Bni1 (PDB 1Y64) because this structure was obtained in the presence of an actin monomers, and it thus brings some insights about the formin-actin contacts. The existing structures obtained from formin mDia1 does not include actin (full length by EM: Maiti et al, 2012; crystal structure of subdomains (without FH1): Otomo et al., 2010 PLoS one). It thus seems relevant, in the context of our investigations, to use a structure where formin-actin contacts could be at least partially inferred.

Further, we superimposed the existing crystal structure of the FH2 mDia1 domain (PDB: 1V9D) with our model and reconstruction and show that the differences are minor (please see the figure in our response to point 1.a, above).

4) The open and closed states are observed from negative staining data. However, the authors can only find one of the states (open) by cryo-EM, which decreases the confidence level of the paper's conclusions. It would be useful for the authors do a little more to try to find the closed conformation by cryo-EM.

Using Cryo-EM we can already recover the most abundant open conformation.

Unfortunately, as pointed out here, the number of particles obtained was too low to enable high resolution and reveal the two observed conformations. Indeed, considering a density of ~ 5 barbed ends par micrograph, the collection of tens of thousands of images would have been necessary, which was not realistic regarding the access we have to latest generation microscopes.

5) It is unclear whether there are additional effects of using FH1-FH2-DAD protein (not FH2 only) for the imaging, as it shows long protrusion at the tip of actin barbed end. To avoid those concerns the authors could use only FH2 domain of mDia1. Also the authors have to note that they used Bni1p structure because there are no published structures of mDia1 so far.

We had indeed tried to use a construct deprived of the flexible FH1 domain but the lower purity of this construct and the presence of aggregates led to the collection of lower quality EM micrographs. As profilin was not included in our assay, FH1 domains were not involved in actin polymerization at the barbed end and thus remain very flexible and unstructured. Consistently, we did not detect any additional electronic density that could result from the FH1 domains.

We indeed point out (p5) that “We used the crystal structure from yeast Bni1p FH2 domains in interactions with an actin filament, rather than the existing one from mammalian mDia1 formin FH2 dimer in isolation (PDB 1V9D), because actin-formin contacts are described in the Bni1p structure.” Minor comments:

1) Figure 1: It would be interesting if imaging is provided for mDia1 bound to filaments which it has nucleated. Would it be possible that binding to pre-formed filaments is different to that for mDia1-nucleated filaments?

This is a good suggestion for further investigations but it extends beyond the scope of this study: as we explain, our attempts to nucleate filaments from mDia1 lead to lower quality micrographs, and the sonication of preformed filaments was our best option. However, we do not expect the translocation mechanism of FH2 to differ, as a function of the nucleation history of the filament, since the formin interacts with a filament whose elongation it has assisted over several subunits.

2) Supplementary figure 2: Numbers of things in the S2 is unclear and poorly described in both results and methods. In particular, figure S2A, the definitions of the black and gray lines (steady state actin) is not clear. Are they containing 5% pyrene actin? Is that actin in polymerization buffer or in monomer-actin buffer? Is that actin incubated with actin polymerization buffer for a certain time before measurement of fluorescent intensity? In figure S2B, how the authors calculate the monomer actin concentration? The authors should provide the information in either results or methods part.

We apologize for the lack of information. Since this is a standard assay, we have now added more details in the Methods section (rather than in the Results section).

All curves shown in figure S2 were obtained with 5% pyrene actin. The gray curve shows the pyrene fluorescence intensity baseline from 1 µM G-actin monomers, obtained in G-buffer. The black curve is the fluorescence intensity at steady-state of 1 µM actin in polymerizing conditions, (after 1 hour of incubation at room temperature, at 5 µM, the sample was diluted without sonication and left for another hour before measuring the fluorescence intensity).

The monomeric actin concentrations shown in figure S2B are derived from the intensity level of pyrene at any time point during the experiment, using the simple equations we now present in the Methods section.

3) Supplementary figure 2 C: The figure and legend are missing in the manuscript. Furthermore, the authors describe that they used Gc-globulin to sequester monomeric actin in solution. Is gc-globulin widely used for actin monomer sequestration?

Thank you for noticing the missing panel which is now back in place. Indeed, Gc globulin is known to sequester G-actin (Van Baelen, H., R. Bouillon, and P. DeMoor. 1980. “Vitamin D binding protein (Gc-globulin) binds actin”. J. Biol. Chem. 255:2270-2272). This is why we have attempted to use it. We could see a slight effect but we did not want to increase the noise within our images with additional proteins that would have made the analysis more complicated.

CROSS-CONSULTATION COMMENTS Reviewer #1 mentions that the authors identify formin densities bound along the actin filament for the first time. I agree that the imaging of the mDia1 along the actin filament using electron microscopy is novel, but the concept of formin binding has already been found and studied well with other formins (PMID 16556604, PMID 24915113) and even mDia1 has poor binding activity compared to other formins. It was really nice of the authors to show the mDia1 side filament binding, but I don't think it is a striking finding.

I have no comment for Reviewer #2.

Reviewer #3 (Significance (Required)):

If the EM refinements and 3D rendering techniques are conducted rigorously (which this reviewer is unable to judge), the data support an existing theory of how FH2 domains interact with the actin barbed end. Overall, the data will be of interest in formin field. However, as written the paper confirms an existing model, and does not represent new insight. Impact would be raised by providing insights from these findings that impact formin function or disease.

We have answered this concern above. The existing models were speculative and not based on direct observations. They relied on data obtained in non-physiological conditions.

Here, we directly observe two distinct conformations in our structural data, and clearly validate one model over the other. This provides a major advancement in our understanding of formin interaction with actin filaments. In addition, we uncovered an unexpected behavior of formin mDia1, which can readily be found along the core of the filament without the aid of additional proteins, and we propose a mechanism based on our data to account for this observation.

Another main point is that the observation of FH2 domains bound along an actin filament, while interesting, is not novel. Others have found this for other formins, but those papers are not referenced here.

The direct binding of formins to the sides of actin filaments is thought to be specific to some particular formins (we now cite additional references in our manuscript, to discuss this point). Formin mDia1, which is a ubiquitous and widely studied mammalian formin (perhaps the most studied), has only been described to diffuse along actin filaments when a capping protein dislodges it from the barbed end (Bombardier et al. Nat Com 2015). Here, we show that formin mDia1 can be found encircling the core of actin filaments, in the absence of any capping protein. This behavior is novel and unexpected. It should open new avenues for research on formin mDia1, as well as on other formins.

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

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Reply to the reviewers

Reviewer #1 (Evidence, reproducibility and clarity (Required)): ____ *A significant criticism of the paper is an assumption that readers will be familiar with all of the findings in the author's previous 2016 paper and the PGL-1 papers by Aoki et al. Minimal context is given for each approach. *

To address this concern, we have added a paragraph in the Introduction section of the revised manuscript.

*Some conclusions are not well supported and require further analysis, proper controls, and more extensive descriptions of the experiments performed. *

We have addressed the reviewer’s concerns as detailed below.

Most importantly, the central conclusion and title of the paper is that composition can buffer the dynamics of individual proteins within liquid-like condensates. In other words, in vitro condensation assays often do not recapitulate LLPS behavior in vivo. That said, the findings in this study would be significantly strengthened and complemented by observing endogenously tagged PGL-3 and PGL-3 mutants in living worms, considering the efficiency of using CRISPR in C. elegans to insert tags and make precise mutations.

The original manuscript already contained data where we microinjected wild-type PGL-3 and mutant PGL-3 proteins (recombinantly purified) into adult C. elegans gonads to assay how the P granule phase supports diffusion of these proteins.

In the revised version, we now include additional data which shows “dynamics buffering” in transgenic worms generated using CRISPR/Cas9 technology. Briefly, we used CRISPR/Cas9 to generate transgenic C. elegans which expresses PGL-3-mEGFP or PGL-3(D425-452)-mEGFP from the native pgl-3 locus. In vitro, wild-type PGL-3-mEGFP protein generates liquid-like condensates. On the other hand, the recombinantly purified PGL-3(D425-452)-mEGFP protein generates condensates that are non-dynamic. In contrast to these observations in vitro, both wild-type PGL-3-mEGFP and PGL-3(D425-452)-mEGFP show similar dynamics (half-time of FRAP recovery) within P granules in vivo.

*To improve readability, the introduction to P granules should be expanded, and include the reasons for looking at the nematode-specific PGL-3 protein among all the other known P granule proteins. A recap of previous findings on PGL-3 phase separation, in vivo and in vitro, is warranted, starting with the significant results of Saha et al 2016. Setting up the investigative questions in the context of recent work on PGL-1 (Aoki, et al) is also necessary. *

To address this concern, we have added a paragraph in the Introduction section of the revised manuscript.

The physiological concentration of PGL-3 should be more transparent, including why some experiments in this study are done at physiological concentrations while others are not. Describing why salt concentrations, crowding agents, and protein abundance are similar or different for each experiment is necessary and relevant. For example, after showing in Figure 1 that PGL-3 protein phase separates, the paragraph starting on line 161 says that it was previously shown that PGL-3 doesn't phase separate at physiological concentrations without RNA. One has to go back to Figure 1 to realize it was done differently than Figure 2 and Saha 2016.

The concentrations of PGL-3 protein and use of crowding agents (if any) have already been specified within figures or figure legends. Salt concentrations used are specified within figure legends or materials and methods section.

We have added the following paragraph to the materials and methods section of the revised manuscript.

“Saha et al. 2016 showed that at physiological concentrations (approx. 1 mM), the PGL-3 protein is unable to phase separate into condensates. At these concentrations, mRNA promotes phase separation of PGL-3. To assay for mRNA-dependence of condensate assembly, it is therefore essential to use physiological concentrations of the PGL-3 protein or mutants (e.g. Figure 2). However, these condensates are generally too small to assay rate of internal rearrangement of PGL-3 molecules within condensates using fluorescence recovery after photobleaching experiments. Therefore, to generate large condensates for measuring internal rearrangement of PGL-3 or mutant molecules, we primarily used higher concentrations of these proteins where binding to RNA is not essential for phase separation. However, to mimic the in vivo P granule phase as closely as possible, we generally added constituent proteins in proportion to their in vivo abundance estimated in Saha et al. 2016.”

The added paragraph in the Introduction section of the revised manuscript may be helpful to the readers. * *

*Statements in the same paragraph like "in contrast to full-length PGL-3, mRNA does not support phase separation..." should be qualified by stating the concentration observed, with or without salts or other crowding agents. Similarly, line 230 "suggests that interactions involving the disordered C-terminal region of PGL-3 are not essential for the fast dynamics" and should be qualified with "at non-physiological concentrations and with XX crowding agents or salt concentration." It would be more consistent if physiological concentrations were consistent from figure to figure, as extra variables weaken some of the stated conclusions. *

We thank the reviewer for this suggestion. However, we feel the statements (without full experimental details within main text) help convey the conceptual essence of the findings better. Of course, all these statements contain reference to figures or prior publications which provide relevant details about experimental conditions.

*The 2010 review reference stating that there are 40 P granule enriched proteins is outdated. More recent reviews put the number much higher. This is relevant because the approach to put PGL-3 in a more physiological environment by including just PGL-1, GLH-1 and mRNA with the condensate assays, out of ~100 P granule enriched proteins, may not be sufficient to conclude "that the influence of complex composition on dynamics is modest" (line 223), or imply that the multicomponent nature of the P granule is reconstituted by adding these components (line 355). *

We revised the text to indicate that P granules contain approx. 70 proteins and added appropriate references.

• *

Based on current information of constitutive P granule components (PGL-1, PGL-3, GLH-1, GLH-2, GLH-3, GLH-4, DEPS-1, MIP-1 and mRNA), (Kawasaki et al, 1998, 2004; Spike et al, 2008a, 2008b; Price et al, 2021; Cipriani et al, 2021; Phillips & Updike, 2022) we reconstituted P granule-like phase in vitro with mRNA, PGL- and GLH- proteins that likely constitute the most abundant components within P granules in vivo (based on concentration estimates in Saha et al. 2016).

We do appreciate the reviewer’s comment that more components can be added to our in vitro reconstitution in addition to the limited set of components used in our study. However, we feel it is interesting to observe that a limited set of components can support dynamics buffering (the main message of the paper). Further, the complementary in vivo experiments show that the P granule phase can also support dynamics buffering.

*Figure 1C needs to include PGL-3(370-693) in the analysis. Figure 1E is also incomplete without a comparison of FRAP recovery between PGL-3(1-452) and full PGL-3 as the control.

*

Fig. 1c already includes data with PGL-3 (370-693) [top row, central panel]. FRAP recovery data with full-length PGL-3 is already available in Supplementary Fig. 2c, g.

*Figure 4C is missing an essential control where PGL-3 and S1 FRAP is performed without PGL-1, GLH-1, and mRNA. *

In the revised version, we have added Supplementary Fig. 5f, where FRAP recovery of the following condensates are plotted together: 1) PGL-3 alone, 2) S1 alone, 3) PGL-3 + PGL-1, GLH-1 and mRNA, 4) S1 + PGL-1, GLH-1 and mRNA.

*It would also help show sup Fig4A in the main figure to show concentration dependence. *

We revised Fig. 4 to address the reviewer’s suggestion.

Consider adding subtitles to supplementary figures.

We considered the suggestion but felt it may not be essential.

*M&M should include an explanation for statistical analysis *

We added a paragraph describing statistical analysis within the Materials and Methods section.

*CROSS-CONSULTATION COMMENTS I am also in agreement with the comments and critiques of reviewers 2 and 3.

* Reviewer #1 (Significance (Required)): The paper by Saha and colleagues investigate the in vitro liquid-liquid phase separation propensity of a P granule protein PGL-3 and its structural domains. The findings largely replicate and support the phase-separation properties of a paralogous protein called PGL-1, as recently described by Aoki et al. 2021. Furthermore, they show that the dynamics demonstrated by recombinant PGL-3 may be maintained or buffered by the complex composition of P granules.

Reviewer #2 (Evidence, reproducibility and clarity (Required)):

*Jelenic et al. describe the effect of partner proteins on the FRAP dynamics of recombinant PGL-3 protein and variants in in vitro condensates and C elegans p-granules. The study shows that the N terminal a-helical dimerization domains is required for condensate formation and modulate of it alters aggregation and the FRAP dynamics of its condensates. Interestingly, a construct including the entire IDR region (370-693) by itself does not phase separate on its own at these conditions. The K126E K129E mutant (known previously to disrupt dimerization) and the deletion mutant abrogate llps. A mutant construct that shuffles the sequence in the region 423-453 called S1 here reduces the helicity and the condensate FRAP dynamics but recovered in the presence of a few P granule components. Also, the reduced dynamics of partially unfolded PGL-3 condensates are also rescued by the p-granule components to a certain degree of the unfolded PGL3 concentrations. This threshold concentration for recovering the condensate dynamics is further reduced in the helix reducing S1 mutant, which is also dependent on the number and the nature of P granule components.

Overall, the study aims to probe how "composition can buffer protein dynamics within liquid-like condensates" - yet several underlying aspects of the study do not fully support that conclusion. The introduction does not sufficiently introduce the known structural information of the two dimerization domains in C elegans PGL proteins for which structures are known. The region is discussed as "alpha helical" but really there are two evolutionarily conserved independently folding dimerization domains (referring to the mutants as "reduced alpha helicity" is not helpful - these are mutations that destabilize a folded domain).*

To address this concern, we have added a paragraph in the Introduction section of the revised manuscript.

*Additionally, the abstract and introduction ignore the aspects of aggregation (touched on in discussion) - this is likely what the disruption to the helical region in residue 450 region is doing (the helix is not on the dimer interface based on homology / sequence identity to the crystal structure of PGL-1 central dimerization domain. *

We think elucidating the molecular mechanism of apparent aggregation of PGL-3 (D425-452) could be an interesting direction for future investigation. Here, we focused our analysis predominantly on the mutant S1 since it generates liquid-like condensates with ~20- fold slower dynamics (compared to wild-type) in contrast to non-dynamic condensates/aggregates. Therefore, influence of other P granule components on the dynamics of PGL-3 in liquid-like condensates is easier to address using the mutant S1 rather than PGL-3 (D425-452). We didn’t find evidence that S1 aggregates as we did not detect aggregates of S1 molecules using fluorescence confocal microscopy and the slow dynamics in condensates of S1 does not change significantly over 24 h (Supplementary Fig. 3f).

However, in the revised version, we now include additional in vivo data with C. elegans expressing the aggregation-prone PGL-3 (D425-452)-mEGFP. Briefly, we used CRISPR/Cas9 to generate transgenic C. elegans which expresses PGL-3-mEGFP or PGL-3(D425-452)-mEGFP from the native pgl-3 locus. In vitro, wild-type PGL-3-mEGFP protein generates liquid-like condensates. On the other hand, the recombinantly purified PGL-3(D425-452)-mEGFP protein generates condensates that are non-dynamic. In contrast to these observations in vitro, both wild-type PGL-3-mEGFP and PGL-3(D425-452)-mEGFP show similar dynamics (half-time of FRAP recovery) within P granules in vivo.

Finally, the "dynamics buffering" is not really clearly established and could also be explained as small concentrations of aggregated proteins act like clients while increasing the concentration results in aggregation and "cross linking" in the entire droplet - and this concentration is never achieved in the in worm experiments so it is not clear. In other words, the change in FRAP dynamics not observed in worms is perhaps not surprising if small amount of recombinant proteins are incorporated into the granules. *

*

Data with the S1 mutant establishes that dynamics buffering can be observed in condensates with different sets of additives both in vitro (Fig. 5a, b) and in vivo (Fig. 4a, b). Further, data with condensates of S1 containing the additives PGL-3 (K126E K129E) or S1 (K126E K129E) demonstrate that dynamics (half-time of FRAP recovery) within S1 condensates, and in turn “dynamics buffering” depend on inter-molecular interactions. With respect to the hypothesis proposed by the reviewer, we did not detect aggregates within S1 condensates using confocal fluorescence microscopy.

In contrast to S1 condensates, condensates containing partially unfolded PGL-3-mEGFP together with PGL-1, GLH-1 and mRNA showed spatial inhomogeneities in fluorescence signal throughout the condensate (Fig. 4g). We have not tested if areas with higher fluorescence signal represent aggregates. It is a possibility that the partially unfolded PGL-3-mEGFP fluorescence signal becomes more homogeneous if higher concentrations of additives (PGL-1, GLH-1 and mRNA) are used. However, the presented data demonstrate the significant effect of the P granule components (PGL-1, GLH-1 and mRNA) on the FRAP recovery rate of partially unfolded PGL-3-mEGFP in condensates (compare figures Fig. 3e and Fig. 4g).

However, consistent with dynamics buffering, the P granule phase in vivo supports wild-type dynamics of different PGL-3 constructs over a range of concentrations - PGL-3(D425-452)-mEGFP at physiological concentration (CRISPR transgenic strain, Fig. 4e) or at higher concentrations (microinjected S1 and partially unfolded PGL-3-mEGFP, Fig. 4b).

• *

*It is also not clear what the mechanism of the changes is - is the protein driven to fold more properly (despite S1 disruption of its conserved sequence) inside the condensate? Does it still self interact and act as a dimerization domain? Does this change disrupt interactions? *

We agree with the reviewer that identifying the precise structural changes of the S1 protein within the condensate vs. dilute phase could be an interesting direction for future investigation. However, we have already discussed the issues raised by the reviewer in the original manuscript.

“Our data is consistent with the model that other regions of S1 molecules cooperate with residues 425-452 (shuffled) to generate stronger inter-molecular interactions. For instance, addition of the mutant S1 (K126E K129E) enhances dynamics of S1 within condensates in contrast to maintaining the slower dynamics observed within condensates of S1 alone. This suggests that the interactions disrupted by the mutations K126E and K129E also contribute to slow S1 dynamics. One possibility is that interactions involving the residues K126 and K129 favor S1 conformations that enhance 425-452 (shuffled)-dependent interactions. Indeed, the mutations K126E K129E have been reported to interfere with interactions among N-termini of PGL-3 molecules (Aoki et al, 2021). While two self-association domains within the α-helical N-terminus of PGL-3 have been mapped (Aoki et al, 2021, 2016), structural insights into those associations are limited. However, PGL-3 shares significant sequence similarity with another protein PGL-1. Crystal structures are available for fragments of the PGL-1 protein that show the two self-association domains at the N-terminus are predominantly α-helical and globular in nature (Aoki et al, 2016, 2021). Therefore, one possibility is that shuffling the sequence 425-452 of PGL-3 or heat-induced unfolding of PGL-3 exposes hydrophobic residues that become available to participate in inter-molecular interactions.”

What is the real mechanism by which PGL-3 phase separates if not via the disordered domains? *

*

We agree with the reviewer that elucidating the detailed mechanism of phase separation of PGL-3 is an interesting direction for future investigation. However, we feel this is not required to support the main message of this manuscript.

Throughout the manuscript, the term "dynamics" is used to indicate FRAP, but it would be better to define what is meant (diffusion of PGL-3 in condensates) instead of using dynamics a term that could mean many things. Secondly, FRAP cannot directly measure liquidity etc (see recent critiques by McSwiggen elife 2019, etc) so it is better to be cautious in the claims. Finally, discussing "dyanmics buffering" adds more terminology where it is not needed - perhaps say "changes to diffusion of PGL-3 in condensates".

We feel it is useful to introduce a term that describes our observation. To our knowledge, our observation is novel and therefore requires a new term to describe it.

However, we do appreciate the concern raised by the reviewer. We used a more generic term “dynamics buffering” in contrast to the more specific “diffusion buffering” since we did not directly estimate diffusion behavior at the ‘single-molecule’ level. However, we already described what we mean by “dynamics buffering” in the text as follows.

“We used condensates of similar size for our analysis (average ± 1 SD of diameter of condensates are 6.4 ± 1.7 mm (Fig. 5a) and 5.9 ± 0.4 mm (Fig. 5b)). Therefore, dynamics buffering here is likely to represent similar diffusion rates of S1 within condensates.”

• *

*The "N-terminus" is not 65% of the protein. One could define this as the N-terminal domain, but again there are two clear folded domains in the first 65% of the protein and this needs to be described better. *

We revised the text to replace the terms “N-terminus” and “N-terminal domain” to “N-terminal fragment”.

*The description of "stickers" and the references to tau and hnRNPA1 are confusing as this is a predominantly ordered domain while those are IDRs. *

• *

We feel this is important as it aids discussing our work in the context of current literature describing the mechanisms of macromolecular phase separation.

The suggestion in the discussion that "P granule components support dynamics by participating in intermolecular interactions wth PGL-3-mEGFP molecules" is not well supported because no interaction assays are performed and no mutaitons are made that disrupt these interactions to test this.

Indeed, we have not conducted interaction assays or mutational analysis to directly test this. However, our detailed analysis with the S1 mutant supports this suggestion.

While partially unfolded PGL-3-mEGFP molecules lose 30% of a-helicity, the a-helicity of the S1 mutant is reduced by 15% compared to wild-type PGL-3. Data with S1 and partially unfolded PGL-3-mEGFP molecules show that loss of a-helicity correlates with slower diffusion of protein molecules within condensates. Using the mutants PGL-3 (K126E K129E) and S1 (K126E K129E), we show that diffusion rate of S1 molecules within condensates depend on inter-molecular interactions, and presence of other P granule components support faster diffusion rate of S1 molecules within condensates. Therefore, we feel it is safe to speculate that intermolecular interactions with P granule components can support dynamics of a “more unfolded” (compared to S1) version of PGL-3 molecule. * *

*More detailed analysis of some of the claims: Claim 1: An a-helical region mediates the phase separation of PGL-3, and the C-terminal disordered region by itself does not phase separate. The N-terminal dimerization is essential for LLPS. The C-terminal IDR interactions with mRNA facilitate the LLPS. Comments: The authors show sufficient experimental data using microscopy and FRAP on truncated constructs with the N-terminal and C-terminal regions - but see above regarding how these are described - a proper domain structure with the folded domains shown and the RGG motifs highlighted should be added and integrated throughout the discussion. *

In the revised version of the manuscript, we described the predicted PGL-3 domains within a paragraph in the introduction: “The interactions that support phase separation of the PGL-3 protein remains unclear. Structural studies on the orthologous PGL-1 protein revealed two dimerization domains. This raises the possibility that PGL-3 also contains similar dimerization domains, and phase separation depends on interactions involving these domains.”

Our Fig. 1a already includes the schematic representation of PGL-3 with predicted N-terminal and Central Dimerization domains and RGG repeats.

*They show that the N-terminus is necessary and adequate for LLPS, and the C-terminus by itself does not phase separate. But, how does the N-terminal domains phase separate? This is not explained - what are the interactions? *

• *

Also, a di-mutant (K126E K129E) that is known, and also authors use SEC-MALS to show their N-terminal construct is consistent with the published results. Disrupting the n-terminal dimerization prevents phase separation, suggesting the importance of these residues in the N-terminus for self-assembly and LLPS. The Microscopy data backs the claim that the mRNA-mediated LLPS is facilitated by binding with C-terminus. However, the m-RNA binding to IDR is not sufficient for LLPS. Yet, the authors do not explain how higher salt prevents phase separation - again the mechanism of phase separation is unclear. Is it multivalent interaction of the two dimerization domains? A basic model (that is tested) would be important.

We agree with the reviewer that elucidating the detailed mechanism of phase separation of PGL-3 is an interesting direction for future investigation. However, we feel this is not required to support the main message of this manuscript.

However, our manuscript already provides some relevant insights as follows.

“To investigate the underlying mechanism further, we began by testing if the N-terminal α-helical region of PGL-3 can self-associate. Our analysis using size exclusion chromatography followed by multi-angle light scattering (SEC-MALS) showed that this PGL-3 fragment 1-452 forms a dimer (Supplementary Fig. 2f). Mutation of two residues (K126E K129E) have been shown to interfere with interactions among the N-termini of PGL-3 molecules (Aoki et al, 2021). We mutated these two residues within the full-length PGL-3 protein (K126E K129E) (Fig. 1a) and found that this mutant PGL-3 (K126E K129E) protein cannot phase separate even at high protein concentrations up to ~130 µM (Fig. 1b, c). Addition of mRNA does not trigger phase separation of this protein at physiological concentrations either (Fig. 2a, b). Taken together, our data is consistent with a model where association among folded N-termini of PGL-3 molecules is essential for phase separation.”

A likely possibility is that phase separation of PGL-3 depends on electrostatic inter-molecular interactions among the folded N-terminal fragment of PGL-3 molecules. Therefore, high salt prevents phase separation.

Are the tags removed to ensure that phase separation is not caused by tags or remaining linker regions? Is the protein purified to be without nucleic acid contamination or other purity metrics?

Most of the experiments were done with only 5% of total protein tagged with 6x-His-mEGFP. No additional tags were present on the constructs. For recombinant expression and purification, proteins were cloned such that it is possible to remove the 6xHis-mEGFP tag following treatment with TEV protease. Following removal of the 6xHis-mEGFP tag, the residual linker is just two amino acid residues long. We used 100% tagged-protein for our experiments only in very few cases (indicated in the figure legends).

To demonstrate purity of recombinant proteins, SDS-PAGE gels with all protein constructs used in this study are shown in Supplementary Fig. 1.

To minimize contamination of nucleic acids, we treated samples with Benzonase during the course of purification.

To assess the extent of nucleic acid contamination, the ratio of absorbance at 260 nm and 280 nm (A260/A280) was monitored. In exceptional cases with high A260/A280 values, we analyzed samples further by purifying RNA from the sample using RNA purification kit (Qiagen) and found that RNA represented 1% or less of the sample mass.* *

Claim2: The N-terminal a-helical region modulates the dynamics within condensates. The IDR region has minimal effect on the fast dynamics of PGL-3. Comments: The authors show that the full-length PGL-3 condensates have modest influence of components by comparing the FRAP half times with or without the P granule components, including mRNA. However, have the authors tried this in the presence of mRNAs for the constructs lacking the IDRs as they have several RGG domains and bind with mRNA and are likely to change the dynamics.

We thank the reviewer for this suggestion. However, this experiment is not essential to support the claim made in the context of homotypic condensates of PGL-3 : “The N-terminal a-helical region modulates the dynamics within condensates. The IDR region has minimal effect on the fast dynamics of PGL-3.”

*The authors report the importance of the N-terminal a-helical region by making a construct that lacks/disrupts a part of the helices lowers the thermal stability and significantly lowers the dynamics of the condensates. Also unfolding of helices is shown to reduce the dynamics. One primary concern is whether these "rescued" protein dynamics imply protein functionality. *

An assay of “functionality” e.g. an enzymatic activity of the PGL-3 protein is not available.

However, we compared the fecundity of C. elegans worms expressing from the native pgl-3 locus, PGL-3-mEGFP or the mutant protein PGL-3(D425-452)-mEGFP, to assay the functionality of P granules in these strains. We found that worms of both genotypes produced similar number of offspring (Fig. 4d). This suggests that deletion of residues 425-452 of PGL-3 does not result in significant loss of function of P granules.

Are these semi denatured proteins refolded in the presence of P-granule components?

We feel that identifying the precise structural changes of the semi-denatured PGL-3 proteins within the condensate vs. dilute phase could be an interesting direction for future investigation.

Finally, it is not clear why the authors chose to disrupt folding of the central dimerization domain?

The manuscript included a paragraph to describe the rationale.

“This suggests that interactions involving the disordered C-terminal region of PGL-3 are not essential for the fast dynamics within condensates. Therefore, we addressed the role of the N-terminal α-helical region (1-452) in driving dynamics. In order to avoid engineering mutations that result in significant misfolding of PGL-3 and concomitant loss of its ability to phase separate, we focused our mutational analysis close to the junction of the folded N-terminus and the disordered C-terminus of PGL-3. Surprisingly, we found that a full-length PGL-3 construct (D425-452) that lacks only 27 residues phase separates into condensates that are non-dynamic (Fig. 3a, c). Sequence analysis of the PGL-3 protein predicts that this region 425-452 spans two α-helices (one complete helix and fraction of a second helix) (Supplementary Fig. 3d). We generated a PGL-3 construct (hereafter called ‘S1’) (Fig. 3a) in which the sequence in the region, 425-452, is shuffled while keeping the overall amino acid composition unchanged. We found that S1 phase separates into condensates that are 20- fold less dynamic than with wild-type PGL-3 (Fig. 3d, Supplementary Fig. 3c).”

Saying that "reduced alpha-helicity of PGL-3 correlates with slower dynamics in condensates" may be factual in these assays but "correlation" should be expanded upon to include mechanism and to me it seems that the statement should read "aggregation of PGL-3 causes slower dynamics in condensates" (both the partially destabilized mutant and the fully unfolded WT show similar effects perhaps to different degrees).

We feel that identifying the precise structural changes of the semi-denatured PGL-3 proteins within the condensate vs. dilute phase could be an interesting direction for future investigation.

We did not use the term "aggregation" since we did not detect aggregates of S1 molecules using fluorescence confocal microscopy.

*CROSS-CONSULTATION COMMENTS I agree with the other reviewer's comments and critiques, I have concerns about the biological relevance and also the biophysical mechanisms. Reflecting on the other reviewers' comments, the papers could provide more depth in one or both of these areas to come to firm conclusions that are either revealing about PGL biology or elucidate a (possible) general biophysical mechanism. *

In the revised version, we now include additional data which shows “dynamics buffering” in transgenic worms generated using CRISPR/Cas9 technology. Briefly, we used CRISPR/Cas9 to generate transgenic C. elegans which expresses PGL-3-mEGFP or PGL-3(D425-452)-mEGFP from the native pgl-3 locus. In vitro, wild-type PGL-3-mEGFP protein generates liquid-like condensates. On the other hand, the recombinantly purified PGL-3(D425-452)-mEGFP protein generates condensates that are non-dynamic. In contrast to these observations in vitro, both wild-type PGL-3-mEGFP and PGL-3(D425-452)-mEGFP show similar dynamics (half-time of FRAP recovery) within P granules in vivo.

Reviewer #2 (Significance (Required)): *Hence, although the authors shows how inclusion of other components can alter the one protein component phase separation, this is done with entirely artificial means of destabilizing the fold of one of the domains which likely leads to aggregation. So the true impact of the work is hard to understand because the mutations impact on the basic biophysical properties of the domain (stability, interaction) are not completely characterized and the reason for disrupting this folding is not clear. *

A major impact of our work is elucidation of a novel “dynamics buffering” property within biomolecular condensates in vitro. Our in vivo data is consistent with this finding.

• *

We have chosen two orthogonal ways of perturbing the PGL-3 protein (i.e. mutations and temperature-dependent unfolding) to assay the effect on diffusion rate against different levels of perturbation (e.g. 30% loss of a-helicity in heat-denatured PGL-3-mEGFP vs. 15% loss of a-helicity in the S1 mutant, compared to wild-type PGL-3). Studying the phase separation behavior of these “artificially-generated” constructs provided the understanding that dynamics of PGL-3 in condensates depends on inter-molecular interactions, and slower dynamics generally correlate with stronger inter-molecular interactions. Further, interactions among two or more P granule components can buffer against large change in dynamics / aggregation within the P granule phase. These insights may lay the groundwork for addressing how more “natural” modifications (e.g., post-translational modifications, high local concentration of “sticky” molecules) may influence dynamics within biomolecular condensates in vivo.

Based on current knowledge of P granule composition, chaperone proteins (e.g. heat-shock family proteins) do not show abundant concentration within P granules. However, it is unclear if chaperone proteins are completely excluded from the P granule phase. Therefore, we speculate that weak interactions among two or more non-chaperone proteins contribute significantly to “dynamics buffering” within the P granule phase in vivo.

In the discussion section of the manuscript, we had speculated that “dynamics buffering” may potentially explain observations reported in the nucleolus: “Similarly, interactions among components could be a potential mechanism of storage of misfolding-prone proteins in non-aggregated state within the liquid-like nucleolus under stress in vivo (Frottin et al, 2019).”

Our finding is also relevant in the context of synthetic biology with applications that require steady diffusion rate of macromolecules during biochemical reactions within biomolecular condensates.

• *

My field of expertise is protein phase separation and protein structure. * *

Reviewer #3 (Evidence, reproducibility and clarity (Required)):

Summary: P granules are liquid condensates found in the developing germlines and embryos of C. elegans. Prior work by the authors and others have established P granules as a tractable model to investigate the basic biophysical properties of liquid condensates. Much of the prior published work focused on specific P granule scaffold proteins, PGL-1 and PGL-3. How attributes of these PGL proteins and the effect of other P granule components affect condensate properties is not fully understood. Here, Jelenic, et al. probe the biophysical properties of PGL-3. Using recombinant protein, they show that an N-terminal, alpha-helical region of PGL-3 is sufficient for liquid condensate formation and that N-terminal assembly is required for this formation. Creation of a scrambled alpha-helical region in PGL-3 and heat treatment affects PGL-3 fluidity. This fluidity can be "rescued" in vivo and in vitro with the inclusion of other P granule factors, including wildtype PGL-3, PGL-1, GLH-1 and mRNA. The authors note an inverse correlation between fluidity and mutant PGL-3 fluorescent intensity. They propose a model that heterotypic compositions of condensates can buffer their fluidity against components with stronger multivalent interactions. *

MAJOR: 1. PGL-3 is a fantastic model to study the biophysical properties of a liquid condensate. But as the authors address in their discussion, the S1 mutant will likely affect the central domain folding, at its minimum causing exposure of a hydrophobic surface not typically exposed in biology. These helices are found at the terminal portion of the domain determined in the crystal structure and as depicted in the authors' Figure 1A. While the cause of S1's enhanced molecular interactions does not affect the in vitro work presented in this manuscript, it does affect how the conclusions connect to the biological nature of P granules and liquid condensates more generally. *

We have chosen two orthogonal ways of perturbing the PGL-3 protein (i.e. mutations and temperature-dependent unfolding) to assay the effect on diffusion rate against different levels of perturbation (e.g. 30% loss of a-helicity in heat-denatured PGL-3-mEGFP vs. 15% loss of a-helicity in the S1 mutant, compared to wild-type PGL-3). Studying the phase separation behavior of these “artificial” constructs provided the understanding that dynamics of PGL-3 in condensates depends on inter-molecular interactions, and slower dynamics generally correlate with stronger inter-molecular interactions. Further, interactions among two or more P granule components can buffer against large change in dynamics / aggregation within the P granule phase. These insights may lay the groundwork for addressing how more “natural” modifications (e.g., post-translational modifications, high local concentration of “sticky” molecules) may influence dynamics within biomolecular condensates in vivo.

Based on current knowledge of P granule composition, chaperone proteins (e.g. heat-shock family proteins) do not show abundant concentration within P granules. However, it is unclear if chaperone proteins are completely excluded from the P granule phase. Therefore, we speculate that weak interactions among two or more non-chaperone proteins contribute significantly to “dynamics buffering” within the P granule phase in vivo.

In the discussion section of the manuscript, we had speculated that “dynamics buffering” may potentially explain observations reported in the nucleolus: “Similarly, interactions among components could be a potential mechanism of storage of misfolding-prone proteins in non-aggregated state within the liquid-like nucleolus under stress in vivo (Frottin et al, 2019).”

Our finding is also relevant in the context of synthetic biology with applications that require steady diffusion rate of macromolecules during biochemical reactions within biomolecular condensates.

• Recombinant PGL-3 experiments added PGL-1, GLH-1 and mRNA simultaneously and measured fluidity. It will be interesting to know which components contribute to fluidity and whether fluidity enhancement of each component is dependent on one another. Addition experiments with each component should be included and/or at least discussed in the main text. *

Our data with S1-mEGFP or PGL-3-mEGFP (pre-heated at 50°C) proteins microinjected into C. elegans gonads, and the transgenic strain expressing PGL-3(D425-452)-mEGFP from the pgl-3 locus showed that the P granule phase can support fast dynamics of these mutant PGL-3 constructs. Since P granules have a complex composition, one possibility is that fast dynamics of these constructs is supported by interactions involving many P granule components. We found that using only a limited set of P granule components (PGL-1, GLH-1 and mRNA) can buffer dynamics of S1 in condensates in vitro.

In absence of a systematic analysis investigating the individual role of approx. 70 P granule proteins in buffering S1 dynamics in condensates in vitro, we have claimed in the text that dynamics-buffering of S1 in condensates is supported by interactions among two or more components. However, we do appreciate the reviewer’s comment and feel it would be interesting to investigate the contribution of individual P granule components towards fluidity in future studies. We have discussed this in the ‘Discussion’ section of the manuscript.

• The biological relevance of PGL-1, GLH-1, and mRNA were not discussed in the main text. How these factors contribute to P granule assembly and function should be mentioned in the Introduction or Results. *

To address this concern, we have added a paragraph in the Introduction section of the revised manuscript.

*MINOR: 1. Line 20, "most non-membrane-bound compartments...have complex composition": Are there examples of condensates that do not have complex composition? *

Not all non-membrane-bound compartments may have been characterized. To accommodate this possibility, we refrained from making a more general statement, but stated “most non-membrane-bound compartments…”.

• Lines 40-43, RNA interactions driving LLPS: Please include citations from the Parker Lab (e.g. Van Treeck and Parker, Cell. 2018 doi: 10.1016/j.cell.2018.07.023) *

We added the reference suggested by the reviewer.

• *

• Line 60, condensates contain hundreds of different proteins and RNA: Please cite at least a few examples of condensates with their components identified. *

We added some references following suggestion by the reviewer.

• Lines 82-84, PGL-3 drives assembly: Please cite Kawasaki, et al. Genetics 2004 for the discovery of PGL-3. *

We added the reference suggested by the reviewer.

• Lines 88-89, PGL-3 N-terminal fragment predominantly alpha-helical: The PGL domain structures should be cited here as supporting evidence that these regions are composed primarily of alpha helices (Aoki, et al 2016, 2021) *

• *

To address this concern, we have added a paragraph in the Introduction section of the revised manuscript.

• Lines 158-159, driving forces for phase separation: This statement should be removed or expanded. The authors point regarding the protein concentrations is not clear here but clarified in the Discussion (Lines 691-693). Recommend removing due to its speculative nature. *

We retained the speculative comment in the results section. We feel that this prepares the readers for the discussion later in the manuscript.

• Lines 210: Add commas before and after "PGL-1 and GLH-1"*

We addressed the reviewer’s suggestion.

• Lines 218-219: add "and" instead of comma between PGL-1 and GLH-1 *

We addressed the reviewer’s suggestion.

• Lines 238-239, alpha-helices: The PGL CDD structure should also be referenced here (Aoki, et al 2016). *

To address this concern, we have added a paragraph in the Introduction section of the revised manuscript.

• Lines 680-682, MEG proteins: Please cite accordingly. *

We added the reference suggested by the reviewer.

• Lines 694-695, heterotypic interactions: Please cite Saha, et al. 2016. *

We added the reference suggested by the reviewer.

• Figure 1: Add space between 1 and mM DTT *

We addressed the reviewer’s suggestion.

• Figure 2b: Please provide statistics between condensate numbers. *

We provide statistics between condensate numbers in Fig. 2b.

• Figure 4A: The region of the germline imaged and analyzed should be mentioned in the caption or the main text. *

We revised the Figure legend of Fig. 4a to address this issue.

• Figure 4B,C: Please include statistics between the FRAP curves. *

We have included statistics comparing FRAP curves in Supplementary Fig. 4a-c.

• Figure 4D: It will be helpful to compare this curve to Figure S4A in the same graph. Please also include graph statistics. *

We have revised Fig. 4 to address the reviewer’s suggestion.

• Figure 5: The data points are difficult to resolve. Recommend use of color.*

We considered the suggestion, but felt it works better in the original form.

• Figure 6: This is a very general model that does not highlight the extensive experimental work performed by the authors. Recommend incorporating PGL-3, mutants and P granule factors into this model. *

We thank the reviewer for appreciating our extensive work. However, we retained the original Fig. 6 for the sake of simplicity.

• Methods, Line 939, C. elegans section: What worms were used? TH623? Please describe the genotype. *

We have included a table listing the strains used in the study and their genotype. * CROSS-CONSULTATION COMMENTS While my review was arguably the more favorable of the three, I agree with the other reviewers' comments and evaluation, particularly with Reviewer #1. As written in my review, my primary concern was the biological relevance of the work.*

Reviewer #3 (Significance (Required)):

Overall, the in vitro work presented investigating the biophysical properties of this minimal P granule system was thorough and well-analyzed, and the manuscript was clearly written. Additional citations and statistics will improve the manuscript and the strength of the conclusions, respectively. The biological relevance of this study to P granule form and function in vivo, and to condensates in vivo, is debatable. This work will interest those who study condensate biology, the biophysics of protein-protein and protein-RNA interactions, and RNA biochemists more generally.

A major impact of our work is elucidation of a novel “dynamics buffering” property within biomolecular condensates in vitro. Our in vivo data is consistent with this finding.

We have chosen two orthogonal ways of perturbing the PGL-3 protein (i.e. mutations and temperature-dependent unfolding) to assay the effect on diffusion rate against different levels of perturbation (e.g. 30% loss of a-helicity in heat-denatured PGL-3-mEGFP vs. 15% loss of a-helicity in the S1 mutant, compared to wild-type PGL-3). Studying the phase separation behavior of these “artificially-generated” constructs provided the understanding that dynamics of PGL-3 in condensates depends on inter-molecular interactions, and slower dynamics generally correlate with stronger inter-molecular interactions. Further, interactions among two or more P granule components can buffer against large change in dynamics / aggregation within the P granule phase. These insights may lay the groundwork for addressing how more “natural” modifications (e.g., post-translational modifications, high local concentration of “sticky” molecules) may influence dynamics within biomolecular condensates in vivo.

• *

Based on current knowledge of P granule composition, chaperone proteins (e.g. heat-shock family proteins) do not show abundant concentration within P granules. However, it is unclear if chaperone proteins are completely excluded from the P granule phase. Therefore, we speculate that weak interactions among two or more non-chaperone proteins contribute significantly to “dynamics buffering” within the P granule phase in vivo.

In the discussion section of the manuscript, we had speculated that “dynamics buffering” may potentially explain observations reported in the nucleolus: “Similarly, interactions among components could be a potential mechanism of storage of misfolding-prone proteins in non-aggregated state within the liquid-like nucleolus under stress in vivo (Frottin et al, 2019).”

Our finding is also relevant in the context of synthetic biology with applications that require steady diffusion rate of macromolecules during biochemical reactions within biomolecular condensates.

*I have expertise in P granules, protein/RNA biochemistry, condensate assembly, and C. elegans. *

References

Aoki ST, Kershner AM, Bingman CA, Wickens M & Kimble J (2016) PGL germ granule assembly protein is a base-specific, single-stranded RNase. Proceedings of the National Academy of Sciences of the United States of America

Aoki ST, Lynch TR, Crittenden SL, Bingman CA, Wickens M & Kimble J (2021) C. elegans germ granules require both assembly and localized regulators for mRNA repression. Nat Commun 12: 996

Cipriani PG, Bay O, Zinno J, Gutwein M, Gan HH, Mayya VK, Chung G, Chen J-X, Fahs H, Guan Y, et al (2021) Novel LOTUS-domain proteins are organizational hubs that recruit C. elegans Vasa to germ granules. Elife 10: e60833

Frottin F, Schueder F, Tiwary S, Gupta R, Körner R, Schlichthaerle T, Cox J, Jungmann R, Hartl FU & Hipp MS (2019) The nucleolus functions as a phase-separated protein quality control compartment. Science 365: 342–347

Kawasaki I, Amiri A, Fan Y, Meyer N, Dunkelbarger S, Motohashi T, Karashima T, Bossinger O & Strome S (2004) The PGL family proteins associate with germ granules and function redundantly in Caenorhabditis elegans germline development. Genetics 167: 645–661

Kawasaki I, Shim YH, Kirchner J, Kaminker J, Wood WB & Strome S (1998) PGL-1, a predicted RNA-binding component of germ granules, is essential for fertility in C. elegans. Cell 94: 635–645

Phillips CM & Updike DL (2022) Germ granules and gene regulation in the Caenorhabditis elegans germline. Genetics 220: iyab195

Price IF, Hertz HL, Pastore B, Wagner J & Tang W (2021) Proximity labeling identifies LOTUS domain proteins that promote the formation of perinuclear germ granules in C. elegans. Elife 10: e72276

Saha S, Weber CA, Nousch M, Adame-Arana O, Hoege C, Hein MY, Osborne Nishimura E, Mahamid J, Jahnel M, Jawerth L, et al (2016) Polar Positioning of Phase-Separated Liquid Compartments in Cells Regulated by an mRNA Competition Mechanism. Cell 166: 1572-1584.e16

Spike C, Meyer N, Racen E, Orsborn A, Kirchner J, Kuznicki K, Yee C, Bennett K & Strome S (2008a) Genetic analysis of the Caenorhabditis elegans GLH family of P-granule proteins. Genetics 178: 1973–1987

Spike CA, Bader J, Reinke V & Strome S (2008b) DEPS-1 promotes P-granule assembly and RNA interference in C. elegans germ cells. Development (Cambridge, England) 135: 983–993

Author Response

Reviewer #1 (Public Review):

Several questions have remained regarding the characteristics of these cells:

1) Based on the transcriptome data in Figure 2, the authors inferred that thymic macrophages are "specialized in lysosome degradation of phagocytosed material and antigen presentation" yet did not show functional data to support these claims. Functional assays such as phagocytosis and antigen presentation are desirable, especially in comparison to other well characterized macrophage populations.

We agree with the reviewer that additional functional characterization of thymic macrophages will strengthen the conclusions of our manuscript. We have performed antigen presentation assay and in vitro phagocytosis assay to functionally characterize the thymic macrophages. Indeed, thymic macrophages seem to be quite good antigen presenting cells – not as good as thymic DCs, but much better than peritoneal macrophages. This is documented in Fig. 3A and B. They were also good phagocytes both in vitro and in vivo as demonstrated in Fig. 3C-G. Surprisingly, peritoneal macrophages were better in the in vitro phagocytosis assay. We attribute this result to thymic macrophages’ poor survival during the sorting and in vitro culture.

2) Do transcriptomes of CX3CR1+ thymic macrophages in old mice significantly differ from those of young mice?

This is a very interesting question that we plan to explore in the future, but we feel it is beyond the scope of the current manuscript.

3) It would be helpful to better graphically show the compositions (both cell number and cell ratio) of thymic macrophage subsets (TIM4+, CX3CR1+, and others) in mice at different ages (1 week, 6 weeks, and 4 months old). It is not straightforward to deduce all the information based on the current data presentation.

We thank the reviewer for the suggestion! Plotting the cell numbers did reveal a peak in young age and then significant decline in the number of Tim4+ cells and a trend for accumulation of Tim4+ cells with age. Unfortunately, older mice show great variability in thymus size, which prevented the Tim4- result from being statistically significant. We have added these data to Fig. 8F.

4) The description of the gating strategy of thymic macrophages for Figure 1 is quite verbose. Adding a step-wise gating strategy of thymic macrophages as a figure panel would be helpful for readers to follow the experimental details.

We thank the reviewer for the suggestion. The description of the gating strategy has been stripped to 2 panels that capture its essence (Fig. 1B).

Reviewer #2 (Public Review):

This work provides by far the most thorough characterization of thymic macrophages. The authors used bulk RNA-seq, single-cell seq and fate mapping animal models to demonstrate the phenotype, origin and diversity of thymic macrophages. Overall the manuscript is well written and the conclusions of the paper are mostly well supported by data.

Some aspects of data acquisition and data analysis need to be clarified.

1) the authors should state what does row min row max in figure2 b,d refer to. is this expression value on log scale? In figure 2d, the authors compared their own RNAseq data with ImmGen seq data, what kind of normalization did the authors apply?

We appologize for not making this clear. The values in Fig. 2b and d (current Fig. 2A and C) are expression values on log scale. We have included this information in the figure.

Our data is part of the IMMGEN dataset. We sorted the cells and sent them to the US for RNA sequencing. That is why we referred to it as “our” data. However, to avoid confusion we changed the wording to clearly reflect that the data are from IMMGEN.

2)The authors used immunofluorescent to identify the localization of two populations of macrophages, where they used merTK staining to indicate all macrophages. However, MerTK expression may not restrict to immune cells. The authors are encouraged to confirm that MerTK only labels macrophages in thymus by co-staining with F4/80 or CD45. Tim4 can also be used in immunofluorescence.

We agree that staining with additional macrophage markers will strengthen our conclusions about ThyMacs localization. We have performed staining with CD64 together with MerTK or Tim4. CD64 and MerTK almost completely overlapped and so did CD64 and Tim4 in the cortex. We could not stain MerTK and Tim4 together because the antibodies are raised in the same species (rat). Additional evidence for the specificity of these markers for thymic macrophages comes from Fig. 3E and F showing the high degree of co-localization of apoptotic cells (TUNEL+) with MerTK or Tim4. Finally, Fig. 4 figure supplement 1 also clearly shows the distribution of TIM4 and CD64 in the whole thymus.

3) The data of Cx3cr1+ cells accumulation with age in thymus is very interesting, and as the author has discussed, might indicate their contribution to thymus involution. However, the authors only showed change of percentage. As the total macrophages numbers decreased with age, it is not clear whether these cells actually "accumulate" with age. It will help us to assess if this increased percentage of Cx3Cr1+ cells is an actual increase of "influx" or due to the decrease of the self-maintain Tim4+ macrophage subsets.

The reviewer is raising a very important point. As the changes in the Tim4+ and Tim4- thymic macrophages proportions with age occur at the background of thymic involution, it is difficult to judge whether Tim4+ cells self-maintain and whether Tim4- cells accumulate. Plotting the cell numbers revealed a peak in young age and then significant decline in the number of Tim4+ cells and a trend for accumulation of Tim4+ cells with age. Unfortunately, older mice show great variability in thymus size, which prevented the Tim4- result from being statistically significant. We have added these data to Fig. 8F.

Reviewer #3 (Public Review):

This study by Zhou et al. focuses on thymic macrophages and shows that two populations can be distinguished with different identities, localization and origin. Authors use several murine reporter and fate-mapping models, coupled with flow cytometry and transcriptomics approach to support their claims.

Overall, the question tackled by this study is interesting, thymic macrophages having a bit being forgotten in the last decade which has seen many studies similar to the one presented here in other organs. So, the stated aim to closing this gap is relevant. But the actual version of the study suffers from many defects, more or less severe, which affect the clarity and the persuasiveness of it.

• About the plan, authors study the origin of the thymic population and provide data in fig 2, 3 & 4 assuming that thymic macs form a homogeneous population. But from fig 5, they distinguish 2 populations and study them separately. So the end of the paper renders obsolete the beginning, that asks for a revision of the whole plan.

We agree with the reviewer that there is more than one way to tell this story and we have been agonizing over our plan. However, we respectfully disagree that the beginning of the paper is made obsolete by the ending for several reasons:

1) The initial figures in our manuscript contain very fundamental characterizaition of ThyMacs. Just as the revelation of a heterogeneity in liver macrophages or lung macrophages (ref) does not render all prior research on these cells obsolete, the initial figures in our manuscript are an essential part of the story. Such data are available for all other studied tissue resident macrophage populations. Removing them will be a disservice to the community.

2) Another reviewer asked for deeper characterization of ThyMacs based on the data in Fig. 2. Accommodating this request will be very difficult if we remove this part.

Nevertheless, we agree that ThyMacs heterogeneity is the central claim of the manuscript and should be introduced earlier. Now, the original figure 5 (current Fig. 4) that described the heterogeneity has been moved before the original figures 3 and 4 (current Fig. 5 and 6). Additional analyses distinguishing Tim4+ and Tim4- ThyMacs has been incorporated in current Fig. 5 and 6.

• The figure 1 is not very clear. The backgating should be added in 1a. Or why not using the color map axis mode from FlowJo to show 3 parameters at a glance? The gating strategy should be more clearly displayed on the figure. On fig 1S3, there are clearly 2 pops in the CX3CR1-GFP mice. Why not starting from this to introduce the two populations?

We thank the reviewer for the suggestion. We have included a color map axis to show MerTK, CD64, and F4/80 in one plot. The description of the gating strategy has been stripped to 2 panels that capture its essence. \We agree that there are several indications for heterogeneity among thymic macrophages, starting with Fig. 1E – the expression of Tim4, and Fig S4c – the expression of CX3CR1-GFP. We have added extra text at the beginning of the paragraph describing current Fig. 4 to point out these facts.

• The figure 2 could be revised also. First, the panel 2a is useless and should be removed. A PC analysis of all the macs would be more useful here. Also, the color code used for the genes is confusing. Why genes up in ThyMacs are red in 2b but only half of them in 2d? Info can be found in the legend but it should be more clear on a graphical point of view.

We have revised Fig. 2 according to the reviewer’s suggestions. The PCA analysis is consistent with the hierarchical clustering and shows that splenic and liver macrophages are most closesly related to ThyMacs. We agree that the presence of red in both heatmaps is confusing and we have changed the color code – color was removed from current Fig. 2A but retained in Fig. 2C.

• For figure 3, what is the timepoint of the panel 3b? Here, authors should show microglia and ThyMacs for both timepoints and conclude based on the comparison. If ThyMacs are as stable as the microglia, no replacement. If not, replacement. For the panel 3f, n=3 is too low to be convinced notably with the standard variation here. And displaying the dot plot with 11% of blood mono from donor while the median being around 20 is not fair, authors should present the most representative plot. For the panel 3h, there are more GFP (in term of MFI) for TEC and ThyMacs than for total cells. How is it possible? TECs and ThyMacs should be in the total cells? Or the gating is not clear enough?

We thank the reviewer for pointing our omissions. Fig. 3b (current Fig. 5B) is from E19.5 and we have added this information to the figure. We also agree that in Fig. 3f (current Fig. 5F) the sample number is too small and the variation too large to make solid conclusions. That is why we have repeated the partial chimeras experiment trying to irradiate as much as possible of the mice without affecting the thymus. We have substituted the data in the Fig. 3e and 3f with the new data. For Fig. 3h, we appologize for not labeling the data clearly. The panels labeled “single, live cells” should be labeled as “thymocytes” as they were obtained without enzymatic digestion that is essential for both TECs and ThyMacs. However, we found an important caveat in the thymus transplant experiment. It appeared that some of the thymus macrophages were GFP positive not because they express GFP but because they have engulfed GFP+ cells. As a result our experiments with embryonic GFP+ thymus transplants overestimate the percentage of donor-derived ThyMacs (all of them were GFP+). We have repeated the thymus transplantation experiments with congenically marked thymuses (CD45.2 donor and CD45.1 host). While this set up did not allow us to use the thymic epithelial cells as positive control because they are CD45-, we did identify host-derived ThyMacs, consistent with Tim4- cells originating from adult HSCs. Thus, we have replaced the previous data in Fig. 3H and 3I with current figures 5H and 5I.

• For figure 4, the EdU staining (4e) is not convincing at all. The signal is very low (as compared to 4c for example.

We agree that signal after 21d chase is a lot weaker than after 2 h (Fig. 4c) or 21d (Fig. 4e) of EdU pulse. The reason we decided to keep this data is that: 1) the thymocytes also have much lower EdU staining after 21d chase compared to 2h and 21d of EdU pulse; 2) The results from EdU staining are very consistent with the data from Ki67 staining, cell cycle analysis, and scRNA-Seq revealing a small population (~5%) of cycling ThyMacs.

• For figure 7, the interpretation of the data and the way to present them are not clear. Authors use an inducible fate-mapping model. The fact that Tim4- loose their signal with time argue for a replacement by non-labelled cells (blood monocytes) whereas Tim4+ ones are stable meaning they self-maintain. It is what authors claim. But how it fits with previous data where they say that Tim4+ derived form CX3CR1+? The explanation that is a bit subtended here but not enough clearly shown is that CX3CR1+ give rise to Tim4+ during embryonic development but is stops after, Tim4 self-renew independently, and CX3CR1+ are slowly replaced by monocytes. As this is the central claim of the paper, it should be most clearly reported and for this, a substantial change of the whole plan is required.

We thank the reviewer for pointing out the need for better explanation. The maintenance of the different populations of ThyMacs is indeed complex and proceeds in different ways in the different periods of life. We have added some extra data to Fig. 7 (current Fig. 8) that we hope will add some clarity to the maintenance of thymic macrophages with age. The new Fig. 8F shows the dynamics of the cell numbers of Tim4+ and Tim4- macrophages with age. Tim4+ cells reach a peak in young mice and decline significantly as mice age. So, we do not think that they are self-maintaining but instead, undergo slow attrition with very limited replacement. These results are consistent with Fig. 6I showing low levels of Mki67 in Tim4+ cells. Tim4- are a different story: they progressively accumulate with age. Although the variability in thymus size and Tim4- macrophages in very old mice is too great for the data to reach significance, the trend is clear.

As for the dynamics of the populations in the embryonic period, we added data formally demonstrating that TIM4+CX3XR1- are derived from CX3CR1+ cells by fate mapping (Fig. 7E-G). We induced re-combination in pregnant ROSA26LSL-GFP mice pregnant from Cx3cr1CreER males at E15.5 when almost all ThyMacs are Cx3cr1+ (Fig. 7A). Just before birth, at E19.5, we could find a substantial proportion of TIM4+CX3CR1- cells among the fate mapped GFP+ macrophages, indicating that Cx3cr1+ cells, indeed, give rise to TIM4+CX3CR1- cells. As pointed out before, this pathway gets exhausted by the first week after birth – at d7 all ThyMacs are TIM4+.

I totally agree with this statement, because different people think differently, and people's brain work and learn in different ways in different stage of growth. It is really surprising that we as a student, all learn from the same method and experiences. And clearly the style of teaching should be change,

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Reply to the reviewers

Reviewer #1 (Evidence, reproducibility and clarity (Required)):

The authors have assembled an enormous amount of statistical data on the genomes and phylogeny of Arctic algae, including the genomes of four new species that they sequenced for this study. Their main finding is that horizontal gene transfer has led to convergent evolution in distantly related microalgae.

**Major comments**

Reviewer #1__: The purpose of the study is not clearly stated in the abstract or the introduction. The authors say (line 93) "Defining the genetic adaptations underpinning these small algal species is crucial as a baseline to understand their response to anthropogenic global change (Notz & Stroeve,2016)." Is this their goal? Or are they just quoting another study? The authors state (line 103) "We extend by sequencing the genomes of four distantly related microalgae...". This is not really a question or a hypothesis. I am sure the authors can provide a more compelling reason to embark on such a labor-intensive study.__

Reply: We agree that the aim was lost in the details and the Introduction is now focused towards the original goal of the study, which was to investigate convergent evolution in a biogeographically isolated ocean. Additional references on the formation and history of the Arctic Basin have been added to the Introduction to provide context. “An ocean has been present at the pole since the beginning of the Cretaceous. Shaped by tectonic processes (Nikishin et al., 2021) the Arctic Ocean has been a relatively closed basin since the Masstrichtian at the end of the late Cretaceous epoch (ca. 70 million years before present), with episodic sea-ice cover since that time (Niezgodzki et al., 2019). This long history suggests limited gene flow from the global ocean over vast time scales and Arctic marine species including microalgae could well have unique adaptations to cold arctic conditions.” Line 78-83.

And following this we provide a clear hypothesis “The potential for lineages of ancient Arctic origin and the episodic input of outside species led us to our hypothesis that Arctic microalgae convergently evolved traits or adaptations aiding survival in an ice-influenced ocean. Line 112-117.

We also discuss both the adaptive and distinct physical environment of the Arctic, and its topographical separation from other ocean regions as dispersal limitation would enhance the Arctic-specific genomic signatures. We now cite the recent paper by Sommeria-Kline et al. (2020), which puts eukaryotic plankton biogeography into a global context (Line 72)

Reveiwer #1__: The most prominent shared trait that the authors found are genes for ice-binding proteins. However, in view of their importance, little information is given about their different types and possible functions.__

Reply: We appreciate the comment and have added information on relevant ice binding proteins found in the Arctic Algae. In addition, we discuss how the functional and secretory diversity of IBP would enhance the survivability of pelagic taxa. Lines 534 to 564.

Although ice binding proteins from multicellular animals and plants are outside the scope of this study, there is a recent review; Bar Doley, Braslavsky and Davies 2016 Annual review of Biochemisty 85: 515-542.

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Reviewer #1__: The HGT of ice-binding proteins is a major focus of this study, but little is said about what previous studies have said about this. What are the previous studies, what are their findings and how do the present findings contribute to this?__

Reply: We agree that this aspect should have been more visible. We incorporated new data to characterize IBPs drawn from MMETSP transcriptomes, and environmental Tara Ocean metagenomes, as well as our Arctic strains. We note that as we take a PFAM-based approach, the IBPs treated are DUF3494/PF11999 domain, which are type 1 IBPs / algal IBPs (Raymond and Remia 2019). As an example of novelty, we identify the position of IBPs from dinoflagellates, within a larger Arctic Clade that included CCMP2293, CCMP2436 and CCMP2097 and Arctic TARA IBP, rendering this a pan-algal IBD clade.

In addition, we were able to resolve the position of anomalous F. cylindrus IBP that fell between two Arctic associated clades (A and B, in our Fig 4). This finding is consistent with F. cylindrus originating in the Arctic as previously suggested and subsequently invading the Southern Ocean.

The recurrent acquisition of multiple diverse IBP isoforms in individual species through HGT events has not been previously reported, and the extent of isoforms in the Arctic was surprising. See for example multiple different IBP forms with separate origins in Pavlovales CCMP2436 (Fig 4). The previous studies are referred to in the context of the phylogeny of the IBD within the results section: Lines 322- 413, and Lines 534-585.

Reviewer #1: Figure 5 on HGT of ice-binding proteins is difficult to follow. It would be clearer if each panel could be described separately, clearly stating its main finding. I doubt that a reader could look at this figure and explain to a colleague what it shows.

Reply: We have revised rearranged the figure (now Fig 4) with Arctic A, B, C and D clearly indicated as well as the two Antarctic dominated clades. The upper schematic includes the deepest phylogeny of algal IBDs to date, incorporating all of UniRef, MMETSP and TARA Oceans. The fasta files underlying the tree and the nexus file used are provided the S1 Data Folder, which is an excel folder with information on the analysis of the data. The callout and order of the clades has been revised to facilitate interpretation of the phylogenies more clearly. The entire section has been completely rewritten.

Reviewer #1: This is also a problem with many of the other figures. For each figure, what is the question being asked and what is its take-home message?

Reply: We agree that the message was lost and have now focused on our original question in our accepted proposal to JGI. “Is there a convergence among arctic microalgae at the genomic level?”. We found some genome properties were common among the Arctic isolates (more unknown PFAMS and several expanded PFAMs). The importance of ice binding proteins in Arctic Isolates and the widespread inter-algal HGT of this important protein among the Arctic strains. The IBP biogeography and phylogeny strongly indicate that the Arctic microalga have acquired IBP locally and that the Antarctic strains have acquired additional isoforms independently from Antarctic bacteria and fungi (Lines 565-585).

Reviewer ____#1____: ____The paper has more data than a reader can absorb. It could be strengthened by reducing the number of figures, simplifying them if possible, and more clearly stating the value of the remaining figures.

Reply. As suggested, we have refocused the paper, removing more speculative statistics based analysis and associated figures. The main conclusions are supported by the 5 main figures. We are now present 5 main figures and 11 supplementary figures (previously 23 downloadable supplementary figures and 40 on-line only figures supporting the support figures). We agree with the reviewer, and we feel the revised version is a more transparent synthesis. Briefly the Figures illustrate the following points. Fig. 1. The multigene tree of available algal genomes and transcriptomes provides a clear framework for judging the divergence of subsequent individual gene and PFAMs phylogenies. Fig. 2 (originally Fig. 3). Indicates the convergence of PFAM domains in the Arctic strains, in contrast to strains from elsewhere. Fig. 3 (originally Figure 4) shows Arctic specific expansions and contraction of PFAM domains, again demonstrating convergent evolution in the Arctic. The figure identifies specific PFAMs that contribute to the within-Arctic convergence. This figure is based on statistical methods independent of Fig 2. Figure 4 is the most extensive IBP phylogeny to date and has been discussed above. Figure 5, which was supplementary in our non-peer reviewed version, shows the biogeographic distribution of IBP, and can be compared to the distributions of the 18S rRNA genes from the four Arctic algae provided as supplementary (S6 Fig.)

**Minor comments**Reviewer #1

1. The figure citations are confusing. E.g., what does "Fig.1- Figure supplement 1" refer to? Does this refer to 1 or 2 figures? Apparently, it refers only to Fig. S1, so many readers will be confused when they look at Fig. 1.

Reply: We apologize for the confusing format; the manuscript had been formatted for the online journal eLife. Our revision follows the more traditional style of PLoS Biology and other Review Commons journals.

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Multiple citations should be in order of publication date, not alphabetical order.

Reply ; We agree that date of publications is quite standard and recognizes priority of publication. Several on line journals no longer follow this rule and citation order will follow the specific style used by our accepting journal.

Reviewer #1 (Significance (Required)): It is well known that useful genes tend to be shared among microorganisms. The present study strengthens previous studies in showing that gene transfer is an important process in polar regions.

Reply: We thank the reviewer for recognizing the importance of our study.

Reviewer #2 ____(Evidence, reproducibility, and clarity (Required)):

This manuscript is the result of a large international collaborative effort, including the US Department of Energy Joint Genome Institute. Its focus is comparative genomics of eukaryotic Arctic algae. The primary data described in the ms are four new genome and transcriptome sequences from diverse Arctic algae, represented by a cryptomonad, a haptophyte, a chrysophyte, and a pelagophyte.

The authors compare these new data to previously published genomic/transcriptomic data from eukaryotic algae with the goal of understanding genome evolution in the Artic. The results of the paper are a series large-scale comparative genomic bioinformatics analyses, including the associated statistical analyses. The key findings center on statistically significant features of Arctic genomes, features that stand out as compared to the genomes of algae that are not primarily found in the Arctic. Together, these findings allow the authors to make various hypotheses and suggestions about genetic adaptations to polar environments.

By far the most significant finding is that the genomes of Arctic algae are enriched in genes encoding proteins with an ice-binding domain, paralleling findings from Antarctic algae. These genes appear to have spread among Arctic algal genomes via horizontal gene transfer, which raises a series of interesting questions. In my opinion, the major conclusions of this paper are supported by the data. Listed below are a few comments that may improve the ms:

Reviewer #2.

1) In today's post-genomics era, everyone seems to be sequencing nuclear genomes. Often what distinguishes high-impact and low-impact genome papers is the number of genomes presented and the quality of the genome assembly. I may have missed it, but reading the main text, the figures/tables, and the supplementary data I was not able to get a sense of the quality of the four genome assemblies from which the main findings are based. I was eventually able to find this information from PhycoCosm (note: some of the links to this site are not working in the ms). My quick scan of the PhycoCosm summary info for the four genomes indicates that the assemblies are highly fragmented, likely because they are based on short-read Illumina sequencing rather than a combination of short and long reads. I think it is important to briefly discuss (and or present) the quality of the assemblies in the ms and to highlight the potential limitations/drawbacks of employing highly fragmented assemblies when carrying out large-scale comparative genomics.

Reply: We agree and the data concerning the genome quality assemblies has been moved to the main text Table 1. The comparison with other paired related strains is provided in an excel folder designated S2 Data Folder.

Reviewer #2.

2) Horizontal gene transfer is undeniably a major driving force in evolution, and one that has shaped genomic architecture across the Tree of Life. I believe the data presented here support a role for HGT in the genome of evolution of Arctic algae, particularly with respect to genes encoding proteins with an ice-binding domain. However, we can all think of numerous instances when authors of genome papers were too quick to point to HGT. Thus, I would urge more caution and balance when presenting the HGT data, including some discussion about factors that could incorrectly lead researchers to conclude a significant role for HGT, such as contamination, gene duplication, mis-assemblies, etc. I'm not suggesting that you change the main conclusions, but just tone down the language in places (e.g., "we reveal remarkable convergence in the coding content ... ").

Reply: We understand the reviewers concerns and now more clearly outline the pipeline we have used to identify HGTs. This included: filtering each genome to remove all possible contaminant sequences first, considering both contig co-presence of vertical- and horizontally-derived genes, and reciprocal and independent annotations of gene sequences in both genome sequences and MMETSP transcriptomes. Retained genes were subjected to simultaneous BLAST analysis and manually curated phylogenies using decontaminated reference datasets. The most parsimonious explanation for our final IBP domain microbial algal clusters (Fig 4) is HGT. On the side of caution, we removed the entire section that identified potential arctic HGT based primarily on a less targeted broad statistical analysis. The focus is now on 3 genes that have clearly identifiable utility in the Arctic, were found to be enriched in Arctic genomes via a separate analysis and had homologs in the Tara Ocean Polar circle data. In addition, we describe more clearly the role of expansion and enrichment of PFAMs and the high proportion genes without an identifiable PFAMs in the Arctic strains as evidence for Arctic convergence separate from potential HGT.

Reviewer #2.

3) The downside of studying protists (as compared to multicellular animals, for instance) is that most are not widely known by the scientific community and even fewer scientists can picture what they actually look like (e.g., Pavlovales sp. CCMP2436). A few more details about the four Arctic algae that make up the focus of this paper might be helpful for the casual reader. My sense is that if at the next departmental meeting I asked my colleagues what a pelagophyte was most would look at me with a blank stare. Moreover, am I right to assume that all four algae are psychrotolerant rather than psychrophilic (Supplement Fig. 1 makes me think otherwise). It might be good to point out the difference in the text.

Reply: High resolution images of each strain are available on the JGI home page for each alga, given the multiple figures we feel photos would not add information.

Reviewer #2

4) I don't think Supp. Table 1 (the Pan-algal dataset) got uploaded correctly during the manuscript submission stage. The first link I click on gives me Supp. Table 2.

Reply: We apologize for this, the format was incorrect for the file designation and there were lost links. We now more actually refer to these as Data Folders as they are excel folders containing multiple sheets, All supplementary links will be verified again on final submission.

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Reviewer #2 (Significance (Required)):

By far the most significant finding from this paper is that the genomes of Arctic algae are enriched in genes encoding proteins with an ice-binding domain, paralleling findings from Antarctic algae. These genes appear to have spread among Arctic algal genomes via horizontal gene transfer, which raises a series of interesting questions. This is not the first paper to present these types of ideas, but it is arguably the broadest analysis yet, at least with respect to eukaryotic algae. This work will be of great interest to polar scientists, phycologists, protistologists, and the genomics community. I am genome scientist studying protists, including algae.

Reply. We thank the reviewer for their insightful comments.

Reviewer #3 (Evidence, reproducibility and clarity (Required)):

**Summary:**

This manuscript is focused on Arctic microalgae, an important yet understudied community in permanently cold ecosystems. By sequencing the genomes of four phylogenetically diverse and uncharacterized polar algae, the authors seek to elucidate genomic features and protein families that are similar in polar species (and differ from their relatives from temperate environments) This work used high-throughput genomic sequencing and computational analysis to demonstrate significant horizontal gene transfer (HGT) in several gene families, including ice-binding proteins. The authors suggest that this HGT is an effector of environmental adaptation to Arctic environments.

**Major comments and experiment suggestions:**

The authors conclude that HGT between arctic species is a driver of polar adaptation. The authors strongly support the claim that HGT is present more frequently in the polar algae examined here. Whether this is adaptive should be further explored though. For instance, ice-binding domains were one PFAM group found at significantly higher frequencies in the polar species - but are all of these species associated with ice? What would be the benefit of IBDs in an alga that is found in the open ocean. Similar with the other domains (Lns 333-335), its not clear whether these are truly adaptive features. ____This is more speculative.

Reply: We agree that detail was lacking and have considerably expanded our introduction on the character of the Arctic Ocean and have stated the goals and underlying hypothesis. Briefly, all surface water organisms that live in the Arctic encounter ice during the year as the ocean freezes in winter, and surface waters reman around negative 1.7 °C for much of the year. This information has been added to the introduction. We have also expanded the discussion on the multiple effects of different IBPs that would be ecologically beneficial for plankton as well as ice-algae and cite relevant experimental studies and reviews.

Reviewer #3) ____HGT was a major conclusion of this study, putting this in a wider perspective would strengthen the conclusion, especially in the context of HGT from prokaryotes. Are there insights on whether IBDs are present in Arctic prokaryotes?

Reply: This is a good question, and we now point out that there were 91 Arctic bacterial and archaeal IBP sequences in our comparative dataset. In contrast to the Antarctic clades, none were closely related to the Arctic strain IBPs (Fig 4). Line 336.

Reviewer #3) ____The data obtained from the genomic works supports the conclusions stronger that ones from transcriptomes, where what genes/domains are present would depend largely on the sampling conditions. This should be emphasized.

Reply: The main rational for using transcriptomes was that more of these are available and enabled us to detect convergences and HGT across a broader taxonomic range than would be possible with genome-only data, where we had access to a total of only 21 microalgal genomes. In general transcriptome studies are aimed at identifying responses under different conditions and rely on comparative expression data, usually 2-fold differences in up or down expression under different growth conditions, see for example Freyria et al. 2022 (Communications Biology). Unlike a transcriptome expression study, our data mining detected any (constitutive or regulated) expression in these unicellular haploid cells, we would have detected genes used under any condition that an algal happened to be growing. IBD was not detected in any of the temperate genomes, and only detected in transcriptomes of Arctic and Arctic-Boreal groups. However, we agree that there may be some limitation of transcriptomes only studies and mention this. Lines 522-528.

Reviewer #3) ____An experiment to determine whether the species are cold extremophiles (psychrophiles) would be useful here to strongly support the data in Figure 1. The authors state that their species can not survive >6C but this is based on experiments done on older studies. Considering the cultures have been maintained as a continuous culture for decades, confirming that they still have psychrophilic characteristic would be useful. This is a straightforward and low cost experiment that requires simply measuring growth rates at several temperatures to define the optimal and confirm that the cells are not viable above 6C.

Reply: These are interesting points, and the broad “background” statements in the original manuscript would require a separate study,and have been deleted. Temperature tolerance experiments are not so simple for cold adapted algae with slow growth rates. Such experiments require specialized incubators to maintain low temperatures. Temperature experiments have been carried out on the cultures in the context of other studies, see for example, Daugberg et al. 2018, J. Phycol. But this is not within the scope of the present study.

We now restrict our conclusions to the specific question of convergence among Arctic strains. We apologize for the misunderstanding on the history of the cultures. They have not been in “continuous culture” but are cryopreserved. We now simply indicate that they grow below 6 °C, which is sufficient to assume that they are likely cryophiles, our experience is that they do not grow well or at all at higher temperatures, our efforts have been to maintain the cultures that are otherwise easily lost. We now make no claims about optimality or limits. Here we simply examined genomes and available transcriptomes that were generated from algae growing at 4-6 °C.

Reviewer #3) ____**Minor comments:**

Defining the species used here as psychrophiles would put the study in context better. The authors relate their finding to Antarctic species (HGT, ice-binding domains, large genomes) all of which are confirmed psychrophiles.

Reply: The temperature definition of psychrophiles is surprisingly high (optimal growth below 15 °C) and this definition of psychrophiles is now given in the introduction. The point is really that there are few isolates from cold surface waters that have been well studied. We now add. “A handful of polar algal genomes have been extensively studied, with 4 of these from around Antarctica and classified as psychrophiles (not being able to grow above 15 °C (Feller & Gerday, 2003)”. Lines 103-107.

Reviewer #3) ____A short rationale on why these species at all would be useful - are they representative of their classes? Do they have psychrophilic characteristics that might make them useful models in the future? Are they widely used now?

Reply: We appreciate the point as the definition of utility in discovery-based science is an open dialog.

We agree that the study requires context and have added our rational for selecting the species for genome sequencing to the introduction. “To address questions on genetic adaptations to this ice-influenced environment, we sequenced 4 phylogenetically divergent microalgae, from 4 algal classes belonging to 3 algal phyla: Cryptophyceae (Cryptophyta), Pavlovophyceae (Haptophyta), Chrysophyceae and Pelagophyceae (both in the Ochrophyta) isolated from the ca. 77 °N, where surface ice flow persists through June (Mei et al., 2002). The four isolates were selected as representatives of different water and ice conditions and phylogeny from available strains collected in April and June 1998 during the North Water Polynya study”.

Reviewer #3) ____Starting algal cultures were maintained in a continuous culture since 1998 and under continuous light since at least 2015, have the authors confirmed that these algae retain their physiological features even after this long time? The accumulation of mutations is a possibility here.

Reply: We apologize for the misunderstanding of the timeline; the history of the cultures was not given in the manuscript and the inferred history is not quite correct. The 2015 date was the year of publication for the MMETSP data. Our continuous light statement is a record of our standard culture conditions. We now elaborate on the material used in the current study. The cultures were deposited in the Bigelow culture collection (now NCMA) in 2002 and cryopreserved once they had been verified and given a culture designation. We obtained fresh cultures in 2005 and these were used for the MMETSP project. We obtained fresh cultures again in 2011, specifically for the JGI genome project. These algae do not grow fast and most of the DNA was sent to JGI in 2012 for most of the isolates. This history is rather long and not relevant, since one would speculate that over the years the algae would tend to lose the ice associated functionality, e.g. they were not frozen in seawater every year for 4 to 6 months or subject to sudden freshwater exposure, when ice melts. We would encourage other researchers to order the cultures and run experiments. We note that many of the 40 or so algae isolated from the same campaign have been used by others for specific studies and at least 8 are in the MMETSP data set. The presence of 18S rRNA and phylogenetic position of the IBP sequences compared to Tara Arctic circle data confirms long-term Arctic presence of each species and the IBP domains in the Arctic without marked changes over the last 20 years.

Reviewer #3) ____Ln381 - The culture collection IDs for each sequenced species should be included here

Reply: we have added the culture IDs throughout.

Reviewer #3) ____Ln. 389 - Algal cells are harvested and used for nucleic acid extraction, the nucleic acids themselves are not harvested

Reply: we agree and corrected the wording

Reviewer #3 (Significance (Required)):

This study is well places in the current state of research on polar alga and represents a significant and very valuable addition to the current knowledge pool. Algae in general are lagging behind other groups of photosynthetic organisms in the number of sequenced and analyzed genomes, despite algae being one of the main primary producers globally. This is even more strongly felt in polar research, where only 4 species have been sequenced, most of which are restricted to Antarctica. There is a true gap in our knowledge when it comes to Arctic species, and this study fills this gap. As the authors correctly state, we need more knowledge on polar environments and the primary producers that support these important ecosystems in light of current climate change trends.

Reply: we appreciate the succinct summary of our study and thank the reviewer for insights and suggestions that have improved the manuscript.

Reviewer field of expertise: Polar algae, stress responses, plant and algal energetics, cell signalling

Reply: We appreciate the incites and perspective steming from the reviewer's expertise.

Relevant key references cited in the reply:

Daugbjerg N, Norlin A, Lovejoy C. Baffinella frigidus gen. et sp. nov. (Baffinellaceae fam. nov., Cryptophyceae) from Baffin Bay: Morphology, pigment profile, phylogeny, and growth rate response to three abiotic factors. Journal of Phycology. 2018;54(5):665-80

Feller, G. and Gerday, C. (2003) Psychrophilic enzymes: Hot topics in cold adaptation. Nat Rev Microbiol, 1, 200-208.

Freyria NJ, Kuo A, Chovatia M, Johnson J, Lipzen A, Barry KW, et al. Salinity tolerance mechanisms of an Arctic Pelagophyte using comparative transcriptomic and gene expression analysis. Communications Biology. 2022;5(1). doi: 10.1038/s42003-022-03461-2

Mei, Z. P., Legendre, L., Gratton, Y., Tremblay, J. E., Leblanc, B., Mundy, C. J., Klein, B., Gosselin, M., Larouche, P., Papakyriakou, T. N., Lovejoy, C. and Von Quillfeldt, C. H. (2002) Physical control of spring-summer phytoplankton dynamics in the North Water, April-July 1998. Deep-Sea Research Part Ii-Topical Studies in Oceanography, 49, 4959-4982.

Niezgodzki, I., Tyszka, J., Knorr, G. and Lohmann, G. (2019) Was the Arctic Ocean ice free during the latest Cretaceous? The role of CO2 and gateway configurations. Global and Planetary Change, 177, 201-212.

Nikishin, A. M., Petrov, E. I., Cloetingh, S., Freiman, S. I., Malyshev, N. A., Morozov, A. F., Posamentier, H. W., Verzhbitsky, V. E., Zhukov, N. N. and Startseva, K. (2021) Arctic Ocean Mega Project: Paper 3-Mesozoic to Cenozoic geological evolution. Earth-Science Reviews, 217.

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Referee #2

Evidence, reproducibility and clarity

This manuscript is the result of a large international collaborative effort, including the US Department of Energy Joint Genome Institute. Its focus is comparative genomics of eukaryotic Arctic algae. The primary data described in the ms are four new genome and transcriptome sequences from diverse Arctic algae, represented by a cryptomonad, a haptophyte, a chrysophyte, and a pelagophyte.

The authors compare these new data to previously published genomic/transcriptomic data from eukaryotic algae with the goal of understanding genome evolution in the Artic. The results of the paper are a series large-scale comparative genomic bioinformatics analyses, including the associated statistical analyses. The key findings center on statistically significant features of Arctic genomes, features that stand out as compared to the genomes of algae that are not primarily found in the Arctic. Together, these findings allow the authors to make various hypotheses and suggestions about genetic adaptations to polar environments.

By far the most significant finding is that the genomes of Arctic algae are enriched in genes encoding proteins with an ice-binding domain, paralleling findings from Antarctic algae. These genes appear to have spread among Arctic algal genomes via horizontal gene transfer, which raises a series of interesting questions. In my opinion, the major conclusions of this paper are supported by the data. Listed below are a few comments that may improve the ms:

1) In today's post-genomics era, everyone seems to be sequencing nuclear genomes. Often what distinguishes high-impact and low-impact genome papers is the number of genomes presented and the quality of the genome assembly. I may have missed it, but reading the main text, the figures/tables, and the supplementary data I was not able to get a sense of the quality of the four genome assemblies from which the main findings are based. I was eventually able to find this information from PhycoCosm (note: some of the links to this site are not working in the ms). My quick scan of the PhycoCosm summary info for the four genomes indicates that the assemblies are highly fragmented, likely because they are based on short-read Illumina sequencing rather than a combination of short and long reads. I think it is important to briefly discuss (and or present) the quality of the assemblies in the ms and to highlight the potential limitations/drawbacks of employing highly fragmented assemblies when carrying out large-scale comparative genomics.

2) Horizontal gene transfer is undeniably a major driving force in evolution, and one that has shaped genomic architecture across the Tree of Life. I believe the data presented here support a role for HGT in the genome of evolution of Arctic algae, particularly with respect to genes encoding proteins with an ice-binding domain. However, we can all think of numerous instances when authors of genome papers were too quick to point to HGT. Thus, I would urge more caution and balance when presenting the HGT data, including some discussion about factors that could incorrectly lead researchers to conclude a significant role for HGT, such as contamination, gene duplication, mis-assemblies, etc. I'm not suggesting that you change the main conclusions, but just tone down the language in places (e.g., "we reveal remarkable convergence in the coding content ... ").

3) The downside of studying protists (as compared to multicellular animals, for instance) is that most are not widely known by the scientific community and even fewer scientists can picture what they actually look like (e.g., Pavlovales sp. CCMP2436). A few more details about the four Arctic algae that make up the focus of this paper might be helpful for the casual reader. My sense is that if at the next departmental meeting I asked my colleagues what a pelagophyte was most would look at me with a blank stare. Moreover, am I right to assume that all four algae are psychrotolerant rather than psychrophilic (Supplement Fig. 1 makes me think otherwise). It might be good to point out the difference in the text.

4) I don't think Supp. Table 1 (the Pan-algal dataset) got uploaded correctly during the manuscript submission stage. The first link I click on gives me Supp. Table 2.

Significance

By far the most significant finding from this paper is that the genomes of Arctic algae are enriched in genes encoding proteins with an ice-binding domain, paralleling findings from Antarctic algae. These genes appear to have spread among Arctic algal genomes via horizontal gene transfer, which raises a series of interesting questions. This is not the first paper to present these types of ideas, but it is arguably the broadest analysis yet, at least with respect to eukaryotic algae. This work will be of great interest to polar scientists, phycologists, protistologists, and the genomics community. I am genome scientist studying protists, including algae.

Consolidated peer review report (23 September 2022)

GENERAL ASSESSMENT

In this manuscript, Tiemann, J., et al. take on a large-scale exploration of how mutations associated with disease impact calculated stability and conservation scores across the entire membrane proteome. The aim was to gain mechanistic insight into the causes of pathogenicity of missense mutations of human membrane proteins and verify whether, as is the case for soluble proteins, mutational destabilisation of membrane proteins can explain disease. To do so, the authors use a framework they previously developed, using measures of stability change (ΔΔG) and sequence conservation (ΔΔE, the GEMME score) to predict fitness effects of mutations with large-scale mutational data (Høie et al., 2022).

By conducting a proteome-wide analysis of missense variants in human membrane proteins, the authors find decisively that pathogenic mutations are heavily enriched within the transmembrane region of membrane proteins. In addition, they report that they can sometimes use their calculated properties to classify residues based on their potential roles in stability or function, and that stability appears to be a major determinant of conservation and likely pathogenicity for GPCRs. 

The authors thus make meaningful strides towards explaining the clinical impact of variants within membrane proteins, a currently under-characterized yet important category of proteins. The analyses have been conducted in a rigorous way, and the data and protocols are openly available. This work will be of interest to researchers working on membrane proteins as well as those applying computational methods to biophysical systems.

On the other hand, the choices made by the authors in terms of presentation make the identification of the main conclusions of the paper challenging. In part, this is likely due to fundamental technical challenges associated with calculating biophysical properties for membrane proteins. In addition, although the analysis was performed at the scale of the proteome, due to the decision to only consider X-ray crystallography structures, the number of proteins analyzed is rather small (15). It thus remains unclear how the findings are transferable to other membrane proteins and how robust the comparison between the different functional classes is. 

RECOMMENDATIONS

Revisions essential for endorsement:

1.     The authors are careful with what they claim, to the point where it becomes difficult to interpret the major messages. It appears there are many contributing factors to noise within these assays, resulting in complex figures that make it hard to interpret the data. The goal of presenting the data without overinterpreting it is noble, and the difficulty of digesting and presenting the comparisons in this work should be emphasized, but the complexity of the results made it difficult for reviewers to interpret without more robust processing. Further, we were not always certain how each result fits into the overall argument, which from our reading is whether the performance of predictors for classifying pathogenic mutations based on conservation and stability calculations provides insight into the mechanisms underlying membrane protein disease. Overall, we feel that clarifying the unifying argument of the manuscript and simplifying the figures would greatly improve the comprehensibility of this work. This could be achieved with one of the following approaches, although we leave the final choice to the authors: 

• The manuscript could attempt to answer the following question: “Can existing methods be used to computationally determine whether pathogenic mutations are due to stability?” It would then explore why this question can or cannot be answered with the current analysis pipeline and existing tools. The answer is likely that the current tools are insufficient and the manuscript would thus point towards a future area of growth to be able to address the question.

• The manuscript could focus on presenting the dataset. The results would be presented as preliminary examples of the kind of information that can be extracted and the type of analysis that may be done. In this case, claims such as “stability causes x% of pathogenic mutations” should be avoided, and the most important aspect of the manuscript would be that it accompanies a well-curated and openly available dataset, and provides links to it. In that context, the authors should mention whether there are existing curated and/or established databases of (human) membrane proteins, and how the dataset of putative membrane proteins compares with these resources.

• The manuscript could focus on presenting the “computational approach”, which consists of mapping ddG-ddE, combined with an analysis of the localization of pathogenic (and non-pathogenic) mutations and the types of mutation (conservative, non-conservative etc.). Revisions would be needed to present results as examples of the kind of information this approach may provide.

• The manuscript could possibly make a clear and compelling case for the idea that mutations of membrane proteins cause disease either because they destabilize the protein or because they occur at sites that are directly involved in function. This would require major revisions of the results and a systematic, clear and robust combined analysis of quadrant-location, protein-region-location, and amino-acid-type substitution.

Related to the above, it would be useful to clarify in the introduction what is expected from the study upfront: did the authors expect that the picture that would emerge would indeed be the same for membrane proteins as for soluble proteins? Are there different degradation pathways for these two classes of proteins and is a loss of stability expected to have different consequences or not? In the end, the role of destabilization is rationalized in terms of buriedness and amount of physico-chemical change upon mutations. Hence, are the results of the study saying something about the mechanisms of disease variants or simply about the physico-chemical composition and topology of membrane proteins? To answer this point, we suggest contextualizing the study more by expanding on the published literature. This would also clarify that the membrane protein folding field is very far behind the soluble protein folding field, and, as a result, that we cannot expect the methods that work for soluble proteins to work for membrane proteins, or even if methods will mature to the point that they do yield predictive results for membrane proteins. 

2.     In general, uncertainties need to be better quantified and discussed and statistical tests included. For example:

• The low correlation of Rosetta estimates of ΔΔG and experimental ΔΔG is 0.47, which means less than 25% of ΔΔG is accounted for by Rosetta. This uncertainty needs to be considered more carefully: it will likely affect the AUC (i.e. is AUC(ΔΔG) < AUC(ΔΔE) because not all mutations are pathogenic due to stability, or is this a mere consequence of the uncertainty of ΔΔG estimates?) and the number of points in the different quadrants (how many of the points in a quadrant are false-positives or false-negatives, etc., and can we guess which they are by using other information such as the protein region, aa-type change, ΔΔE value, etc?). 

• A variant may fall in the “wrong” ΔΔG-ΔΔE quadrant because of the mentioned (large) ΔΔG error, but also because of ΔΔE errors. This needs to be considered. Some estimate of the ΔΔE error needs to be made (e.g. by bootstrapping the alignment). Even in an ideal case in which ΔΔE is dependent only on ΔΔG, i.e. that both ΔΔG_Rosetta and ΔΔE are estimates of a “true” ΔΔG, not all points would fall in a y = x line in the ddE-ddG plane. How many points would there be in each of the quadrants because of mere estimation errors?

• As the authors state, quadrant IV has few points. But it also seems that there are more blue points than red points in regions further away from the axes. Could the author comment on this observation? Is there a tendency for the ΔΔG measure to “over predict” pathogenicity ?

• Within the manuscript the authors widely compare different groupings to drive their narrative. For example, on line 115 the authors discuss the enrichment of pathogenic mutations within the transmembrane domains, which then leads to many subsequent explorations of why TMs may be involved in disease. For this comparison, there is a large and visible significant difference, thus there may not be a need for a statistical test for significance. However, there are many other comparisons that are harder to interpret due to multiple different groupings, complex data representation, and at its core a fundamentally complex study. In these cases, we would like to see more robust statistical tests. For example, on line 184, after breaking up data in 2B based on ΔΔG and ΔΔE cutoffs, the authors write “...only a few variants (14.2%) falling in the quadrant of low ΔΔE and ΔΔG…” – it is unclear what a few means or if this is a significant reduction in variants compared to other quadrants. 

3.     Regarding the performance of Rosetta to measure ΔΔGs:

• The authors state that pathogenic mutations causing loss of stability are more often located in the interior of the protein (buried), implying bigger physico-chemical property changes. Isn’t that expected from Rosetta design? Indeed, while the analysis of the distribution of variants among protein regions (buried, etc.) and mutation-type (hydrophobic-to-hydrophobic, etc.) does add additional information to support the hypothesis that in some cases stability loss causes disease, it is important to recognize that this is not completely independent evidence because any ΔΔG predictor should somehow capture the observed patterns. 

• ROC curves are used to determine how well ΔΔG guides pathogenicity, as a follow up to the observations that pathogenic mutations are enriched in TM regions of membrane proteins. The intuition here is that deleterious mutations within TMs are likely disrupting folding and therefore a ΔΔG-based predictor should do relatively well. However, the authors find that Rosetta-based ΔΔG calculations do not do well in all membrane proteins with benign-like and pathogenic mutations (Figure 2A) and solved crystal structures. In contrast, ΔΔG works quite well when trained solely on GPCRs (Figure 3A). The interpretation of this could be that stability is not a major driver of membrane protein disease – however, in many cases it is, such as Rhodopsin and CFTR. In contrast, another explanation is that Rosetta doesn’t predict stability well for mammalian membrane proteins, and in fact the authors discuss this at length in the limitations of the study section, explaining this is because Rosetta is trained on many bacterial beta barrel membrane proteins. We appreciated this section but would have preferred more of this discussion earlier on as it could aid in understanding why the ΔΔG predictors don’t perform accurately, as presented in Figure 2A. 

• Could the authors clarify what they mean by “where the Rosetta energy function suggested a potential incompatibility between the experimental structure and the Rosetta energy function”? 

4.     Regarding ΔΔE, in the present work, there is an implicit assumption that the constraints that operate during evolution of the aligned sequences, across species, as captured by GEMME, are the same constraints that affect the variants within a population, and therefore determine whether a variant will be pathological/non-pathological. This is a major assumption that needs to be spelled out and discussed. Mentioning this will help interpret “misplaced” points of the ΔΔE-ΔΔG map.

Additional suggestions for the authors to consider:

1.     The comparison of pathogenic/non-pathogenic mutations should consistently be made across the various sections of the paper. In too many cases in the present version of the paper this comparison is not emphasized. In some cases, the distribution of variants is described, without clearly differentiating pathogenic from non-pathogenic. In other cases, only pathogenic variants are considered, without comparing with the non-pathogenic cases.

2.     Moving the section on the two specific proteins to the end of results would likely improve the flow of the paper. The A/B x ΔΔE-ΔΔG plane analysis would be presented first, then the A/B x ΔΔE-ΔΔG x “protein regions” analysis, and finally the A/B x ΔΔE-ΔΔG x regions x “aa-type” analysis before ending with examples.

3.     The choice to restrict the analysis to X-ray crystallography structures from the PDB is not obviously well suited. Indeed, the coverage of membrane proteins by the PDB is rather low, and the authors found that less than 30% of all annotated human membrane proteins have at least some part resolved. One of the potential advantages of the AlphaFold database is to improve this coverage, and the analyses presented by the authors would thus benefit from considering predicted models displaying high confidence values.

4.     In Figure 2, the authors define two classes of variants in their dataset, group A (pathogenic variants) and group B (benign or non-pathogenic with an allele frequency > 9.9 · 10^-5). Then they tested their models’ ability to distinguish between groups A and B by constructing ROC curves for Rosetta ΔΔG and GEMME ΔΔE. To visualize variant effects and further classify variants, they plotted individual variants along a ΔΔG vs. ΔΔE plot. They then use this plot to further classify variants based on their combined ΔΔG and ΔΔE values. The allele frequency cutoff is so important for generating group B that all downstream analysis is dependent on this. But because these residues are coming from a much more limited set of proteins, we think it would be useful to include a comparison showing that the gnomad allele frequency > 9.9 x 10^-5 cutoff remains informative for differentiating between benign and pathogenic residues.

5.     In Figure 3, the authors apply their analysis to variants across all GPCRs, as well as just GPCR transmembrane regions. The AUC curves in panel A are much more accurate when applied to just this protein family, as also seen in panel B where variants fall into very clear subpopulations within each quadrant. The illustration and category definitions on the left of panel C are a helpful guide for the discussion of different variant types and their relevance to stability of the protein versus function in a unique way, however the plot on the right of panels C and D is confusing and not immediately intuitive making it difficult to consider comparisons that are discussed within the text. Indeed, the authors state that “Pathogenic variants in GPCRs, especially in the transmembrane region, lose function mostly by loss of stability”. Comparing these two panels, it is concluded that the pathogenic variants that do not lose stability are more often found in the TM regions of GPCRs compared to all datasets. This is somewhat confusing and the numbers supporting this affirmation in Fig 3C seem quite low.

6.     The authors do not extensively discuss their results in the context of the membrane protein field nor the specific membrane proteins they highlight such as Rhodopsin and GTR1 (Figure 4). For Rhodopsin, at least, there has been extensive work done on its folding by Johnathan Schlebach’s lab and others, including a mutational scan. It could be useful to at least contextualize and contrast results here with previously published work. 

7.     In Figure 5, the authors consider whether the identities of the starting and mutant residues correlate with their overall quadrants. Panel A is extremely difficult to interpret. We are  also unsure how robust any differences are likely to be, given the uneven sampling and the small number of samples in some of the boxes. Narrowing the comparisons (changed vs. unchanged property, A vs B) would likely improve comprehension and may be more meaningful. Panel B is, on the other hand, a wonderful example of how to clearly display complex, multidimensional data in a comprehensible way. The well-demonstrated association of hydrophobicity and transmembrane stability is beautifully demonstrated directly from the data, and the potential discordance with evolutionary conservation as well. We find this correlation even more striking given that the hydrophobicity scale used here was explicitly determined in the context of transmembrane regions, but the variants are drawn from all regions of the targets. We were curious to know what percentage of these are drawn from the transmembrane vs. soluble regions of the targets.

REVIEWING TEAM

Reviewed by:

Willow Coyote-Maestas Paper Discussion Group, UCSF, USA: membrane proteins; high throughput experimental variant screening; developing assays for measuring how mutations break membrane proteins in order to explore how mutations alter folding, trafficking, and function of membrane proteins (see Appendix for group members).

Julian Echave, Professor, Universidad Nacional de San Martín, Argentina: theoretical and computational study of biophysical aspects of protein evolution.

Elodie Laine, Associate Professor, Sorbonne Université, France: development of methods for predicting the effects of missense mutations using evolutionary information extracted from protein sequences and/or structural information coming from molecular dynamics simulations.

Curated by:

Lucie Delemotte, KTH Royal Institute of Technology, Sweden

APPENDIX

Willow Coyote-Maestas Paper Discussion Group:

Feedback was generated in a meeting of the journal club involving:

Willow Coyote-Maestas

Christian Macdonald

Donovan Trinidad

Patrick Rockefeller Grimes

Matthew Howard

Arthur Melo

(This consolidated report is a result of peer review conducted by Biophysics Colab on version 1 of this preprint. Minor corrections and presentational issues have been omitted for brevity.)

Artykuł przedstawia podłoże rozwoju metod rozpoznawania dokumentów oraz wyszukiwania informacji do 1939 roku, czyli do momentu, w którym Vannevar Bush napisał artykuł „As We May Think”, opublikowane potem w 1945 roku.

Artykuł przekonuje do tego, że pomysł Busha nie był ani tak oryginalny, ani tak rewolucyjny, jak się go przedstawia. Autor przedstawia także stanowiska innych badaczy czy wynalazców, którzy mieli zarzuty względem projektu Memeksu.

Autor skupia się przede wszystkim na osobie Emanuela Goldberga i jego wynalazku wyszukiwarki mikrofilmów. Przedstawia także powody, które spowodowały, że jego wynalazek był pomijany i zapomniany.

I think that this is very important to understand. If you know where or with what your students succeed then you can use those strengths to help them in other areas where they may not feel as confident.

orienting statements - I feel like this gives the article a clear end and conclusion which ultimately reveals the direction of the essay

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Reply to the reviewers

We are very grateful about the thorough reading and deep understanding of the work that these 4 reviewers have provided. Although it is evident that they still request an improved profiling of some aspects, it is very encouraging that all four think the work is very interesting, original, insightful and adds a new layer of knowledge to the regulation of DNA damage sensing and repair. It is also very rewarding that the four reviewers estimate that this work will sew connections between different fields and interest a broad readership. This is why we have designed here a very deep revision, tailored to satisfy all the raised concerns except one, and this just for technical reasons.

Please find below the original reviewers’ comments and our answers to them preceded by the symbol “>”:

Reviewer #1 (Evidence, reproducibility and clarity (Required)): Ovejero et al. report an increase in lipid droplet (LD) abundance after long (from 120' on) exposure of budding yeast cells to DNA damaging agents zeocin and camptothecin (CPT). Next, they analyze DNA damage signaling in yeast mutants that impair triacylglycerol (TAGs) or sterol (STEs) esterification. They observe a slight anticipation in Rad53/CHK2 phosphorylation (indicative of DDR signaling) in yeast stem mutants, as well as in yeast cells or human cells lines pre-treated with oleate upon zeocin treatment. Yeast stem mutants are sensitive to zeocin and captothecin, but only confer sensitivity to hydroxyurea upon combination with tagD mutations. Authors relate these phenotypes to a somewhat decreases DSB resection in yeh2D mutants (expected to have reduced steryl esters pools) and RPA-foci in steD yeast cells. Next, a reduction in single strand annealing recombination repair events upon zeocin treatment is reported using a genetic reporter in steD mutants and oleate-treated cells. From these data they conclude that inability to process sterols in response to DSBs leads to an exacerbated DDR and prevents DNA repair. Next, it is shown that Flag-tagged Tel1 distinctly interacts with mono-phosphate phosphoinositides, including PI(4)P. An interaction in vivo is also inferred through Proximity Ligation Assays (PLA) using anti-PI(4)P and anti-ATM antibodies in human cell lines, which was moderately downregulated upon treatment with MMS or zeocin. Over-expression of the Osh4/OSBP1 transporter, which consumes PI(4)P, increased the number of Tel1 (nuclear) foci upon zeocin treatment. Conversely Sac1 ablation, in which accumulation of PI(4)P is expected, abrogated nuclear Tel1 foci formation and reduced telomere length (a phenotype related to lack of Tel1 function). From these results authors conclude that Tel1 availability in the nucleus is influenced by PI(4)P availability. Lastly, treatment with an OSBP1 inhibitor led to a cell line and damaging agent -variable reduction of ATM phosphorylation and a mostly non-significant reduction of DNA resection, measured by native BrdU detection, in response to CPT treatment. Overall, authors conclude that i) biding of Tel1/ATM to PI(4)P modulates its functional availability in the nucleus, and that ii) DNA damage elicits the esterification and storage of sterols toward LDs, which contributes to tritate Tel1/ATM away from the nucleus dampening the DDR and affecting long-range resection.

Major comments: While the conclusion that Tel1/ATM binds PI(4)P and this interaction modulates Tel1/ATM functional availability at the nucleus is convincing, the conclusion that DSBs elicit a change in the metabolism of this lipid to "control" Tel1/ATM function is not demonstrated. The notion that sterol processing occurs in response to DSBs is not sufficiently supported by the data presented, as the increase in LD numbers is observed much after activation of the DDR (Rad53 phosphorylation) in Zeozin-treated yeast cells.

We are afraid that we have not been clear enough in explaining the kinetics giving rise to our model. As indicated by the reviewer, our work shows, through kinetic studies, that the storage of sterols within LD occurs at later stages than the activation of the DDR by Tel1 and Rad53 phosphorylation. Tel1 foci decline is necessary for subsequent engagement of downstream DNA long-range resection. Since we propose that sterol storage within LD is a means to attenuate Tel1 engagement at DSBs, it is thus logical (and thus compatible with the data we show) that LD number increase occurs simultaneously with Tel1 foci decrease, at late stages of the reactionWe will include this explanation and graph in the revised version of the work.

In addition, evidence is not provided on the mechanisms by which PI(4)P metabolism would be controlled, which would be expected to be DDR-independent as they are placed upstream of this signaling pathway in the author's model.

The key mechanism through which, in the end, PI(4)P metabolism will be controlled, is the esterification of sterols within LD. Given that, as clarified above, LD formation in response to DSBs occurs “late” (i.e., after 120 min), it is not excluded that the DDR itself can instruct, through phosphorylation of some effector(s), LD formation. In other words, by ordering LD formation, the DDR would be launching a self-limiting mechanism. In support, we now know, although we do not show in this work, that eliminating key DDR proteins prevents the formation of LD in response to DNA damage. Because of this, we have undertaken an educated-guess approach and chosen critical or rate-limiting enzymes in LD biology either possessing an S/T-Q cluster domain (predicted to be a phosphorylation substrate for the DNA Damage Response kinases (1), and/or retrieved in phospho-proteomic screens as specific DDR targets (2,3). This adds up to 28 proteins in S. cerevisiae and 45 proteins in Homo sapiens. Importantly, the emergent candidates fall into two identical categories in both organisms. To provide initial support for their pertinence, we have generated a point mutant in the putative S/T-Q cluster of one of the yeast candidates. Of high relevance, we find that the concerned mutant is impaired in correctly triggering LD formation in response to DNA damage, and we have now obtained a specific funding to pursue this characterization that, as such, constitutes a different work from the one presented in this manuscript. We hope that the reviewer is now convinced yet that she/he agrees in keeping this information for subsequent manuscript(s).

The damaging agents used have been suggested to alter the redox metabolism and even lipid peroxidation (Kitanovic 2009, Mizumoto 1993, Krol 2015, Todorova 2015, Ren 2019, Singh 2014). Hence it is possible that PI(4)P changes are not due to DSBs, but an indirect though relevant effect. In absence of direct evidence supporting an active regulation of PI(4)P dynamics in response to DNA breaks, this conclusion remains speculative and this should be noted in the manuscript.

We fully agree with the reviewer that the used genotoxins are triggering a myriad of effects which could elicit the same phenomenon by indirect means. Yet, we want to stress that the use of camptothecin, which elicits a very robust LD formation phenotype (Figure 1C), is very likely specific, as it is proven as a potent and direct trapper of Top1 onto DNA after having cleaved it. Nevertheless, we propose in the next paragraph two specific experiments to dismiss this problem, please see immediately below.

Authors conclude that LD is specific to DSB induction. This seems an overstatement as they just reported LD increases in response to two agents that also induce other kinds of DNA damage. To also strengthen the link between DSBs and PI(4)P modulation of Tel1 function, authors should analyze LD numbers, Rad53 phosphorylation and Tel1 nuclear re-localization in response to HO-induced DNA breaks (e.g., using the system employed in Figure 3C).

We humbly think that enzymatically-induced DNA breaks will both activate Rad53 phosphorylation and Tel1 nuclear concentration, as this has already been established, thus requiring no further exploration. Yet, it is very important to assess the reviewer’s suggestion concerning whether enzymatically-induced DNA breaks also trigger the formation of LD. To this end, we will perform two complementary studies in which, instead of using HO, which cuts only a few times in the genome, we will:

1. a) exploit the naturally DSB-accumulating mutant rad3-102, which we previously characterized in the past (4), and which we already exploit in this work for recombination analyses (Figure S4A), to evaluate whether it endogenously harbors more LD in comparison with the WT.
2. b) we have recently created a tool in which gRNAs targeted to different subsets of transposons in the genome can drive Cas9 to create DSB in a dose-dependent manner ((9), under revision in Genetics). We will use this system to monitor the LD formation in response to Cas9-triggered cuts. In addition, on figure 5A, significant differences in GFP-Tel1 foci abundance between WT and steD or yeh2D cells are only observed after 210', way after the slight effect on Rad53 phosphorylation is observed. This is at odds with the conclusion that Tel1 association to STEs modulates DDR signaling.

We are afraid that we have not been clear enough in explaining the kinetics giving rise to our model. As indicated by the reviewer, our work shows, through kinetic studies, that the storage of sterols within LD occurs at later stages than the activation of the DDR by Tel1 and Rad53 phosphorylation. Tel1 foci decline is necessary for subsequent engagement of downstream DNA long-range resection. Since we propose that sterol storage within LD is a means to attenuate Tel1 engagement at DSBs, it is thus logical (and thus compatible with the data we show) that LD number increase occurs simultaneously with Tel1 foci decrease, at late stages of the reactionWe will include this explanation and graph in the revised version of the work.

Minor comments:

Figure S1D and E, experiments should be carried out to include time points in which LD accumulation and cell cycle arrest are observed upon zeocin treatment (i.e., up to 210' as in Figure 1A)

We will provide cytometry profiles of cells at 210 min. These data exist already in our laboratory.

How do authors explain increased single strand annealing recombination frequencies in steD and oleate-treated wild type cells (Figure 4A). Should it not be expected that increased STEs also impair recombination induced by endogenous damage?

Only ste∆ (and not +oleate) indeed manifests an increase in basal recombination frequencies, likely arising from endogenous damage. Although the increase is observed, it is not significant. We agree anyway with the reviewer that, was the experiment to be repeated more times, the increase may be found significantly different. We do not have any honest proposal to explain this.

Data presented in figure 4B and 4C are not fully convincing. Performing time course experiments might help concluding if the differences observed represent a relevant defect in DSB processing.

We will perform a Pulsed Field Gel Electrophoresis (PFGE) kinetcis in response to zeocin with or without oleate pre-loading to reinforce the conclusion.

Is Figure 5B referring to Flag-tagged Tel1 or GFP-tagged Tel1 as stated in the figure legend?

There is a misunderstanding here, as the mentioned Figure 5B corresponds to P-ATM immunofluorescences in human cells, not to any tagged Tel1 experiment.

Treatment with the ATM inhibitor AZD0156 increased PI(4)P-ATM PLA signals. From these authors conclude that "association of ATM and PI(4)P inversely correlated with the need for ATM within the nucleus. Do they imply that treatment with ATM-inhibitors reduces the requirement for ATM function in the nucleus? The interpretation of this result should be further elaborated to sustain this conclusion.

We may have conveyed a wrong notion at this point. We do not imply at all that ATM inhibitors reduce the need for ATM in the nucleus. Instead, we imply that, by reinforcing ATM attachment to Golgi-resident PI(4)P, ATM inhibitors end up titrating ATM away from the nucleus. We will clarify our explanation to avoid misunderstandings.

An increase of GFP-Tel1 foci upon OSH4 overexpression is described on Figure 7B. These are described as nuclear in the results, but no reference is made in the figure or legend as to how nucleus positions are addressed in these experiments. This should be clarified.

We systematically combine the tagging of a nucleoplasmic protein (mCherry-Pus1) with the detection of GFP-Tel1 foci, as to unambiguously assess the nuclear position of Tel1 foci. We will include this explanation and the corresponding mCherry-Pus1 channel to clarify this.

Also, WT controls and quantifications should be included in the experiments shown on Figure 7C.

These experiments are quantified from the moment we did them, although we did not include such quantifications in the present version for the sake of space. We will do so in the revised version.

Reviewer #1 (Significance (Required)):

While the conclusion of lipid metabolism responding to DSBs is not convincing, the observation that Tel1/ATM function is modulated by PI(4)P biding is significant and advances the understanding on the function and regulation of this key kinase in promoting genome integrity maintenance. This is an unanticipated result which is highly novel and has implications for the modulation of Tel1/ATM function through pharmacological manipulation of lipid metabolism. This finding would be of broad interest for scientists working on the response to DNA damage and the maintenance of genome integrity. This reviewer belongs to that group and has limited expertise to evaluate the lipid metabolism genetic manipulation in the manuscript.

Reviewer #2 (Evidence, reproducibility and clarity (Required)):

The authors show that cytoplasmic PI4P have a regulatory role on ATM response to DNA double strand breaks. The process involves a balance between exchange of PI4P between Golgi and ER in exchange of esterified sterols. The study is of interest, however provides indirect evidences to support their conclusions.

Major comments : 1). Since the major conclusion relates to PI4P association with ATM in basal conditions to keep ATM outside nucleus and known presence of PI4P, ATM in other organelles of a cell, further experiments such as cell fractionation experimental that show golgi specific interaction would support the main conclusion.

In continuation of 1st comment, since PI4P in substrate of PI4 phosphoinositol kinases, is there a competition between PI4kinases and ATM for PI4P binding should be addressed through immunoprecipitation studies.

First of all, we need to specify here that PI4kinases will phosphorylated PI4 to create PI(4)P. Thus, PI(4)P is the product, and not the substrate, of PI4kinases. We therefore do not expect any competition between such kinases and ATM.

Second, we take good note of the reviewer’s concern that the pool of PI(4)P at the Golgi may be shared, and also that it would be important to reinforce the notion of the relative subcellular localization of ATM under different treatments. To this end, we will perform the following integrative experiment:

Immunoprecipitation of PI(4)P could theoretically be done using our specific antibody, yet the IP efficiency of a lipid cannot be verified by western blot. Further, there are PI(4)P pools elsewhere in the cell that would mess up with interpretations. We therefore dismiss the use of anti-PI(4)P as a tool to perform immunoprecipitations.

Instead, to explore the impact of PI(4)P levels on ATM both at the Golgi and within the nucleus, we will split our cultures in two to either immunoprecipitate specific cytoplasmic Trans-Golgi Network-associated proteins (we will test separately TGN46 and GOLPH3); or the nuclear ATM-interacting factor MRE11 from nuclei, then blot for co-immunoprecipitated ATM. The relative co-immunoprecipitated ATM is expected to vary under different treatments to which the cells will be exposed, namely:

• untreated
• zeocin, to trigger ATM need in the nucleus
• OSBP inhibition (+/- zeocin), to stabilize PI(4)P at the Golgi
• PIK93, an inhibitor of PI4 kinases that prevents PI(4)P synthesis

2). The authors claim that the ATM retention is the main function of PI4P in Golgi. The authors should rule out the possibility that the phenotype observed on DNA damage response is not due to non availability of PI4P substrate for PI4P kinases, that have recently been shown to participate in genome integrity maintenance.

We want to explain that we do not intend to say that PI(4)P main function at the Golgi is ATM retention, as PI(4)P is a molecule binding and modulating multiple proteins, as for example the aforementioned GOLPH3. We will first revise our text to correct it, in case we have conveyed this incorrect notion, as it stems from the reviewer’s comment.

Second, the reviewer evokes the notion that PI(4)P can be the substrate of a second phosphorylation, which could give rise to PI(3,4)P or to PI(4,5)P, which could still undergo remodeling into PI(3)P, for example. Recent work by Dr Michael Sheetz’s lab demonstrated that this set of phosphoinositides serves to drive the nucleation and activation of the ATR-Chk1 branch of the DNA Damage Response upon genotoxic stress, yet was completely inert with respect to the ATM-Chk2 branch (5). To rule out the possibility, as evoked by the reviewer, that the oleate-induced DDR phenomena we describe relate to these other events, we have now explored the response of the ATR-Chk1 branch when comparing the response of zeocin-treated cells that have been pre-loaded or not with oleate. We observe that the ATR-Chk1 branch is unaltered by oleate loading. Thus, we can now propose that the PI(4)P branch exclusively modulates the ATM-Chk2 axis.

3). Does Oleate treatment influences Rad53 protein levels in addition to its phosphorylation that affect DNA damage response may be addressed.

Exponential cultures from three different WT, three different ste∆ and three different yeh2∆ strains have now been taken and pre-loaded for 2 hours with 0.05% oleate, then total levels of Rad53 (without induction of DNA damage) assessed. We can now formally say that basal levels of Rad53 protein are not altered by this incubation. We will include this control in the revised manuscript.

4). Does Yeh2 deletion reduces LDS should be checked.

We frequently use yeh2∆ cells in our studies. In particular, we have recently published work characterizing the phenotype of this strain with respect to the formation of lipid droplets in the nucleus (6). We are currently exploiting those same sets of data to quantify the total number of LD in order to satisfy the reviewer’s concern.

5). Figure 4D representation should show % of phospho reduction of initial activation and a better western blot image should be shown that show equal loading of samples.

We are currently repeating these gels and blots for the sake of clarity, as requested.

6). In immunoprecipitation experiments, kindly include isotypee IgG controls as well to rule out non-specificity.

Of course, this important control will be included every time.

Minor points: 1). Figure S1F do not show oleate treatment as presented in results section.

We will revise the accurate naming.

2). A better gel for S4B should be presented with ponceau of the same gel.

We are currently repeating this gel and associated blot for the sake of clarity, as requested.

3). Nuclear PI4Ps has also been previously reported, an explanation to the specific interaction of ATM and PI4P in the Golgi should be addressed/discussed.

We take it that the reviewer is referring here to the recent work by Fáberová et al (7) in which PI(4)P and PI(4,5)P were described as very dynamic in the nucleus, and mostly related then to mRNA transcription, splicing and export. We will reinforce the connection of our phenomenon to the Golgi-associated pool of PI(4)P thanks to the co-immunoprecipitation experiments proposed above, and will timely contextualize these in light of the paper by Fáberová and co-workers in the revised version. Thank you for reminding us of this work.

Reviewer #2 (Significance (Required)):

The current work definitely adds a layer in our understanding to ATM regulation and cross-talk between different PIKK family of kinases. ATM localisation in extra nuclear regions of a cell has been described earlier with significant impact on cell physiology such as mitochondria etc., ATM retention at golgi and limiting nuclear ATM levels is significant advance at ATM activity regulation, while signifying non canonical function of PI4P.

Reviewer #3 (Evidence, reproducibility and clarity (Required)):

Summary:

In this manuscript, the authors propose that ATM/Tel1 signaling is regulated in a spatiotemporal manner during genotoxic stress both in yeast and mammalian cells. They show that Lipid droplets accumulate in response to genotoxic stress. As a consequence, there is a decrease of exchange of PI4P from the Golgi to ER, thus dampening ATM/Tel1 signaling by sequestering this kinase into the Golgi. The authors combined findings in yeast and mammals showing that this mechanism is conserved throughout eukaryotes. For this purpose, they use a vast number of techniques that support their proposed model.

Major comments:

The conclusions were made based on evidence combining yeast genetics, immunofluorescence, DNA end resection analysis and pharmacological interventions. The hypothesis that ATM is kept away from the nucleus by physically interacting with PI4P at the Golgi, thus allowing processive repair is bold and contributes for a better understanding of the choreography of the DDR kinases during DSB repair. However, many of the experiments in yeast and mammals show only mild phenotypes and there is no evidence that this mode of ATM dampening impact cell viability in mammals.

We agree with the reviewer that the effects associated to the reported phenomenon are indeed mild. This is a fact. We would like to remind that the metabolism of sterols is finely controlled, and at many different levels, in a very complex manner. For example, sterol increases in the cell will immediately be compensated by reduced synthesis, while synthesis inhibition will immediately promote uptake from the medium, and/or release from stores (for example, see (8)). As a natural consequence, the window of manipulation and, more importantly, the strength of the phenotypes we can uncover are small.

Therefore, I have some comments and suggestions of experiments that I think could improve the quality of the manuscript. I believe that most of these new experiments does not require much time and resources.

• Does oleate treatment in RPE-1/Huh-7 cells induce loss of viability? An experiment showing loss of viability like MT-assay or decreased cell proliferation would reinforce the importance of the mechanism proposed.

This experiment was already included in the previous version, yet it may have escaped the attention of the reviewer. We show in Figure S2E that oleate treatment restricts viability in Huh-7 cells alone, and also worsens their tolerance to zeocin. Perhaps we should reconsider moving this result to the main figures so that it does not go unnoticed.

• In yeast there is evidence that a ste delta strain show sensitivity to zeocin/CPT, but there is no experiment showing the same effect on cells lacking Yeh2. Since both strains share similar phenotypes, it would be interesting to show that increased kinetics of Rad53 signaling leads to sensitivity to genotoxins.

We have now performed this experiment, we will include the matching information for yeh2∆ cells, which agrees with the predictions.

• The conclusion that ste delta cells exposed to zeocin leads to unproductive events due to defects in DNA-end resection could be reinforced by a decrease in Rad52 foci. It has been previously shown by the group of Dr. Marcus Smolka, that inhibition of DNA-end resection decreases Rad52 foci (https://doi.org/10.1083/jcb.201607031). Since the authors were able to monitor Rad52-YFP (Figure S1A), it shouldn't consume time and resources.

The reviewer is right that this experiment should not be time- or resources-consuming. We will evaluate the accumulation of Rad52 foci in response to the concerned genotoxin in ste∆ cells.

• Since the authors propose that there is a DNA repair defect due to inhibition of long-range DNA-end resection, it would be important to monitor gamma-H2A(X) signal either in yeast or mammals.

Taking into consideration the reviewer’s suggestion, we have now performed anti-yH2AX immunofluorescence of all the implied conditions (genotoxins +/- oleate pre-load) and will quantify them to answer the concern.

• How do the authors exclude the possibility that yeast mutants or oleate treatment in yeast/mammalian cells change membrane permeability allowing an increase in genotoxin concentration?

Although this is a very reasonable criticism, we want to remind the data we present in Figure S4A in which we use the naturally DSB-bearing rad3-102 cells for recombination analyses, showing that, in the absence of any genotoxin, the same phenotype also applies. Yet, we want to reinforce the notion that LD formation in response to DSB can also occur when the breaks are not chemically, but physically, induced. To this end, and also to match a related request by Reviewer 1, we will:

1. a) exploit the naturally DSB-accumulating mutant rad3-102 (4) to evaluate whether it endogenously harbors more LD in comparison with the WT.
2. b) we have recently created a tool in which gRNAs targeted to different subsets of transposons in the genome can drive Cas9 to create DSB in a dose-dependent manner ((9), under revision in Genetics). We will use this system to monitor the LD formation in response to Cas9-triggered cuts. In addition, on figure 5A, significant differences in GFP-Tel1 foci abundance between WT and steD or yeh2D cells are only observed after 210', way after the slight effect on Rad53 phosphorylation is observed. This is at odds with the conclusion that Tel1 association to STEs modulates DDR signaling.

We are afraid that we have not been clear enough in explaining the kinetics giving rise to our model. As indicated by the reviewer, our work shows, through kinetic studies, that the storage of sterols within LD occurs at later stages than the activation of the DDR by Tel1 and Rad53 phosphorylation. Tel1 foci decline is necessary for subsequent engagement of downstream DNA long-range resection. Since we propose that sterol storage within LD is a means to attenuate Tel1 engagement at DSBs, it is thus logical (and thus compatible with the data we show) that LD number increase occurs simultaneously with Tel1 foci decrease, at late stages of the reactionWe will include this explanation and graph in the revised version of the work.

• It would be interesting to investigate genetic interactions between ste delta (or yeh2delta) and yeast mutants with DNA-end resection problems (exo1delta; sae2delta). For instance, it has been shown that Sae2 antagonizes checkpoint signaling by competing with Rad9 to DSB sites (https://doi.org/10.1073/pnas.1816539115). Also, cells lacking Sae2 show an increase in Rad53 signaling due to increased Tel1 Signaling. Therefore, an epistatic effect between these two pathways would reinforce the hypothesis of the manuscript.

we will build the double mutant sae2∆ yeh2∆ and assess the potential epistatic behavior they may display with respect to some key phenotypes (Tel1 foci formation, Rad53 phosphorylation…).

• The authors showed that Tel1-GFP does not accumulate in the nucleus in cells lacking Sac1 (Figure 7C). Tel1 is important to cope with increased DSBs in the absence of Mec1, thus avoiding genomic instability. Cells lacking both Mec1 and Tel1 show a sick phenotype with an exponential increase in gross chromosomal rearrangements and sensitivity to genotoxins. Therefore, does deletion of Mec1 (and Sml1) in sac1 delta phenocopies a mec1tel1 delta? Alternatively, does pharmacological inhibition of ATR in the presence of the OSBP1 inhibitor causes loss of viability or chromosomal aberrations?

We will delete SAC1 in mec1∆ sml1∆ and compare the fitness, through growth drop assays, with respect to the mutant tel1∆ mec1∆ sml1∆.

We will expose cells either to OSBP1 inhibitor, ATR inhibitor, or both, and assess the phosphorylation of their downstream common effector H2AX. Additionally, we will assess the effect on cell growth of the combination of ATRi and OSBP1i using synergy matrices. We will determine if the combination of both drugs synergizes or not to impair cell proliferation and reduce cell viability.

• Finally, it seems strange to me that ATR/Mec1 signaling is not mentioned throughout the entire manuscript. Does PI4P pathway affect only ATM/Tel1? In Figure 2D, an antibody against phospho-CHK1 could be used to monitor ATR signaling. In line with that, I would like to see in the discussion how these new findings are in line with evidence from a 2019 paper showing that phophoinositides PIP2 and PIP3, but not PI4P are important for ATR signaling (DOI: 10.1038/s41467-017-01805-9). They showed that a nuclear pool of PIP2 increases upon DNA damage induction and rapidly accumulates at DNA lesions. This event is important for the recruitment of ATR. Since PI4P is substrate for PIP2 synthesis and there is a nuclear pool of PI4P and PIP2, I think it is important to discuss if the results presented here are in line with these previous findings.

The reviewer evokes recent work by Dr Michael Sheetz’s lab demonstrating that a different set of phosphoinositides serves to drive the nucleation and activation of the ATR-Chk1 branch of the DNA Damage Response upon genotoxic stress, yet was completely inert with respect to the ATM-Chk2 branch (5). We have now explored, also to satisfy a similar concerned raised by Reviewer 2, the response of the ATR-Chk1 branch when comparing the response of zeocin-treated cells that have been pre-loaded or not with oleate. We observe that the ATR-Chk1 branch is unaltered by oleate loading. Thus, we can now propose that the PI(4)P branch exclusively modulates the ATM-Chk2 axis.

We will of course give the needed credit to this work and contextualize our findings accordingly.

Minor comments:

• Line 124: The correct is Figure S1E, lower panel and not Figure S1F -Lines 127-128: Figure S2A does not show zeocin treatment

Both minor mistakes will be corrected.

Reviewer #3 (Significance (Required)):

Together, these new findings, if corroborated by others, might be important to open new lines of investigation in basic and translational research regarding human diseases as explored in the discussion section. I believe this paper will attract attention not only from the DDR field but also from other areas of research such as nutrient and lipid signaling both in yeast and mammals. I hope I was able to collaborate in this review, since my main expertise is in the area of DNA damage signaling using budding yeast as an organism model.

Reviewer #4 (Evidence, reproducibility and clarity (Required)):

This is a very interesting study where Sara et al. demonstrated a link between lipid metabolism with DNA repair response (DDR). In this study, they have proposed ATM as a novel PI4P-effector. The sterol deposition into lipid droplets impacts the Golgi PI4P level due to lipid exchange machinery facilitated by OSBP1, therefore regulating the cytosolic retention of ATM due to PI4P binding. Although how lipid droplets in the cytosol sense the DNA damage and control the initiation of DDR by regulating ATM is still unclear, this study linked lipid biology/PI signaling to DNA damage repair and showed the evolutionary conservation of PI signaling and DNA repair machinery from yeast to humans. The experiments are well designed, nicely controlled, with a high quality of data presentation. With some improvements, this work could be a very interesting study attracting a broad readership.

In their model, ATM is PI4P-bound and sequestered inside the cytosol under basal conditions. Upon genotoxic stress, activation of OSBP1 removes PI4P and free PI4P-bound ATM for nuclear translocation of DNA repair. This could also be interpreted as genotoxic stress-induced PIP-kinase activity, where PI4P is processed into PIP2 or PIP3, somehow redirecting ATM into the nucleus to initiate its activation for DDR. Those aspects should be discussed and improved.

Both Reviewers 2 and 3 have somehow evoked a similar concern. More precisely, the work by Dr Michael Sheetz’s lab demonstrating that a different set of phosphoinositides serves to drive the nucleation and activation of the ATR-Chk1 branch of the DNA Damage Response upon genotoxic stress, yet was completely inert with respect to the ATM-Chk2 branch (5). We have now explored, to satisfy all reviewers’ concerns, the response of the ATR-Chk1 branch when comparing the response of zeocin-treated cells that have been pre-loaded or not with oleate. We observe that the ATR-Chk1 branch is unaltered by oleate loading. Thus, we can now propose that the PI(4)P branch exclusively modulates the ATM-Chk2 axis.

Additionally, we will of course give the needed credit to this work and contextualize our findings accordingly.

Upon stress, there is nuclear activation of p53-phosphoinositide (PI) signalosomes and PIP-kinases. Also, there is a significant PIP2 pool inside the nucleus with an involvement in DNA damage repair. Those papers and their relevance to the current study need to be discussed. If ATM is a novel PI4P-effector, there is also nuclear PI4P formation or nuclear PI4P accumulation upon stresses based on recent studies; how the ATM interacts with PIPn in the nucleus upon translocation? A know ATM substrate p53 is PIP2/PIP3 bound in the nucleus based on recent studies. Will ATM prefer to interact with other PIPn-bound proteins in the nucleus or PIPn regulate their interaction needs to be discussed.

These additional notions are in line with the previous paragraph presented by the reviewer, and our answers too. We will provide a constructive overview of all these ideas in the revised version of the manuscript.

Major points: 1. The PI4P-ATM complex is supported only by PLA and PIP strips. Need more robust biochemical characterization of the interaction: co-IP, lipid binding, and/or in vitro constitution.

We agree with the need to perform assays in which PI(4)P is embedded in a bilayer, as to confidently assess whether Tel1 can bind it in that context. We have now performed a pilot experiment in which we have confronted purified FLAG-Tel1 to liposomes harboring PI(4)P. Western blot analysis using anti-FLAG antibody shows the encouraging result that FLAG-Tel1 can be found there. As a control, we have performed the same process but in the absence of any liposomes. We observe that a residual fraction of FLAG-Tel1 can nevertheless be found in this control, most probably because the buffer used during the liposome assay makes part of FLAG-Tel1 precipitate.To avoid this type of background and to increase our trust in the results, we propose to perform the liposome assay but on a discontinuous density gradient, so that liposomes will be retrieved in the top layer (and bound FLAG-Tel1 with them, if that is the case), while any precipitated FLAG-Tel1 will be in the bottom fraction (liposome floatation assay). As a further control, we will include the same liposomes but lacking PI(4)P. We expect to be successful in the floatation assays. If we are not, we will repeat the experiment presented above to be confident that the observed increase is reproducible.

1. The use of drug inhibitors only in the final figure is problematic. KD or KO experiments should be performed to confirm that ATM and the exchanger are the relevant targets.

We have now used siRNAs against the exchanger protein, OSBP1, with a very high silencing rate success. We have next monitored the activation status of the chromatin-associated ATM target KAP1, in order to monitor the predicted decrease of ATM activity specifically inside the nucleus. Our results confirm the role of OSBP1, by KD experiments as requested by the reviewer, in attenuating ATM nuclear participation.

1. Poor quality of some WBs (e.g Fig. S1F).

We have now repeated the Western Blot to detect Rad53-P in response to 20 mM HU in WT versus ste∆cells.

1. Lack of statistical analyses for some data (e.g. Fig. 1B-E)

We had already included, in the previous version, the complete statistical analyses corresponding to Figures 1B to E and evoked here by the reviewer. They were indeed included in Figure S1C, and our brief reference to them in the text may have escaped her/his attention. We will make a clear reference to this in the revised version.

Additional clarification points:

Figure 1: No representative images were shown for quantifications in Figure 1C, D, E.

If the reviewer / editor estimates it pertinent, we can of course include them. Yet, they will be very redundant with the images displayed in Figure 1A.

Line 121: Should be Figure S1E, upper panel. Line 124: Should be Figure S1E, lower panel. Figure 2D-E, please show the quantification of the ratio of pCHK2/CHK2 with an N=3

We will correct / include the requested changes.

Figure S2B: needs quantification of NileRed staining to conclude induction in LD formation

We will quantify the LD as requested.

Figure 3C, to show the selectivity of ATM-binding toward PI4P, PLA of ATM with other PIPn species should be assessed, such as PI3P, PI4,5P2, and PI3,4,5P3.

We have provided an overview of the binding preferences of ATM with respect to the full battery of phosphoinositides in the strip-binding assay shown in Figures S5C and 6B. Other than that, we are afraid that PLA studies as the ones we develop in the current manuscript for PI(4)P are not feasible, since no reliable antibodies exist for most of the phosphoinositide species evoked by the reviewer.

Figure S6A, PI4P level could be assessed by IF staining using PI4P antibody besides using PI4P sensor.

We will use our PI(4)P antibody to monitor by immunofluorescence the behavior of this molecule in response to either MMS or zeocin, as suggested.

References

1. Cheung HC, San Lucas FA, Hicks S, Chang K, Bertuch AA, Ribes-Zamora A. An S/T-Q cluster domain census unveils new putative targets under Tel1/Mec1 control. BMC Genomics. 2012;
2. Bensimon A, Schmidt A, Ziv Y, Elkon R, Wang SY, Chen DJ, et al. ATM-dependent and -independent dynamics of the nuclear phosphoproteome after DNA damage. Sci Signal. 2010;
3. BastosdeOliveira FM, Kim D, Cussiol JR, Das J, Jeong MC, Doerfler L, et al. Phosphoproteomics Reveals Distinct Modes of Mec1/ATR Signaling during DNA Replication. Mol Cell. 2015;
4. Moriel-Carretero M, Aguilera A. A Postincision-Deficient TFIIH Causes Replication Fork Breakage and Uncovers Alternative Rad51- or Pol32-Mediated Restart Mechanisms. Mol Cell. 2010;37(5):690–701.
5. Wang YH, Hariharan A, Bastianello G, Toyama Y, Shivashankar G V., Foiani M, et al. DNA damage causes rapid accumulation of phosphoinositides for ATR signaling. Nat Commun. 2017;
6. Kumanski S, Forey R, Cazevieille C, Moriel-Carretero M. Nuclear Lipid Droplet Birth during Replicative Stress. Cells. 2022;11(1390).
7. Fáberová V, Kalasová I, Krausová A, Hozák P. Super-Resolution Localisation of Nuclear PI(4)P and Identification of Its Interacting Proteome. Cells. 2020;9(5):1–17.
8. Luo J, Yang H, Song BL. Mechanisms and regulation of cholesterol homeostasis. Nat Rev Mol Cell Biol [Internet]. 2020;21(4):225–45. Available from: http://dx.doi.org/10.1038/s41580-019-0190-7
9. Coiffard J, Santt O, Kumanski S, Pardo B, Moriel-Carretero M. A CRISPR-Cas9-based system for the dose-dependent study of 4 DNA double strand breaks sensing and repair 5 6. bioRxiv [Internet]. 2021;1–37. Available from: https://doi.org/10.1101/2021.10.21.465387.

I want to focus this annotation on one word in particular, that at first I overlooked: gammon. Taken at face value, gammon is a British term for a smoked or cured ham. Thus, it would be easy to not think much of the line: “Well, that Sunday Albert was home, they had a hot gammon, / And they asked me in to dinner, to get the beauty of it hot.” However, gammon also has two other definitions: 1. To defeat an opponent in backgammon, another board game which shares similarities to chess. and 2. To hoax or deceive. First, alluding to backgammon within “A Game of Chess” provides interesting parallels and reflections on what it means to be within a game. From Middleton’s play, we know that chess is strongly affiliated with seduction and lust. While this may be a stretch, I believe that backgammon acts as a contrast to chess as a representation of what society was before the War and deterioration of creativity and individualism that Eliot constantly references within The Wasteland. Chess is the younger of the two, and has a belligerent connotation (possibly in reference to The Great War), in comparison to the meditative nature of backgammon. To be engaged in chess is a cerebral battle, and in Eliot’s mind, England is losing. Moreover, in Sukhbir Singh’s journal article, “Gloss on "Gammon" in "The Waste Land", II, Line 166”, he mentions the importance of the characterization of the gammon as “hot.” Singh deems the gammon aphrodisiac, and believes that “hot” refers to the Duke’s “flaming appetite” and “hot lust.” Singh’s opinion fits nicely into the second alternate definition of gammon, which is to hoax or deceit. In this case, the unnamed woman in the poem has most likely fallen into her “flaming appetite” and participated in an affair. Singh believes that this lack of love is Eliot’s reflection of societal deterioration.

Watson acknowledges some arguments that may come from his views, but still believes psychology to stop focusing on consciousness but rather on the behavior.

Anthropomorphism is when you think about animals or objects as if they were human. For instance, pet owners might observe human-like qualities in their pets, believing that their pet is experiencing an emotional state similar to what a human feels. https://psychcentral.com/health/why-do-we-anthropomorphize#anthropomorphism

I haven't really explored this idea before, but it makes complete sense! When we take time to reflect on what we already know, what we have just learned, and ask questions about what else these ideas may relate to, we get the big picture. I think this idea could apply to connecting ideas and it could also be about connecting people (like in out Twitter chats) so that we have more resources or better support to continue the learning process.

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Reply to the reviewers

1. General Statements [optional]

In our work, we quantified the abundance and positions of major kinetochore proteins within the metaphase kinetochore in budding yeast using single-molecule localization microscopy. Based on these measures, we revised the current model of the kinetochore and provided a nanoscale view of the complex.

We now revised our manuscript according to reviewers’ points. We performed new analyses to quantify the measurement errors and to justify our data analysis workflows. We further exploited the correlation-based analysis and found a correlation between the spreads of kinetochore proteins perpendicular to the spindle axis and their positions along the axis. We also discussed the potential non-centromeric pools and revised our model of the kinetochore. Further information on our analyses was now provided to improve the clarity. Changes to the text were implemented to better reflect our data. Information from relevant works was incorporated to better connect this work to the field.

We thank the reviewers for their points, which help us show the rigorousness of our analyses, further demonstrate the potential of our work, and improve clarity.

2. Point-by-point description of the revisions

Reviewer #1 (Evidence, reproducibility and clarity (Required)):

The authors have developed a rigorous methodology for using single-molecule imaging of exogenously labeled kinetochore proteins to count and estimate their copy numbers and the average distance from the kinetochore protein Spc105. Although the method is technically sound, its application to the kinetochore raises some crucial questions below. My biggest concern is the effect of non-centromeric pools of the centromeric proteins Cse4, Cep3, and Ctf19 on the estimated copy number per kinetochore. The authors should be able to address most, if not all, questions by presenting a more in-depth data analysis.

Major points

1. Accounting for tilt of the yeast spindle relative to the image plane: It is not clear to me how the authors ascertain whether the spindle being imaged is nearly parallel to the image plane. In the companion fission yeast study, spindle poles are used for this purpose, but this study seems to rely only on the labeled kinetochore proteins. The criteria used to select the in-plane spindles should be clearly defined.

We thank the reviewer for pointing this out. We selected the in-plane spindles based on their average PSF size, which informs the z positions of the center of the kinetochore cluster (for simplicity, now all ’half-spindle’ was changed to ‘kinetochore cluster’). To calibrate the z position of kinetochore clusters, we first measured the width of the kinetochore cluster by fitting a cylindrical distribution. Overall, the kinetochores are likely symmetrically distributed around the spindle axes. Therefore, the height and the width of a kinetochore cluster should be the same. We then calibrated the z positions of the PSF size based on fluorescent bead data. Next, we plugged in the cylindrical distribution to the calibration curve to correlate the mean PSF size and position of the kinetochore cluster. We only took the kinetochore clusters with a mean PSF size

1. The effects of PSF depth on counting kinetochore proteins: The authors use a well-characterized nuclear pore protein as the reference to estimate kinetochore protein counts per half-spindle. Although this method appears rigorous in principle, I am unsure about the effect of the spatial distribution of kinetochores on the accuracy of the estimated number. Nuclear pore proteins are all localized within an 100 nm away from the focal plane even when the spindle is perfectly parallel to the focal plane. A discussion of this possibility, its effect on the protein count/distance estimates, and any mitigating factors is essential to highlight the caveats associated with the conclusions.

Based on the cylindrical distribution (see please the reply to point 1) of kinetochore clusters and their positions in z, we calculated the upper and lower boundaries of the distribution of kinetochore proteins in z, given a specific mean PSF size cutoff of a kinetochore cluster. Regardless of how stringent the cutoff is (130 and 135 nm), we made sure the boundaries do not exceed the imaging depth defined by our choice of the PSF size filtering (

1. Presentation of the cross-correlation analysis: The authors use cross-correlation for an unbiased calculation of the axial separation between a protein of interest and Cse4, but I am curious about the structure of the underlying data, and the intensity image in Figure 1 is not easy to examine. It will be helpful to include more analysis of the underlying data for at least a subset of the proteins (e.g., proteins at short, intermediate, and long distances from Cse4) as supplementary data.

2. The authors should include X and Y projections of the cross-correlation function.

3. Do the widths of cross-correlation functions (i.e., their spread perpendicular to the spindle axis) match across all proteins and experiments? This should be an almost invariant characteristic of the measurements, assuming that proteins within each kinetochore tightly cluster around the 25 nm microtubule. This line of thinking makes the large width of the cross-correlation shown in Figure 1 somewhat surprising.

4. It will also be interesting to test if the correlation between the positions of Spc105 molecules, especially perpendicular to the spindle axis, is comparable to the known separations between adjacent microtubules in the yeast spindle (the authors could use Winey et al. 1995 for serial-section EM of yeast spindles for comparison).

The reviewer is interested in the spread, or the size of the distribution, of a protein in a kinetochore along and perpendicular to the spindle axis. This is an interesting idea and can be done practically. However, the information can be more easily obtained based on auto-correlation instead of cross-correlation, due to its better signal-to-noise ratio along the dimension perpendicular to the spindle axis. Cross-correlations in that dimension are convoluted with background localizations and different localization precisions of the two channels. These factors are hard to interpret and disentangled. In auto-correlations, although the background is still present, it can be modeled and then removed easily, as now mentioned on page 15 lines 500-516.

Accordingly, we performed auto-correlation analysis on all the proteins and compared them to simulations representing different sizes. We find that the size of the distribution correlates to the position of the protein along the spindle axis. The results are now included as the new Fig. S5 and discussed on page 6 lines 169-176.

The cross-correlation analysis was based on only the position of the maximum value, not the projections. To keep the figure concise, we decided not to include the projections. However, the auto-correlation analysis was indeed based on projections, which we now included in Fig. S5.

Regarding the correlation between the positions of Spc105 molecules, we believe the reviewer actually refers to the correlation between the positions of kinetochores. Auto-/cross-correlations contain the information of the cluster sizes, based on the first peak (as shown in Fig. S5), and the relative distance (if the pattern is periodic). Unfortunately, the positions of kinetochores perpendicular to the spindle axis are not periodically distributed. Therefore, we cannot comment on the separations between adjacent microtubules.

1. Cse4 count (4 per kinetochore) and the model presented: One of the surprising conclusions of the study is that there are two nucleosomes associated with each microtubule attachment, with Mif2/CENP-C potentially interacting with both nucleosomes. There are two critical issues that the authors must consider.

(1) Fluorescent protein chimeras of Cse4 and CBF3 and COMA complex members do not exclusively localize to kinetochores. Biochemical studies show that both Cse4 and CBF3 proteins interact with non-centromeric DNA, e.g., see work from the Biggins lab regarding Cse4 over-expression and also from the Henikoff group that used ChIP-seq. I can't think of a similar reference for the CBF3 complex, but the DNA-binding proteins are also likely to interact with other parts of the genome. The non-centromeric protein is visible as a significant background fluorescence in wide-field microscopy, e.g., see Cep3 localization here: https://images.yeastrc.org/imagerepo/viewExperiment.do?id=202308&experimentGroupOffset=3&experimentOffset=0&experimentGroupSize=3

Similar background fluorescence can be detected for Cse4 and Ctf19. This extra-centromeric localization of Cse4, Cep3, and Ctf19 makes it possible that the protein counts included by the authors are "contaminated" to some extent by the extra-centromeric protein. The authors should discuss this possibility and how it might affect their counts.

After consideration, we agree with the reviewer that, specifically, a fraction of counted Cse4 molecules should be considered non-centromeric. We agree that the previous data is certainly sufficient to conclude it. The reviewer made a similar suggestion about COMA and CBF3 subcomplexes. In recent years a substantial portion of inner kinetochore components has been reconstituted. In Harrison et al. 2019, the Ctf19 complex structure has been solved. Two copies of the complex were observed. Therefore, the non-centromeric pool of COMA is certainly possible and we now made the adjustments to the text (page 8, lines 219-225) and Fig. 4. Accordingly, we now also modified the abstract (page 1, lines 26-27) and restructured the sections (page 10) to accommodate the different possibility of Cse4 copy numbers. While, fluorescence imaging of CBF3 presents a signal throughout the nuclear region we observed only four copies of Cep3 (part of CBF3). A CBF3 structure also has been resolved by Yan et al. 2018, in which the complex was proposed to exist as a dimer. This translates into four copies of Cep3. Therefore, we find it more suitable to leave all observed Cep3 (CBF3) molecules within a kinetochore model.

(2) The model drawn in Figure 4 makes explicit assumptions about the positioning of the four Cse4 molecules (or two nucleosomes) in each kinetochore relative to the rest of the kinetochore components. Yet, the data shown do not justify this specific arrangement. Lawrimore et al. 2011 claim that the non-centromeric Cse4 nucleosomes must be randomly distributed in the pericentromeric chromatin to evade detection in biochemical tests. Therefore, the nearest-neighbor analysis suggested above will be valuable for gaining new insights into the relative positioning of the centromeric- and non-centromeric Cse4 nucleosomes. A similar analysis for Cep3 and Ctf19 will also be helpful. If stereotypical positioning of these molecules cannot be detected, then the model should be revised accordingly (alternative models that are also consistent with the data can be included).

The reviewer has pointed out that Lawrimore et al. 2011 proposed and justified the existence of a non-centromeric Cse4 pool. This arrangement, also potentially along other inner kinetochore components, makes sense and our data did not indicate it otherwise. Therefore, we now revised our model accordingly by applying changes in the main text on page 10 lines 302-305 __as well as in __Fig. 4.

(3) I suggest one experiment that can help the authors better understand protein organization in one kinetochore. Joglekar et al. 2006 used a dicentric chromosome to isolate single kinetochores on the spindle axis to test the assumption that each kinetochore consists of approximately the same number of molecules of kinetochore proteins. The strains are easy to construct (transform existing strains with a linearized plasmid). Single kinetochores can be seen with a low but reasonable frequency. I leave the decision to perform the experiment to the authors' discretion depending on whether the experiment will be worth the effort in strengthening or enhancing their conclusions.

We performed the suggested experiment using the strain published in Joglekar et al. 2006 (kindly provided by Prof. Kerry Bloom) with Cse4 additionally tagged with mMaple. However, we always observed several super-resolved Cse4 clusters (likely of several kinetochores) overlapping with Nuf2-GFP diffraction-limited signal, therefore unable to assign a single isolated kinetochore to the lagging centromere.

1. Information regarding the degree of correction applied to calculate protein count per half-spindle: It will be helpful to include data regarding the degree of correction applied to the expected and measured numbers of NPC protein as supplementary data so that the readers can see the magnitude of this correction relative to the measured counts.

We would like to clarify that we did not correct the data. Instead, we calibrate the copy number, given that the copy number of Nup188 per NPC is known. We assume the same ratio between localization and copy number applies to both Nup188 and the kinetochore proteins. We now include a new Table S4 listing calibration factors of all experiments shown in Fig. 3.

Minor points:

1. McIntosh et al. JCB 2013 used microtubule plus-ends in serial section electron micrographs of yeast spindles to align the centromeric region and found a disk-shaped structure that roughly corresponds to the size of a single nucleosome ~ 80 nm away from the tip of the microtubule and centered the microtubule axis. The authors should refer to this finding in their discussion of the model that they present with two nucleosomes. In my opinion, this is compelling evidence for a nucleosome-like structure serving as the kinetochore foundation.

We agree with this reviewer's comment. The study, among others, present compelling evidence for a point-centromere. We now included the finding in the discussion on page 10, lines 293-294.

1. As discussed by the authors, the number of Cse4 molecules per kinetochore has been the subject of some controversy. Biochemical data from the Biggins group and ChIPseq data from the Westermann group (Altunkaya et al. 2016 Current Biology) strongly suggest that Cse4 molecules can only be found centered on the centromeric sequence. The latter reference should be included in the discussion.

Thank you for pointing this out. Indeed, this is important. We have now added the relevant reference in the discussion on__ page 10 lines 291-292__.

1. Although microscopy-based methods have estimated anywhere from 1, 2, to 6 Cse4 molecules per kinetochore, these studies generally agree on the stoichiometry between Cse4 and the rest of the kinetochore proteins, e.g., Ndc80 complex proteins are ~ 4-fold more abundant that Cse4, etc. The present study seems to disagree with protein stoichiometry. The authors may find it worthwhile to note this feature of their data.

We now discuss the stoichiometry difference between our results and others on page 11 lines 322-324.

1. Omission of the Dam1 complex from this study is disappointing to me personally, but I am sure that the authors have good reasons for this. They should briefly comment on the absence of the Dam1 complex in this study.

To provide information on the Dam1 complex, we imaged Ask1, a component of the complex. The measured positioning and copy number of the protein are now included in Fig. 2 and Fig. 3 respectively, and described and discussed in respective parts of the manuscript.

Reviewer #1 (Significance (Required)):

Cieslinski and colleagues present a single-molecule localization-based study to define the copy numbers and relative organization of kinetochore proteins in budding yeast. These numbers confirm and significantly refine prior measurements of the same aspects of the kinetochore. They also raise new questions and point to new research directions. The measurements also reveal a model of the protein organization of the budding yeast kinetochore in metaphase. For these reasons, the manuscript is of significant interest to the cell division field.

Reviewer #2 (Evidence, reproducibility and clarity (Required)):

In this study, Cielinski and colleagues have applied single molecule localization microscopy to map the positions of proteins in the yeast kinetochore. This has not been reported previously and this study is both well-conducted and the data appear solid. They also use a modification of this technique to assess the stoichiometry of kinetochore proteins. The results that they obtain are broadly in line with several previous studies that use other methodology. There may be an improvement in accuracy using this new approach that has not been obtained previously and there are some important novel conclusions from this work. I would like the authors to address the following concerns prior to publication:

Major points

1. One interesting finding is that there is a discrepancy in the length of both the MIND and NDC80 complexes (from crystallographic data) with their relative positions. The authors suggest that the outer complexes could be twisted or rotated in respect of the spindle axis. It would be great if the authors could illustrate this in their model (or discuss it in the text), to demonstrate the required angle of twist/rotation of both complexes to account for the discrepancy. A twisted filament structure to the outer kinetochore does have some implications for its response to tension - a key determinant of kinetochore-microtubule attachment. It also may provide some flexibility to the structure under tension.

The discussion about this discrepancy has now been incorporated in the main text, page 9 lines 263-267. For clarity, we only partially reflect this in our schematic model (Fig. 4A; the MIND complex) but we already reflected this in the illustrative structural model in Fig. 4B.

1. For the experiment with cycloheximide, the authors state "Although we observed minor changes in copy numbers, the overall effect of CHX was small." For some proteins, Cse4i for example, there appears to be a significant decrease in intensity (30-40%) after cycloheximide treatment, see Figure S3. While the conclusion that tag maturation does not affect copy number measurements is sound, I suggest modifying this section to reflect the data.

We now modified the section accordingly by pointing out that Cse4i under CHX measurements led to reduction of the signal. The modification can be found on page 8 lines 207-211.

1. Page 5. The statement "These data agree reasonably well with previous diffraction-limited dual-color microscopy studies ..." provides readers with little ability to compare the data. I would like to see a supplementary figure comparing these new data with previous studies, especially those of Joglekar et al 2009, see Figure 3 in this paper.

We thank the reviewer for suggesting such a table. This will allow readers a direct comparison of the data between our study and Joglekar at al. 2009. The comparison can be found in new Table S1 __and __Fig. S4, which are now mentioned on page 5.

1. In terms of the distances quoted, are they in one dimension (as per Jogelkar et al 2009) or in three? The results section is entitled "...positions of kinetochore proteins along the metaphase spindle axis", which suggests a single dimension. Please make this very clear in the results section. In the discussion, is the statement "we mapped the relative positions of 15 kinetochore proteins along the kinetochore axis", which is not entirely clear. It seems from the methods that this is one dimension "...we determined the average distance between the two proteins along the spindle axis. “I suggest clarifying the results section briefly and clearly to indicate that this is a single dimension being measured and also using consistent wording of the axis measured throughout the text.

We agree the previous description may not be clear to the viewers. We now changed the text accordingly in the results section, page 5 lines 129-130.

Minor points:

Abstract: I would drop "all" from "For all major kinetochore proteins...", since full characterisation was performed on 14 proteins (9 in terms of copy number).

We now deleted “all” in the abstract as the reviewer suggested__.__

Page 2: "trough" to through.

Corrected.

Page 2 "S. cerevisiae" to italics

Corrected.

Methods p11. How do the MKY strains relate to common yeast genetic backgrounds? (e.g. are they S288C?).

MKY strains are derivative of S288C. The information was now updated in the Methods section and in Table S2.

Reviewer #2 (Significance (Required)):

This manuscript, together with an accompanying one from Virat et al., are nice complementary studies that provide the first single molecule localization studies of the yeast kinetochore. Although other labs have used super-resolution methods to study individual kinetochore proteins; both of these new studies map distances between many proteins at the kinetochore and thus are able to produce maps of the overall kinetochore structure. Like the previous study using standard resolution methods (Joglekar et al, 2009. Current Biology 19, 694-699); these studies will likely provide a benchmark for future studies on eukaryotic kinetochore architecture, including those in mammalian systems. Additionally, this work will appeal to super-resolution microscopists.

My expertise is as a yeast kinetochore cell biologist.

Author Response

Reviewer #1 (Public Review):

I'm curious about whether the microscopy provided any information about when secretory vesicles leave the TGN. Do they leave throughout the lifetime of a TGN structure, or do they leave in a burst when a TGN structure disperses as marked by loss of Sec7? This information might take us a step closer to understanding how secretory vesicles are made.

Given the limitations of our current imaging set-up with regards to high-speed 3D two-color microscopy, we were unable to capture a large number of these events and therefore cannot make concrete statements about this, however, the quantified events did not appear to be preceded or followed by additional events, suggesting some temporal separation.

Reviewer #2 (Public Review):

The authors are encouraged to integrate their data together better with published biochemistry and structural work into more complete mechanisms for vesicle trafficking, tethering and fusion. The manuscript would be improved by a clearer model(s) of how these factors come together to carry out exocytosis.

This suggestion has been addressed by the addition of a new model figure (Figure 9).

Moreover, many conclusions (especially as they appear in the Results and Figures) are written as if they are well supported by the data (or others' data), when they are often speculative, or reasonable alternative explanations exist. The authors should be clear about which conclusions are well supported, and which are hypotheses. (e.g. Fig 6I, which is a terrific figure, but some of the "conclusions/statements" are speculations).

We have made textual changes to make clearer distinctions between conclusions that are supported by the data, and which are more speculative.

The mechanistic and experimental definitions for the start/end of "tethering" and "fusion" are not clearly stated in the main text, which leads to confusion when examining the arrival of different factors (and seems to lead to circular arguments about what is defining what). Are these definitions well supported by the previously published and current data? E.g. is the disappearance of GFP-Sec4 really equal to the fusion event? Without data showing membrane-merger or content delivery, this needs to be described as an assumption that is being made.

Early in the results, we now define precisely what we interpret as the start of tethering and time of fusion. Unfortunately, thus far, all attempts at designing a cargo marker suitable for defining membrane fusion have not succeeded, however, we believe the observations in Figure 4 strongly support assumption that loss of GFP-Sec4 signal coincides with fusion.

The Sro7 results and conclusions are complicated, and not always carefully supported, for several reasons: there is a functionally redundant paralog Sro77, and data shows Sro7 can bind to Sec4, Sec9 and Exo84 in exocyst (Brennwald, Novick and Guo labs). The authors should be clearer, as they seem to pick and choose which interactions they think are relevant for different observations.

We did not intend to “pick-and-choose” relevant interactions and now more clearly state what our Sro7 results mean.

The assumption that yeast Sec1 behaves similarly to other Sec1/Munc18 proteins for "templating" SNARE complex assembly, e.g. Vps33 in Baker et al, is unlikely, given the binding studies from a number of labs (Carr, McNew, Jantti). Furthermore, the evidence for Sec1 interaction with exocyst suggests that they may work together (Novick, Munson labs). Previous data from the Guo lab (Yue et al 2017) and new BioRxiv data from the Munson/Yoon labs suggest that exocyst may play key roles in SNARE complex assembly and fusion.

We did not mean to imply that the exocyst does not play a meaningful and critical role in SNARE complex assembly and fusion. This was an unintentional omission, which we have now addressed in the text. Our interpretation of the published meaning of SM-protein “templating” is that SM’s facilitate the alignment of the critical zero-layer ionic residues in the SNARE motifs, which may be possible regardless of affinity to single SNARE motifs. Indeed, for Sec1 specifically, it may be possible that this exact function is of lower importance relative to, perhaps, the stabilization and protection of trans-SNARE complexes prior to membrane fusion. Future studies may clarify this.

There is concern that the number of molecules of each of the factors measured is accurate, and how the authors really know that they are visualizing single vesicle events (especially with data showing that "hot-spots" may exist). For example, why is the number of molecules of exocyst is ~double or more than that previously observed (Picco et al; Ahmed et al with mammalian exocyst).

Estimating the numbers of molecules is subject to some variation due to fluorescent tags used and to some extent where the protein is tagged. Since different tags were used in the earlier studies, being within a factor of two is not that surprising.

For puncta of exocyst subunits in the mother or moving towards the plasma membrane, what is the evidence that they are actually on vesicles? The clearest argument seems to be the velocity at which they move, but this could be due to the direct interaction of exocyst with the myosin (which is a tighter interaction in vitro than exocyst-Sec4 binding), rather than being on vesicles. Furthermore, do all the exocyst complexes in the cell show this behavior, or could these be newly synthesized/assembled complexes?

Transport of the exocyst by myosin alone without a vesicle seems very unlikely, as this myosin V needs to be activated by binding vesicle-associated Sec4 (Donovan et al., 2012, 2015). Moreover, transport of just two exocyst complexes by a myosin dimer would be very hard to detect. Nonetheless, we have added an additional supplementary figure (Figure 1 Supplement 5C) illustrating a clear example of exocyst complex colocalization with a secretory vesicle in the mother cell which we hope will quell fears that the exocyst complex is indeed on secretory vesicles, albeit in small numbers, during this stage of transport.

With regard to the exocyst octamer leaving at the time of "fusion," the authors should discuss Ahmed et al.'s finding of Sec3 leaving prematurely in mammalian cells, as well as data from the Toomre lab.

We did reference this earlier work in mammalian cells and indicate that it differs from the situation in yeast. We don't have anything insightful to be drawn from these differences.

Reviewer #3 (Public Review):

In this context, it is notable that dual-channel imaging appears to be made by sequential, not simultaneous, acquisition, which deserves a currently missing comment. Moreover, given the weight that image acquisition plays in this project, it might be described and justified better.

As noted above, we have expanded our description of the microscopy. We took two-color images sequentially as our microscope is not configured with a beam-splitter for simultaneous imaging.

This referee could not fully understand the routine of image acquisition, specifically, the continuous movement of the stage in the Z-axis as images are streamed (to the RAM or to the disk? the latter takes time, line 177); does it mean that Z-stepping is solely governed by the exposure time? The CCD camera penalizes pixel size (16 µm) at the expense of achieving outstanding quantum efficiency. The optical path includes a 100x objective and a 2x magnification lens to compensate for the large camera pixel size, thereby achieving 0.085 µm/pixel, but these lenses 'waste' part of the fluorescent signal. One wonders if the CMOS camera (6.5 µm pixel size) coupled with a 63x objective wouldn't be appropriate? A brief discussion on this choice would be helpful for readers.

We now discuss the microscopy in more detail and why we use an EMCCD rather than aCMOS camera.

It is remarkable that Sec2 and Sec4 are recruited to membranes even before a vesicle is formed (Fig 6I). I find somewhat weak the evidence that RAB11s 'mark' the TGN, and disturbing the fact that RAB11 reaches the PM (does GFP tagging prevent GAP accession?). I should like to recommend strongly that the authors integrate into the introduction/discussion information on the late steps of exocytosis available for Aspergillus nidulans, another ascomycete that is particularly well suited for studying this process. Here RAB11 is not a late Golgi resident but is transiently (20 s) recruited to TGN cisternae in the late stages of their 120 s maturation cycle to drive the transition between Golgi and post-Golgi (Pantazopoulou MBoC, 2014). Recruitment of RAB11 to the TGN is preceded by the arrival of its TRAPPII GEF (Pinar, PNAS 2015; Pinar PLOS Gen 2019), a huge complex that is incorporated en bloc to the TGN (Pinar JoCS, 2020). Upon RAB11 acquisition RAB11 membranes engage molecular motors (Penalva, MBoC 2017) to undertake a several-micron journey that transports them to a vesicle supply center located underneath the apex (review, Pinar & Penalva, 2021). Here is where Sec4 is located, strongly indicating that there is a division of work between two Rabs each mediating one of the two stages between the TGN and the membrane (Pantazopoulou, 2014, MBoC).

In the general comments above, we discuss the possible artifact of tagged Ypt31 on the PM. In the Discussion, we now compare our results in S. cerevisiae with the findingss in A. nidulans.

Reviewer #1 (Public Review):

Grande et al report the results of a series of functional connectivity experiments that build upon and extend results reported in Maass et al. (2015). The authors conducted three separate but interrelated analyses with a primary aim of characterising entorhinal-hippocampal processing pathways in the human brain.

The first analysis served to identify subregions within the entorhinal cortex (EC) that preferentially connect with the retrosplenial cortex (RSC), posterior parahippocampal cortex (PHC) and perirhinal areas 35 (A35) and 36 (A36). The results of this analysis revealed that the RSC and PHC preferentially connect with the anterior medial EC and posterior medial EC respectively while A35 and A36 preferentially connect with the anterior lateral EC and posterior lateral EC respectively. In a second analysis, the authors evaluated patterns of functional connectivity between the four entorhinal subregions identified in Analysis 1 and specific subfields of the hippocampus, namely the subiculum and CA1. The authors provide evidence that each EC subregion preferentially connects with specific regions along the transverse (medial-lateral) axis of the subiculum and CA1.

In a third analysis, the authors investigated whether 'object' and 'scene' information is differentially processed within EC subregions and along the transverse axis of the subiculum and CA1. Results revealed that the posterior medial EC and distal (medial) subiculum were preferentially engaged by 'scene' stimuli. In contrast, anterior regions of the EC and the CA1/subiculum border were equally engaged by 'object' and 'scene' stimuli. The authors propose that the posterior medial EC and distal subiculum may represent a unique route for scene/contextual information flow while anterior regions of the EC and the CA1/subiculum border may be involved in integrating both 'scene' and 'object' information.

Overall, the study was well-motivated, well-designed and appropriately analysed to address the research questions. The conclusions of the paper are well supported by the data.

The primary novelty of these results relate to the characterisation of how the RSC, PHC, A35 and A36 functionally connect with different portions of the EC and how, in turn, these EC subregions preferentially connect along the medial-lateral axis of the subiculum and CA1. These new and detailed insights will have an impact on and advance current theoretical models of entorhinal-hippocampal functional organisation in the human brain with implications for our understanding of human memory processing and its dysfunction.

The study also provides new evidence regarding the functional organisation of EC-hippocampal circuitry as it relates to 'object' and 'scene' processing. Results of this component of the analysis support accumulating evidence that medial portions of the hippocampus and EC are preferentially engaged during scene-based cognition.

Taken together, the results of this study inform and extend current theoretical models of entorhinal-hippocampal information processing pathways in the human brain.

A major strength of the study is the detailed approach used to investigate each cortical region of interest (ROI), to characterise their functional connectivity with subregions of the EC and, in turn, how these EC subregions functionally relate to hippocampal subfields. The authors take advantage of the rich dataset acquired at 7T to gain new insights into entorhinal-hippocampal functional interactions.

While the detailed approach noted above is a major strength of the study, it is also the source of some weaknesses. For example, when manually segmenting small ROIs (such as hippocampal subfields), quality assurance measures are important to give the reader confidence that the ROI masks are, as accurately as possible, measuring what we think they are measuring. A weakness of this study in its current form is that no quality assurance measures have been presented for the ROIs. The authors provide no metrics relating to intra- or inter-rater reliability (e.g., DICE metrics) for the manually segmented ROIs. Also, it can be difficult to warp small ROIs such as hippocampal subfields to EPI images with sufficient accuracy. No data is presented to assure readers that the ROIs (manually segmented on structural images and then warped to EPI space) were well aligned with the EPI images.

It is also important to note that the subiculum mask used in this study appears to encompass the entire 'subicular complex' inclusive of the subiculum, presubiculum and parasubiculum. Importantly, the pre- and parasubiculum are located on the medial most aspect of the 'subicular complex' but this region is referred to throughout the current study as the 'distal subiculum'. Therefore, results attributed to the distal subiculum likely also reflect functional activation of the pre- and parasubiculum. Indeed, this makes sense considering accumulating evidence that the pre- and parasubiculum are preferentially engaged during scene-based cognition. Interpretation of results relating to the 'distal subiculum' should, therefore, be interpreted with this in mind.

Author Response

Reviewer #1 (Public Review):

This well-written paper combines a novel method for assaying ubiquitin-proteasome system (UPS) activity with a yeast genetic cross to study genetic variation in this system. Many loci are mapped, and a few genes and causal polymorphism are identified. A connection between UPS variation and protein abundance is made for one gene, demonstrating that variation in this system may affect phenotypic variation.

The major strength of the study is the power of yeast genetics which makes it possible to dissect quantitative traits down to the nucleotide level. The weakness is that is not clear whether the observed UBS variation matters on any level, however, the claims are suitable to moderate, and generally supported.

We agree with the reviewer that understanding how causal variants for ubiquitin-proteasome system (UPS) activity affect other molecular, cellular, and organismal phenotypes is an important area of future research.

The paper provides a nice example of how it is possible to genetically dissect an "endo-phenotype", and learn some new biology. It also represents a welcome attempt to put the function of a mechanism that is heavily studied in molecular cell biology in a broader context.

We thank the reviewer for these kind words.

Reviewer #2 (Public Review):

In this manuscript, the authors developed an elegant quantitative reporter assay to identify quantitative trait loci that regulates N-end rule pathway, a major quality control mechanism in eukaryotes. By crossing two yeast species with divergent proteostasis activity, they generated a population that showed broad variation in proteostasis activity. By sequencing and mapping the underlying loci, they have identified several genes that regulate N-end rule activity. They then verified them using precise genetic tools, validating the power of their approach.

Overall, it is a very solid manuscript that would be highly interesting for the quality control field.

In general, I really liked this manuscript for these reasons:

• Uses fluorescent timers elegantly to quantitatively measure protein degradation.

• Validates the approach in depth, showing the readers how the tool works.

• Uses the power of yeast genetics and bulk segregant analysis to map loci that may have small effects.

• Validates the mapped loci using precise genetic tools.

In a field that is dominated by biochemistry, this manuscript will be a fresh breath of air…

We thank the reviewer for their thoughtful evaluation of our work and these kind words.

Reviewer #3 (Public Review):

This manuscript, "Variation in Ubiquitin System Genes Creates Substrate-Specific Effects on Proteasomal Protein Degradation" studies the genetic basis of differences in protein degradation. The authors do so by screening natural genetic variation in two yeast strains, finding several genes and often several variants within each gene that can affect protein degradation efficiency by the Ubiquitin-Proteasome system (UPS). Many of these variants have "substrate-specific effects" meaning they only affect the degradation of specific proteins (those with specific degrons). Also, many variants located within the same genes have conflicting effects, some of which are larger than others and can mask others. Overall, this study reveals a complex genetic basis for protein degradation.

Strengths: Revealing the genetic basis for any complex trait, such as protein degradation, is a major goal of biology. The results of this paper make a significant step towards the goal of mapping the genes and variants involved in this specific trait. Fine mapping methods are used to home in on the specific variants involved and to measure their effects. This is very nicely done and provides a detailed view of the genetic basis of protein degradation. Further, the GFP/RFP system used to quantify the efficiency of the protein degradation system is a very elegant system. Also, the completeness of the analysis, meaning that all 20 N-degrons were studied, is impressive and leads to very detailed findings. It is interesting that some genetic variants have larger and opposite effects on the degradation of different N-degrons.

We thank the reviewer for these positive comments.

Weaknesses: Some of the results discussed in this paper are not surprising. For example, the finding that both large effect and small effect genetic variants contribute to this complex trait is not at all surprising. This is true of many complex traits.

We agree with the reviewer that the number and patterns of QTLs we observe are perhaps not unexpected given that most traits are genetically complex. However, we also note that our results stand in stark contrast to previous efforts to understand how natural genetic variation affects the UPS, which have focused almost exclusively on large-effect mutations in UPS genes that cause rare Mendelian disorders. We have therefore chosen to retain our discussion of the complex genetic architecture of the UPS.

The discussion of human disease is also a bit extensive given this study was performed on yeast. It might be more productive to use these findings to understand the UPS better on a mechanistic level. Why does the same genetic variant have opposite effects on the degradation of different degrons, even in cases where those degrons are of the same type?

Following the reviewer’s suggestion we have removed multiple references to human disease from the introduction. We retained paragraph 3 of the introduction (previously, lines 43-55, pg. 2, para. 2 in the revised manuscript), which discusses disease-causing mutations in UPS genes, because the examples presented highlight two important motivations for our work: (1) individual genetic differences create variation in UPS activity and (2) much of our knowledge of how natural genetic variation affects the UPS comes from these rare, limited examples. However, we have re-written the paragraph to focus on these points and removed descriptions of the clinical manifestations of the disorders mentioned.

We agree with the reviewer that understanding the mechanistic basis of substrate-specific variant effects on distinct N-degrons is important. However, doing so would require additional experiments that we argue are outside the scope of the current study.

Overall, this manuscript excels at mapping the genetic basis of variation in the UPS system. It demonstrates a very complex mapping from genotype to phenotype that begs for further mechanistic explanation. These results are important to the UPS field because they may help researchers interrogate this highly conserved essential system. The manuscript is weaker when it comes to the broader conclusions drawn about the relative importance of large vs. small effects variants on complex traits, the amount of heritability explained, and the effects of genetic variation on protein abundance vs transcript abundance. Though in the case of protein vs transcript, I feel the cursory examination of the trends is perhaps at an appropriate level for the study, as it is mainly meant to show these things differ rather than to show exactly how and why they differ.

We state that the distribution of QTL effect sizes for UPS activity consists of many QTLs with small effects and few QTLs of large effects. While this result is similar to patterns observed for other complex traits, it differs dramatically from the results of previous studies of genetic influences on the UPS, which have been largely confined to large-effect variants. Given these differences, we think it is appropriate and worthwhile to emphasize the complex genetic architecture of UPS activity.

We agree that estimating the fraction of heritability explained by our QTLs and variants would be valuable. However, as noted in our response to Reviewer 1, the QTL mapping method we used does not permit ready calculation of heritability estimates due to its pooled nature.

The reviewer is correct in noting that the primary goal of our RNA-seq and proteomics experiments was to provide an initial exploration of the effects of causal variants for UPS activity on global gene expression at the protein and mRNA levels. While a comprehensive dissection of the effects of this and other causal variants is an important area of future work, our results here show broad changes in global gene expression and establish that the causal UBR1 variant affects gene expression at the protein and mRNA levels.

Reviewer #4 (Public Review):

Overall the paper is clear and well-written. The experimental design is elegant and powerful, and it's a stimulating read. Most QTL mapping has focused on directly measurable phenotypes such as expression or drug response; I really like this paper's distinctive approach of placing bespoke functional assays for a specific molecular mechanism into the classical QTL framework.

We thank the reviewer for their thoughtful evaluation of the work and positive comments.

I think this example of her reference to religion demonstrates its significance amidst her hardships.

An explicit reference in 1931 in a section on note taking to cross links between entries in accounting ledgers. This linking process is a a precursor to larger database processes seen in digital computing.

Were there other earlier references that are this explicit within either note making or accounting contexts? Surely... (See also: Beatrice Webb's scientific note taking)

Just the word "digital" computing defines that there must have been an "analog' computing which preceded it. However we think of digital computing in much broader terms than we may have of the analog process.

Human thinking is heavily influenced by associative links, so it's only natural that we should want to link our notes together on paper as we've done for tens of thousands of years (at least.)

one of the issues. I think this is especially jarring to the reader because most of us have used an Uber before, or other forms of public transportation and these "terms" are very different.

Author Response

Reviewer #1 (Public Review):

The manuscript shows that bone is resorbed during the early steps of limb regeneration in urodeles, and osteoclasts are required for this process. In case of impaired resorption, integration of newly-formed tissue with the original bone shaft is compromised. The manuscript further shows that wound epithelium is required for bone resorption and suggests that it induces osteoclastogenesis or migration of osteoclasts. Furthermore, the authors showed that the formation of novel skeletal elements is initiated while the resorption of the old one is still actively ongoing.

The study is well designed, conclusions are relatively well supported, and data are presented in a clear way. Two new models of transgenic axolotls have been created. The strongest and most important finding is that partial bone resorption is required for tissue reintegration. My main concern is the novelty of this study, which is quite limited in my opinion.

Specifically, resorption of bone stump during limb regeneration has been shown before in various model organisms.

The role of osteoclasts in this process has not been well characterized in urodeles but has been shown during the regeneration of a mouse digit.

It is reasonable to anticipate that similarly, osteoclasts are resorbing bone in salamanders, especially since this is the only cell type known for bone resorption.

Thus, this observation, despite being nicely and thoroughly done, is of limited interest.

The role of wound epithelium in bone histolysis is well demonstrated via skin flap experiments in this manuscript. However, upon skin flap surgery no limb regeneration occurs, implying wound epithelium is a key tissue triggering all the processes of limb regeneration. Accordingly, the absence of bone histolysis in such conditions can be secondary to the absence of any other part of the regenerative process, e.g., blastema formation, macrophage M1 to M2 transition, reinnervation, etc. The proposed link between wound epithelium and osteoclastogenesis (i.e., Sphk1, Ccl4, Mdka) is very superficial and very suggestive.

No functional evidence was provided to confirm these connections. Finally, the authors showed that new bone formation occurs while resorption of the bone stump is still ongoing. This is a nice observation, but again, rather indirect as it is based on the dynamics of bone resorption and bone formation in different animals. Due to high variability among animals, direct evidence, like double staining for osteoclasts and blastema markers would address this point more precisely.

We consider that our work provides evidence, for the first time, that skeletal resorption in early stages of regeneration has a durable impact by affecting tissue integration. We show that this process occurs in a short and conserved time, which provides a window of interest for comparative research with other models, and interventional therapies. To our knowledge, limb regeneration is studied mainly in amphibians, as they are the only established lab model with this ability. Some lizards, geckos and possibly iguanas, have been reported to regrow an appendage albeit lacking the regenerative fidelity amphibians have. In an established regeneration lab model, such as the axolotl, the study of regeneration-induced resorption has been scarce.

During murine digit tip, osteoclasts are recruited to the amputation site and resorb the bone in a similar time frame as we show here in the axolotl. Ablating osteoclasts delays the regeneration time, however, no study has been conducted on the impact of tissue integration. Additionally, a key difference between mouse digit and adult axolotl limb regeneration is that the new skeletal elements are built fundamentally different: direct ossification (bone on top of bone) in mouse, versus endochondral ossification (cartilage on top of osteo-cartilage elements) in the axolotl limb. The tissue integration of the latter may present different challenges worth exploring to understand its regulation. What this work adds, is a characterization of the temporal and cellular dynamic of regeneration-induced resorption, the interaction of osteoclasts with skeletal cells and lastly, the impact on tissue integration.

Based on previous studies in mammals, it is reasonable to anticipate the presence and role of osteoclasts in salamanders. However, the growing body of work in the field, as well as our own work in the axolotl, have shown that extrapolations of mammalian skeletal biology to other species come with their risks.

We agree that the role of the wound epithelium (WE) in skeletal histolysis will require further and extensive work. The evidence shown here, provides a glimpse of the complex response and crosstalk of the WE with the tissue underneath, and we hypothesize this response is tailored to the tissue composition exposed during the injury.

Finally, following the reviewer’s advice, we have conducted new experiments to prove the temporal connection between skeletal resorption and regeneration, showing that these processes occur simultaneously.

Reviewer #3 (Public Review):

This study outlines the role of osteoclast-mediated resorption in integrating the skeletal elements during limb regeneration, using axolotls that can regenerate the entire limb upon amputation. Using calcium-binding vital dyes (calcein and alizarin red), the authors first demonstrated that a large portion of amputated skeletal elements is resorbed prior to blastema formation. They further show that 1) inhibiting bone resorption by zoledronic acid impairs proper integration of the pre-existing and regenerating skeletal elements, 2) removing the wound epithelium using the full skin flap surgery inhibits bone resorption, and 3) bone resorption and blastema formation are correlated. The authors reached the major conclusion that bone resorption is essential for successful skeletal regeneration. Notably, this study applies a well-established and elegant axolotl limb regeneration model and transgenic reporter strains to reveal the potential roles of resorption in limb regeneration.

Strengths:

1. The authors utilized a well-established axolotl limb regeneration model and applied elegant vital mineral dyes and transgenic reporter lines for sequential in vivo imaging. The authors also provided quantitative assessment by examining multiple animals, particularly in the early sections, ensuring the rigor and the reproducibility of the study.

2. The authors further performed important interventions that can impinge upon successful limb regeneration, including inhibition of bone resorption by zoledronic acid and impairment of the wound epithelium by full skin flap surgery. These procedures gave rise to useful insights into the relationship between bone resorption and successful limb regeneration.

3. The imaging presented in this manuscript is of exceptionally high quality.

Weaknesses:

1. Despite the high quality of the work, many analyses in this study are incomplete, making it insufficient to support the major conclusion. For example, in Figure 4, the authors did not provide any quantitative assessment to show how zol affects the integration of the skeletal elements (angulation?), which seems to be essential for supporting the conclusion. Likewise in Figure 7, the analyses of EdU+ cells and Sox9 reporter expression were not included in zol-treated animals. Similarly in Figure 5, quantification of osteoclasts was not performed with the full skin flap surgery group. Analyses of only normally regenerated animals are not sufficient to support many of the conclusions.

2. The phenotype of zol-treated animals in limb regeneration is somewhat disappointing. Although zol-treated animals show decreased blastema formation and unresorbed pre-existing skeletal elements, limb regeneration still occurs and the only phenotype is a relatively minor defect in skeletal integration. It is possible that zol-induced defect in blastema formation is not directly linked to the failure of integration at a later stage. I find this “weakness” a bit subjective.

3. As an integration failure of the newly formed skeleton still occurs in untreated animals, it is not entirely clear how the authors can attribute this defect to a lack of bone resorption. More quantitative analyses would be necessary to demonstrate the correlation between zol treatment and lack of integration.

Taking into consideration the reviewer’s concerns, we have improved our analysis of integration phenotype. The assessment of integration success was carried out using a score matrix and with it, we correlated the extent of resorption with integration efficiency more accurately. We believe our results provide sufficient evidence to support this correlation.

When we first saw the phenotype of zol-treated animals, we were far from disappointed, we were actually intrigued that we could observe a significant failure in tissue integration after removing the function of osteoclasts in an early phase of regeneration. All or nothing results are exciting, subtle results on the other hand, could prove more informative, and we think this is the case here. Our treatment does not inhibit regeneration, but disrupts tissue integration, opening another fascinating aspect of regeneration: how old tissue is capable of functionally integrate newly-formed tissue?

The integration phenotypes observed in the un-resorbed limbs does not resemble anything reported in the field so far. Moreover, the range of phenotypes observed led us to better determine its correlation with resorption. Importantly, the presence of integration failures in untreated animals allowed us to look into ECM organization at this old-new tissue interphase, while highlighting the normal occurrence of imperfect regeneration in the axolotl limb.

Finally, we have included new results to complement the conclusions presented at the end of our work. Albeit we observed differences in blastema size in zol-treated animals, we did not observe difference in the amount of EdU+ cells, which reveals that the skeleton cannot be used as a reference for assessing blastema location. This conclusion is complemented with our in vivo assays in which we observed condensation of cartilage despite resorption still occurring. We consider our conclusions to be justified and supported by the assays presented in our work.

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

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Reply to the reviewers

Manuscript number: RC-2022-01594

Corresponding authors: Hidehiko Kawai and Hiroyuki Kamiya

1. General Statements [optional]

We would like to extend our gratitude to the Editor and both Reviewers for their constructive and insightful comments to our manuscript. We deeply appreciate the Reviewers’ careful consideration of our work, in result of which we think the paper has greatly improved. Below, we have responded to all points raised by the Reviewers.

2. Point-by-point description of the revisions

Reviewer #1 (Evidence, reproducibility and clarity (Required)):

The analysis of mutations in mammalian, including human, genomes has been of interest for many decades. Early DNA sequencing technologies enabled direct identification of mutations in target genes provided that the mutant genes could be readily isolated. This requirement stimulated the development of shuttle vector plasmids that carried a mutation marker gene and could replicate in both mammalian and bacterial cells. These were used in experiments in which the plasmids, treated with a mutagen, would be passaged through mammalian host cells after which the progeny plasmids were introduced into an indicator bacterial strain. Colonies with mutant marker genes could be distinguished by color or survival, the plasmids recovered, and the sequence of the mutant gene determined. The shuttle vector plasmid that became the most widely used contained as the marker the supF amber suppressor tyrosyl tRNA gene positioned in the plasmid such that deletion mutations associated with mammalian cell transfection were selected against. Although various improvements have been introduced since its introduction in the mid-1980s, including bar codes to distinguish independent from sibling mutations (in the early 1990s), the basics of the system have been maintained, and it and variations are still in use. The Kamiya group has made several adjustments to the supF shuttle vectors, including the construction of indicator bacterial strains based on survival of bacteria containing mutant supF genes (the initial system relied on colony color). They have published many studies of mutagenesis by various agents, error prone polymerases, etc. In the current submission they describe a comprehensive approach to identifying mutations in the supF gene that exploits Next Generation Sequencing technology that can identify the full spectrum of mutations including those that escape detection in phenotypic screens. The study is exhaustive and presents a methodical validation of each component of their approach. They report UV induced mutations, the mechanism of which has been well characterized in previous literature. They also describe a category of multiple mutations, which had been observed in the early work with the supF plasmids, and whose relationship to UV photoproducts is most likely indirect.

*We thank the Reviewer for their very insightful feedback to our manuscript and their positive assessment. We have added some discussion points based on the essential references mentioned in the Reviewer’s comments, which we believe made the explanation of our study more complete. *

Major comments: This manuscript presents a technical advance on the use of the supF mutation reporter system. The extent of the validation of each component of the system, including the bar code is rigorous. Their data on the nature and location of UV induced mutations are in very good agreement with previous studies with supF and other reporter genes, a further validation of their approach. Their discussion of the mechanism of the UV induced mutations is in accord with prior work from other laboratories. However, their interpretation of the multiple mutations, although reasonable in invoking a role for APOBEC deamination of cytosines (see eLife. 2014; 3: e02001 for another discussion of this issue), overlooks a much earlier study on the same topic that showed that nicks in the vicinity of the marker gene are mutagenic and can induce multiple mutations (Proc Natl Acad Sci 1987 84:4944-8). It would be useful for the authors to consider their data on the multiple mutations in the light of the earlier analysis. Furthermore, a check to verify the covalently closed circular integrity of the plasmid preparations would be an important quality control and could reduce the mutagenesis observed in 0 UV controls.

We thank the Reviewer for the valuable comments that made our manuscript clearer and more emphatic. We are hereby addressing all of the Reviewer’s concerns. The available data accumulated from previous studies have proved the high sensitivity of the supF assay as a mutagenesis assay, which now has been clearly supported by the results in the current study. We believe that this NGS assay will be able to fulfil the data requirements to tackle many questions related to mutagenesis, thanks to the simplicity and cost-effectiveness of the procedure. However, to meet the experimental objectives, the preparation and analysis of the library are crucially important procedures in the stages of initial setting up of the assay. The covalently closed circular integrity of the vector library is definitely one of the important points we should pay attention to when performing this assay. After the construction of the BC12-library, we have to check the quality of the library by agarose gel electrophoresis. The background mutation frequency and the sequence of the library itself (uploaded as described in the DATA AVAILABILITY section of this manuscript) also needs to be analyzed by NGS before the experiment. We are also routinely constructing the double-stranded shuttle vector from a single-stranded circular DNA with a variety of site-specific damaged oligonucleotides. The treatment with T5 exonuclease followed by purification is absolutely essential to decrease the background mutation frequency. Without the treatment with the exonuclease, cluster mutations may be increased under specific experimental conditions. For this study, we carried out the conventional supF assay using the BC12-library purified after T5 exonuclease treatment. However, in this case the process of purification slightly increased the mutant frequency of the BC12-library to about 2 x10-4 (corresponding to 1x10-6/bp).Therefore, when setting up the essay, we have to consider the background control that we will need for the data analysis. In response to the Reviewer’s comments, we have now added the following paragraph in the DISCUSSION section:

Page 16, line 25:

”5) For the supF assay, spontaneous cluster mutations at TC:GA sites were often observed, and it was well illustrated in an earlier study that a nick in the shuttle vector was a trigger for these asymmetric cluster mutations (54). Therefore, we need to be aware of the quality of each library and how it affects the outcome of each analysis, especially for detection of very low levels of mutations. Depending on the purpose of the experiments, in the preparation of covalently closed circular vector libraries it is essential to eliminate the background level of mutations. In fact, the in vitro construction of the library of double-stranded shuttle vectors from single-stranded circular DNA requires the process of treatment with T5 Exonuclease, which drastically decreases background mutations.”

Minor points The authors state that only 30% of the base sequence of the supF gene can be "used for dual-antibiotic selection on the indicator E. coli". An earlier review (Mutation Res 220: 61,1989) indicated that within the mature tRNA region single or tandem mutations had been reported at 87% of sites, using the colony color assay. The direct NGS analyses would be indifferent to phenotype, and one would expect the maximum number of mutable sites would be recovered from this approach. It would be helpful for an explicit statement regarding the number of mutant sites to be in the Discussion, as this should strengthen the case for the NGS strategy.

We thank the Reviewer for the helpful comment. These are important points we should indeed mention. This method will complement previous data, and especially the data from titer plates will provide us with non-biased mutation spectra for the whole analyzed region. We have now explained in detail about the coverage of mutation spectra in the DISSCUSSION section.

Page 14, line 14:

“The mutation spectra of single or tandem base-substitutions for inactive supF genes identified by using the blue-white colony color assays were comprehensively summarized in an earlier review article, and it was noted that the mutations were detected at 86 sites within a 158-bp region covering the supF gene (54%) and at 74 sites within the 85-bp mature tRNA region (87%), thus demonstrating the great sensitivity of the supF assay system for analysis of mutation spectra (19). However, obtaining reliable datasets by the conventional supF assay requires skill and experience, especially for studies where the mutations of interest are induced with low frequency. The method has been advanced by the construction of indicator bacterial strains with different supF reporter genes which allow selection based on survival of bacteria containing mutant supF genes. However, the fact that the supF phenotypic selection process relies on the structure and function of transfer RNAs that may be differently affected by different mutations means that the improvement of the efficiency of the selection process may cause loss of coverage of the mutation spectra, as it is under our experimental conditions, where the coverage is about 30% (19,20).”

Page 15, line 4:

“From this point of view, we believe that we can secure a sufficient number of experiments to improve the accuracy of the analysis and to confirm the reproducibility of the experiments. Furthermore, the data from colonies grown on titer plates provides us, at least in principle, and with the exception of large deletions and insertions, with non-biased mutation spectra for the whole analyzed region.

Supplementary Figure 1 shows the organization of 8 supF reporter plasmids. Were these discussed in the text and employed in the experiments? It was not clear in the text.

We thank the Reviewer for the helpful comment. It was indeed not clear which vectors we used and why we constructed a series of vectors. Now, we have added the vectors we used for the constructions of the library and each experiment in the RESULTS and MATERIALS AND METHODS sections. Since this is quite important for us and, we believe, the readers, we also added the explanations in the DISCUSSION section, detailing why we have constructed a series of shuttle vectors, as follows:

Page 19, line 36:

“Mutational signatures identified in cancer cells are emerging as valuable markers for cancer diagnosis and therapeutics. Innumerable physical, chemical and biological mutagens, including anticancer drugs, induce characteristic mutations in genomic DNA via specific mutagenic processes. The mutation spectra obtained here by using the presented advanced method were in good agreement with accumulated data from previous papers where the conventional method had been used, with the advantage that our method provided less-biased mutation spectra data. As described above, the datasets presented here highlighted novel mutational signatures and also cluster mutations with a strand-bias, which could be associated with the processes of replication, transcription, or repair of DNA-damage, including a single strand break (a nick). In this study, eight series of supF shuttle vector plasmids were constructed, as presented in Supplementary Figure S1; however, the analysis was carried out using N12-BC libraries prepared from either pNGS2-K1 (Figures 1-4) or pNGS2-K3 (Figures 5-10). The pNGS2-K1/-A1/-K4/-A4 and pNGS2-K2/-A2/-K3/-A3 vector series contain an M13 intergenic region with opposite orientations relative to the supF gene, which allows us to incorporate specific types of DNA-damage at specific sites in the opposite strand of the vector library. Also, the pNGS2-K1/-A1/-K3/-A3 and pNGS2-K2/-A2/-K4/-A4 vector series contain the SV40 replication origin, which enables bidirectional replication and transcription, at opposite sides of the supF gene. Although this is still preliminary data, it is notable that the spontaneously induced mutations for the different vectors in U2OS cells were not significantly different. Therefore, the here presented mutagenesis assay with NGS, by using these series of libraries, can be applied in many different types of experiments to address both quantitative and qualitative features of mutagenesis. It is possible to design series of libraries containing DNA lesions or sequences suitable for the investigation of specific molecular mechanisms, such as TLS, template switching, and asymmetric cluster mutations.”

CROSS-CONSULTATION COMMENTS Comment on the issue raised by Reviewer #2 regarding plasmids with unrepaired DNA damage introduced into E. coli after passage through U2OS cells: treatment of the plasmid harvest with Dpn1 eliminates un-replicated plasmid DNA. Also, SV40 T antigen drives run away replication of the plasmids, which contain the SV40 origin of replication. This greatly dilutes plasmids with remaining UV photoproducts.

Reviewer #1 (Significance (Required)):

Significance This is a comprehensive description of a technical advance for the analysis of mutations based on the most widely used system for reporting mutations in mammalian, including human, cells. As costs for NGS decline it is likely to become the approach of choice.

Reviewer #2 (Evidence, reproducibility and clarity (Required)):

In this manuscript, the authors developed a novel mutagenesis assay by combining the conventional supF forward mutagenesis assay with NGS technology. The manuscript is well written, providing design, methods, and results of the experimental system in very much details, which this reviewer highly evaluates. However, the manuscript may be too long and could be more concise. In addition, this reviewer is afraid that main figures seem difficult to fit printed pages (especially multi-paneled figures of large size, such as Fig. 5 through 8). The authors should re-organize the figures by reducing size and/or moving partly to supplementary information.

We thank the Reviewer for the helpful comments to our manuscript. It is true that the multi-paneled figures were too large, and we have now re-analyzed and optimized most of the figures by reducing size, transferring to Supplementary Figures, and separating one figure into two. Although the number of Figures and Supplementary Figures have now increased, we believe that it has become easy to follow for readers and to fit printed pages. *We considered carefully the Reviewer’s remark about the length of the manuscript, but we feel that the text was already as concise as we could make it, and we have already left out some more detailed explanations. *

1. Some UV-induced DNA damage (typically CPD) is repaired only slowly in human cells, so that the replicated plasmid DNAs recovered from U2OS cells may still contain damage and possibly induce mutations in E. coli after transfection. As the result of high sensitivity of NGS analysis, it is worried that such mutations could be also included in the results. To obtain even more accurate mutational characteristics in mammalian cells, the authors could consider to treat the DNA samples with photolyases before transformation of E. coli. The authors could consider to discuss on this point.

*We thank the Reviewer for the helpful comment, indeed Dpn I treatment is one of the very important procedures for avoiding analysis bias. We have now expanded the explanation why the libraries have to be treated with Dpn I, as follows: *

Page 11, line 4:

“the libraries were extracted from the cells, and treated with dam-GmATC-methylated DNA specific restriction enzyme Dpn I to digest un-replicated DNAs that contain UV-photoproducts.”

1. It is quite intriguing that multiple mutations in a single BC clone tend to occur in the same DNA strand. Is there any trend in a distance between the mutated sites? Considering participation of TLS polymerases in the first round of replication, it may be interesting if multiple DNA lesions occur in relatively close positions so that TLS polymerases elongate the DNA strand without switching back to replicative polymerases.

We thank the Reviewer for the valuable and insightful suggestions for this assay. We have analyzed the positions of SNSs in multiple-mutations shown in Supplementary Figures S11 and S12. As the reviewer mentioned, we may be able to address the mechanisms of TLS switching in mammalian cells by using this assay. In this study, the obtained non-biased mutation spectra of multiple mutations may not be enough for the static analysis, but our results indicate that multiple mutations were induced at relatively close positions. It would be interesting if we could address the mechanisms of TLS polymerase switching. We believe that the accumulation of large numbers of non-biased mutation spectra will provide us with growing opportunities to address more questions in mutagenesis. We have now added the Supplementary Figures S11 and S12, as well as the following discussion points:

Page 14, line 6:

“5) The distance between two SNSs in multiple mutations induced by UV irradiation was relatively shorter than the theoretically expected based on the sequence (Supplementary Figures S11 and S12).”

Page 18, line 27:

“In addition, the positions of SNSs in the multiple mutations were closer to each other compared to the theoretically expected positions (Supplementary Figures S11 and S12), which may reflect switching events involving TLS polymerases. It should be noted that the presented data for the distance between two SNSs in the multiple mutations was analyzed from the data from selection plates in order to secure a sufficient number of mutations, and therefore, there may be a bias due to hot spots associated with the selection process. However, the results from the limited number of mutations from the titer plates are similar to these from the selection plates. It can be proposed that this assay may also be applied for analysis of TLS polymerases in mammalian cells.”

1. This reviewer is wondering whether the results of mammalian cells are influenced by transcription-coupled repair in this experimental system. Because the SV40 replication origin functions as bidirectional promoters, the supF region may be transcribed in U2OS cells so that DNA damage on transcribed strands may be removed more efficiently than non-transcribed strands. Please comment on this, if relevant.

*We thank the Reviewer for the insightful comments. This issue is also very important and interesting, and should be addressed in the mutagenesis research. That is exactly the reason why we presented series of vectors for the assay in this paper. The SV40 replication origin has an effect on the background mutations, which this is also dependent on the experimental conditions. However, this needs to be confirmed by further studies. We hope the idea for these constructions will be helpful for many laboratories. We have now added the following parts in the DISCUSSION section. *

Page 18, line 36:

“Mutational signatures identified in cancer cells are emerging as valuable markers for cancer diagnosis and therapeutics. Innumerable physical, chemical and biological mutagens, including anticancer drugs, induce characteristic mutations in genomic DNA via specific mutagenic processes. The mutation spectra obtained here by using the presented advanced method were in good agreement with accumulated data from previous papers where the conventional method had been used, with the advantage that our method provided less-biased mutation spectra data. As described above, the datasets presented here highlighted novel mutational signatures and also cluster mutations with a strand-bias, which could be associated with the processes of replication, transcription, or repair of DNA-damage, including a single strand break (a nick). In this study, eight series of supF shuttle vector plasmids were constructed, as presented in Supplementary Figure S1; however, the analysis was carried out using N12-BC libraries prepared from either pNGS2-K1 (Figures 1-4) or pNGS2-K3 (Figures 5-10). The pNGS2-K1/-A1/-K4/-A4 and pNGS2-K2/-A2/-K3/-A3 vector series contain an M13 intergenic region with opposite orientations relative to the supF gene, which allows us to incorporate specific types of DNA-damage at specific sites in the opposite strand of the vector library. Also, the pNGS2-K1/-A1/-K3/-A3 and pNGS2-K2/-A2/-K4/-A4 vector series contain the SV40 replication origin, which enables bidirectional replication and transcription, at opposite sides of the supF gene. Although this is still preliminary data, it is notable that the spontaneously induced mutations for the different vectors in U2OS cells were not significantly different. Therefore, the here presented mutagenesis assay with NGS, by using these series of libraries, can be applied in many different types of experiments to address both quantitative and qualitative features of mutagenesis. It is possible to design series of libraries containing DNA lesions or sequences suitable for the investigation of specific molecular mechanisms, such as TLS, template switching, and asymmetric cluster mutations.”

1. page 13: Please check whether the description of Fig. 9C is correct (6th line, graph on top; 9th line, bottom graph).

We thank the Reviewer for carefully checking our manuscript, it was mislabeled in the text. Now, following the Reviewer’s comments, most figures have been changed from the figures in the previous submission. We appreciate the careful review.

CROSS-CONSULTATION COMMENTS Reviewer #1 gives quite relevant comments as an expert of the mutagenesis field. It would improve this manuscript greatly for the authors to make appropriate modifications according to his/her suggestions.

Reviewer #2 (Significance (Required)):

It is quite convincing that this method has a great potential to give much more extensive information on mutational characteristics, most importantly, by eliminating the bias caused by phenotypic selection. Therefore, this work certainly must be worth being published in an appropriate journal.

This really illustrates the disconnect between what provokes stress or despair between the two worlds. Privilege check. SO THE BEGINNING PARTS ARE CREATING DISTANCE. I think the later parts will make us see that while we live two different lives, we live right next to each other. And nothing is as far as it may seem.

Made me think of flat-earth documentary on Netflix.

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Reply to the reviewers

Reviewer #1:

Review of "Identifying novel regulators of placental development using time series transcriptomic data and network analyses."

The authors present a detailed bioinformatic assessment of mouse developmental time series of the placenta. They apply current data mining and analysis methods to identify protein-centred networks that are likely enriched to specific cell types of the placenta. They then translate these findings to humans using statistical comparisons of human single-cell sequencing data of the placenta. Lastly, they use knock-down experiments to validate the conserved functional importance of the hub genes in the mouse protein networks in human cells.

The strengths of this paper are the rigorous data mining methods and the functional translation to humans from mice. There are no critical weaknesses to the article. There is a blend of statistical analysis with anecdotal or hand curation from databases and the literature, but it is unclear if these curated finings are circumstantial or statistically meaningful. In the end, the hypothesis seems to hold in that 4/4 gene knocked down in the human cells gave a migration phenotype.

Comments, questions, critique:

1. Given the translational aims of the paper, more introduction/discussion material on the comparative aspects of mice and humans are needed. Are giant cells and EVT the same? What are the cell equivalents that you are discovering? The Soncin et al. paper is cited, but I think underused. This publication contains time series data on mice and humans and could be used as external validation of clusters, networks, and other analyses. Other publications to consider for context are

2. Cox B, et al. Mol Syst Biol 5: 279.

3. Silva JF, Serakides R. 2016. Cell Adhes Migr 10: 88-110. (specifically discusses migration difference between the species placentae)

We thank the reviewer for the comment and valuable resources. We agree that more information about the similarities and differences between the migratory cells needs to be provided. We have added the following details in the introduction of the manuscript:

“Although there are certain differences between the mouse and human placenta (Hemberger, Hanna, and Dean 2020; Soncin, Natale, and Parast 2015), they do express common genes during gestation, including common regulators and signaling pathways involved in placental development (Cox et al. 2009; Soncin et al. 2018; Soncin, Natale, and Parast 2015; Watson and Cross 2005). For example, Ascl2/ASCL2 and Tfap2c/TFAP2C are required for the trophoblast (TB) cell lineage in both mouse and human models (Guillemot et al. 1994; Kuckenberg, Kubaczka, and Schorle 2012; Varberg et al. 2021). Another example is the HIF signaling pathway, which regulates TB differentiation in both mouse and human placenta (Soncin, Natale, and Parast 2015).”

“Although the structure of the placenta is not identical between mouse and human, certain mouse placental cell types are thought to be equivalent to human placental cell types (Soncin, Natale, and Parast 2015). For example, parietal TGCs and glycogen TBs have been described as equivalent to human extravillous trophoblasts (EVTs) (Soncin, Natale, and Parast 2015). Mouse TGCs are not as invasive as human EVTs (Soncin, Natale, and Parast 2015), and they have different levels of polyploidy and copy number variation (Morey et al. 2021); however, both EVTs and TGCs are able to degrade extracellular matrix to enable TB migration into the decidua (Silva and Serakides 2016).”

Added to discussion:

“These genes were selected primarily based on the network analyses, but also based on expression data from human cells to account for possible differences between mouse and human placental gene expression.”

As the reviewer suggested, we used the Soncin et al., 2015 data for validation. Only 6,317 of the 11,713 protein-coding genes used for hierarchical clustering were detected in the mouse dataset in Soncin et al., 2015. This issue could be because the Soncin data was generated using microarrays.

Nevertheless, we still compared our e7.5 and e9.5 hierarchical groups with: (1) Soncin et al. gene clusters in mouse that were downregulated over time, had highest expression from e9.5-12.5, or were upregulated over time; and (2) Soncin et al. gene clusters in human that were best correlated with mouse clusters and were either downregulated over time or upregulated over time. We observed a general consensus that our e7.5-hierarchical group had the highest percent of agreement with Soncin et al. gene groups that are downregulated over time, and our e9.5-hierarchical group had the highest percent of agreement with Soncin et al. gene groups that either have highest expression at e9.5-e12.5 or genes that are upregulated over time. This data is added below, described in the results section 1, and included in Supplementary Table S1.

Comparison with Soncin et al. mouse data:

Having expression > 0 (in Soncin et al.) and being in any hierarchical clusters

E7.5-hierarchical genes (down-regulation trend)

E9.5-hierarchical genes (up-regulation trend)

Cluster 2, 3 and 7 (Soncin et al., downregulation trend)

1009

800 (79.3%)

279 (27.7%)

Cluster 6 (Soncin et al., highest at e9.5 – e12.5)

120

51 (42.5%)

110 (91.7%)

Cluster 1, 4 and 5 (Soncin et al., upregulation trend)

1019

415 (40.7%)

881 (86.5%)

Comparison with Soncin et al. human data:

Having expression > 0 (in Soncin et al.) and being in any hierarchical clusters

E7.5-hierarchical genes (down-regulation trend)

E9.5-hierarchical genes (up-regulation trend)

HS Cluster 5 (Soncin et al., downregulation trend)

164

92 (56.1%)

52 (31.7%)

HS Cluster 2 and 4 (Soncin et al., upregulation trend)

111

44 (39.6%)

72 (64.9%)

The following statement was added to the result section:

“Second, we compared our hierarchical groups with previously published mouse and human placental microarray time course data from Soncin et al., 2015 (Soncin, Natale, and Parast 2015). Despite the technical differences between the datasets, we observed a consensus that our e7.5 hierarchical cluster had the highest percent of overlap with Soncin et al. gene groups that are downregulated over time, and our e9.5 hierarchical cluster had the highest percent of overlap with Soncin et al. gene groups that either have highest expression at e9.5 - e12.5 or genes that are upregulated over time (Supplementary Table S1).”

Clustering represented in Figure 1B, was this a supervised model? Why only three clusters?) Did you specify that there would be three models and force each gene profile into one of the categories? How robust are the fits? A fitted model might be a better approach as you can specify the ideal models (early high, late high and mid-high), then determine each gene profile that fits each model and only assess those genes with a significant fit to the model. Forcing clustering to the three-model fit likely gives many poorly fitting profiles. While in the end, this works out, it may be due to applying other post hoc methods for gene enrichment, where noise distributes randomly.

We carried out unsupervised transcript clustering using hierarchical clustering (agglomerative approach using Euclidean distance and complete linkage). The resulting dendrogram was cut at the second highest level to obtain three clusters. We have added additional validation with different numbers of clusters (k = 3, 4 and 5) and quantification of agreement between different clustering methods to show the robustness of the hierarchical clusters. We acknowledge that hierarchical clustering could be sensitive to noise and could result in poorly fitted transcripts in each group; however, it was a necessary first step for us to identify genes relevant to the distinct placental processes at the three timepoints. Acknowledging this disadvantage, we only focused the analyses on genes that are differentially expressed over time and were present in the timepoint hierarchical groups.

We added the additional analysis as Supplementary Figure S1, and the following statements were added in the results section:

"First, we used three different algorithms, K-means clustering, self-organizing maps, and spectral clustering, to validate the trends of the expression levels in hierarchical groups, as well as the number of transcript groups (k = 3, 4 and 5). Only with k = 3 did we obtain groups with median expression level trends consistent in all four algorithms (Supplementary Figure S1). Moreover, with k = 3, the maximum percent of agreement (see Materials and Methods) between hierarchical clusters and clusters obtained using each of the different algorithms was 70.34-87.26% (Supplementary Figure S1), while the maximum percent of agreement between hierarchical clusters and clusters obtained from other algorithms decreases to between 55.67-65.72% with k = 4 and 54.81-59.19% with k = 5.”

We agree model-based clustering could be an alternative approach and have added it to the discussion section:

“Combining hierarchical clustering with differential expression analysis, we were able to identify gene groups using an unsupervised approach. It has also been shown that for times-series analyses with fewer than eight timepoints, pairwise differential expression analysis combined with additional methods identifies a more robust set of genes (Spies et al. 2019). Alternatively, model-based clustering using RNA-seq profiles (Si et al. 2014) could also be useful for gene group identification. However, it is still important to evaluate the robustness and functional relevance of the fitted models by carrying out additional downstream analyses.”

Several statements are made about the conservation of importance between mouse and human hub genes. For example, "We predict these highly expressed genes to be generally important for TB function and processes such as cell migration, a term associated with multiple timepoint specific networks (Figure 2A)." While your knock-down assay of migration results shows these hub genes to be necessary to humans, what do they mean to the mouse? You did not use mouse TSC to assess functional importance concurrently. You note a small number of genes as of known importance, "127 hub genes of which 16 have been annotated as having a role in placental development". Were the others knocked out but lack a developmental phenotype or not assessed? Are these functionally redundant in the mouse or not involved in the same processes between the species?

To assess the possible role of hub genes in mouse development more comprehensively, we extended our search for gene functions on the Mouse Genome Informatics (MGI) database to include not only placenta related GO and MGI phenotype terms (defined as “genes with known roles”), but also embryo related GO and MGI phenotype terms (defined as “genes with possible roles”). We included embryo related terms as “genes with possible roles” because embryonic lethal mouse knockout lines frequently have placentation defects, and because defects in placental development can be associated with the development of other embryonic tissues (Brown and Hay 2016; Perez-Garcia et al. 2018; Woods, Perez-garcia, and Hemberger 2018). This change resulted in an increase in the number of genes with relevant functions in mouse, including several annotated as embryonic lethal or with abnormal embryonic growth (see Supplementary Table S6). With the additional annotations:

• 6 out of 17 hub genes of e7.5 networks have known/possible roles.
• 17 out of 28 hub genes of e8.5 networks have known/possible roles.
• 48 out of 127 hub genes of e9.5 networks have known/possible roles. We also carried out randomization tests to determine if the number of known/possible genes we identified were significant. Randomization tests were carried out with the following procedure: for each timepoint, from the respective timepoint-specific groups, we sampled 10,000 gene sets of the same number as the hub gene numbers. Then we counted the number of known/possible genes in each random set. A p-value is calculated as the number of times a random gene set has ≥ known/possible genes than the observed number, divided by 10,000. We found that the number of genes with known/possible roles at each time point are statistically significant (Supplementary Figure S3). This result indicates that the gene sets we identified are significantly associated with relevant phenotypes in mouse.

The remaining hub genes are unannotated as related to placental or embryonic functions in the MGI database. Based on that, it is difficult to determine if they lack a relevant phenotype, or if there has not been a detailed assessment of the placenta.

Added to section 2 of the result section:

“Briefly, genes annotated under any GO or MGI phenotype terms related to placenta, TB cells, TE and the chorion layer are considered as having a “known” role in the placenta. Genes annotated under terms related to embryo are considered as having a “possible” role in the placenta, because embryonic lethal mouse knockout lines frequently have placentation defects, and because defects in placental development can be associated with the development of other embryonic tissues (Brown and Hay 2016; Perez-Garcia et al. 2018; Woods, Perez-garcia, and Hemberger 2018). Hereafter, such genes are referred to as “known/possible genes”. In the e7.5 networks, there were 17 hub genes in which six genes were known/possible. The number of hub genes that are labelled as known/possible is statistically significant when comparing to random gene sets selected from the e7.5 timepoint-specific group (Supplementary Figure S3). In the e8.5 and e9.5 networks, 17 out of 28 and 48 out of 127 hub genes were known/possible, respectively. Similar to e7.5, the number of hub genes labelled as known/possible in e8.5 networks and e9.5 networks were both statistically significant when comparing to random gene sets selected from the corresponding timepoint-specific groups (Supplementary Figure S3). These results indicate that the gene sets we identified are significantly associated with relevant phenotypes in the mouse.”

For the four genes that we tested in HTR-8/SVneo cells, we also added more information about the current known role of the gene in mouse.

Added to the discussion section:

“We identified hub genes and their immediate neighboring genes which could regulate placental development and confirmed the roles of four novel genes (Mtdh, Siah2, Hnrnpk and Ncor2) in regulating cell migration in the HTR-8/SVneo cell line. These genes were selected primarily based on the network analyses, but also based on expression data from human cells to account for possible differences between mouse and human placental gene expression. Previous studies suggested these four candidates are functionally important in mouse. Mtdh has been suggested to regulate cell proliferation in mouse fetal development (Jeon et al. 2010). The Siah gene family is important for several functions (Qi et al. 2013). Of relevance to the placenta, Siah2 is an important regulator of HIF1α during hypoxia both in vitro and in vivo (Qi et al. 2008). Moreover, while Siah2 null mice exhibited normal phenotypes, combined knockouts of Siah2 and Siah1a showed enhanced lethality rates, suggesting the two genes have overlapping modulating roles (Frew et al. 2003). Hnrnpk-/- mice were embryonic lethal, and Hnrnpk+/- mice had dysfunctions in neonatal survival and development (Gallardo et al. 2015) . Ncor2-/- mice were embryonic lethal before e16.5 due to heart defects (Jepsen et al. 2007). According to the International Mouse Phenotyping Consortium database (Dickinson et al. 2016), Ncor2 null mice also showed abnormal placental morphology at e15.5. However, none of these genes have been studied in TB migration function.”

In determining conservation between mouse and human networks, were only 1:1 orthologs examined or did you consider more complex 1:many mapping conditions between the two species?

In this work, we used only one-to-one orthology between mouse and human avoid duplication while sampling in the enrichment tests. We added this detail in the method section. However, as found in Cox et al., 2009, genes with one-to-many orthologs could be highly intriguing and should be investigated in future studies.

Should the migration assay be normalized to survival/adhesion? If 70,000 cells were seeded but had 50% cell death (or reduced adhesion), then it may appear to be poor migration. Should the migration be evaluated as a ratio of top to bottom cell densities to control for poor adhesion or survival?

We thank the reviewer for bringing up this important point. Unfortunately, with the method we used we cannot quantify the densities on top, because the cells on top need to be scraped off prior to measuring the cells at the bottom (the two densities cannot be measured separately). To help with this concern, in a separate experiment we instead counted cell numbers 48-hours post-transfection for cells treated with target gene siRNA and cells treated with negative control siRNA to determine if apoptosis or changes in proliferation rate could be leading to changes in the observed migration. From this data, we determined that none of the siRNA knockdowns resulted in a significant change of cell counts (p-value > 0.05). We do note that Siah2 siRNA #1 has some decrease in counts (p-value = 0.081) and Ncor2 siRNA #1 and #2 have some increase in cell counts (p-value = 0.081 and p-value = 0.077) (Supplementary Figure S7). Additional follow up experiments we have performed with our targets of interest, which are out of the scope of this paper, demonstrate that different pathways and processes could be involved in the resulting decrease in migration we observed (we are following up experimentally in more detail for each gene). Proliferation and other assays could also be used to further examine the increase in Ncor2 cell counts that were observed. We have added the cell count results and additional text to the discussion.

Added to results, section 4:

“When comparing the number of cells 48 hours post-transfection for cells treated with target gene siRNA to cells treated with negative control siRNA, we determined that none of the target gene siRNA treatments resulted in significant changes in cell counts. We do note that Siah2 siRNA #1 has some decrease in cell counts (p-value = 0.081), and Ncor2 siRNA #1 and Ncor2 siRNA #2 have some increase in cell counts (p-value = 0.081 and p-value = 0.077) compared to negative control treated samples (Supplementary Figure S7). This provides evidence that, in general, the reduction in cell migration capacity was likely not due to the target gene impacting the rate of cell death.”

To the discussion:

“Moreover, we observed that cell counts generally were not decreased upon target gene knockdown compared to negative control knockdown. However, more detailed analysis and process specific assays are needed. For example, future studies assessing each gene’s role in cell adhesion, cell-cell fusion, cell proliferation and cell apoptosis can be done to better understand their roles in placental development.”

Reviewer #1 (Significance (Required)):

This significantly advances previous publications on this topic by functionally testing the discovered genes.

This highlights an excellent data mining strategy for a developmental disease using mice and translating to humans.

The audience is likely developmental biologists and reproductive specialists.

My expertise is bioinformatics and developmental biology.

Reviewer #2 (Evidence, reproducibility and clarity (Required)):

The authors used RNA-seq data from mouse fetal placenta at e7.5, e8.5, and e9.5 to create timepoint-specific gene expression interaction networks to find genes that they predicted would regulate placental development. They confirmed four novel candidate genes and showed that in the transfected human trophoblast HTR-8/SVneo cell line, these four candidates reduced cell migration capacity. Additionally, the authors show that bulk RNA-seq data can be used to infer cell-type composition and when used with single-cell RNA-seq, can be a powerful tool to study the biological processes that involve multiple cell-types.

Overall, the authors are rigorous in their analyses, their conclusions appear sound, and the work could be an asset to the broader placental biology field. However, although the authors present an approach that future studies might find useful to replicate and their work has produced numerous novel transcripts/genes that warrant further investigation, the approach is not entirely novel, and could be expanded/improved (as suggested by the authors in the discussion), particularly with regard to validation of the genes/networks identified. Major and minor comments are listed below.

Major comments:

1) The authors used clustering and differential expression analysis to define sets of timepoint-specific genes. However, it was not clear to me the benefits of this approach. Why would using this approach be better than differential expression analysis alone such as in a typical ANOVA?

We have added more discussion on this matter to explain our approach. We believe using hierarchical clustering and pairwise differential expression analysis can help identify gene lists with higher confidence. These are the new details we added to the discussion section:

“Combining hierarchical clustering with differential expression analysis, we were able to identify gene groups using an unsupervised approach. It has also been shown that for times-series analyses with fewer than eight timepoints, pairwise differential expression analysis combined with additional methods identifies a more robust set of genes (Spies et al. 2019). Alternatively, model-based clustering using RNA-seq profiles (Si et al. 2014) could also be useful for gene group identification. However, it is still important to evaluate the robustness and functional relevance of the fitted models by carrying out additional downstream analyses.”

2) Related to number 1 above, although the authors are interested in timepoint-specific transcripts, the author's methods would filter out possibly interesting transcripts that turn on and off during development. The authors might want to check to see if there are transcripts that are up in e7.5 and then down in e8.5 but then up again in e9.5. Also, the author's methods seem to include transcripts that are not exclusive to one timepoint (i.e. are up in e7.5 and e8.5 but not e9.5). It might be interesting to differentiate transcripts that are exclusive to one timepoint from those that are in more than one timepoint.

We thank the reviewer for their valuable comment. We agree genes that turn on and off during the time course could be very interesting. In performing this analysis, we found that the number of such genes is rather small (38 genes that are up-regulated at e7.5 compared to e8.5 and up-regulated at e9.5 compared to e8.5). These genes were not enriched for processes that we observed with timepoint-specific gene groups, such as “trophoblast giant cell differentiation” (e7.5-specific genes), “labyrinthine layer development” (e8.5- and e9.5-specific genes), "blood vessel development” (e7.5- and e9.5-specific genes) and “response to nutrient” (e9.5-specific genes) (Supplementary Table S3). They are generally enriched for processes related to cytokine production and regulation of secretion.

We also agree that it is interesting to differentiate transcripts that are exclusive to one time point from those that are in more than one time point. In the revised manuscript, we added additional analysis for genes that belong to multiple timepoint groups due to different transcripts of the same gene being annotated as timepoint-specific, and genes unique to each timepoint (Added to results section 1):

“It is possible that timepoint-specific groups share genes that have timepoint-specific transcripts. Indeed, we identified 37 genes shared between e7.5 and e8.5, 5 genes shared between e7.5 and e9.5, and 109 genes shared between e8.5 and e9.5 (Supplementary Table S3). We found that genes only present at one timepoint (timepoint-unique genes) were generally enriched for similar terms as the full group of timepoint-specific genes (Supplementary Table S3). However, terms related to the development of labyrinth layer like “labyrinthine layer morphogenesis” and “labyrinthine layer blood vessel development” were only enriched when using all e8.5-specific genes but not when using e8.5 timepoint-unique genes. Moreover, we found that, unlike genes shared between e9.5 and e7.5, genes shared between e9.5 and e8.5 were enriched for processes such as “blood vessel development” and “insulin receptor signaling pathway”. This observation may indicate that different transcripts of the same genes could be expressed at different timepoints for the continuation of certain biological processes.”

3) In the network analysis it would be interesting and helpful to the reader to highlight, if any, nodes or terms that were found to be significant (i.e. hubs or genes that have a high centrality metric etc.) in both the STRING and GENIE3 networks or overlap the networks created by the two different algorithms to compare them. This might help readers better rank genes when using these data to decide what genes are most important at each timepoint.

We observed only one hub gene shared among networks inferred by the two methods (Vegfa in the e9.5 networks). However, hub genes of networks inferred by one method could be nodes in networks inferred by the other method. Hence, we have added lists of such genes in section 2. Interestingly, many of these genes have known roles in placental development. In terms of biological functions shared between the networks at the same timepoints, there were multiple interesting processes such as “positive regulation of cell migration”, “epithelium migration” and “vasculature development”, which we highlighted in Figure 2A.

In the revised manuscript, we have added the following details in different paragraphs of section 2 of the results:

“Although the networks inferred by the two methods did not share any hub genes, hub genes identified with one method could be members of the other method’s networks. These hub genes are Mmp9 (e7.5_1_STRING), Frk, Hmox1, and Nr2f2 (e7.5_2_GENIE3) (Table 1). This observation strengthens the potential roles of Frk gene in placental development.”

“Hub genes identified with one method and present in the other method’s networks are Hsp90aa1, Akt1, and Mapk14 (e8.5_1_STRING), Dvl3 and Msx2 (e8.5_2_GENIE3) (Table 1).”

“Hub genes identified with one method and present in the other method’s networks include important genes such as Rb1 (Sun et al. 2006), Yap1 (Meinhardt et al. 2020) (e9.5_1_GENIE3) and Vegfa (e9.5_2_STRING) (Table 1). Notably, Vegfa is the only hub gene identified with both of the network inference methods.”

4) The author's conclusion that network analysis can be used to identify genes more likely associated with specific placental cell types is very likely true, but I think that the conclusion would be more impactful if the authors reported how the method compares to simply taking a list of differentially expressed genes and looking for cell type enrichments using their favorite enrichment software. For example, if a gene is highly connected in a particular network that has been identified as SCT-specific, but that gene isn't considered an SCT "marker" by the placental biology research community, it would be interesting to highlight that it is prevalent in a previously published scRNA-seq dataset or a dataset that has isolated that particular cell type to show the advantages of using networks to find placental cell type specific genes.

We completely agree with the reviewer’s point and have now added a randomization analysis to compare the enrichment using PlacentaCellEnrich (PCE) with genes in networks and random genes (Supplementary Figure S6). We randomly sampled 10,000 gene sets with the same sizes as the subnetworks from their corresponding hierarchical groups and carried out PCE analysis. These tests showed that the enrichments of cell type-specific genes were only significant with the subnetwork genes but not the random genes. The randomization tests added a valuable highlight that the network genes are highly relevant to cell type-specific genes in the human placenta, and therefore provided more confidence in the gene lists obtained from the network analyses.

We also further checked the expression of the hub genes in other independent data in order to identify hub genes that are potentially cell type specific markers. For example, we observed that Dvl3 (e8.5_2_GENIE3) and Olr1 (e9.5_3_STRING) have been shown to be differentially expressed in SCT compared to other TB subtypes (human trophoblast stem cells, EVT (Sheridan et al. 2021) or endovascular TB (Gormley et al. 2021)).

We added the following detail in the results, section 3:

“Importantly, randomization tests showed that the enrichment of cell type-specific genes were only significant in these subnetworks but not in random gene sets selected from corresponding timepoint hierarchical groups (Supplementary Figure S6), which highlights the biological relevance of the gene network modules.”

Added to the discussion section:

“Moreover, hub genes could be used to identify potential novel markers for the cell types corresponding to their subnetworks. For example, hub genes of subnetworks enriched for SCT-specific genes such as Dvl3 (e8.5_2_GENIE3) and Olr1 (e9.5_3_STRING) are not established SCT marker genes, but are in fact differentially expressed in SCT compared to human trophoblast stem cells, EVT (Sheridan et al. 2021) or endovascular TB (Gormley et al. 2021). In general, combining network analysis with existing gene expression data from single cell or pure cell populations will allow identification of novel cell-specific marker genes to help future studies focused on different TB populations.”

5) While the selection of genes for validation was limited by the model system available for testing, the authors should recognize that the genes/networks identified here should first and foremost be validated in a mouse model (by knockdown/overexpression studies using mouse trophoblast stem cells or by evaluation of placenta/embryo in a KO/transgenic mouse model). Whether or not the data are relevant to human placentation is (at least initially) irrelevant. While we recognize that these are difficult studies requiring significant time and resources, as is, the data and results will have significantly less impact than if even a limited amount of such validation could be performed.

We thank the reviewer for this valuable comment. Based on this comment and the suggestions from reviewer #1, we have added the following points to the manuscript to discuss the relevance of the genes in the mouse models, and further explain our gene choices:

To assess the possible role of hub genes in mouse development more comprehensively, we extended our search for gene functions on the Mouse Genome Informatics (MGI) database to include not only placenta related GO and MGI phenotype terms (defined as “genes with known roles”), but also embryo related GO and MGI phenotype terms (defined as “genes with possible roles”). We included embryo related terms as “genes with possible roles” because embryonic lethal mouse knockout lines frequently have placentation defects, and because defects in placental development can be associated with the development of other embryonic tissues (Brown and Hay 2016; Perez-Garcia et al. 2018; Woods, Perez-garcia, and Hemberger 2018). This change resulted in an increase in the number of genes with relevant functions in mouse, including several annotated as embryonic lethal or with abnormal embryonic growth (see Supplementary Table S6). With the additional annotations:

• 6 out of 17 hub genes of e7.5 networks have known/possible roles.
• 17 out of 28 hub genes of e8.5 networks have known/possible roles.
• 48 out of 127 hub genes of e9.5 networks have known/possible roles. We also carried out randomization tests to determine if the number of known/possible genes we identified were significant. Randomization tests were carried out with the following procedure: for each timepoint, from the respective timepoint-specific groups, we sampled 10,000 gene sets of the same number as the hub gene numbers. Then we counted the number of known/possible genes in each random set. A p-value is calculated as the number of times a random gene set has ≥ known/possible genes than the observed number, divided by 10,000. We found that the number of genes with known/possible roles at each time point are statistically significant (Supplementary Figure S3). This result indicates that the gene sets we identified are significantly associated with relevant phenotypes in mouse.

The remaining hub genes are unannotated as related to placental or embryonic functions in the MGI database. Based on that, it is difficult to determine if they lack a relevant phenotype, or if there has not been a detailed assessment of the placenta.

Added to section 2 of the result section:

“Briefly, genes annotated under any GO or MGI phenotype terms related to placenta, TB cells, TE and the chorion layer are considered as having a “known” role in the placenta. Genes annotated under terms related to embryo are considered as having a “possible” role in the placenta, because embryonic lethal mouse knockout lines frequently have placentation defects, and because defects in placental development can be associated with the development of other embryonic tissues (Brown and Hay 2016; Perez-Garcia et al. 2018; Woods, Perez-garcia, and Hemberger 2018). Hereafter, such genes are referred to as “known/possible genes”. In the e7.5 networks, there were 17 hub genes in which six genes were known/possible. The number of hub genes that are labelled as known/possible is statistically significant when comparing to random gene sets selected from the e7.5 timepoint-specific group (Supplementary Figure S3). In the e8.5 and e9.5 networks, 17 out of 28 and 48 out of 127 hub genes were known/possible, respectively. Similar to e7.5, the number of hub genes labelled as known/possible in e8.5 networks and e9.5 networks were both statistically significant when comparing to random gene sets selected from the corresponding timepoint-specific groups (Supplementary Figure S3). These results indicate that the gene sets we identified are significantly associated with relevant phenotypes in the mouse.”

For the four genes that we tested in HTR-8/SVneo cells, we also added more information about the current known role of the gene in mouse.

Added to the discussion section:

“We identified hub genes and their immediate neighboring genes which could regulate placental development and confirmed the roles of four novel genes (Mtdh, Siah2, Hnrnpk and Ncor2) in regulating cell migration in the HTR-8/SVneo cell line. These genes were selected primarily based on the network analyses, but also based on expression data from human cells to account for possible differences between mouse and human placental gene expression. Previous studies suggested these four candidates are functionally important in mouse. Mtdh has been suggested to regulate cell proliferation in mouse fetal development (Jeon et al. 2010). The Siah gene family is important for several functions (Qi et al. 2013). Of relevance to the placenta, Siah2 is an important regulator of HIF1α during hypoxia both in vitro and in vivo (Qi et al. 2008). Moreover, while Siah2 null mice exhibited normal phenotypes, combined knockouts of Siah2 and Siah1a showed enhanced lethality rates, suggesting the two genes have overlapping modulating roles (Frew et al. 2003). Hnrnpk-/- mice were embryonic lethal, and Hnrnpk+/- mice had dysfunctions in neonatal survival and development (Gallardo et al. 2015) . Ncor2-/- mice were embryonic lethal before e16.5 due to heart defects (Jepsen et al. 2007). According to the International Mouse Phenotyping Consortium database (Dickinson et al. 2016), Ncor2 null mice also showed abnormal placental morphology at e15.5. However, none of these genes have been studied in the context of TB migration.”

Minor comments:

1) In the GO analysis, why not use a combination of hypergeometric and binomial distribution for enrichment decisions?

We used hypergeometric tests as in the default setting of ClusterProfiler. GO enrichment with hypergeometric test for differentially expressed genes was also suggested in Rivals et al., 2007 (Rivals et al. 2007). Combination of hypergeometric and binomial tests will be of great use when carrying out enrichment for cis-regulatory domains where there is a higher chance of sampling a gene randomly (McLean et al. 2010).

We have added this detail in the method section to make the analysis clearer.

2) In Figure 2B, are there any genes that are both hub nodes (diamonds) and annotated as having placental functions (squares)? If so, it might be good to show that in some way.

We agree this is necessary and have altered the presentation in Figure 2. In the revised manuscript, we have added an additional list of hub genes as genes with possible roles. The figure now shows hub genes with known placental functions (diamonds), hub genes with possible functions (hexagons) and hub genes without related annotation (rounded squares). Non-hub genes are now not shown to avoid crowdedness.

3) It might improve the deconvolution analysis to employ more than one method and recent reports have shown that the cell-type signature data is the most important parameter with the main factors influencing performance being biological (such as where the sample was taken) rather than technical (https://doi.org/10.1038/s41467-022-28655-4).

We agree the conclusion would have been further confirmed if we could employ another deconvolution method. Upon literature search, we found another tool, CAM (N. Wang et al. 2016), that had similar approaches to LinSeed which aims to infer cell proportions without reference. However, the tool has been taken down from Bioconductor and is not currently maintained. As a result, to the best of our knowledge, LinSeed is the only deconvolution tool that is completely reference-free.

We also tried carrying out the deconvolution analysis with another method, DSA (Zhong et al. 2013), with a limited number of marker genes obtained through literature review. However, when the marker genes are highly correlated in multiple cell types, the models failed to infer meaningful proportions.

We acknowledge that we need additional single cell RNA-seq data or marker genes obtained from pure cell populations to make more concrete conclusions for the deconvolution analysis. We hope with future studies, there will be more evidence supporting our observations.

We have added this acknowledgement in the results section:

“The identification of these cell groups could have resulted from noise introduced by both biological and technical variation, which is challenging to overcome when using a small sample size or analyzing without prior knowledge in the deconvolution analysis.”

Added to the discussion section:

“Nevertheless, we acknowledge that our deconvolution analysis and cell type annotations were limited due to the absence of matching scRNA-seq data, data from pure cell populations, or extensive cell marker lists. As these types of information are available, deconvolution analysis can be used to identify species-specific cell types or correcting for confounding effects prior to DEA (Sutton et al. 2022).”

4) The above report also shows that there are ways to correct for cell-type composition differences in DEA which might be interesting to look when using bulk data from different timepoints in future studies when focusing on different biological processes and not timepoint-specific transcripts.

We agree correcting for cell proportion prior to differential expression analysis will be interesting for future studies. When single cell RNA-seq data or more extensive marker gene lists are available, deconvolution analysis will be of great use for this purpose.

We have added this in the discussion section (also mentioned in point #3):

“Nevertheless, we acknowledge that our deconvolution analysis and cell type annotations were limited due to the absence of matching scRNA-seq data, data from pure cells, or extensive cell marker lists. As these types of information become more available, deconvolution analysis can be used to identify species-specific cell types or correcting for confounding effects prior to DEA (Sutton et al. 2022).”

5) Could the authors speculate as to possible reason(s) that an siRNA knockdown would give variable results functionally, while the actual gene expression appears to be consistently and sufficiently downregulated? Did the authors evaluate protein levels following siRNA knockdown?

Following the reviewer’s comment, we have evaluated protein levels for each target gene and each siRNA. For the genes that gave variable results between siRNAs (MTDH and NCOR2), we did not observe a change in their ability to reduce protein levels (Supplementary Figure S7). It is therefore possible that there are off-target effects for one of the siRNAs. We considered this possibility in designing the project, which is why we tested two siRNAs per target gene. Although siRNA off-target effects may be present, visual inspection of the migration experiments indicate that transfection with each of the siRNAs reduces migration capacity. We have added the possibility of off-target effects in the discussion section:

“We observed that while all siRNAs were able to decrease cell migration capacity, there was variability in the amount of decrease, even when comparing two siRNAs targeting the same gene. This observation did not seem to be associated with differences in transcript or protein knockdown levels and could be due to different off-target effects for different siRNAs.”

6) As mentioned in the discussion, finding genes that have timepoint dependent isoforms would an interesting and novel addition to the manuscript.

Protein isoforms would be interesting to study. Here we focused on different mRNA transcripts. We carried out additional GO analysis on the genes unique to each timepoint and genes shared among timepoints. This was also done in response to major comment 2:

In the revised manuscript, we added additional analysis for genes that belong to multiple timepoint groups due to different transcripts of the same gene being annotated as timepoint-specific, and genes unique to each timepoint (Added to results section 1):

“It is possible that timepoint-specific groups share genes that have timepoint-specific transcripts. Indeed, we identified 37 genes shared between e7.5 and e8.5, 5 genes shared between e7.5 and e9.5, and 109 genes shared between e8.5 and e9.5 (Supplementary Table S3). We found that genes only present at one timepoint (timepoint-unique genes) were generally enriched for similar terms as the full group of timepoint-specific genes (Supplementary Table S3). However, terms related to the development of labyrinth layer like “labyrinthine layer morphogenesis” and “labyrinthine layer blood vessel development” were only enriched when using all e8.5-specific genes but not when using e8.5 timepoint-unique genes. Moreover, we found that, unlike genes shared between e9.5 and e7.5, genes shared between e9.5 and e8.5 were enriched for processes such as “blood vessel development” and “insulin receptor signaling pathway”. This observation may indicate that different transcripts of the same genes could be expressed at different timepoints for the continuation of certain biological processes.”

7) Although outside the scope of this manuscript, it might be interesting to look at the effects of knocking down network genes on the networks themselves and in combination with a phenotypic readout such as a migration assay. With numerous knockouts and migration assay readouts, one could possibly find a better method to rank the genes within the networks.

We agree with this comment. Upon literature search, we realized this approach has been used in previous studies on other biological contexts such as virus entry (A. Wang et al. 2010; A. Wang, Ren, and Li 2011) and cancer cell growth (Paul et al. 2021). Although these studies used different network inference strategies from ours, their in silico gene knockouts proved to be effective for the candidate selection. However, the knockout process (both computationally and experimentally) may not be trivial; therefore, we agree the approach will be useful for future studies.

CROSS-CONSULTATION COMMENTS

I mostly agree with the other two reviewers.

It is not clear to me that additional KD experiments (i.e. ones that might affect fusion, proliferation, apoptosis), as proposed by Reviewer #3, would be that much more informative. There are many differences between mouse and human placentation, and these model systems (HTR8 and BeWo) are not truly representative of either. The additional data mining/computational work would be more useful and enhance data interpretation.

Reviewer #2 (Significance (Required)):

The authors use RNA-seq of mouse placenta at e7.5, e8.5, and e9.5 to show that timepoint-specific expression patterns are highly correlated with certain biological processes and point to the existence of certain cell types in the sample. While focused on early post-implantation mouse placental development, the author's methods could be transferrable to other timepoints, species, and organs. Furthermore, with their method they uncover what appears to be several novel, early placental, developmentally important genes and their results might be of interest to those in the field studying placental development.

Reviewer #3:

Summary:

This paper is an analysis of RNA-seq data from the mouse human placenta at embryonic day from 7.5 to 9.5 days. Bioinformatics was used to pinpoint genes networks, and tentatively connect with human cell populations. Wet experiments were performed on the HTR8/SV neo trophoblast cell model.

The introduction clearly posits the reasons why mouse models were chosen, and presents some examples of genes that are conserved between human and mouse placentas, before presenting the major steps of mouse placental development at the crucial periods analyzed.

The results are divided into four parts:

1. Identification of genes that are specific of fetal tissues at the three days studied
2. A network analysis of the genes using classical bioinformatics tools (String, Genie3) to identify gene modules
3. A connection with the human placenta at the level of cell-specific expression profile is then analyzed
4. A in vitro validation on a trophoblast cell model using siRNA to Knockdown genes identified in the in silico part of the paper. Three clustering methods were used to classify the genes according to their profile (at which time point they have the highest level). The function associated are dispatched into three logical physiological events (7.5: proliferation and ectoplacental cone development, 8.5 attachment of the placenta -chorioallantoidian at this stage- , and 9.5: syncytiotrophoblast constitution and labyrinth development, structures essential for growth and exchange).

Mostly minor comments:

Quality of the transcriptomics data: 6 replicates per condition (some being pools at E7.5 and 8.5) is a lot, and I congratulate the authors to have make such effort. This says a lot about the technical quality of their results. Nevertheless, there is no comment on the exclusion of two samples in the further analysis based upon the PCA. Could the authors comment upon the reasons why these two samples behave so differently from the others?

We thank the reviewer for the comment. We reviewed the RNA concentration and quality prior to sequencing, and did not observe that the outliers were of lower quality. After sequencing, quality control metrics (obtained with FastQC), also did not indicate that the two outliers were of poor quality. Based on the PCA, it is also unlikely that two samples were swapped. One possibility is that the tissues obtained for these samples were diseased in some way. However, this is difficult to confirm, so we did not want to speculate about this in the manuscript. We did exclude the two samples to ensure the accuracy of our downstream analyses.

Rq: at this stage some statistics of the degree of enrichment in keyword should be provided (such as Enrichment Scores, normalized or not, and False Discovery Rates, to be able to evaluate the actual robustness of the genes network identified. In addition, it seems that the authors supervised the 'keywords' and 'ontologies' toward placental function. A more agnostic approach could be very relevant, such as identifying the ontologies associated to for instance the set of genes that are highest at 8.5 days, by comparing them with preliminary datasets accessible via the GSEA platform of the BROAD institute or similar sites such as Webgestalt. This does not mean that the placental-targeted approach is not useful, but to have a more global overview is in my opinion indispensable.

We agree and this is a good point. We have now added a stringent approach to determine if the placenta-targeted terms are truly relevant to the gene networks. We performed randomization tests using random gene sets sampled from hierarchical groups of the same time point. These tests showed that the selected terms are significant in the networks when compared to gene groups of the same size from the timepoint specific hierarchical groups (Supplementary Figure S3). Moreover, we have added the specific -log10(q-value) of some highlighted enriched terms in the main text, so together with Figure 2A, the degree of enrichment of these terms can be shown in a clearer way.

We have added this detail in the result section:

“Compared to e8.5 and e9.5 networks, e7.5 networks had a higher rank or fold change and were significantly enriched for the GO terms “inflammatory response” (e7.5_1_STRING: -log10(q-value) = 22.82 and e7.5_2_GENIE3: -log10(q-value) = 3.95) and “female pregnancy” (e7.5_2_GENIE3: -log10(q-value) = 4.1) (Figure 2A, Supplementary Table S5). The term “morphogenesis of a branching structure”, which can be expected following chorioallantoic attachment around e8.5, was not enriched at e7.5, but was enriched in multiple e8.5 and e9.5 networks (e8.5_1_STRING: -log10(q-value) = 1.73, e8.5_2_GENIE3: -log10(q-value) = 1.72, e9.5_1_STRING: -log10(q-value) = 4.01, e9.5_1_GENIE3: -log10(q-value) = 1.54, e9.5_2_STRING: -log10(q-value) = 14.33, and e9.5_2_GENIE3: -log10(q-value) = 2.2). After chorioallantoic attachment finishes, nutrient transport is being established. Accordingly, we observed the following enrichments: “endothelial cell proliferation” (highest ranked in e9.5_2_STRING: -log10(q-value) = 15.91), “lipid biosynthetic process” (only significant after e7.5, highest ranked in e9.5_3_STRING: -log10(q-value) = 17.63), “cholesterol metabolic process” (only significant after e7.5, highest ranked in e9.5_2_GENIE3: -log10(q-value) = 2.76 and e9.5_3_STRING: -log10(q-value) = 7.79), and “response to insulin” (only significant after e7.5, highest ranked in e9.5_1_GENIE3: -log10(q-value) = 1.67).”

“Using randomization tests, we observed the majority of these GO terms (10 out of 11 terms) were significantly enriched when using the network genes but not random gene sets (significance level of 0.05; the term “vasculature development” having p-value = 0.0549 and 0.0575 in with subnetwork e9.5_1_GENIE3 and e9.5_3_GENIE3, respectively) (see Materials and Methods, Supplementary Figure S3). This analysis demonstrates that the network genes were highly relevant to the biological functions of interest. Moreover, the observed GO terms strongly aligned with the processes enriched when using the full lists of timepoint-specific genes (Supplementary Table S3), indicating the representative characteristics of the network genes. While the current analysis focuses on the biological processes related to placental development, there are other terms significantly enriched, which can be found in Supplementary Table S5.”

This is partially done in the part 2 of the results, but it would be relevant to do it on the group of highly expressed genes and not only on the clusters found by the algorithm of sting and genie3.

We have added GO analysis for timepoint-specific genes and also observed highly relevant processes being enriched (Supplementary Table S3). This additional analysis has also helped strengthen the relevance of the network genes, as the observed terms with network genes aligned well with the terms enriched with the full lists of genes.

Rq: in the second part of the results, everything is descriptive but no hierarchy is given to facilitate the understanding and to try to generate a few 'take-home messages' for the reader.

We agree with the comment and have adjusted the writing accordingly. We have added the following statements in section 2 of the result section:

“In summary, we identified 18 subnetworks across three timepoints for downstream analyses, some of which were enriched, according to GO analysis and randomization tests, for specific terms relating to placental development (Figure 2A).”

“These results indicate that the gene sets we identified are functionally relevant in the mouse models.”

“In summary, we have identified hub genes in networks at each timepoint. Analyzing the annotations of hub genes using the MGI database demonstrated that the hub genes are biologically relevant to mouse development and will be strong candidates for future investigation.”

The network analysis is well presented in Figure 2. I wonder whether the author could add systematically besides the three examples that are given the network analysis for the other enrichment network that are described (the four at e7.5, the 6 at e8.5 and the 8 at e9.5).

We have added the additional figures in Supplementary Figure S3.

The deconvolution of the 3rd part of the results to try to connect the mouse results to the human cell situation is interesting. I suspect that given the terms of the mouse placentas used, it would be relevant to focus on 1st trimester human placental cells.

The reference dataset we used in the PlacentaCellEnrich analysis was from human 1st trimester placenta samples. For the Placenta Ontology analysis, we were limited to the provided database from (Naismith and Cox 2021); however, it will be interesting to revisit the analysis when the database is extended.

We have specified that the reference data in PlacentaCellEnrich analysis was from human 1st trimester placenta in the methods section:

“For PlacentaCellEnrich, cell-type specific groups were based on the single-cell transcriptome data of first trimester human maternal-fetal interface from Vento-Tormo et al.”

As previously mentioned, this is a highly descriptive paragraph, and two or three sentences at the end of each paragraph of the results would be in my opinion indispensable to present the most important observations of the results in an intelligible way. Overall, the data presented by the authors, are not obviously 'raw data', but an effort of interpretation should be done by the authors to underline the importance of their results, and to stress among these results which are the most important, and which are the most relevant for placental development and human health.

We agree with the comment and have adjusted the writing accordingly. We have added this summary paragraph at the end of section 3 of the result section:

“In summary, we have demonstrated that the identification of timepoint-specific gene groups and densely connected network modules can be used to infer the cellular composition of bulk RNA-seq samples. We used independent human datasets from different sources to annotate the cell types in each timepoint’s samples. As a result, from the bulk RNA-seq data we were able to observe that at e7.5 and e8.5, there was a high proportion of different TB populations, whereas at e9.5, the placental tissues consisted of multiple cell types such as TB, endothelial and fibroblast cells.”

In the last part, which is very important in this type of paper, four genes were selected. A choice of highly expressed genes was made (which can in fact be discussed, some transcriptional factors may have a crucial importance with relatively low levels of expression). The efficiency of the siRNA was overall excellent. The authors showed that each of these siRNA is efficient to inhibit cell migration in the HTR8/SVneo model.

The migration assays are quantified, but there is a inherent limit of the cell model: the authors analyzed only cell migration, but not other very important parameters. One of them is trophoblast fusion, an issue that can be studied in another trophoblast cell model, the BeWo cells, which are induced to fuse under forskolin. It would be highly relevant to test the siRNA identified in this respect, since fusion is a very conspicuous feature of trophoblast cells in mice as well as in humans. Other relevant endpoints such as proliferation markers, apoptosis markers, oxidative stress markers could be studied in the KD cell models. Alternatively, it would have been interesting to evaluate the overall effect of the siRNA by transcriptomics and check whether the modified gene expression leads to specific profiles characteristic of a certain moment of placental development in mice, or proportion of various cells in the human placentas. Without asking for further experiments the authors should mention these limits in their discussion.

We completely agree with this comment and are investigating each of our candidate genes in more detail in ongoing studies. As we have already learned that each gene is involved in different processes and pathways, we feel that these studies are out of the scope of the current paper. However, we have added this point to our discussion section:

“However, more detailed analysis and process specific assays are needed. For example, future studies assessing each gene’s role in cell adhesion, cell-cell fusion, cell proliferation and cell apoptosis can be done to better understand their roles in placental development.”

In sum, I feel that this paper provides an excellent dataset, but that the authors should make an additional effort of redaction to extract the most important conclusions of their paper. This would increase its impact for a wider public.

Thank you. We have attempted to do so in the revised version.

Reviewer #3 (Significance (Required)):

The context is well introduced, but explanatory and synthesis sentences are missing at the end of each paragraph. I am relatively competent in bioinformatics methods, including deconvolution, and rather expert in cell biology. Therefore I feel comfortable to evaluate this paper.

References:

Brown, Laura D., and William W. Hay. 2016. “Impact of Placental Insufficiency on Fetal Skeletal Muscle Growth.” Molecular and cellular endocrinology 435: 69. /pmc/articles/PMC5014698/ (August 24, 2022).

Cox, Brian et al. 2009. “Comparative Systems Biology of Human and Mouse as a Tool to Guide the Modeling of Human Placental Pathology.” Molecular Systems Biology 5: 279. /pmc/articles/PMC2710868/ (July 20, 2022).

Dickinson, Mary E. et al. 2016. “High-Throughput Discovery of Novel Developmental Phenotypes.” Nature 2016 537:7621 537(7621): 508–14. https://www.nature.com/articles/nature19356 (July 20, 2022).

Frew, Ian J. et al. 2003. “Generation and Analysis of Siah2 Mutant Mice.” Molecular and Cellular Biology 23(24): 9150. /pmc/articles/PMC309644/ (July 27, 2022).

Gallardo, Miguel et al. 2015. “HnRNP K Is a Haploinsufficient Tumor Suppressor That Regulates Proliferation and Differentiation Programs in Hematologic Malignancies.” Cancer Cell 28(4): 486–99. http://www.cell.com/article/S1535610815003050/fulltext (August 24, 2022).

Gormley, Matthew et al. 2021. “RNA Profiling of Laser Microdissected Human Trophoblast Subtypes at Mid-Gestation Reveals a Role for Cannabinoid Signaling in Invasion.” Development (Cambridge, England) 148(20). https://pubmed.ncbi.nlm.nih.gov/34557907/ (August 15, 2022).

Guillemot, François et al. 1994. “Essential Role of Mash-2 in Extraembryonic Development.” Nature 371(6495): 333–36. https://www.nature.com/articles/371333a0 (December 21, 2021).

Hemberger, Myriam, Courtney W. Hanna, and Wendy Dean. 2020. “Mechanisms of Early Placental Development in Mouse and Humans.” Nature Reviews Genetics 21(1): 27–43. http://dx.doi.org/10.1038/s41576-019-0169-4.

Jeon, Hyun Yong et al. 2010. “Expression Patterns of Astrocyte Elevated Gene-1 (AEG-1) during Development of the Mouse Embryo.” Gene expression patterns : GEP 10(7–8): 361. /pmc/articles/PMC3165053/ (July 27, 2022).

Jepsen, Kristen et al. 2007. “SMRT-Mediated Repression of an H3K27 Demethylase in Progression from Neural Stem Cell to Neuron.” Nature 450(7168): 415–19. https://www.nature.com/articles/nature06270 (July 27, 2022).

Kuckenberg, Peter, Caroline Kubaczka, and Hubert Schorle. 2012. “The Role of Transcription Factor Tcfap2c/TFAP2C in Trophectoderm Development.” Reproductive BioMedicine Online 25(1): 12–20. http://www.rbmojournal.com/article/S1472648312001010/fulltext (December 21, 2021).

McLean, Cory Y. et al. 2010. “GREAT Improves Functional Interpretation of Cis-Regulatory Regions.” Nature Biotechnology 28(5): 495–501. http://dx.doi.org/10.1038/nbt.1630.

Meinhardt, Gudrun et al. 2020. “Pivotal Role of the Transcriptional Co-Activator YAP in Trophoblast Stemness of the Developing Human Placenta.” Proceedings of the National Academy of Sciences of the United States of America 117(24): 13562–70. https://www.ncbi.nlm.nih.gov/geo/ (April 8, 2022).

Morey, Robert et al. 2021. “Transcriptomic Drivers of Differentiation, Maturation, and Polyploidy in Human Extravillous Trophoblast.” Frontiers in Cell and Developmental Biology 9: 2269.

Naismith, Kendra, and Brian Cox. 2021. “Human Placental Gene Sets Improve Analysis of Placental Pathologies and Link Trophoblast and Cancer Invasion Genes.” Placenta 112: 9–15.

Paul, Abhijit et al. 2021. “Exploring Gene Knockout Strategies to Identify Potential Drug Targets Using Genome-Scale Metabolic Models.” Scientific Reports 2021 11:1 11(1): 1–13. https://www.nature.com/articles/s41598-020-80561-1 (July 27, 2022).

Perez-Garcia, Vicente et al. 2018. “Placentation Defects Are Highly Prevalent in Embryonic Lethal Mouse Mutants.” Nature 555(7697): 463. /pmc/articles/PMC5866719/ (August 11, 2022).

Qi, Jianfei et al. 2008. “The Ubiquitin Ligase Siah2 Regulates Tumorigenesis and Metastasis by HIF-Dependent and -Independent Pathways.” Proceedings of the National Academy of Sciences of the United States of America 105(43): 16713. /pmc/articles/PMC2575485/ (September 20, 2021).

Qi, Jianfei, Hyungsoo Kim, Marzia Scortegagna, and Ze’ev A. Ronai. 2013. “Regulators and Effectors of Siah Ubiquitin Ligases.” Cell biochemistry and biophysics 67(1): 15. /pmc/articles/PMC3758783/ (July 27, 2022).

Rivals, Isabelle, Lé On Personnaz, Lieng Taing, and Marie-Claude Potier. 2007. “Databases and Ontologies Enrichment or Depletion of a GO Category within a Class of Genes: Which Test?” Bioinformatics 23(4): 401–7.

Sheridan, Megan A. et al. 2021. “Characterization of Primary Models of Human Trophoblast.” Development (Cambridge, England) 148(21). /pmc/articles/PMC8602945/ (August 15, 2022).

Si, Yaqing, Peng Liu, Pinghua Li, and Thomas P. Brutnell. 2014. “Model-Based Clustering for RNA-Seq Data.” Bioinformatics 30(2): 197–205. https://academic.oup.com/bioinformatics/article/30/2/197/217752 (July 18, 2022).

Silva, Juneo F., and Rogéria Serakides. 2016. “Intrauterine Trophoblast Migration: A Comparative View of Humans and Rodents.” Cell Adhesion and Migration 10(1–2): 88–110. http://dx.doi.org/10.1080/19336918.2015.1120397.

Soncin, Francesca et al. 2018. “Comparative Analysis of Mouse and Human Placentae across Gestation Reveals Species-Specific Regulators of Placental Development.” Development (Cambridge) 145(2).

Soncin, Francesca, David Natale, and Mana M. Parast. 2015. “Signaling Pathways in Mouse and Human Trophoblast Differentiation: A Comparative Review.” Cellular and Molecular Life Sciences 72(7): 1291–1302.

Spies, Daniel, Peter F. Renz, Tobias A. Beyer, and Constance Ciaudo. 2019. “Comparative Analysis of Differential Gene Expression Tools for RNA Sequencing Time Course Data.” Briefings in Bioinformatics 20(1): 288. /pmc/articles/PMC6357553/ (July 18, 2022).

Sun, Huifang et al. 2006. “An E2F Binding-Deficient Rb1 Protein Partially Rescues Developmental Defects Associated with Rb1 Nullizygosity.” Molecular and Cellular Biology 26(4): 1527. /pmc/articles/PMC1367194/ (February 6, 2022).

Sutton, Gavin J. et al. 2022. “Comprehensive Evaluation of Deconvolution Methods for Human Brain Gene Expression.” Nature Communications 2022 13:1 13(1): 1–18. https://www.nature.com/articles/s41467-022-28655-4 (July 27, 2022).

Varberg, Kaela M. et al. 2021. “ASCL2 Reciprocally Controls Key Trophoblast Lineage Decisions during Hemochorial Placenta Development.” Proceedings of the National Academy of Sciences of the United States of America 118(10). https://www.pnas.org/content/118/10/e2016517118 (December 21, 2021).

Wang, Anyou, S. Claiborne Johnston, Joyce Chou, and Deborah Dean. 2010. “A Systemic Network for Chlamydia Pneumoniae Entry into Human Cells.” Journal of Bacteriology 192(11): 2809–15. https://journals.asm.org/doi/10.1128/JB.01462-09 (July 27, 2022).

Wang, Anyou, Li Ren, and Hong Li. 2011. “A Systemic Network Triggered by Human Cytomegalovirus Entry.” Advances in Virology 2011.

Wang, Niya et al. 2016. “Mathematical Modelling of Transcriptional Heterogeneity Identifies Novel Markers and Subpopulations in Complex Tissues.” Scientific Reports 2016 6:1 6(1): 1–12. https://www.nature.com/articles/srep18909 (July 27, 2022).

Watson, Erica D., and James C. Cross. 2005. “Development of Structures and Transport Functions in the Mouse Placenta.” Physiology 20(3): 180–93.

Woods, Laura, Vicente Perez-garcia, and Myriam Hemberger. 2018. “Regulation of Placental Development and Its Impact on Fetal Growth — New Insights From Mouse Models.” Frontiers in Endocrinology 9(September): 1–18.

Zhong, Yi et al. 2013. “Digital Sorting of Complex Tissues for Cell Type-Specific Gene Expression Profiles.” BMC Bioinformatics 14(1): 1–10. https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-14-89 (July 27, 2022).

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Referee #1

Evidence, reproducibility and clarity

Review of "Identifying novel regulators of placental development using time series transcriptomic data and network analyses."

The authors present a detailed bioinformatic assessment of mouse developmental time series of the placenta. They apply current data mining and analysis methods to identify protein-centred networks that are likely enriched to specific cell types of the placenta. They then translate these findings to humans using statistical comparisons of human single-cell sequencing data of the placenta. Lastly, they use knock-down experiments to validate the conserved functional importance of the hub genes in the mouse protein networks in human cells. The strengths of this paper are the rigorous data mining methods and the functional translation to humans from mice. There are no critical weaknesses to the article. There is a blend of statistical analysis with anecdotal or hand curation from databases and the literature, but it is unclear if these curated finings are circumstantial or statistically meaningful. In the end, the hypothesis seems to hold in that 4/4 gene knocked down in the human cells gave a migration phenotype.

Comments, questions, critique

1. Given the translational aims of the paper, more introduction/discussion material on the comparative aspects of mice and humans are needed. Are giant cells and EVT the same? What are the cell equivalents that you are discovering? The Soncin et al. paper is cited, but I think underused. This publication contains time series data on mice and humans and could be used as external validation of clusters, networks, and other analyses. Other publications to consider for context are

a) Cox B, et al. Mol Syst Biol 5: 279.

b) Silva JF, Serakides R. 2016. Cell Adhes Migr 10: 88-110. (specifically discusses migration difference between the species placenta)

1. Clustering represented in Figure 1B, was this a supervised model? Why only three clusters?) Did you specify that there would be three models and force each gene profile into one of the categories? How robust are the fits? A fitted model might be a better approach as you can specify the ideal models (early high, late high and mid-high), then determine each gene profile that fits each model and only assess those genes with a significant fit to the model. Forcing clustering to the three-model fit likely gives many poorly fitting profiles. While in the end, this works out, it may be due to applying other post hoc methods for gene enrichment, where noise distributes randomly.

2. Several statements are made about the conservation of importance between mouse and human hub genes. For example, "We predict these highly expressed genes to be generally important for TB function and processes such as cell migration, a term associated with multiple timepoint specific networks (Figure 2A)." While your knock-down assay of migration results shows these hub genes to be necessary to humans, what do they mean to the mouse? You did not use mouse TSC to assess functional importance concurrently. You note a small number of genes as of known importance, "127 hub genes of which 16 have been annotated as having a role in placental development". Were the others knocked out but lack a developmental phenotype or not assessed? Are these functionally redundant in the mouse or not involved in the same processes between the species?

3. In determining conservation between mouse and human networks, were only 1:1 orthologs examined or did you consider more complex 1:many mapping conditions between the two species?

4. Should the migration assay be normalized to survival/adhesion? If 70,000 cells were seeded but had 50% cell death (or reduced adhesion), then it may appear to be poor migration. Should the migration be evaluated as a ratio of top to bottom cell densities to control for poor adhesion or survival?

Significance

This significantly advances previous publications on this topic by functionally testing the discovered genes.

This highlights an excellent data mining strategy for a developmental disease using mice and translating to humans.

The audience is likely developmental biologists and reproductive specialists.

My expertise is bioinformatics and developmental biology.

Overall it seems that climate change has affected the author or they are worried for others but others may say different. I truly don't think humans take full accountability for this but may play some part in it.

Epistemic bubble vs. echo chamber

hey that's what I said at the beginning of this piece

It's interesting that this machine focuses on retrieval of a person's personal memories, whereas we're more concerned with retrieving other people's ideas from the internet and from archives

he had no idea man

machines for advanced analysis forced their way into the extensive market by becoming more familiar and user-friendly, but they are still essentially the same machine

this is interesting in thinking about current ideas about what should be relegated to machines-- thinking about the debates around whether or not AI can really create art

can we dig into his thing about girls and keys

now our problem is navigating the sheer amount of things compressed and actually being able to effectively consult them

reminds me of our discussion about the move away from the 'solitary genius' of western tradition to a more collaborative approach towards knowledge-building in which everyone specializes in something different

interesting that Bush characterizes the war as little more than an 'exhilarating' blip in the careers of professional scientists

This is a really interesting way to think about smartphones/tablets; there's less of a burden on us to carry a ton much information in our minds because it's so easy to pull out our phones and reference almost anything in the world.

I read online somewhere that humans were originally only supposed to live in small groups of 25 people or less, and that we are not built to handle the thousands of people we see on social media in a day. I do not know if the first part is accurate, but I believe we are no meant to see as much as we do. While we can interact much more than just face to face allows, we don't need to, and it may be too much. The amount of tragedy we witness everyday online certainly isn't healthy and I don't think our brains are supposed to handle all of that at once. So I agree that access to a larger society is definitely a double-edged sword.

I remember in middle school every year for our digital literacy class we had to do a lesson about our digital footprint. And it really got instilled in us that you need to be careful what you put online because it will never go away. I remember we would be told stories of people losing their job because of something they posted about years before and even if they didn't use that platform anymore they got fired because their company didn't want to be associated with someone like that. I think this idea should be instilled into anyone who uses the internet because it is really helpful.

Author Response

Reviewer #2 (Public Review):

Klein et al. have developed a high-throughput tracker to evaluate operant conditioning in Drosophila larvae. Employing this device, they train larvae to prefer bending towards one specific side (left or right), by using as unconditioned stimulus (US) the optogenetic activation of dopaminergic and serotoninergic neurons, demonstrating that larvae are able to perform this behaviour. Furthermore, they show that serotoninergic neurons alone are sufficient to mediate the reward signal, and that specifically serotoninergic neurons in the VNC are required for this behaviour. However, they do not show whether serotoninergic VNC neurons are sufficient. The results are interesting and novel. Operant conditioning had been shown for Drosophila adult. Furthermore, the existence of VNC circuits sufficient for operant conditioning had been shown for other species, as the authors point out in the discussion. Nonetheless, the genetic dissection to identify serotonine expressing neurons as mediators of operant conditioning in the Drosophila larva, and the identification of VNC serotonine cells as necessary are new. Furthermore, given the experimental advantages of the Drosophila larva, including genetic accessibility and a full connectome, the findings open the door to future research into the circuit mechanisms of operant conditioning. I have some comments that I think would be important to address.

The high-throughput tracker is impressive. However, there is no sufficient documentation to ensure that an expert would be able to easily reproduce it. All of the hardware assembly files, the list of materials, as well as the electronic circuit maps and all of the required software needs to be appropriately documented and uploaded onto a public repository. This is a basic requirement when publishing new hardware/software, particularly in an open journal such as eLife.

We have now included all the documentation and CAD files for the high-throughput tracker. The software is publicly available in the following Github repository (https://github.com/ZlaticLab/multi-larva-tracker-scripts-public). The CAD files are available in the Supplementary materials of the paper.

• The differences observed in the results of operant conditioning are very subtle (see for example figure 3c), which means that it is extremely important that statistic analyses are correctly made. The sample number (n) for these experiments is really high (n>100) and for what I understood is not equivalent to the number of animals, because the same animal can generate n >1, eg. n = 2 or n =3 if it collides one or two times, as each time it collides a new identity is given to the larvae. This means that the datapoints collected are not independent, and I think in that case a Wilcoxon rank-sum test is not the appropriate test to take. I recommend the authors and eLife editors to consult with an expert in this type of statistics. Alternatively, the authors could, for each experiment, take into account only the data from larvae that did not collide, and for those that collide only take into account the data before the collision. This can be calculated easily as they just need to exclude from their analysis in each experiment all of the larval IDs where the ID is larger than the initial number of larvae identified by the software.

We apologise if we did not clarify sufficiently that we only took into account (for each time bin) larvae that did not collide. Within the Materials and methods, we describe how objects retained for analysis had to satisfy several criteria. The first criterion is that the object needed to be detected in every frame of the given 60 s bin. In this way, the object identity is stable throughout the bin - a reflection that the object did not collide with another object. In other words, within a single time bin, the same animal only contributes once. Text has been added to the Materials and methods to clarify that this first criterion is selecting for larvae that did not collide.

The reviewer mentions that Wilcoxon rank-sum test is not the appropriate nonparametric test for dependent samples. We agree. In accordance with this, the test used for within-bin comparisons was Wilcoxon signed-rank, which is also nonparametric but is for dependent samples. We believe, then, that there is no need to reconsider the statistical tests used.

-The finding that serotoninergic neurons in the VNC, which with the line they used amount to only 2 neurons per VNC hemisegment, are required for operant conditioning is very interesting. It would be great if they could also test whether they are sufficient. It seems that they would just need to make two split Gal4 lines one for tsh and one for tph, so the experiment does not seem too difficult and would significantly add to their findings.

Generating new intersections is beyond the scope of this already large study which has been significantly impacted by the pandemic. We have therefore added the following sections below explaining that we have identified candidate serotonergic neurons that are required for operant learning and that identifying specific single neuron types that may be sufficient would be an exciting avenue for future follow-up work.

In the Results section entitled, “Serotonergic VNC neurons may play role in operant conditioning of bend direction” we have added:

“The Tph-Gal4 expression pattern contains two neurons per VNC hemisegment (with the exception of a single neuron in each A8 abdominal hemisegment, Huser2012). Future experiments exclusively targeting a single serotonergic neuron per VNC hemisegment could be valuable in determining whether they are sufficient for operant learning.”

In the Discussion section entitled: “Automated operant conditioning of Drosophila larvae”

“Furthermore, developing sparser lines that target single serotonergic and dopaminergic neuron types will enable the identification of the smallest subsets of neurons that are sufficient for providing the operant learning signal. Behavioural experiments with these genetic lines may have the added benefit of mitigating conflicting or non-specific reinforcement signalling.”

Author Response

Reviewer #1 (Public Review):

The manuscript is clear and well-written and provides a novel and interesting explanation of different illusions in visual numerosity perception. However, the model used in the manuscript is very similar to Dehaene and Changeux (1993) and the manuscript does not clearly identify novel computational principles underlying the number sense, as the title would suggest. Thus, while we were all enthusiastic about the topic and the overall findings, the paper currently reads as a bit of a replication of the influential Dehaene & Changeux (1993)-model, and the authors need to do more to compare/contrast to bring out the main results that they think are novel.

Major concerns:

1) The model presented in the current manuscript is very similar to the Dehaene and Changeux 1993 model. The main difference is in the implementation of lateral inhibition in the DoG layer where the 1993 model used a recurrent implementation, and the current model uses divisive normalization (see minor concern #1). The lateral inhibition was also identified as a critical component of numerosity estimation in the 1993 model, so the novelty in elucidating the computational principles underlying the number sense in the current manuscript is not evident.

If the authors hypothesize that the particular implementation of lateral inhibition used here is more relevant and critical for the number sense than the forms used in previous work (e.g., the recurrent implementation of the 1993 model or the local response normalization of the more recent models), then a direct comparison of the effects of the different forms is necessary to show this. If not, then the focus of the manuscript should be shifted (e.g., changing the title) to the novel aspects of the manuscript such as the use of the model to explain various visual illusions and adaptation and context effects.

Thank you for bringing up these issues. We acknowledge that there was a lack of clear explanations for the key differences between the proposed model and that of Dehaene & Changeux (hereafter D&C). Please see our revisions below where we: 1) explain the D&C model and its limitations in more in detail; 2) our critical changes to the D&C model; and 3) how those critical changes allow a novel way to explain numerosity perception.

The paragraph in the Introduction where we first introduce D&C is modified to read:

“The computational model of Dehaene and Changeux (1993) explains numerosity detection based on several neurocomputational principles. That model (hereafter D&C) assumes a one-dimensional linear retina (each dot is a line segment), and responses are normalized across dot size via a convolution layer that represents combinations of two attributes: 1) dot size, as captured by difference-of-Gaussian contrast filters of different widths; and 2) location, by centering filters at different positions. In the convolution layer, the filter that matches the size of each dot dominates the neuronal activity at the location of the dot owing to a winner-take-all lateral inhibition process. To indicate numerosity, a summation layer pools the total activity over all the units in the convolution layer. While the D&C model provided a proof of concept for numerosity detection, it has several limitations as outlined in the discussion. Of these, the most notable is that strong winner-take-all in the convolution layer discretizes visual information (e.g., discrete locations and discrete sizes yielding a literal count of dots), which is implausible for early vision. As a result, the output of the model is completely insensitive to anything other than number in all situations, which is inconsistent with empirical data (Park et al., 2021).”

The revised Discussion describes our critical modifications to D&C and their consequences.

“At first blush, the current model might be considered an extension of Dehaene and Changeux (1993). However, there are four ways in which the current model differs qualitatively from the D&C model. First, the D&C model is one-dimensional, simulating a linear retina, whereas we model a two-dimensional retina feeding into center-surround filters, allowing application to the two-dimensional images used in numerosity experiments (Fig. 1A). Second, extreme winner-take-all normalization in the convolution layer of the D&C model implausibly limits visual precision by discretizing the visual response. For example, the convolution layer in the D&C model only knows which of 9 possible sizes and 50 possible locations occurred. In contrast, by using divisive normalization in the current model, each dot produces activity at many locations and many filter sizes despite normalization, and a population could be used to determine exact location and size. Third, extreme winner-take-all normalization also eliminates all information other than dot size and location. By using divisive normalization, the current model represents other attributes such edges and groupings of dots (Fig. 1B) and these other attributes provide a different explanation of number sensitivity as compared to D&C. For example, the D&C model as applied to the spacing effect between two small dots (Fig. 4A) would represent the dots as existing discretely at two close locations versus two far locations, with the total summed response being two in either case. In contrast, the current model gives the same total response for a different reason. Although the small filters are less active for closely spaced dots, the closely spaced dots look like a group as captured by a larger filter, with this addition for the larger filter offsetting the loss for the smaller filter. Similarly, as applied to the dot size effect (Fig. 4B), the D&C model would only represent the larger dots using larger filters. In contrast, the current model represents larger dots with larger filters and with smaller filters that capture the edges of the larger dots, and yet the summed response remains the same in each case owing to divisive normalization (again, there are offsetting factors across different filter sizes). The final difference is that the D&C model does not include temporal normalization, which we show to be critical for explaining adaptation and context effects.”

In sum, the current model explains a wider range of effects by using representations and processes that more closely reflect early vision. The change to two-dimensions allows application to real images. The inclusion of temporal normalization allows application to temporal effects. The change from winner-take-all to divisive normalization might appear to be a parameter setting, but it’s one that produces qualitatively different results and explanations (e.g., representations of edges and groupings that are part of the explanation of selective sensitivity to number). These behaviors are consistent with empirical data and are qualitatively different from that of the D&C model. Now that we’ve highlighted the ways in which this model differs qualitatively from the D&C model, we hope that our original title still works.

Reviewer #2 (Public Review):

This is a very interesting and novel model of numerosity perception, based on known computational principles of the visual system: center-surround mechanisms at various scales, combined with divisive normalization (over space and time). The model explains, at least qualitatively, several of the important aspects of numerosity perception.

Firstly, the model makes major and minor predictions. Major: the effect of adaptation, at least 30%, as well as impendence of several densities and dot size; minor: tiny effects like irregularity, around 6%. I think it would make sense to separate these. To my knowledge, it is the first to account for adaptation, which was the major effect that brought numerosity into the realm of psychophysics: and it explains it effortlessly, using an intrinsic component of the model (divisive normalization), not with an ad-hoc add-on. This should be highlighted more. And perhaps, the fit can be more quantitative. Murphy and Burr (who they cite) showed that the adaptation is rapid. How does this fit the model? Very well, I would have thought.