interesting narrative shaping here.

scientifically, yes our understanding of our autonomic nervous system is super important to the healing the things that are wild and disturbing above — but jumping to the next part is where I get lost.

Agree a lot of our pain is from: "is the belief that we do not belong in this world." which I think we'll have to work on to fix the climate thing.

Author Response

Reviewer #1 (Public Review):

[...] My main technical concern lies in the choice of decomposition filter for SEP and alpha oscillations, and the conclusions the authors draw from that. Specifically, a CCA spatial filter is optimized here for the N20 component, which is then identically applied to isolate for alpha sources, with the logic being that this procedure extracts the alpha oscillation from the same sources (e.g., L359). I have no issues (or expertise) with using the CCA filter for the SEP, but if my understanding of the authors' intent is correct, then I don't agree with the logic that using the same filter isolate for alpha as well. The prestimulus alpha oscillation can have arbitrary source configurations that are different from the SEP sources, which may hypothetically have a different association with the behavioral responses when it's optimally isolated. In other words, just because one uses the same spatial filter, it does not imply that one is isolating alpha from the same source as the SEP, but rather simply projecting down to the same subspace - looking at a shadow on the same wall, if you will. To show that they are from the same sources, alpha should be isolated independently of the SEP (using CCA, ICA, or other methods), and compared against the SEP topology. If the topology is similar, then it would strengthen the authors' current claims, but ideally the same analyses (e.g., using the 1st and 5th quintile of alpha amplitude to partition the responses) is repeated using alpha derived from this procedure. Also, have the authors considered using individualized alpha filters given that alpha frequency vary across individuals? Why or why not?

Indeed, applying the same spatial filter to EEG signals with different spatial arrangements of the sources can lead to the extraction of neuronal activity which does not originate from the very same sources. We had chosen our approach, as it is well known that the generators of the early SEP components and the generators of the prominent somatosensory alpha rhythm co-reside at similar sites in the primary somatosensory cortex (e.g., Haegens et al., 2015). Therefore, we considered our approach appropriate to specifically focus on neural activity from the somatosensory region both in the frequency band of the SEP as well as of the alpha rhythm. Yet, we agree with the reviewer that it should be acknowledged that we may have missed or mixed-up effects of alpha activity from other sources by using this procedure (which might have led to different conclusions otherwise). In order to account for this, we repeated our analyses with an SEP-independent reconstruction of the oscillatory effects in source space (“whole brain analysis”). For this, we first reconstructed the sources of alpha activity using eLORETA and head models based on participant-specific MRI scans, and estimated the respective effects independently for all sources across the cortex using both linear-mixed effects models (LME) as well as a binning approach for the Signal Detection Theory (SDT) parameters sensitivity d’ and criterion c (consistent with the previous analyses in our manuscript). In the LME analyses, both the effects of pre-stimulus alpha activity on N20 amplitudes as well as on perceived stimulus intensity were strongest in the right primary somatosensory cortex – in accordance with the sources of the originally extracted tangential CCA component of the SEP (see Supplementary Figure 1 for Peer Review). Also, using the binning approach to examine the relation or pre-stimulus alpha activity with SDT parameter criterion c, the effects were most pronounced around the right somatosensory regions (Supplementary Figure 2 for Peer Review), yet these effects did not survive statistical correction for multiple comparisons (FDR-correction with p<.01). However, when performing the same binning analysis for our region of interest (ROI), the hand area in BA 3b of the right somatosensory cortex, a significant effect or pre-stimulus alpha on criterion c was indeed confirmed, t(31)=-2.951, p=.006, CI95%=[-.173, -.032]. Furthermore, in line with our previous CCA results, for sensitivity d’, neither the whole brain analysis nor the ROI analysis showed effects of pre-stimulus alpha amplitude, t(31)=0.633, p=.531, CI95%=[-.083, .157]. Taken together, the findings we report in our original manuscript for pre-stimulus alpha activity obtained with the spatial CCA filter can thus be replicated with a SEP-uninformed source reconstruction, both using LMEs for a “whole-brain analysis” as well as SDT analyses in a ROI-based approach. We therefore conclude that the relationships between pre-stimulus alpha activity, N20 potential of the SEP, and perceived stimulus intensity can indeed be attributed to neural activity from the same (or at least very similar) sources in the primary somatosensory cortex.

Addressing the question on filtering alpha activity in individualized frequency bands, we considered this option, too. However, the rather short length of our pre-stimulus window (-200 to -10 ms) constitutes a natural limit for the frequency resolution in the alpha range and slightly different filter ranges (adjusted with regards to the individual alpha peak frequency) are thus unlikely to lead to large differences in the estimation of pre-stimulus alpha amplitudes. Therefore, we refrained from using individualized frequency bands here and focused on the more generic approach using one common alpha band (8-13 Hz) for all participants, which should also facilitate direct comparisons with previous studies on pre-stimulus oscillatory effects.

In the same vein, both alpha and N20 amplitude relate to perceptual judgement, and to each other. I believe this is nicely accounted for in the multivariate analysis using the SEM, but the analysis that partitions the behavioral responses using the 20% and 80% are done separately, which means that different behavioral trials are used to compute the effect of N20 and alpha on sensitivity and criterion. While this is not necessarily an issue given that there IS a multivariate analysis, I would like to know how many of those trials overlap between the two analyses.

This is an interesting point indeed. We included both the binning analyses and the multivariate analyses in our manuscript as we believe they offer complimentary views on the data, and also allow a direct comparison to previous studies in the field (e.g., Iemi et al., 2017). In fact, the trial overlap between the extreme bins of the alpha and N20 data were rather small.

Since the expected trial overlap is 20% when partitioning the data into quintiles randomly, the effect-driven increments and reductions in trial overlap in our data appear to be rather small. However, they showed the expected directions: Larger alpha amplitudes were associated with more negative N20 amplitudes (and vice versa). Presumably, these small differences in trial overlap reflect the rather small effect sizes we also observed in the multivariate analyses. We have added this information to our revised manuscript in the following way to give the reader a better picture of the underlying data for the binning analyses (page 9, lines 137 ff.): “(Please note that this procedure resulted in a different trial selection as compared to the SDT analysis of pre-stimulus alpha activity. Please refer to Fig. 2—figure supplement 2 for further details on the trial overlap.)”

At multiple points, the authors comment that the covariation of N20 and alpha amplitude in the same direction is counterintuitive (e.g., L123-125), and it wasn't clear to me why that should be the case until much later on in the paper. My naive expectation (perhaps again being unfamiliar with the field) is that alpha amplitude SHOULD be positively correlated with SEP amplitude, due to the brain being in a general state of higher variability. It was explained later in the manuscript that lower alpha amplitude and higher SEP amplitude are associated with excitability, and hence should have the opposite directions. This could be explicitly stated earlier in the introduction, as well as the expected relationship between alpha amplitude and behavior.

Thank you for pointing out this unclarity. We have now made this rationale more explicit already at an early point in the introduction (page 3, lines 26 ff.): “According to the baseline sensory excitability model (BSEM; Samaha et al., 2020), higher alpha activity preceding a stimulus indicates a generally lower excitability level of the neural system, resulting in smaller stimulus-evoked responses, which are in turn associated with a lower detection rate of near-threshold stimuli but no changes in the discriminability of sensory stimuli (since neural noise and signal are assumed to be affected likewise).”

Furthermore, I have a concern with the interpretation here that's rooted in the same issue as the assumption that they are from the same sources: the authors' physiological interpretation makes sense if alpha and N20 originated from the same sources, but that is not necessarily the case. In fact, the population driving the alpha oscillation could hypothetically have a modulatory effect on the (separate) population that eventually encodes the sensory representation of the stimulus, in which case the explanation the authors provide would not be wrong per se, just not applicable. A comment on this would be appreciated in the revision.

Our extensive additional analyses suggest that the sources of behaviorally relevant alpha and N20 activity were located at very similar cortical sites. Nevertheless, this is not a proof that exactly the same neuronal populations were involved (for example, alpha and N20 effects could originate from different cortical layers). Therefore, we have added this potential limitation to our revised manuscript in the following way (page 19, lines 379 ff.): “Furthermore, with the present data, we cannot unambiguously conclude that the observed relation between pre-stimulus alpha activity and initial SEP indeed involved the very same neuronal populations – which may represent a limitation of the hypothesized mechanism. However, all approaches to localize these effects pointed to very similar cortical regions as discussed in the following section.”

In addition, given how closely related the investigation of these two quantities are in this specific study, I think it would be relevant to discuss the perspective that SEPs are potentially oscillation phase resets. Even though the SEP is extracted using an entirely different filter range, it could nevertheless be possible that when averaged over many trials, small alpha residues (or other low freq components) do have a contribution in the SEP. If the authors are motivated enough, a simulation study could be done to check this, but is not necessary from my point of view if there is an adequate discussion on this point.

Indeed, the phase reset mechanism may be a possible alternative explanation for relations between oscillations and later parts of the ERP. However, the N20 potential reflects the very first excitation of the cortex in response to a somatosensory stimulus and should therefore represent a textbook example of an additive response (EPSPs are added to ongoing background activity). Moreover, the N20 response should be over long before a possible phase reset in lower frequencies (such as alpha frequencies) would start to play a role (Hanslmayr et al., 2007; Sauseng et al., 2007). Nevertheless, we ran additional control analyses (including a simulation study) in order to exclude that some odd combination of phase-locking and filter residues led to the present findings: Please see Essential Revision #4 for details and how we included these considerations in our revised manuscript.

Reviewer #2 (Public Review):

[...] The main weaknesses of the manuscript becomes most apparent with respect to the stated impact that "The widespread belief that a larger brain response corresponds to a stronger percept of a stimulus may need to be revisited.". I am not really sure if there are many cognitive neuroscientists, that would actually subscribe to such a simplistic relationship between evoked responses and perception and that temporal differentiation (early vs late responses) and the biasing influence of prestimulus activity patterns are becoming increasingly recognized. So rather than actually changing a dominant paradigm, this work is an (excellent) contribution to a paradigm shift that is already taking place.

Thank you for this feedback. We agree that the paradigm shift away from simplistic assumptions about the relationship between variability of neural responses and perception is already taking place and that this is already being appreciated by many scientists in the field. Also, we agree that the present study contributes more evidence to this emerging notion rather than changing the whole field. However, we do think that particularly the observation of opposite amplitude modulations of initial somatosensory evoked responses associated with presented stimulus intensity on the one hand and pre-stimulus excitability state on the other, provides a novel perspective for our understanding of how fundamental features of sensory stimuli are processed at initial cortical levels. Following your suggestions to tone down claims about the controversiality as well as to avoid over-generalization, we have therefore adjusted the impact statement of this manuscript to: “Larger evoked responses during initial cortical processing may reflect states of lower excitability.”

Furthermore, we have adjusted similar statements throughout the manuscript accordingly.

Also it should be considered that with regards to the analysis approach using CCA, the claims are mainly restricted to BA3b: i.e. while I also think that this is a strength of the current study, one should refrain from overinterpreting the results in a very generalized manner. The authors do include some "thalamus" and "late" evoked response patterns as well, however that presentation of the results is somewhat changed now as compared to the N20 (e.g. using LMEs rather than comparison of extremes; not using SEMs). The readablity of results and especially the comparison of effects would profit from a more coherent approach.

We agree that our findings indeed have the specific focus on the N20 component and thus on its generators in BA3b. We did not intend to suggest that the effects we observed for this initial cortical response can be readily generalized to other (later) ERP components, too. However, we do believe (and hypothesize) that similar mechanisms may be in place for corresponding initial cortical responses in other sensory modalities, too – yet it is clear that we cannot test this generalization with the current study. To avoid misunderstandings of these interpretations and their limitations, we have further specified these aspects in the Discussion.

Regarding our analyses of the later SEP (i.e., N140 component) and thalamus-related activity (i.e., P15 component), we initially decided to use linear-mixed effects models as they are mathematically equivalent to the way the sub-equations of the structural equation model were constructed (Table 2 in the manuscript). Nevertheless, we have now additionally run binning analyses to make a direct comparison also with Signal Detection Theory (SDT) parameters possible: For the N140 component, there was a significant effect on criterion c, t(31)=-3.010, p=.005, but no effect on sensitivity d’, t(31)=0.246, p=.807. For the P15 component, no effects emerged either for criterion c or sensitivity d’, t(12)=1.201, p=.253, and t(12)=-0.201, p=.844, respectively. These findings correspond well to the previous LME analyses and may indeed further facilitate the comparison with the findings for the N20 potential and pre-stimulus alpha activity. Therefore, we have added these complimentary analyses to our manuscript in the following way:

Results: “In addition, the SDT analysis based on binning of the P15 amplitudes into quintiles neither suggested a relation with criterion c nor with sensitivity d’, t(12)=1.201, p=.253, and t(12)=-0.201, p=.844, respectively.” (page 14, lines 241 ff.)

“These findings were in line with a separate SDT analysis: N140 amplitudes were associated with an effect on criterion c, t(31)=-3.010, p=.005, but no effect on sensitivity d’ emerged, t(31)=0.246, p=.807.” (page 15, lines 263 ff.)

Discussion: “Crucially, our data are at the same time consistent with previous studies on somatosensory processing at later stages, where larger EEG potentials are typically associated with a stronger percept of a given stimulus (e.g., Al et al., 2020; Schröder et al., 2021; Schubert et al., 2006), as both our SDT and LME analyses of the N140 component showed.” (page 19, lines 367 ff.)

“Yet, neither our SDT analyses nor the LME models of the thalamus-related P15 component supported this notion.” (page 21, lines 414 ff.)

Methods (page 32, lines 681 ff.): “The effects of the EEG measures pre-stimulus alpha amplitude, N20 peak amplitude, P15 mean amplitude, and N140 mean amplitude on the SDT measures sensitivity d’ and criterion c were examined using a binning approach: […]”

I have some concerns whether the relationship between large alpha power and more negative N20s could be driven by more trivial factors rather than the model explanations the authors develop in the discussion. Concretely the question whether phase locking of large alpha power along with >30 Hz high pass filtering could produce a similar finding as shown e.g. in Figure 2c. This is an important issue, as prestimulus alpha influences the N20 amplitudes as well as the perceptual reports.

Indeed, potential phase-locking of alpha oscillations to stimulus onset and filter-related effects are important issues that could potentially offer an alternative explanation for the observed relationship between amplitudes of pre-stimulus alpha activity and the N20 potential of the SEP. Although such pre-stimulus alpha locking is rather unlikely in a paradigm with jittered stimulus onsets (in our case uniformly distributed between -50 ms and +50 ms; corresponding to a whole alpha cycle), we have run the following control analyses to fully exclude this possibility:

First, we analyzed whether pre-stimulus alpha phase values were distributed uniformly and whether these phase distributions differed between high and low alpha amplitudes as well as between high and low N20 amplitudes. The phase of pre-stimulus alpha activity was obtained from a Fast-Fourier transform in the pre-stimulus time window from -200 to -10 ms, applied to unfiltered, but otherwise identically pre-processed data as in the original manuscript (i.e., applying the spatial filter of the tangential CCA component). For the FFT, we used zero padding (extending the pre-stimulus data segments to 2048 data points each) in order to obtain an interpolated frequency resolution of around 3 Hz. The phase was extracted at the frequency 9.766 Hz (i.e., the closest available frequency to 10 Hz). As visible from Supplementary Figure 3 for Peer Review, pre-stimulus alpha phases were distributed uniformly across all five quintiles of both alpha and N20 amplitudes. This observation was confirmed by the Rayleigh test (testing for deviations from a uniform distribution; Berens, 2009): Neither in the concatenated phase data of all participants, z=1.130, p=.323, nor in single-participant analyses within every alpha amplitude or N20 amplitude bin, we found evidence for a non-uniform distribution of alpha phase, all p>.367 (after Bonferroni correction for multiple testing). Thus, there was no phase-locking of pre-stimulus alpha activity that could serve as a trivial alternative explanation of the relationship between pre-stimulus alpha amplitude and N20 amplitude.

Second, in order to examine whether the combination of our temporal filters (30 to 200 Hz band-pass for the SEP, and 8 to 13 Hz band-pass for alpha activity) could have led to the present findings, we additionally re-ran our analysis pipeline with simulated data: We mixed exemplary SEP responses with constant amplitudes (unfiltered; derived from within-participant averages), with simulated alpha band activity with randomized amplitude fluctuations, and pink noise, reflecting neural background activity as is typical for the human EEG. The SEP onsets were chosen according to our original experimental paradigm with inter-stimulus intervals of 1513 ms and a jitter of ±50 ms. Next, we filtered these mixed signals between 30 and 200 Hz in order to extract the single-trial SEPs, and estimated the pre-stimulus alpha amplitudes between -200 and -10 ms in the same way as was done in the original manuscript (i.e., by filtering the mixed signal between 8 and 13 Hz). This procedure was repeated for 32 generated data streams, containing 1000 SEPs each (corresponding to our empirical dataset of 32 participants). The resulting average SEPs did neither show a visually detectable difference between the five alpha amplitude quintiles nor indicated a random-slope linear-mixed-effects model any relation between pre-stimulus alpha amplitude and N20 amplitude on a single-trial level, βfixed=-.0005, t(255.16)=-.094, p=.925. Therefore, our findings cannot be explained by filter artifacts or residual activity leaking from the alpha frequency band to the frequency band of the N20 potential.

Third, we re-analyzed our empirical EEG data in time-frequency space to obtain a more detailed view of the effects of pre-stimulus alpha activity on N20 amplitudes. For this, we decomposed our pre-processed but unfiltered data with wavelet transformation (complex Morlet wavelets) and calculated linear-mixed effects models on the relation between signal amplitudes in the time-frequency domain and single-trial N20 amplitudes as obtained from our original analyses. As shown in Supplementary Figure 5 for Peer Review, the time-frequency representations of the effects on N20 amplitudes indeed indicated a specific role of the alpha band, with its effects (i.e., already 200 ms before stimulus and in the upper alpha frequency range) separated from the time- and frequency range of the N20 potential of the SEP (i.e., from ~20 ms after stimulus onwards and above ~20 Hz). In addition, we ran the same analysis for the behavioral effect (i.e., perceived stimulus intensity). Also here, pre-stimulus effects were predominantly visible in the alpha band. Of note, there were also strong effects in the beta band. These may be interesting to study further in future studies – in particular, whether they reflect independent physiological processes or rather harmonics of the alpha band. Furthermore, these time-frequency representations suggest that the studied pre-stimulus effects might have been even more pronounced if we had analyzed the data in pre-stimulus time windows from -300 to -10 ms. However, in order to avoid inflating effect sizes by post-hoc data digging (“p-hacking”), we prefer to keep the original, a priori chosen time window for the main analyses of the manuscript. Yet, these onsets of pre-stimulus effects at around -300 ms may be of interest for future work. Taken together, these time-frequency analyses further support the notion that the observed relation between pre-stimulus alpha activity and N20 amplitudes is not due to technical issues (such as filter leakage and phase-locking) but rather reflects genuine neurophysiological effects of alpha oscillations on SEPs.

We have added the time-frequency analysis, as well as the SEP simulation analysis as figure supplements to Figure 2 in our revised manuscript (page 8) since we believe that these control analyses comprehensively show that the observed effects were (a) specific to the alpha band and (b) not due to any data processing-related artifacts.

It is important to emphasize that the model develop is a post-hoc one, i.e. the authors do not develop already in the discussion various alternative scenario results based on different model predictions. Therefore there is no strong evidence in support of the specific one advanced in the discussion.

Thank you for raising this issue. Indeed, we cannot prove with the current findings that our proposed physiological model of the relation between alpha oscillations and the SEP is the correct model (or that it is at least the best one out of a selection of possible alternative models). To do so, future studies would be needed that can actually directly measure and/or manipulate differences in membrane potentials and trans-membrane currents. Rather, we aimed with the present study to associate a physiological meaning with the concept of excitability changes in the human EEG – offering a hypothesis that may be worthwhile to be studied (and either confirmed or rejected) in future studies. We have tried to make this motivation more explicit in the Discussion section (page 20, lines 384 ff.): “Also, we would like to emphasize that the presented mechanism reflects a hypothesized model, which shall be further supported or falsified with more targeted studies, for example, directly quantifying membrane potentials and trans-membrane currents in relation to different excitability states in somatosensation.”

Author Response

Reviewer #1 (Public Review):

[...] The manuscript is excellently written and discusses the simulation results clearly and succinctly. The resolution of the simulations is very impressive and yields unprecedented insight into the effect of merozoite shape on alignment dynamics, which has important implications for how effectively the parasite can survive and multiply. The conclusions reached by the authors are certainly justified by the simulation data. In particular, the authors are careful not to draw conclusions beyond the limits of their study, and acknowledge other factors which may influence the merozoite shape, such as internal structural constraints and the energy of invasion following successful alignment.

We thank the reviewer for a thorough reading of our manuscript and the very positive judgement.

Regarding weaknesses of the manuscript, some of the explanations of the trends observed in the simulation data could be expanded slightly, to help gain a deeper understanding of the competition between adhesion and RBC deformability underlying the alignment dynamics. These are described in more detail below.

1. Line 114 and lines 120-129: The discussion here of the trends observed in Figure 1 (including why the LE shape has a larger energy compared to the OB shape despite having a smaller adhesion area) is somewhat vague and should be developed further. For example, currently there is only a video showing the egg-like shape and a second video comparing the LE shape to a spherical shape - it would be helpful to have a further video comparing the LE and OB shapes and the different RBC deformations they cause. Moreover, the explanation of the energy/mobility of each shape in terms of curvatures (e.g. the OB shape having "lower curvature at its flat side") could be made more precise. I would expect that the adhesion area depends on how close the principal curvatures of the merozoite surface are to being equal and opposite to the natural curvatures of the RBC, since this determines the bending energy associated with wrapping the merozoite and forming short bonds. This would explain why the spherical shape is most mobile (its principal curvatures are constant so there is no region where at least one is relatively small), and why alignment is most likely to occur in the dimple of the RBC where the membrane is naturally concave-outward. For a given adhesion area, the deformation energy should depend on the difference in principal curvatures in the contact region, with a larger difference causing more bending of the RBC membrane. This difference is larger for the LE shape, since one principal curvature remains large at each point on the surface, compared to the OB shape whose principal curvatures are both small on the 'flat side' where contact is most likely to occur.

We have expanded the discussion of these results to make it clearer. Furthermore, a new video was generated to visually see differences between different shapes.

1. Lines 175-176: Given that the ratio A_m/A_s (adhesion area to total surface area) plays a key role in the probability of alignment, the authors should be more quantitative at this point. How does the ratio A_m/A_s (as measured directly, or indirectly e.g. by the area under the probability distributions inside the alignment region in figures 3a,b) scale with the system parameters, such as the adhesion strength and the off-rate k_off? Can it be estimated from an energy balance between RBC bending/stretching and the average adhesion energy?

A change in A_m as a function of adhesion strength can be estimated analytically for a sphere, as was done in Hillringhaus et al. Biophys. J. 117:1202, 2019. For small deformations, there is essentially a competition of bending and adhesion energies, while for strong adhesion, stretching-elasticity contribution becomes important. We have included this theoretical result into the manuscript and discuss its implications.

1. Line 197-198 and Figure 4c: Why is the deformation energy associated with the OB shape much lower than all other shapes for values of k_off/k_on^{long} smaller than 2?

For k_off/k_on^{long} < 2, the magnitude of local curvature has a pronounced effect. For the OB shape, a large adhesion area is formed over the area with very low curvature, and close to the rim where the curvature is large, the adhesion strength may not be strong enough to induce membrane wrapping and deformation. For other shapes, the adhesion strength is large enough to lead to partial wrapping of the parasite by the membrane over moderate curvatures. As a result, the integrated deformation energy is significantly lower for the OB shape than for the other shapes in this regime of adhesion strengths. We have added this clarification to the manuscript.

1. Alignment requires that the distance between the merozoite apex and RBC membrane is very small, and the alignment criteria necessitate examining small changes in the apex angle \theta from \pi. Can the authors comment on how sensitive are the results to the numerical discretisation used?

The discretization length does affect the tightness of the alignment criteria. In our simulations, the average discretization length of the RBC membrane is about l0=0.2 m. The half circumference length of a parasite (corresponding to angle ) is R, which is equal to about 12 l0 for R=0.75 m, such that our angle resolution with respect to the parasite size is 0.1. Therefore, we use 0.2 for the alignment criteria, which is large enough to avoid strong discretization effects. Simulations with a finer discretization are possible, but they become very expensive computationally.

Reviewer #2 (Public Review):

[...] A major strength of the results is that it investigates an unstudied problem in malarial pathogenesis. The results pertaining to adhesion strength may be informative for preventing the organism from invading red blood cells. A primary weakness is that there is too little detail provided in the methods for this reviewer to adequate assess the computational method. Secondly, the results are somewhat inconclusive. While the egg-shape performs better than certain other shapes, there is no clear final understanding why this shape is preferred over the spherical or short ellipsoidal shapes. However, this possibly provides some clues as to why a certain malarial species does actively adopt a spherical shape during red blood cell binding and invasion.

We thank the reviewer for a positive judgment of our manuscript. We have significantly expanded the methods section, so it should contain now all necessary simulation details. We agree with the reviewer that the conclusions about shape advantages/disadvantages are equivocal to some extent, but this is exactly what our simulation data show. However, from our data it is clear that the two shapes (i.e. egg-like and sphere) stand out, and they also correspond to real examples of merozoite shapes. As the reviewer points out, we do discuss some clues for the importance of parasite shape in the alignment process.

Overall, the authors achieved their aims by quantitatively assessing the affect of parasite shape and adhesion strength on cell alignment, which is a proxy for invasion. The discussion at the end of the manuscript provides an accurate evaluation of the results that puts them into the context of invasion. While to some extent the results presented here are inconclusive, I do think that this paper achieves an important goal for its field. This is an understudied area pertinent to a major disease. This manuscript has the potential to bring questions of the biophysics of malarial invasion out to the broader community, specifically introducing these questions to biophysicists as well as microbiologists. Furthermore, the results naturally lead to new questions. If the spherical and egg shapes do not confer a strong advantage, then these specific shapes must also play a role in other processes. The authors do suggest some possibilities in the Discussion. That their remain interesting questions is a great spur for future work.

Thank you for emphasizing the importance of multidisciplinarity. We also hope that our work will ignite interest in different communities, as only a multidisciplinary effort can bring us much closer to understanding of parasite alignment and invasion, which clearly include a combination of different mechanical and biochemical processes.

Author Response

Reviewer #1 (Public Review):

[...] Their studies were complemented by transcriptomics and metabolomics and these results support the general conclusions that pollen contains diverse carbon sources which could be used in complementary ways by the different species, which have diverse metabolic capabilities encoded in their genomes.

Reply: We thank the reviewer for the positive assessment of our manuscript.

One of the points that was not completely explored in the paper is what happens in the simplified diet both in vitro and in the Bee gut. They propose in the discussion that in the presence of few and simple carbon sources (sugars) there is competition for nutrients and competitive exclusion is driving loss of some species. But this is not fully addressed in the paper.

Reply: All four species can colonize the gut individually and grow on their own in axenic cultures when providing the simple sugars or the pollen as the only carbon source. When cultured together, all four strains are stably maintained in the presence of pollen. However, three of the four strains steadily decrease in abundance in the simple sugars. These findings are, in our opinion, consistent with the consumer-resource model (more resources = more species that can coexist) and the competitive exclusion principle which predicts that if two or more strains compete for the same nutrients they will not be able to coexist. We have added a corresponding section on line 423-425.

The system they use (with 4 closely related bacterial species) is a simplified system. Therefore, it is not clear if the same general findings will hold in more complex systems. But the results supporting that nutrient complexity (in diet) and metabolic diversity (from the microbial side) are key factors to enable co-existence and persistence of complex microbiota communities are strong and likely generalizable. Although, it is possible that with other communities and other hosts other factors will also come into play. Nonetheless, the current study is important because it sets a good example for how these questions can be addressed to study more complex systems.

Reply: It is true that bacterial coexistence does not necessarily need to be dependent on the nutrient complexity and that in other communities the host, the structure of the environment, or cross-feeding activities may play a more important role. We have discussed this point in the revised manuscript starting on line 423 and on line 427.

Overall, the study described here is complete, and rigorous, except for a few points that still need to be addressed and clarified. Namely, it would be interesting to understand what drives exclusion of some members of the community in the simplified diet.

Reply: See our reply above.

Importantly, the current study opens the door for new studies (including in vitro studies) on the identification of network interactions that are important for Microbe-Microbe interactions that enable co-existence in other systems. Additionally, this study also highlights the importance of identifying the relevant nutritional (and metabolic) conditions for addressing those questions given the importance of the metabolic context in shaping microbe-microbe interactions.

Reply: Thank you. We agree.

Reviewer #2 (Public Review):

[...] Strengths: The use of community profiling, transcriptomics, and metabolomics adds depth, as does the comparison of defined culture conditions to the host environment. The main conclusions drawn by the authors is that the presence of pollen is necessary for gut species to coexist, and that the different species, although closely related, respond in distinct ways to nutrients in pollen and consume different profiles of nutrients from pollen.

Reply: We thank the reviewer for the positive feedback and the many valuable comments which helped us to further strengthen our manuscript.

Weaknesses: The main weakness I see with this work is the choice of in vitro comparison conditions. The strains are cultured either on pollen or sugar water, whereas in vivo bees are fed a diet of pollen and sugar water, or only sugar water. A direct comparison is possible between the strains grown on sugar water in vitro or in vivo, but I think that in several places, the authors may have to reconsider or modify their interpretations comparing in vitro culture on pollen/pollen extract with the in vivo growth of the community on pollen and sugar water. Because there is sugar in the bee diet, differences in assembly dynamics, transcription, or metabolite consumption between pollen-containing culture conditions and the bee gut might stem from the dietary intake of sugar, or from an aspect of the host environment.

Reply: We agree with the reviewer that the nutrient conditions that were used in vitro and in vivo are not identical and may have impacted the relative abundance of some of the community members, the transcriptional profiles, or the metabolite changes. Nevertheless, we believe that our experimental design is valid to test the main hypothesis of our study, i.e. a complex, pollen-based diet facilitates coexistence, while simple sugars lead to the dominance of a single strain independent of the environment (culture tube versus host). An important point to consider here is that bees will pre-digest the consumed pollen, and partially absorb dietary nutrients such as amino acids, glucose, and fructose, before they reach the bacteria in the hindgut. Consequently, the in vivo and in vitro conditions will never be the same even if we would have used the identical nutrients in our treatments. Also, pollen by itself contains glucose, fructose, and sucrose. So, although we have not added glucose to the in vitro pollen condition, this simple sugar was present in the corresponding condition. We have added a corresponding section in the discussion on line 402-422. This said, while we cannot recapitulate the exact same nutritional conditions in vitro, we still think that our main conclusions hold which is that we can recapitulate the pollen-dependent coexistence found in vivo.

Reviewer #3 (Public Review):

[...] Overall, the paper is strong and the arguments and conclusions put forth are well supported by the data. I only have a few suggestions:

Reply: We thank the reviewer for the positive evaluation of our manuscript.

1) The study focuses on one strain each of the 4 Firm-5 species; however, there is diversity within each species. This is only briefly mentioned in the paper at the very end, and I think the authors should address this a bit more directly. In particular, they have previously generated a large amount of genomic data from some of these other strains, so it is likely possible to infer or speculate, based on this data, whether they expect different strains within each species to utilize similar nutrients. Also, I'm wondering if the authors can comment on how their findings could extend to the related bumble bee gut microbiome. Such a discussion would help enhance the applicability and importance of this study.

Reply: We agree that the large amount of strain-level diversity within a given species is an important point to consider. However, we would like to not expand this point much further as it would require a relatively complex genomic analysis. Also, considering that many of the strain-specific transcriptional changes are in genes shared with the other species, I am not sure how much such an analysis would reveal. Anyways, we plan to compare the coexistence between strains from the same versus another lineages in a follow-up study.

As for the bumble bees, we currently do not know how many strains or species of Lactobacillus Firm5 can coexist in bumble bees. Therefore, we feel that a discussion extending to bumble bees would be too speculative. However, we included a sentence in the discussion which states that since pollen facilitates coexistence, it follows that dietary differences are likely to influence the diversity of Lactobacillus Firm5 and give the example of the Asian honey bee, which seems to only harbor one species of this phylotype. See line 479-488.

2) It is interesting that different species ended up dominating in the in vivo vs. in vitro simple sugar-based communities. What do the authors think may be behind this difference?

Reply: This is indeed an interesting point. We have not used the same sugars in vivo (sucrose) and in vitro (glucose). Moreover, the nutritional and physicochemical conditions in the hindgut are likely different from those found in a culture tube. We have mentioned that these are potential reasons for the observed differences in the relative abundance of different community members between in vivo and in vitro conditions on line 402-422 of the manuscript.

3) Since the observed coexistence of these gut microbes is largely due to nutritional niche partitioning, it would be helpful if the authors can comment on the natural variation of key pollen derived metabolites, and if/how we could expect ecological variation in the bee microbiome due to plant pollen availability based on biogeography and seasonality.

Reply: We agree and have included a corresponding sentence in the discussion on line 479. See also our reply to point 1.

4) The supplementary information is nicely documented and accessible, but I think it would be even more useful if genome-wide data for the RNA-seq results, not just for select genes, are made available. Furthermore, I suggest including descriptive titles and labels within the supplementary Excel files, as there are many separate sheets and it is not always clear what each one shows.

Reply: This has been included in the revised manuscript.

Identity, Mexican;

Time in the US, Immigration Status, Being secretive, Hiding/lying, In the shadows, Living undocumented; Reflections, The United States, US government and immigration; Feelings, Frustration; Time in the US, Jobs/employment/work, Documents, Driver's license, Social security card/ID

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Reply to the reviewers

We thank the reviewers for their thoughtful comments. We were delighted the reviewers found our results “compelling”, “striking”, “well presented”, “implications exciting”, “excellent results! really nice!”, “this microscopy is beautiful!” and “translational-dependence (of mRNA localization) in a transcript-specific way without perturbing translation globally”, which is a “complete surprise, and opens exciting doors to investigate how translation leads to mRNA organization and its connection to **tissue development” and “may represent a new pathway of mRNA transport”.

We also appreciated the comments regarding the “wide appeal”, “broad readership of readers”, and “broad interest” the reviewers gave to our manuscript regarding its impact, and also the comments of “well-written (and) well-cited”.

We can address all the concerns raised by the reviewers. In addition to textual changes, we will add the following to the Results section:

1. Additional quantitation of smFISH beyond Figure 2;
2. Addition of a negative (uniformly distributed) mRNA control and its quantitation;
3. Western blots for our ΔATG lines to determine what and how much protein is made.
4. Unbiased nuclear masking. Our specific responses are shown below, in blue.

Reviewer #1

**Major comments**

Fig. 1: Main and supplementary figures present smFISH signals for eight localized mRNAs, while in the results section authors describe that they analyzed twenty-five transcripts. Authors should explain the choice of transcripts presented in the paper.

We will include a panel in Fig. S1E to show every mRNA that we tested, and we will edit Table 1 to describe the observed subcellular localization.

We will edit the text, adding a few sentences to clarify, along the lines of: “O**ur survey revealed mRNAs with varying degrees of localization within epithelia that we divided into three classes: CeAJ/membrane localized, perinuclearly localized, and unlocalized (Fig. 1 and S1 and Table 1).” and “The rest of our tested mRNAs did not possess any evident subcellular localization at any of the analyzed embryonic stages/tissues and were not further investigated (Fig. S1E and Table 1).”

Moreover, smFISH signal of different localized mRNAs in epidermal cells was visualized at different stages (bean, comma or late comma), and authors did not comment what was the reason of such conditions. This may make transcripts localization results difficult to interpret, as further analysis showed that mRNA localization varied in a stage-specific manner.

We have clarified this point now in Figure legend 1: “Specific embryonic stages were selected for each transcript based on the highest degree of mRNA localization they exhibited.”

Did author used smFISH probes designed against endogenous mRNAs for all tested transcripts?

We did not. We clarify this point now in Materials and methods: “All probes were designed against the endogenous mRNA sequences except dlg-1 (some constructs), pkc-3, hmp-2, spc-1, let-805, and vab-10a, whose mRNA were detected with gfp probes in their corresponding transgenic lines (Table S2). An exception to this is Fig. S1A where we used probes against the endogenous dlg-1 mRNA.”.

Marking dlg-1 mRNA as dlg-1-gfp suggests that smFISH probe was specific for gfp transcript. Is it true? If yes, authors should compare localization of wild-type endogenous dlg-1 mRNA with that of the transcript encoding a fusion protein, to confirm that fusion does not affect mRNA localization.

Yes, in Fig. 1C we show smFISH for GFP (i.e., the tagged dlg-1 only). In Fig. S1A, we show smFISH against endogenous dlg-1. Tagged and endogenous dlg-1 mRNAs are both localized. We clarified this point in the main text: “Five of these transcripts were enriched at specific loci at or near the cell membrane: laterally and at the CeAJ for dlg-1 (Fig. 1C for endogenous/GFP CRISPR-tagged dlg-1::gfp mRNA and S1A for endogenous/non-tagged dlg-1 mRNA), (…)”. And in the Supplemental figure legend (Fig. S1A): “Endogenous/non-tagged dlg-1 mRNA shows CeAJ/membrane localization like its endogenous/GFP CRISPR-tagged counterpart.”

Fig. 2B: Authors conclude that at later stages of pharyngeal morphogenesis mRNA enrichment at the CeAJ decreased gradually in comparison to comma stage. Data do not show statistically significant decrease in ratio of localized mRNAs - for dlg-1: bean: 0.39{plus minus}0.09, comma: 0.29{plus minus}0.08, 1.5-fold: 0.30{plus minus}0.09; for ajm-1: bean: 0.36{plus minus}0.08, comma: 0.30{plus minus}0.05, 1.5-fold: 0.28{plus minus}0.09.

t-test (one-tailed) analysis revealed a significant difference between bean and comma stages for both dlg-1 and ajm-1 mRNAs. Statistical analysis and data will be provided.

Fig. 4: What was the difference between the first and the second __ΔATG transgenic line? Authors should analyze the size of the truncated DLG-1 protein that is expressed from the second Δ__ATG transgenic line that localizes to CeAJ. Knowing alternative ATGs and protein size may suggest domain composition of the truncated protein. This will allow to confront truncated protein localization with the results from.

We will perform a Western blot to determine the size and levels of proteins produced.

Fig. 5. Moreover, to prove that the localization of dlg-1 mRNA at the CeAJ is translation-dependent, additional experiment should be performed where transcripts localization will be analyzed in embryos treated with translation inhibitors such as cycloheximide (translation elongation inhibitor) and puromycin (that induces premature termination).

We believe this comment might refer to Fig. 4. If this is the case: drugs like cycloheximide and puromycin affect the translation of the whole transcriptome, whereas with our ΔATG experiment, we aimed to target the translation of one specific transcript and avoid secondary effects. Nevertheless, we understand Reviewer #1’s concern and will include a second experiment. In our hands, cycloheximide and puromycin have never worked in older embryos (it’s hard to get past the eggshell and into the embryo). Instead, we will use stress conditions, which induce a “ribosome drop-off” (Spriggs et al., 2010). Heat stress has been shown to decrease polysome occupancy (Arnold et al., 2014). We, therefore, have used heat-shock at 33°C for 30’, and the results are now shown in Fig. S4. These show the loss of RNA localization upon heat shock.

**Minor comments**

In the introduction section authors should emphasize the main goal and scientific significance of the paper.

We added this sentence to state the significance before summarizing the results: “To investigate the impact of mRNA localization during embryonic development, we conducted a single molecule fluorescence in situ hybridization (smFISH)-based survey (…)” and “Our data demonstrate that the dlg-1 UTRs are dispensable, whereas translation is required for localization, therefore providing an example of a translation-dependent mechanism for mRNA delivery in C. elegans.” To state the significance.

Fig 1A: It's hard to distinguish different colors on the schematics. Schematics presents intermediate filaments that are not included in the Table 1.

We modified Table 1 based on this and other reviewers’ comments.

Fig. 1C: dlg-1 transcript is marked as dlg-1-gfp on the left panel and dlg-1 on the right panel.

Corrected.

Fig. 2B: Axis labels and titles are not visible, larger font size should be used.

We will modify the graph (following Reviewer #2’s suggestion) and axes label and title sizes will be taken into account.

Fig. 5C: Enlarge the font size.

Will do.

Fig. S2: Embryonic stages should be marked on the figure for easier interpretation.

Added.

Reviewer #2

Major comments

Figure 2 requires a negative (or uniformly distributed) mRNA control for comparison. Figure 2C should be quantified. The plot quality should be improved, and appropriate statistical tests should be employed to strengthen the claimed findings.

We will add a negative control (jac-1 mRNA), and quantify Fig. 2C as well. Plots will be changed accordingly to the suggestion.

Most claims of perinuclear mRNA localization are difficult to see and not well supported visually or statistically. The usage of DAPI markers, membrane markers, 3D rendering, or a quantified metric would bolster this claim. Also, sax-7 is claimed to be perinuclear and elsewhere claimed to be uniform then used as a uniform control. Please explain or resolve these discrepancies more clearly.__

Regarding perinuclear mRNAs:

We are not trying to make a big statement out of these data as perinuclear (ER) localization of mRNAs coding for transmembrane/secreted proteins is well known. The aim of our study was to describe transcript localized at or in the proximity of the junction. However, we thought it was worth mentioning these examples of perinuclearly localized mRNAs (hmr-1, sax-7, and eat-20) for two reasons: scientific correctness – show accessory results that might be interesting for other scientists – and use as positive controls for our smFISH survey – these mRNAs were expected to localize perinuclearly for the reasons mentioned above. We will rewrite the text to make these points clearer.

Regarding sax-7 mRNA:

sax-7 mRNA localizes perinuclearly in sporadic instances (Fig S1C), but it is predominantly scattered throughout the cytoplasm (i.e., unlocalized). It presumably localizes perinuclearly in a translation-dependent manner as sax-7 codes for a transmembrane protein that would be targeted to the ER. We have described this ER-type of localization in the introduction and reiterated it partially in the first paragraph of the results. sax-7 UTRs are therefore presumably not responsible for subcellular localization, which would instead depend on a signal sequence. We will better clarify this point in the main text.

The major concern about the paper is the data display and interpretation of Figure 5C. I'm not comfortable with the approach the authors took of blurring out the nucleus. A more faithful practice would be to use an automated mask over DAPI staining or to quantify the entirety of the cell. If the entirety of the cell were quantified, one could still focus analysis on specific regions of relevance. The interpretations distinguishing membrane versus cytoplasmic localization (or mislocalization) are hard to differentiate in these images especially since they are lacking a membrane marker. The ability to make these distinctions forms the basis of Tocchini et al's two pathways of dlg-1 mRNA localization. These interpretations also heavily rely on how the image was processed through the different Z-stacks, and it's not clear to me how that was done. For example, the diffusion of mRNA in figure 5F and 5I are indistinguishable to my eye but are claimed to be different.

In the images, the nuclei have been blurred to allow the reader to focus on the cytoplasmic signal and not on the nuclear (transcriptional) signal as it is not meaningful for this study. In the quantitation, the nuclear signal has been unbiasedly and specifically removed from the analysis by cropping out the DNA signal from the other channels. The frontal plane views of the seam cells in Fig. 5 show maximum intensity projections (MIPs) of 3 Z-stacks (0.54 µm total) that each contain nuclei and, therefore, the transcriptional signal (schematics in Fig. 5B). We will clarify these points in the text.

Regarding cytoplasmic versus membrane-associated mRNAs, although we did not have a membrane marker, we relied on the brightness of the DLG-1::GFP signal to identify the cell borders (i.e., membranes) after over-exposure. This approach allowed us to discern apicobasal and apical sides for the intensity profile analyses. We will clarify this point as well in the text and, in parallel, we will try a different approach using transverse sections on top views to clarify our data.

To my eye, it seems that Figure 5 could be more faithfully interpreted to state that DGL-1 protein localization depends on the L27-SH3 domains. The Huk/Guk domains are dispensable for DLG-1 protein localization; however, through other studies, we know they are important for viability. In contrast, dlg-1 mRNA localization requires all domains of the protein (L27-Guk). It is exceptionally interesting to find a mutant condition in which the mRNA and protein localizations are uncoupled. It would be very interesting to explore in the discussion or by other means what the purpose of localized translation may be. Because, in this instance, proper mRNA localization and protein function are closely associated, it may suggest that DLG-1 needs to be translated locally to function properly.

We will rewrite the Results and Discussion to clarify our model. We agree that L27 and SH3 domains are critical, but we also detected effects of the HooK/GuK domains. We have refined our model to describe functions of the N and C termini for membrane or junctional localization.

The manuscript requires an improve materials & methods description of the quantification __procedures and statistics employed.__

We will add these points.

Minor & Major comments together - text

Summary statement: Is "adherent junction" supposed to be "adherens junction?"

Corrected.

Abstract: Sentence 1, I think they should add a caveat word to this sentence. Something like "...phenomenon that can facilitate sub-cellular protein targeting." In most instances this isn't very well characterized or known.

Corrected.

In the first paragraph, it might be good to mention that Moor et al also showed that mRNA localize to different regions to alter their level of translation (to concentrate them in high ribosome dense regions of the cell).

Added as follows: “For example, a global analysis of localized mRNAs in murine intestinal epithelia found that 30% of highly expressed transcripts were polarized and that their localization coincided with highly abundant regions in ribosomes **(Moor, 2017).”

There are some new studies of translation-dependent mRNA localization - that might be good to highlight - Li et al., Cell Reports (PMID: 33951426) 2021; Sepulveda et al., 2018 (PCM), Hirashima et al., 2018; Safieddine, et al 2021. Also, Hughes and Simmonds, 2019 reviews membrane associated mRNA localization in Drosophila. And a new review by Das et al (Nat Rev MCB) 2021 is also nice.

We will add them to the text.

Parker et al. did not show that the 3'UTR was dispensable for mRNA localization. They showed the 3'UTR was sufficient for mRNA localization.

Quoting from the paper Parker et al.: “3′UTRs of erm-1 and imb-2 were not sufficient to drive mRNA subcellular localization. Endogenous erm-1 and imb-2 mRNAs localize to the cell or nuclear peripheries, respectively, but mNeonGreen mRNA appended with erm-1 or imb-2 3′UTRs failed to recapitulate those patterns (Fig. 4A-D).” We will make this point clearer in the rewritten text.

In the second paragraph, the sentence about bean stages is missing one closing parenthesis.

Corrected.

Last paragraph: FISH is fluorescence, not fluorescent.

Corrected.

Both "subcellular" and "sub-cellular" are used.

Corrected.

Minor comments – Figures

Figure 1

o Figure 1A is confusing. It's not totally clear what the rectangles and circles signify. There are many acronyms within the figure. Which of the cell types depicted in the figure are shown here? For example, for the dorsal cells, which is the apical v. basal side?

We tried to simplify the cartoon for a general C. elegans epithelial cell. We followed schematics already shown in previous publications to maintain consistency. Acronyms and color-codes are listed in the corresponding figure legend and have been better clarified.

o Some of the colors are difficult to distinguish, particularly when printed out or for red/green colorblind readers. Is erm-1 meant to be a cytoskeletal associated or a basolateral polarity factor?

We understand the issue, but unfortunately, with 8 classes of factors, shades of gray might not solve the problem. We tried to circumvent the red-green issue changing red to dark grey. Furthermore, we added details about shapes to the figure legends. We will work to make the colors work better.

ERM-1 is a cytoskeletal-associated factor.

o The nomenclature for dlg-1 is inconsistent within "C".

Corrected.

o Please specify what the "cr" is in "cr.dlg-1:-gfp" in the legend.

Added.

Figure 2

o Can Figure 2C be quantified in a similar manner to 2A/2B?

Currently our script cannot do that, but we will try to optimize it to be able to quantify this type of images.

o 2B - please jitter the dots to better visualize them when they land on top of one another

Yes, we will.

o Please include a negative control example, a transcript that is not peripherally localized for comparison.

Yes, we will.

o There is no place in the text of the document where Fig 2C is referenced

Corrected (it was wrongly referred to as “2B”).

o I can't see any discernable ajm-1 localization in Fig 2A.

We added some arrowheads to point at specific examples and increased the intensities of the corresponding smFISH signal for better visualization.

o I can't see any dlg-1 pharyngeal localization in Fig2C.

We added some arrowheads to point at specific examples and increased the intensities of the corresponding smFISH signal for better visualization.

o More details on how the quantification was performed would be welcome. Particularly, in 2B, what is the distance from the membrane in which transcripts were called as membrane-associated? What statistics were used to test differences between groups?

We will add a full description of the script used as well as the statistic details.

Figure 3

o Totally optional but might be nice: can you make a better attempt to approximate the scale of the cartoon depiction?

The UTRs, especially the 5’ one, are much smaller than the dlg-1 gene sequence. A proper scaling of the cartoon to the actual sequences, would draw the attention away from the main subjects of this figure, the UTRs. Nevertheless, we made sure it is clear in the corresponding figure legend that the cartoon is not in scale: “The schematics are not in scale with the actual size of the corresponding sequences. UTR lengths: dlg-1 5’UTR: 61 nucleotides; sax-7 5’UTR: 63 nucleotides; dlg-1 3’UTR: 815 nucleotides; unc-54 3’UTR: 280 nucleotides.”

o The GFP as an asterisk illustration may be confusing for some readers. Could you add another rectangular box to depict the gfp coding sequence?

Corrected.

o This microscopy is beautiful!

Thanks Reviewer #2!

o Were introns removed? Is the endogenous copy still present?

All the transgenes were analyzed in a wild-type background, therefore, yes, the endogenous copy was still present. All the transgenes possessed introns. We will change the corresponding text as follows: “To test whether the localization of one of the identified localized mRNAs, dlg-1, relied on zip codes, we generated extrachromosomal transgenic lines carrying a dlg-1 gene whose sequence was fused to an in-frame GFP and to exogenous UTRs.”. In the figure “dlg-1 ORF” has been replaced with “dlg-1 gene”.

o The wording in the legend "CRISPR or transgenic" may be confusing as Cas9 genome editing is still a form of transgenesis.

We added “extrachromosomal” to clarify the nature of the mRNA.

o The authors state that the 5'-3'UTR construct produces perinuclear dlg-1 transcripts but in the absence of DAPI imaging, it's not clear that this is the case.

We could not find such a statement, but we tried to clarify the localization of these mRNAs in the text: “The mRNA localization patterns of the two UTR reporters were compared to the localization of dlg-1 transcripts from the CRISPR line (“wild-type”, Fig. 3A; Heppert et al., 2018), described in Fig. 2. Both reporter strains showed enrichment at the CeAJ and localization dynamics of their transcripts that were comparable to the wild-type cr.dlg-1 (Fig. 3B). These results indicate that the UTR sequences of dlg-1** mRNA are not required for its localization.”

o Which probe set was used? The gfp probe?

Yes, please see the main text: “Given that the transgenic constructs were expressed in a wild-type background, smFISH experiments were conducted with probes against GFP RNA sequences to focus on the transgenic dlg-1::GFP mRNAs (cr.dlg-1 and tg.dlg-1).”

o Here, sax-7 is used as a uniform control, but sax-7 is claimed in Fig S1B-D as being perinuclear. This is a bit confusing.

sax-7 mRNA localizes perinuclearly in sporadic instances (Fig S1C), but it is predominantly scattered throughout the cytoplasm (i.e., unlocalized). It presumably localizes perinuclearly in a translation-dependent manner as sax-7 codes for a transmembrane protein that would be targeted to the ER. We have described this ER-type of localization in the introduction and reiterated it partially in the first paragraph of the results. sax-7 UTRs are therefore presumably not responsible for any subcellular localization, which would instead rely on a signal sequence. We will better clarify this point in the main text.

Figure 4

o Excellent results! Really nice!

Thanks Reviewer #2!

o Fig 4A. The GFP depicted as a circle is strange.

We changed it into a rectangle.

o Fig 4A. Can you include the gene/protein name for easy skimming?

Added.

o Fig 4B. the color here is too faint and it is unclear what is being depicted. Overall, this part of the figure could be improved.

We are optimizing the coloring and simplifying the schematics.

o Were the introns removed?

No, the introns were maintained in this and in all our transgenic lines. We described our transgenic lines in the materials and methods section (now with more detail). What we depict in the scheme (Fig. 4A) is the mature RNA (now specified in the figure), therefore no introns depicted. We will also specify this in the main text.

Figure 5

o Fig 5A. can you add the gene/protein name

Added.

o Fig 5B. Can you make the example apicobasal (non-apical) mRNA more distinctive? If it had its own peak in the lower trace, the reader would more clearly understand that this mRNA will be excluded from apical measurements whereas it will be included in apicobasal measurements.

We actually wanted to show this specific example: a cytoplasmic mRNA and a junctional mRNA may seem close from the apicobasal analysis (partially overlapping peaks that Reviewer #2 mentioned). With the apical analysis, instead, we can show that these mRNAs are actually not close, and they belong to two different compartments (cytoplasm and junction). We would therefore like to keep the current scheme, while better clarifying this point in the corresponding figure legend.

o D' - I' The grey font is too light.

Noted. We will change it.

o D' - I' The inconsistent y-axis scaling makes it difficult to compare across these samples. Can you set them to the same maximum number?

The values are indeed quite different. We tried to use the same scale, but this would make some of the data unappreciable. The idea was to evaluate, within each graph, how mRNA and protein are localized relative to the junctional marker. We will make this clearer in the text.

o D' - I' The x-axis labels are formatted incorrectly

Corrected.

o The practice of masking out the nucleus appears to remove potentially important mRNAs that are not nuclear localized. This could really impact the findings and interpretation. Instead, consider an automated DAPI mask.

The masking on the images is not the same used for the analysis: in the images, a shaded circle has been drawn on the DNA channel and moved onto its corresponding location in the other channels or merges. For the analysis, the DNA signal has been specifically removed in the channel with the smFISH signal. Given that the analysis has been performed on maximum intensity projections of 3 Z-stacks, we believe we did not remove any non-nuclear mRNA. We will clarify this point in Materials and methods.

o I can't see what the authors are calling membrane diffuse versus cytoplasmic. This is making it hard for me to see their "two step" pathway to localization.

We will add in Fig. 5B-C an example of a membrane localized mRNA. Furthermore, we will add transverse sections of membrane and cytoplasm to make the date clearer to the reader.

o Can more details of the quantification be included? How were Z-sections selected, chosen for inclusion? Which Z-sections and how many were selected?

We will add the details to Materials and methods.

o Also, why do these measurements focus on what I think are the seam cells when Lockwood et al., 2008 show the entire epithelium that is much easier to see?

We are focusing on the seam cells at the bean stage as these are the cells and the embryonic stage where we see the highest localization of dlg-1 mRNA in the wild-type.

o Please name these constructs to correlate the text more explicitly to the figures.

Added.

o How many embryos were analyzed for each trace? How many embryos showed consistent patterns?

We will add the details of the analysis to Materials and methods.

o Why were these cells used for study here? Lockwood et al., 2008 use a larger field of epithelial cells for visualization.

As stated before: we are focusing on the seam cells at the bean stage as these are the cells and the embryonic stage where we see the highest localization of dlg-1 mRNA in the wild-type.

Figure 6

There are major discrepancies between what this figure is depicting graphically and what is described in the text. Again, I'm not comfortable making the "two step" claims this figure purports given the data shared in Figure 5.

We are planning to re-write the last part of the results to better clarify our two-step model. A two-step model had been previously suggested in McMahon et al., 2001, where they could show that DLG-1 and AJM-1 (referred to in that publication as JAM-1) are initially localized laterally and only later in development are then enriched apically. Our data agree with McMahon very well, so we used the earlier study as a start. We will cite and explain this paper in greater depth during the rewriting.

**Minor comments - Tables & Supplemental Figures**

Table 1

I think this table could be improved to more clearly illustrate which mRNAs were tested and what their mRNA localization patterns were (for example, gene name identifiers included, etc). Could the information that is depicted by gray shading instead be added as its own column? For example, have a column for "Observed mRNA localization"

We modified Table 1 based on these and the other reviewers’ comments.

Can you add distinct column names for the two columns that are labeled as "protein localization - group"

We modified Table 1 based on these and the other reviewers’ comments.

Can you also add which of these components are part of ASI v. ASII (as described in the introduction?)

A new table has been added with the factors belonging to the two adhesion systems (same color code as in Table 1).

Supplemental Figure 1

It is hard to see that some of these spots are perinuclear. More information (membrane marker, 3D rendering, improved metrics) is required to support this claim.

We are not trying to make a big statement out of these data as perinuclear localization for mRNAs coding for transmembrane/secreted proteins is well known. The aim of our study was to describe transcript localized at or in the proximity of the junction. We thought it was worth mentioning these examples of perinuclearly localized mRNAs (hmr-1, sax-7, and eat-20) for two reasons: scientific correctness – show accessory results that might be interesting for other scientists – and use as positive controls for our smFISH survey – these mRNAs were expected to have a somewhat perinuclear localization for the reasons mentioned above.

What do these images look like over the entire embryo, not just in the zoomed in section?

We added a column with the zoom-out embryos.

sax-7 localization in S4 looks similar but a different localization claim is made.

sax-7 mRNA can localize perinuclearly in sporadic instances (Fig S1C), but is predominantly scattered throughout the cytoplasm (i.e., unlocalized). It presumably localizes perinuclearly in a translation-dependent manner as sax-7 codes for a transmembrane protein that would be targeted to the ER. We have described this ER-type of localization in the introduction and reiterated it partially in the first paragraph of the results. sax-7 UTRs are therefore presumably not responsible for any subcellular localization, which would instead rely on a signal sequence. We will better clarify this point in the main text.

Supplemental Figure 2

Before adherens junctions even exist dlg-1 go to the membrane - this is really neat!

Thanks Reviewer #2!

Supplemental Figure 3

Technical question: If either 5 or 3 stack images are used, how does this work? Do they have different z-spacings? Or do they do 5-stack images represent a wider Z-space?

This is the sentence under question: “Maximum intensity projections of 5 (1.08 µm) (A) and 3 (0.54 µm) (B) Z-stacks”. The space between each Z-stack image is constant in all our imaging and its value is 270 nm. When we consider 5 planes, the distance from the 1st to the 5th is 4 x 270 nm = 1.08 µm, whereas for 3 planes will be 2 x 270 nm = 0.54 µm.

Supplemental Figure 4

Line #2 retains translation and keeps mRNA localization.

Totally optional, but consider showing both lines in the main figure to illustrate the two possibilities.

Noted.

Materials and methods - how did they created the ATG mutations? Is it an array? - why does one translate, and one doesn't?

We will clarify this point in Materials and methods: “dlg-1 deletion constructs ΔATG (SM2664 and SM2663) and ΔL27-PDZs (SM2641) were generated by overlap extension PCR using pML902 as a template.”.

We will perform a Western blot to clarify Reviewer #2’s last point. Currently we do not know what peptide is translated, but the comparison with our full-length control will probably shed some light on the issue.

Reviewer #3

Major comments

The smFISH results are striking and implications exciting. The conclusions made from the smFISH results reported in all Figures will be strengthened considerably by quantifying the mRNA localized to the defined specific subcellular regions. At the very least, localization to the cytoplasm versus the plasma membrane should be determined as performed in Figure 2B, but quantifying finer localization will enhance the conclusions made about regional localization (e.g. CeAJ versus plasma membrane mRNA localization in Figure 5). Inclusion of a non-localizing control in Figures 1-4 will enable statistical comparisons between mRNA localizing and non-localizing groups.

We will add more quantitation, statistics, and negative controls.

The script used for smFISH quantitation should be included in the methods or published in an accessible forum (Github, etc). Criteria for mRNA "dot" calling should be defined in the methods. All raw smFISH counts should also be reported.

We will add the full description of the script in Materials and methods, and we will provide the raw data in an additional supplementary table.

Figure 2: What is the localizing ratio of a non-localizing control mRNA (e.g. jac-1)? Including an unlocalized control with quantitation would strengthen the localization arguments presented.

Yes, we will add quantitation for an unlocalized mRNA.

Figure 5: Quantifying colocalization of mRNA and protein (+/- AJM-1) will strengthen the arguments made about mRNA/protein localization.

Yes, we will quantify Fig. S5 to have a full picture of the cells (the images in Fig. 5 represent only a portion of the cell).

Discussion of the CeAJ mRNA localization mechanism is warranted. Do the authors speculate that the newly translated protein drives localization during translation, similar in concept to SRP-mediated localization to the ER, or ribosome association is a trigger to permit a secondary factor to drive mRNA localization, or another model?

Unfortunately, this is hard to say at the moment as we do not have any data regarding where translation actually occurs. We will add a conjecture to the Discussion.

Minor comments

Please complete the following sentence: "We identified transcripts enriched at the CeAJ in a stage- and cell type-specific."

Corrected.

It would be helpful to provide reference(s) for the protein localization summary in Table 1.

Added.

Figure 2B: Did dlg-1 and ajm-1 localize at similar ratios? Appropriate statistics comparing the different ratios may be informative.

We will modify the graph (following Reviewer #2’s suggestion) and add the requested details.

Figure 2: In the paragraph that begins, "Morphogenesis of the digestive track," the text should refer to Figure 2C? If not, the text requires further clarification.

Corrected.

Figure 2: Reporting the smFISH localizing ratios of 8E and 16E will be informative.

We will add the information.

Please include citations when summarizing the nonsense-mediated decay NMD mechanism and AJM-1 identifying the CeAJ.

Added.

The sentence, "Embryos from our second __Δ__ATG transgenic line displayed a little GFP protein and some dlg-1::gfp mRNA," should refer to Figure S4.

Added.

An immunoblot of this reporter versus wild type may be informative regarding the approximate position of putative alternative start codon.

We will perform a Western blot to verify the size of the protein product produced.

Figure 5: N's and repetitions performed should be included for localization experiments.

Yes, we will add them here and in all the other quantifications we will add to the manuscript.

Please clarify that the "the mechanism of UTR-independent targeting is unknown in any species" refers to dlg-1 mRNA localization.

Added.

"Our findings suggest..." discussion paragraph should reference Figure 6.

Added.

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Referee #2

Evidence, reproducibility and clarity

Summary:

Tocchini et al. screened apical junction and cell membrane proteins for mRNA localization. They identified multiple proteins that are translated from localized mRNAs. Of these, dlg-1 (Discs large) mRNA localizes to cell cortices of dorsal epithelial cells, endoderm cells, and epidermal (seam) cells and is dependent on active translation for transport. The manuscript dissects the contributions of different DLG-1 protein domains to mRNA localization.

A major strength of the paper is the way it assesses translational-dependence in a transcript-specific way without perturbing translation globally. The authors cleverly combine mutations in ATG start sites with a knock down of the non-sense mediated decay pathway. This allows Tocchini et al to examine whether dlg-1 mRNA depends on active translation for localization, which it does. The authors observe an interesting finding, that the domains required for protein localization can be separated from those required for mRNA localization. Namely, mRNA localization (but not protein localization) requires C-terminal domains of the protein.

My major points of concern focus on the presentation and interpretation of Figure 5. In this figure, the blocking approach used seems confounding, the observations described by the authors are not visible, the quantification is confusing, and the interpretations seem like an over-reach. The

Major comments:

• Figure 2 requires a negative (or uniformly distributed) mRNA control for comparison. Figure 2C should be quantified. The plot quality should be improved, and appropriate statistical tests should be employed to strengthen the claimed findings.

• Most claims of perinuclear mRNA localization are difficult to see and not well supported visually or statistically. The usage of DAPI markers, membrane markers, 3D rendering, or a quantified metric would bolster this claim. Also, sax-7 is claimed to be perinuclear and elsewhere claimed to be uniform then used as a uniform control. Please explain or resolve these discrepancies more clearly.

• The major concern about the paper is the data display and interpretation of Figure 5C. I'm not comfortable with the approach the authors took of blurring out the nucleus. A more faithful practice would be to use an automated mask over DAPI staining or to quantify the entirety of the cell. If the entirety of the cell were quantified, one could still focus analysis on specific regions of relevance. The interpretations distinguishing membrane versus cytoplasmic localization (or mislocalization) are hard to differentiate in these images especially since they are lacking a membrane marker. The ability to make these distinctions forms the basis of Tocchini et al's two pathways of dlg-1 mRNA localization. These interpretations also heavily rely on how the image was processed through the different Z-stacks, and it's not clear to me how that was done. For example, the diffusion of mRNA in figure 5F and 5I are indistinguishable to my eye but are claimed to be different.

• To my eye, it seems that Figure 5 could be more faithfully interpreted to state that DGL-1 protein localization depends on the L27-SH3 domains. The Huk/Guk domains are dispensable for DLG-1 protein localization; however, through other studies, we know they are important for viability. In contrast, dlg-1 mRNA localization requires all domains of the protein (L27-Guk). It is exceptionally interesting to find a mutant condition in which the mRNA and protein localizations are uncoupled. It would be very interesting to explore in the discussion or by other means what the purpose of localized translation may be. Because, in this instance, proper mRNA localization and protein function are closely associated, it may suggest that DLG-1 needs to be translated locally to function properly.

• The manuscript requires an improve materials & methods description of the quantification procedures and statistics employed.

Minor & Major comments together:

Text

• Summary statement: Is "adherent junction" supposed to be "adherens junction?"

• Abstract: Sentence 1, I think they should add a caveat word to this sentence. Something like "...phenomenon that can facilitate sub-cellular protein targeting." In most instances this isn't very well characterized or known.

• In the first paragraph, it might be good to mention that Moor et al also showed that mRNA localize to different regions to alter their level of translation (to concentrate them in high ribosome dense regions of the cell).

• There are some new studies of translation-dependent mRNA localization - that might be good to highlight - Li et al., Cell Reports (PMID: 33951426) 2021; Sepulveda et al., 2018 (PCM), Hirashima et al., 2018; Safieddine, et al 2021. Also, Hughes and Simmonds, 2019 reviews membrane associated mRNA localization in Drosophila. And a new review by Das et al (Nat Rev MCB) 2021 is also nice.

• Parker et al. did not show that the 3'UTR was dispensable for mRNA localization. They showed the 3'UTR was sufficient for mRNA localization.

• In the second paragraph, the sentence about bean stages is missing one closing parenthesis.

• Last paragraph: FISH is fluorescence, not fluorescent.

• Both "subcellular" and "sub-cellular" are used. Minor comments - Figures

• Figure 1

o Figure 1A is confusing. It's not totally clear what the rectangles and circles signify. There are many acronyms within the figure. Which of the cell types depicted in the figure are shown here? For example, for the dorsal cells, which is the apical v. basal side? o Some of the colors are difficult to distinguish, particularly when printed out or for red/green colorblind readers. Is erm-1 meant to be a cytoskeletal associated or a basolateral polarity factor? o The nomenclature for dlg-1 is inconsistent within "C". o Please specify what the "cr" is in "cr.dlg-1:-gfp" in the legend.

• Figure 2

o Can Figure 2C be quantified in a similar manner to 2A/2B? o 2B - please jitter the dots to better visualize them when they land on top of one another o Please include a negative control example, a transcript that is not peripherally localized for comparison. o There is no place in the text of the document where Fig 2C is referenced o I can't see any discernable ajm-1 localization in Fig 2A. o I can't see any dlg-1 pharangeal localization in Fig2C. o More details on how the quantification was performed would be welcome. Particularly, in 2B, what is the distance from the membrane in which transcripts were called as membrane-associated? What statistics were used to test differences between groups?

• Figure 3

o Totally optional but might be nice: can you make a better attempt to approximate the scale of the cartoon depiction? o The GFP as an asterisk illustration may be confusing for some readers. Could you add another rectangular box to depict the gfp coding sequence? o This microscopy is beautiful! o Were introns removed? Is the endogenous copy still present? o The wording in the legend "CRISPR or transgenic" may be confusing as Cas9 genome editing is still a form of transgenesis. o The authors state that the 5'-3'UTR construct produces perinuclear dlg-1 transcripts but in the absence of DAPI imaging, it's not clear that this is the case. o Which probeset was used? The gfp probe? o Here, sax-7 is used as a uniform control, but sax-7 is claimed in Fig S1B-D as being perinuclear. This is a bit confusing.

• Figure 4

o Excellent results! Really nice! o Fig 4A. The GFP depicted as a circle is strange. o Fig 4A. Can you include the gene/protein name for easy skimming? o Fig 4B. the color here is too faint and it is unclear what is being depicted. Overall, this part of the figure could be improved. o Were the introns removed?

• Figure 5

o Fig 5A. can you add the gene/protein name o Fig 5B. Can you you make the example apicobasal (non-apical) mRNA more distinctive? If it had its own peak in the lower trace, the reader would more clearly understand that this mRNA will be excluded from apical measurements whereas it will be included in apicobasal measurements. o D' - I' The grey font is too light. o D' - I' The inconsistent y-axis scaling makes it difficult to compare across these samples. Can you set them to the same maximum number? o D' - I' The x-axis labels are formatted incorrectly o The practice of masking out the nucleus appears to remove potentially important mRNAs that are not nuclear localized. This could really impact the findings and interpretation. Instead, consider an automated DAPI mask. o I can't see what the authors are calling membrane diffuse versus cytoplasmic. This is making it hard for me to see their "two step" pathway to localization. o "F" looks the same as "I" to me, but the authors claim they represent different patterns and use these differences as the basis for their claim that X. o Can more details of the quantification be included? How were Z-sections selected, chosen for inclusion? Which Z-sections and how many were selected? o Also, why do these measurements focus on what I think are the seam cells when Lockwood et al., 2008 show the entire epithelium that is much easier to see? o Please name these constructs to correlate the text more explicitly to the figures. o How many embryos were analyzed for each trace? How many embryos showed consistent patterns? o Why were these cells used for study here? Lockwood et al., 2008 use a larger field of epithelial cells for visualization.

• Figure 6

o There are major discrepancies between what this figure is depicting graphically and what is described in the text. Again, I'm not comfortable making the "two step" claims this figure purports given the data shared in Figure 5.

Minor comments - Tables & Supplemental Figures

Table 1

• I think this table could be improved to more clearly illustrate which mRNAs were tested and what their mRNA localization patterns were (for example, gene name identifiers included, etc). Could the information that is depicted by gray shading instead be added as its own column? For example, have a column for "Observed mRNA localization"

• Can you add distinct column names for the two columns that are labeled as "protein localization - group"

• Can you also add which of these components are part of ASI v. ASII (as described in the introduction? Supplemental Figure 1

• It is hard to see that some of these spots are perinuclear. More information (membrane marker, 3D rendering, improved metrics) is required to support this claim.

• What do these images look like over the entire embryo, not just in the zoomed in section?

• sax-7 localization in S4 looks similar but a different localization claim is made.

Supplemental Figure 2

• Before adherens junctions even exist dlg-1 go to the membrane - this is really neat! Supplemental Figure 3

• Technical question: If either 5 or 3 stack images are used, how does this work? Do they have different z-spacings? Or do they do 5-stack images represent a wider Z-space?

Supplemental Figure 4

• Line #2 retains translation and keeps mRNA localization.

• Totally optional, but consider showing both lines in the main figure to illustrate the two possibilities.

• Materials and methods - how did they created the ATG mutations? Is it an array? - why does one translate, and one doesn't?

Significance

The authors discover that dlg-1, ajm-1, and hmr-1 mRNAs (among others) are locally translated, and this represents an important conceptual advance in the field as these are well studied proteins and important markers. This is the first study to illustrate translation-dependent mRNA localization in C. elegans, to my knowledge. The mechanisms transporting these mRNAs and their associated translational complexes to the membrane may represent a new pathway of mRNA transport and is therefore significant. The authors identify domains within DLG-1 responsible which is a nice advance. If they are unable to order the events of association as they claim in Figure 5 (and that I dispute), this doesn't detract from the impact of the paper.

Other high-profile studies have recently been published that echo how mRNA localization to membranes can be observed for transcripts that encode membrane-associated proteins (Choaib et al., Dev Cell, 2020; Li et al., Cell Reports, 2021 (PMID: 33951426); and Reviewed in Hughes & Simmonds, Front Gen, 2019). These recent findings underscore the impact of Tocchini et al.'s paper. Similar studies have identified mRNAs localizing through translation dependent mechanisms to a variety of different regions of the cell (Sepulveda et al., eLife, 2018; Hirashima et al., Sci Reports, 2018; Safieddine, et al., Nat Comm, 2021; and reviewed in Ryder et al., JCB 2020). Given the timely nature of these findings and the recent interest in these concepts, a broad readership of readers should be interested in this paper.

My field of expertise is in mRNA localization imaging and quantification. I feel sufficiently qualified to evaluate the manuscript on all its merits.

I think that technology has developed to the point that it is both a tool and an environment. When I use it to write a paper, it is a tool, but it becomes an environment when using it to interact with my classmates. Things like search engines are more ambiguous. They are a tool in how they help me achieve the goal of finding what I am looking for, but they immerse me into the environment created by websites and documents. Things like google street view are, without a doubt, in my mind, a tool and environment. They both help me find the place I was looking for and immerse me into the environment and visually experience it.

I think that awareness is key for being able to teach within a context we as adults may be familiar with. In this particular experience there was almost a system of checks and balances to make sure the students hypotheses and ideas stayed at the forefront of their research.

Author Response:

Reviewer #1:

By sequencing a large number of SARS-CoV-2 samples in duplicate and to high depth, the authors provide a detailed picture of the mutational processes that shape within-host diversity and go on to generate diversity at the global level.

1) Please add a description of the sequencing methods and how exactly the samples were replicated (two swaps? two RNA extractions? two RT-PCRs?). Have any limiting dilutions been done to quantify the relationship between RNA template input and CT values? Also, the read mapping/assembly pipeline needs to be described.

Limiting dilutions were not performed however the association between Ct and discordance between replicates was explored. Samples with Ct>=24 were found to have considerable discordance between replicates, likely resulting from a low number of input RNA molecules. This is described in the first section of the results and illustrated in Figure 1 - figure supplement 3.

We have now added additional sections to the methods to better describe the sequencing and mapping pipelines.

Sequencing: A single swab was taken for each sample. Two libraries were then generated from two aliquots of each sample with separate reverse transcription (RT), PCR amplification and library preparation steps in order to evaluate the quality and reproducibility of within-host variant calls. The ARTIC protocol v3 was used for library preparation (a full description of the protocol used available at dx.doi.org/10.17504/protocols.io.be3wjgpe).

Alignment and variant calling: Alignment was performed using the ARTIC Illumina nextflow pipeline available from https://github.com/connor-lab/ncov2019-artic-nf...

2) I find the way variants are reported rather unintuitive. Within-host variation is best characterized as minor variants relative to consensus (or first sample consensus when there are multiple samples). Reporting "Major Variants" along with minor variants conflates mutations accumulated prior to infection with diversity that arose within the host. The relative contributions of these two categories to the graphs in Fig 1 would for example be very different if this study was repeated now. Furthermore, it is unclear whether variants at 90% are reversions at 10% or within-host mutations at 90%. I'd suggest calling variants relative to the sample or patient consensus rather than relative to the reference sequence (as is the norm in most within-host sequencing studies of RNA viruses).

We are grateful for this comment and have tried to improve and clarify the reporting of variants to align with previous literature.

Our original classification intended to classify non-reference sites as fixed changes (VAF>95%) or within-host variants (which we called “minor variants”). While we chose 95% as a cutoff (which may have been confusing), the results are analogous with a 99% cutoff, as variants in this set essentially have VAF~100%, and nearly all are expected to have occurred in a previous host. Thus, the previous classification intended to cleanly separate inter-host (fixed) mutations from within-host mutations, to compare their patterns of selection and their mutation spectra.

Following the reviewer’s request, we have modified this classification to better align with other studies of RNA viruses by defining the majority allele at a site as the “consensus”. We note that the results remain largely similar, since the vast majority of within-host variants identified had a low VAFs (<<50%) with the majority/consensus allele most often corresponding to the reference (Wuhan) base.

When considering recurrent mutations we now discuss the number of times variants are observed at each location within a sample. This avoids the issue of how variants are polarised.

3) It is often unclear how numbers reported in the manuscript depend on various thresholds and parameters of the analysis pipeline. On page 2, for example, the median allele frequency will depend critically on the threshold used to call a variant, while the mean will depend on how variation is polarized. Why not report the mean of p(1-p) and show a cumulative histogram of iSNV frequencies on a log-log scale including. I think most of these analyses should be done without strict lower cut-offs or at least be done as a function of a cut-off. In contrast to analyses of cancer and bacteria, the mutation rates of the virus are on the same order of magnitude as errors introduced by RT-PCR and sequencing. Whether biological or technical variation dominates can be assessed straightforwardly, for example by plotting diversity at 1st, 2nd, and 3rd codon position as a function of the frequency threshold. See for example here:

https://academic.oup.com/view-large/figure/134188362/vez007f3.tif [academic.oup.com]

There are more sophisticated ways of doing this, but simpler is better in my mind.

It would be good to explore how estimates of the mean number of mutations per genome (0.72) depend on the cut-offs used. A more robust estimate might be 2\sum_i p_i(1-p_i) (where p_i is the iSNV frequency at site i) as a measure of the expected number of differences between two randomly chosen genomes. Ideally, the results of viral RNA produced of a plasmid would be subtracted from this.

The reviewer raises a number of important points that we have tried to address and clarify.

We think that the quality of our variant calls is supported by several lines of evidence, including: (1) the use of the ShearwaterML calling algorithm, which uses a base-specific overdispersed error model and calls mutations only when read support is statistically above background noise in other genomes, (2) we use two independent replicates from the RT step, (3) we provide several biological signals that cannot be expected to arise from errors, including the fact that the mutation spectra of low VAF iSNVs called in our study recapitulate that of consensus mutations and the clear signal of negative selection acting on iSNVs. We note that this dN/dS analysis is closely related to the suggestion by the reviewer of comparing the frequency of mutations at positions 1/2/3 of a codon.

To address this comment in the manuscript, we have amended the text to include these arguments and we provide two new supplementary figures: (1) a figure of the frequency of mutations at the three codon positions, as requested by the reviewer, and (2) the mutation spectra of low VAF iSNVs, demonstrating the quality of the mutation calls. Similar to the finding in Dyrak et al., (2019), and as expected from the dN/dS ratios, the distribution of variant sites is dominated by variants at the third position and not equally distributed as one might expect if errors were dominating the signal.

We have amended the relevant section of the text to read:

“To reliably detect within-host variants with the ARTIC protocol, we used ShearwaterML, an algorithm designed to detect variants at low allele frequencies. ShearwaterML uses a base-specific overdispersed error model and calls mutations only when read support is statistically above background noise in other genomes \cite{Gerstung2014-av,Martincorena2015-ef} (Methods). Two samples were excluded, as they had an unusually high number of low frequency variants unlikely to be of biological origin, leaving 1,179 samples for analysis, comprising 1,121 infected individuals of whom 49 had multiple samples. For all analyses we used only within-host variants that were statistically supported by both replicates (q-value<0.05 in at least one replicate and p-value<0.01 in the other, Methods). Within each sample, we classified variant calls as `consensus' if they were present in the majority of reads aligned to a position in the reference or as within-host variants otherwise. The allele frequency for each variant was taken as the frequency of the variant in the combined set of reads for both replicates.”

...

“The use of replicates and a base-specific statistical error model for calling within-host diversity reduces the risk of erroneous calls at low allele frequencies. We noticed a slight increase in the number of within-host diversity calls for samples with high Ct values, which may be caused by a small number of errors or by the amplification of rare alleles and that could inflate within-host diversity estimates (Figure 1 - figure supplement 3) \cite{McCrone2016-se}. However, the overall quality of the within-host mutation calls is supported by a number of biological signals. As described in the following sections, this includes the fact that the mutational spectrum of within-host mutations closely resembles that of consensus mutations and inter-host differences and the observation of a clear signal of negative selection from within-host mutations, as demonstrated by dN/dS and by an enrichment of within-host mutations at third codon positions \cite{Dyrdak2019-xk} (Figure 1 - figure supplement 4).”

Whilst we believe the remaining variant calls are reliable we acknowledge that how variants are polarised could impact some of the summary statistics reported. To help improve this we have amended Figure 1 to include a cumulative histogram of within-host variant frequencies on a log-log scale as suggested by the reviewer. We have also included estimates of the mean value of sqrt(p(1-p)) (indicating an estimate of the standard deviation of within-host variants assuming a Bernoulli distribution). We have also replaced the estimates of the mean number of mutations per genome with the expected number of differences between two randomly chosen genomes. The amended Figure 1C now displays a histogram of the expected number of differences between two genomes for each sample rather than the mean number of mutations.

4) This paper provides an important baseline characterization of within-host diversity, while the patterns themselves are not extremely surprising. It is thus important that the data are provided in a form that facilitates reuse. It would be helpful to provide intermediate analysis results in addition to the raw reads in the SRA and the shearwater calls. I would like to see simple csv tables with the number of times A,C,G,U,- was observed at every position in the genomes for every sample. This would greatly facilitate the reuse of the data.

We have now added raw count tables for each sample and each replicate to the GitHub repository. We have also archived this data using Zenodo to ensure it remains easily accessible.

Reviewer #2:

The paper by Tonkin-Hill and colleagues describes the analysis of intra-host variation across a large number of SARS-CoV-2 samples. The authors invested a lot of effort in replicate sequencing, allowing them to focus on more reliable data. They obtained several important insights regarding patterns of mutation and selection in this virus. Overall, this is an excellent paper that adds much novelty to our understanding of intra-host variation that develops during the time course of infection, its impact on transmission, and what we can or cannot learn on relationships between samples.

We are grateful to the reviewer for their positive comments.

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Reply to the reviewers

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Reply to the Reviewers

We thank the Referees for their evaluation and their useful comments.

---

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

The MS from Bonaventure and colleagues used a CRISPR to identify novel IFN-induced antiviral effectors targeting HIV-1. One hit, the DEAD Box helicase DDX42, while not itself part of the IFN response, exerts a substantial inhibitory effect on HIV-1 replication when over expressed, and gives a several fold boost to viral replication when knocked down in cells. The effect of DDX42 KO or O/E is manifest at reverse transcription and PLA analysis suggests and interaction with incoming virions. Moreover, DDX42 appears to exert an inhibitory effect generally against retroviruses and retroelements, with evidence that it associates with viral/transposon RNA. The authors further show that DDX42 has antiviral against a range (but not all) RNA viruses, with very striking phenotypes seen especially with Zika, CHIKV and SARS CoV2, with DDX42 associating with dsRNA in infected cells. These data suggest DDX42 is a constitutively expressed a broad-spectrum inhibitor of a range of mammalian RNA viruses. The manuscript is very well written, the data is of good quality and clearly DDX42 is having a general effect on viral replication. The results are novel, important and potentially of wide interest. Where the MS is somewhat lacking is understanding whether DDX42 has direct antiviral activity or is globally affecting cellular RNA metabolism. Some important areas for the authors to consider are:

• DDX42 has a potential role in splicing and/or RNA metabolism so I think it would be important to see whether there is any clear global change in gene expression in knockout or knockdown cells cells vs control that might be suggestive of a generalized effect.

Responses

We thank the reviewer for this important question. Indeed, DDX42 didn’t impact the replication of 2 negative strand RNA viruses and this suggested that DDX42 didn’t have a global impact on the target cells, but we could not formally exclude a generalized effect. Therefore, we have performed RNA-seq analysis in order to evaluate the impact of DDX42 depletion (using 3 different siRNAs targeting DDX42 in comparison to a CTRL siRNA in U87-MG cells, and 2 different siRNA in comparison to a CTRL siRNA in A549-ACE2 cells, in samples obtained in 3 independent silencing experiments). The RNA-seq data (See Supplemental File 1 and Figure S5) showed that only 63 genes are commonly differentially expressed by the 3 siRNAs targeting DDX42 in U87-MG cells and only 23 of these genes were also found differentially expressed in A549-ACE2 cells depleted for DDX42. Importantly, the identity of these genes could not explain the observed antiviral phenotypes. These data are in favor of the absence of generalized effect on the target cells, which could have explained the antiviral phenotypes of the sensitive viruses.

• The HIV experiments in primary cells are only one round at present. Does the DDX42 knockdown enhance viral replication in multiround? Does it lead to more viral PAMPs for PRRs to induce IFN?

Responses

We agree with the reviewer that it would have been very informative to measure the impact of DDX42 knockdown in multiround infections in primary T cells. However, we tried several times to do this experiment (with primary T cells from several donors) and we were not successful: indeed, DDX42 KO appeared to slow down cell division, which could be taken into account for a short, one-cycle experiment (i.e. 24 h) 3 days post-Cas9/sgRNA electroporation by adjusting the number of cells at the time of infection. However, DDX42 KO appeared quite toxic in longer experiments, with cells stopping to grow.

The question regarding the generation of more viral PAMPs for PRRs to induce IFN is also very interesting. We know from published work (including ours) that primary T cells don’t normally produce IFN following HIV-1 infection (see for instance Bauby and Ward et al, mBio 2021). However, one can indeed hypothesize that as more viral DNAs are produced in the absence of DDX42, perhaps the primary T cells could detect them and produce IFN. To address this question in primary T cells, we would have needed to be able to perform multiround infections, which was not possible, as mentioned above. Moreover, we could not test this hypothesis in the cell lines that we used, such as U87-MG/CD4/CXCR4 cells, as they are unable to produce IFN following HIV-1 infection.

• More could be made mechanistically of the lack of sensitivity of Flu and VSV to DDX42. In particular showing whether or not DDX42 interacts with the RNA of the insensitive virus, or whether DDX42/virus or dsRNA interactions by PLA occur with Flu would highlight the relevance of these observations to the antiviral mechanism.

Responses

This is an excellent remark. We have now performed RNA immunoprecipitation experiments using 2 viruses targeted by DDX42 (CHIKV and SARS-CoV-2) and 1 virus that is insensitive to DDX42 (IAV) (See New Figure 4J-L): whereas CHIKV and SARS-CoV-2 RNAs could be specifically pulled-down with DDX42 immunoprecipitation, this was not the case for IAV RNA. This strongly argues for a direct mechanism of action of DDX42 helicase on viral RNAs.

Reviewer #1 (Significance (Required)):

---

__ The role of helicases in host defence are of wide interest and importance. This has the potential to be a very important study that deserves a wide audience. However in my opinion it needs some further mechanistic insight along the lines I have suggested.

Responses

As mentioned above, we have now added important data: First, DDX42 is able to interact with RNAs from targeted viruses (and not from an insensitive virus); Second, we have checked that DDX42 didn’t have a substantial impact on the cell transcriptome. Taken together, these data are clearly in favour of a direct mode of action of DDX42.

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

In this brief report, the authors use a CRISPR screening approach to identify cellular proteins that limit HIV infection. The screen itself is elegantly designed and most of the top hits are components of the interferon signaling pathway that would be expected to emerge from such a screen, thus providing confidence in the results. The authors followed up on DDX42 as a new hit identified in their screen and confirmed that targeting DDX42 with distinct guide RNAs resulted in increased HIV infection in at least 3 cell lines. Conversely, DDX42 overexpression inhibited infection. They also confirmed a role for DDX42 in inhibiting HIV infection in primary macrophages and CD4 T cells using siRNA and CRISPR KO strategies, respectively. They also demonstrate that DDX42 inhibits several other divergent lentiviruses as well as Chikungunya virus and SARS-CoV-2, but not influenza virus. These data convincingly show that DDX42 plays a role in inhibiting many lentivirus and positive sense RNA virus infections. Using PCR assays for reverse transcription products they conclude that DDX42 inhibits an early process in the HIV life cycle occurring after virus entry, though the statistical significance of these differences is not clear. They further use proximity ligation assays to suggest that DDX42 is in proximity to HIV-1 and SARS-CoV-2 replication complexes. Mechanistically, these data are largely unsatisfying as they do not provide specific insight into how DDX42 so broadly inhibits virus replication. Overall, the manuscript presents a significant advance, it also has some weaknesses as listed below.

1. Statistical analysis is not included in any of the figures.

Response

Statistical analyses have now been included.

Many of the figure legends do not state how many independent biological replicates the figures are based on.

Response

The number of biological replicates for each panel is stated at the very end of each figure legend.

Detailed mechanistic understanding of DDX42 effects on virus replication is not provided by the manuscript.

Response

As mentioned in response to Reviewer 1, we have now added data showing that DDX42 could interact with RNAs from targeted viruses but not from an insensitive virus, arguing for a direct antiviral mode of action of this Dead-Box helicase.

Reviewer #2 (Significance (Required)):

DDX42 is a new antiviral protein identified and confirmed in this manuscript. It was also identified as one of many hits in a genome wide CRISPR screen for cellular proteins that regulate SARS-CoV-2 infections, but was not followed up. Thus, the identification and confirmation of DDX42 antiviral activity is highly significant for both the HIV and SARS-CoV-2 fields. This high significance may compensate to some extent for the lack of mechanistic insight contained in this initial report.

**Referees Cross-commenting**

I find the comments of the other reviewers to be fair and reasonable, and I concur that the work is overall important and novel. It seems that reviewers generally agreed that some additional mechanistic insights would be desirable for publication in a high impact journal. Reviewer 1 makes some good suggestions in this regard. As for mouse experiments, I would reserve these for a follow up manuscript.

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

---

__In this manuscript, Bonaventure et al report the results of a screen to identify cellular inhibitors of HIV-1 infection in IF treated cells. They identify DDX42 as such a factor though, unexpectedly, DDX42 did not turn out to be an ISG. Strikingly, DDX42 turns out to inhibit a wide range of retroviruses as well as retrotransposons and + sense, but not - sense, RNA viruses among which SARS-CoV2 turns out to be especially sensitive to DDX42, with siRNAs specific for SARS-CoV2 DDX42 increasing viral RNA expression by a startling 3 orders of magnitude, compared to only an 2-5 fold positive effect with HIV-1.

Response

We agree with the reviewer that DDX42’s impact on HIV-1 may appear as somewhat modest, however, it is highly reproducible across cell lines and primary cells and, more importantly, it is observed upon depletion of the endogenous protein (either by KO or silencing) in target cells that are highly permissive to viral replication, such as activated primary CD4+ T cells. We therefore believe that these findings, combined with the findings that other positive-strand RNA viruses are targeted, are of high interest.

Reviewer #3 (Significance (Required)):

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__I found this paper generally convincing and technically sound though the emphasis was odd and clearly driven more by the history of how this work was done than by the actual results obtained. Specifically, the emphasis is on HIV-1 yet the most interesting data are the dramatic effects seen with Chikungunya and SARS2. If I was writing this paper, I would delete figure 4 and focus this paper entirely on retroviruses and retrotransposons. In that form, I think it would be competitive at PLoS Pathogens or perhaps EMBO Journal. The RNA virus work shown in figure 4 could then be figure 1 of a new, high impact, paper looking at the mechanism of action of DDX42 as an inhibitor of + sense, but not - sense, viral gene expression. Though Wei et al do mention DDX42 in their SARS-CoV2 screening paper this is certainly not a major theme of that paper so I don't think that would be a problem.

Responses

We thank the reviewer for this comment. We had hesitated to present the manuscript as suggested by the reviewer (i.e. focusing only on HIV-1, retroviruses and retroelements) and prepare a second manuscript with the remaining data. We’ve finally decided against it, as we believe that showing a broad antiviral effect of DDX42 on +strand RNA viruses increases the impact of our findings.

On another note, a conditional DDX42 KO mouse has been generated by the Wellcome trust Sanger institute and it would greatly improve this manuscript if they could show an in vivo a result similar to figure 3F using MLV.

Responses

We thank the reviewer for this information. We completely agree that in vivo work would be a massive plus and we will be planning to explore this in the future, but not at this stage as it would require specific funding and resources.

Author Response:

Reviewer #1:

This study focuses on how the vmPFC supports delay discounting. The authors tested patients with vmPFC lesions (N=12) and healthy controls (N=41) on a delay discounting (DD) task with two additional conditions: (1) reward magnitude and (2) cues that should evoke episodic future thinking (EFT).

The authors replicate their previous finding that patients with vmPFC lesions show steeper DD, and report two novel findings: (1) DD in patients is insensitive to reward magnitude, suggesting that vmPFC is critical for reward magnitude to modulate DD; (2) vmPFC patients show normal effects of EFT cues on DD, such that all subjects discounted less in the presence of cues that promote episodic future thinking. These findings have important implications for how vmPFC contributes to delay discounting, as they suggest that vmPFC is not necessary for prospective thinking to affect the evaluation of future rewards.

1) A potential issue with the EFT finding is that it rests on accepting the null hypothesis of no group differences. However, there are reasons to assume this is not a trivial null result due to a lack of statistical power. Specifically, there is a significant effect of EFT within the vmPFC patient group and there is a significant group difference for the effect of reward magnitude. Assuming comparable power to detect effects of EFT and reward magnitude, it seems unlikely that the non-significant EFT effect is simply a lack of power. In any case, this caveat has to be considered when interpreting the effect.

We have added a discussion of this caveat on p. 10, which reads: “Before discussing this finding further, we note that it rests on accepting the null hypothesis of no group differences in the EFT effect on DD between vmPFC patients and controls. It is unlikely, however, that this null finding simply reflects a lack of statistical power, for example due to a small sample size. First, the null effect on group differences indeed reflects a significant within-participant effect, with greater regard for future amounts in the EFT compared to the Standard condition in vmPFC patients. Second, together with the preservation of the EFT effect, we found a significant reduction of the magnitude effect in the same vmPFC patient sample. Bayesian analyses confirmed greater evidence in favour of the null compared to the alternative hypothesis regarding group differences in the EFT effect on DD.”

2) It is somewhat surprising that the authors had such a strong prediction about the absence of group differences for the EFT effect. Based on previous work (Bertossi et al., 2016a, b), one could expect a smaller EFT effect in the VMPFC group. The authors appear to put much weight on the results by Ghosh et al. 2014, which suggest that vmPFC is critical for schema reinstatement. The rationale for this strong prediction is not very clear from the introduction.

We have now reframed our hypotheses as suggested by the reviewers and the editors. In the Introduction, we now make only the hypothesis of a reduced EFT effect on DD in vmPFC patients, which is based on previous evidence of an EFT impairment in vmPFC patients. We present the hypothesis that vmPFC is critical for schema instantiation only in the Discussion, as an explanation of the null finding on group differences on the EFT effect.

Thus, p. 5 now reads: “Concerning prospection, previous studies have observed an EFT effect on DD, such that people discount future rewards less steeply if cued to imagine personal future events during intertemporal choice (Peters and Büchel, 2010; Benoit et al., 2011). Considering that vmPFC is implicated in prospection (Schacter et al., 2012) and that vmPFC patients are impaired in EFT (Bertossi et al., 2016a,b; Bertossi et al., 2017), vmPFC patients' DD should remain steep even when EFT cues are provided, because patients may nevertheless fail to construct the vivid future events that might be needed to counteract DD. Thus, we predict a reduced EFT effect on DD in vmPFC patients compared to healthy controls.”

Reviewer #2:

Ciaramelli et al. address a timely and theoretically important issue with respect to the functional role of the vmPFC in decision-making more generally, and temporal discounting in particular. Strong points of the paper include 1) a theoretically important research question and 2) much-needed lesion data on two important behavioral effects in temporal discounting: the magnitude effect, and a modulation of discounting via episodic future thinking. Weaker points of the paper include 1) lack of clarity for a number of methodological issues (group comparisons & control group for the AI data, inconsistency analysis) and 2) many remaining open questions with respect to how vmPFC patients might have utilized the EFT cues, and whether different processes were at work compared to controls.

We thank the reviewer for this positive evaluation of the paper and address the reviewer’s comments below.

Major points:

1) The authors note that their interpretation of the preserved EFT effects in the vmPFC patients in terms of e.g. semantic processing remains speculative, but is supported by the finding of intact external details production following vmPFC damage in earlier studies. But was this also the case in the present data set? This remains unclear, because for the AI data, only z-scores relative to some earlier control group (Kwan et al. 2015) are reported (Table 1 and Supplement p. 30). Was this control group matched to the patients? And since the referenced Kwan et al. (2015) paper reports only on six patients (presumably the patients from the Canada site?) - what about the patients from the Italian site, which control group were their AI data compared to?

The Crovitz data of the Canadian patients are unpublished (the Kwan et al., 2015 paper is not about vmPFC patients, but about 6 MTL patients). We compared them to a sample of 18 age-matched healthy controls, a subset of those included in Kwan et al. (2015). The 4 Italian patients were part of the vmPFC sample tested on EFT (and episodic memory) in Bertossi et al. (2016). We compared their performance with that of the 11 healthy controls from the same study who were age-matched to the patients.

This is clarified on p. 17, which reads: “The results of the Italian patients (a subset of those included in Bertossi et al. 2016b) were contrasted with those of the 11 healthy controls from the same study (all males; Bertossi et al., 2016b) who were age-matched to the patients (vmPFC patients: M = 47.75, SD = 5.25; healthy controls: M = 41.63, SD = 11.89, t13 = -0.97, p = 0.34). The results of the Canadian patients (unpublished) were contrasted with those of 18 healthy controls (10 males; a subset of those included in Kwan et al., 2015) age-matched to the patients (vmPFC patients: M = 61.00, SD = 9.83; healthy controls: M = 67.94, SD = 13.57, t22 = 1.15, p = 0.26).”

2) Directly related to my previous point: The methods section states that external details were in the normal range in the vmPFC group (mean z-score for EFT = -.73) but from Table 1 we can see that 8/10 patients in fact exhibit a negative z-score. This suggests that a direct group comparison of the external details scores would very likely reveal a significant group difference. Generally, it would help to report to actual control data here, not just the z-scores, and report the respective group comparisons.

We now report the Crovitz data in Table 2 and have run two ANOVAs on internal and external details separately in vmPFC patients and controls tested in Italy and in Canada. As the two ANOVAs show, we confirm that both patient groups produced fewer internal (episodic) details but a similar number of external details during EFT (as well as episodic remembering) than healthy controls. Therefore, the previously reported EFT problems for internal (but not external) details in vmPFC patients also apply to the patients tested here.

P. 17 now reads: “As for the Italian sample, an ANOVA on the details produced with Group (vmPFC patients, healthy controls), Time (Past, Future), and Detail (internal, external) as factors showed a significant effect of Time (F1,13 = 14.66, p = 0.002, partial η2 = 0.53), such that all participants produced more details for past than future events (18.19 vs. 15.37). There were also significant effects of Group (F1,13 = 6.16, p = 0.02, partial η2 = 0.32) and Detail (F1,13 = 9.14, p = 0.009, partial η2 = 0.41), qualified by a Group x Detail interaction (F1,13 = 8.99, p = 0.01, partial η2 = 0.40). Post hoc Fisher tests showed that vmPFC patients produced fewer internal details (11.45 vs. 25.51; p = 0.004) but a similar number of external details than controls (11.39 vs. 11.96; p = 0.89). No other effect was significant (p > 0.31 in all cases). The same ANOVA on the Canadian sample revealed an effect of Group (F1,22 = 17.76, p = 0.0003, partial η2 =20.44), qualified by a significant Group x Detail interaction (F1,22 = 4.72, p = 0.04, partial η = 0.18), again indicating that vmPFC patients produced fewer internal details (10.63 vs. 31.78; p = 0.0003) but a similar number of external details than controls (16.79 vs. 25.65; p = 0.09). No other effect was significant (p > 0.32 in all cases).”

3) The description of the inconsistency analysis was somewhat unclear. The authors use the procedure suggested by Johnson & Bickel (2008), which makes sense, given the overall analytical approach that focuses on the analysis of indifference points. However, this procedure is based on a comparison of adjacent indifference points. In contrast, the authors are referring to the number of inconsistent choices - this is either a typo, or a different procedure. I think the former, because the reported absolute numbers (e.g. means around 1) and the single subject plots in the supplement appear to reflect the number of inconsistent ID points rather than choices. If this is the case, I disagree with the statement that the "mean number of inconsistent choices was very low" (p. 10) - as this probably reflects the mean number of inconsistent indifference points and not choices, about 1 out of 6 ID points was inconsistent in the vmPFC group, which is a lot.

We apologize for lack of clarity. Yes, we are referring to indifference points (as in our previous study; Sellitto et al., 2010), not single choices. Inconsistent preferences are defined as “data points in which the subjective value of a future outcome (amount = R) at a given delay (R2) was greater than that at the preceding delay (R1) by more than 10% of the amount of the future outcome (i.e., R2 > R1 + R/10, as in Sellitto et al., 2010).” To avoid confusion, we have now corrected the expression ‘inconsistent choice’ to ‘inconsistent preference’ throughout the paper, and have eliminated the claim about the low number of inconsistent choices in vmPFC patients.

4) The EFT cues are suggested to help vmPFC patients to "circumvent their initiation problems" (p. 12) but I am not sure I follow this logic. First, the AI procedure typically entails external cues as well, and here vmPFC patients showed impairments (Table 1, but see my point 1 above). Second, some of the cited papers (e.g. Verfaellie et al., 2019) also used specific event cues, and still observed reduced internal details production in vmPFC patients.

The AI (Crovitz) procedure uses external cues but typically these are words that are not particularly meaningful to the participants (indeed, they are the same for all participants). e.g., Imagine attending a Fourth of July cookout a few years from now; Verfaellie et al., 2019) but, again, these cues are the same for all participants. We used personalized cues, which were events that participants (1) had selected themselves, and (2) had already planned or found them plausible in their future, and therefore presumably were the most self-relevant and familiar to the participants, including patients. We think that these events may have been effective in activating self- and event- relevant schemata. We clarify this point on p. 11, which reads: “We propose, therefore, that subject-specific event cues, which were self-relevant and familiar to the participants because they had been selected by participants themselves, and were already planned or were plausible in their future, acted as external triggers of self- and situation-relevant schemata, helping to circumvent vmPFC patients’ EFT initiation problems. Their intact MTLs allowed them to construct episodic future events, which were then integrated into intertemporal choice, reducing DD.” As we note on p. 14, indeed, vmPFC patients are capable of imagining detailed experiences if they are guided to choose for themselves a specific moment from an extended future event to narrate in detail (Kurczek et al., 2015). Of course, we agree with the Reviewer’s point below that this interpretation is speculative at this point.

5) One shortcoming with the paper is that no data are available that could inform how vmPFC patients might have utilized the EFT cues, and whether the processes at work might have differed from those in controls. Many points mentioned in the discussion (self-referential processing, semantic processing, activation of schemata, self-initiation vs. external cueing etc.) thus necessarily remain conjecture.

We agree with the Reviewer, and we admit in several parts of the Discussion that this interpretation is speculative at this point. However, the interpretation that we offer seems the most plausible to us at this time, considering what we know about the role of the vmPFC (vs. the MTL) in event construction and the absence of the EFT effect on DD in MTL patients. We also propose an alternative interpretation, but the pattern of findings on the EFT effect on DD makes it less likely to us. On p. 12, we state, “An alternative interpretation of the DD modulation is that EFT cues simply shifted attention towards the future, or conferred a positive valence to it, as we encouraged positively valenced EFT. If so, however, one should consistently observe an EFT-induced benefit on DD also in patients with MTL lesions, but this is not the case (Kwan et al., 2015; Palombo et al., 2015).”

Reviewer #3:

In this manuscript, Ciaramelli et al. examined the decision-making behavior of 12 patients with vmPFC damage in a delay discounting task. The authors carried out two manipulations in this task: 1. They presented participants with small and large offers for both the immediate and delayed reward (magnitude manipulation), 2. They prefaced decisions with a cue prompting participants to vividly imagine an event in their future that was expected to occur at the same delay as the proposed larger offer (episodic future thinking (EFT) manipulation). Compared to age and education matched healthy controls, patients with vmPFC damage showed steeper discounting of delayed rewards, particularly when the amounts offered were large (reduced effect of magnitude). However, like controls, vmPFC damaged patients displayed shallower discounting of delayed rewards following the EFT manipulation.

The manuscript is clear and concise in its presentation of the results, while still providing a detailed description of the behavior of these patients. This paper is also a good example of how pooling participants from multiple institutions can increase statistical power in a study of patients with focal brain damage targeting a fairly specific cognitive question. The positive results of the study mostly replicate previous findings. While the null result for the EFT manipulation is novel, the finding is hard to interpret. The authors state that they predicted that the EFT manipulation would not change discounting behavior in vmPFC damaged patients a priori despite the deficits of these patients in EFT in previous papers, which are also replicated here. However, I do not know why the authors would design their task in such a way to test for a null result. It is also not clear if this null result is observed for the reason proposed by the authors (that the EFT cues externally activate this process), or if this result is null for some other reason that is not accounted for here. As the authors do not provide a direct test for their hypothesized rationale for predicting this null result, the findings are hard to interpret.

We agree with the reviewer’s and editor’s point that this paradigm does not allow testing whether subject-specific, personally relevant cues, such as those we used, are indeed effective in externally initiating EFT in vmPFC patients. Therefore, we concur that, for the sake of clarity, this is best presented only as speculative discussion of the preserved EFT effect on DD in vmPFC patients. In the Introduction, therefore, we now formulate only the hypothesis based on previous evidence of impaired EFT in vmPFC patients (e.g., Bertossi et al., 2016a,b, Verfaellie et al., 2019), which would lead to the prediction of a reduced EFT effect in vmPFC patients. We present the hypothesis that vmPFC is critical for schema instantiation only in the Discussion, as an explanation of the null finding on group differences on the EFT effect.

P. 5 now reads: “Concerning prospection, previous studies have observed an EFT effect on DD, such that people discount future rewards less steeply if cued to imagine personal future events during intertemporal choice (Peters and Büchel, 2010; Benoit et al., 2011). Considering that vmPFC is implicated in prospection (Schacter et al., 2012) and that vmPFC patients are impaired in EFT (Bertossi et al., 2016a,b; Bertossi et al., 2017), vmPFC patients' DD should remain steep even when EFT cues are provided, because patients may nevertheless fail to construct the vivid future events that might be needed to counteract DD. Thus, we predict a reduced EFT effect on DD in vmPFC patients compared to healthy controls.”

Overall, this manuscript makes a relatively modest contribution to our knowledge about the function of vmPFC during inter-temporal choice. It bolsters previous claims about how vmPFC damage impacts delay discounting and EFT, while not revealing new information about how vmPFC specifically contributes to the processes involved in these behaviors and why damage to this region impacts intertemporal choice in this way.

We concur with the reviewer that our findings confirm previous evidence that vmPFC is necessary for balanced DD and for EFT. However, we think that our finding of a complete abolishment of the magnitude effect together with a complete preservation of the EFT effect on DD in vmPFC patients configures a remarkable theoretical advancement on the role of vmPFC in intertemporal choice. Indeed, it shows that during intertemporal choice vmPFC is more prominently implicated in reward valuation than in prospection. This finding is important for current theories of intertemporal choice, and is surprising considering previous demonstrations of impaired EFT in vmPFC patients (a finding that was replicated in the current study), and therefore has important implications also for theories relating to the role of vmPFC in EFT. Finally, we note that the paper focuses on one important facet of impulsivity following damage to the vmPFC in humans: steep DD. Our findings, therefore, may inform the clinical management of impulsivity in patients with vmPFC damage or dysfunction, delineating the contextual manipulations that are or are not expected to push the reach of patients' choice into the future.

Author Response:

Reviewer #1:

This manuscript shows cell to cell variability in the relative levels of Sox2 and Brachyury (Bra) expression by individual cells within the region of the epiblast containing axial progenitors (the progenitor zone, PZ). Accordingly, some cells express high Bra and low Sox2 levels, others high Sox2 and low Bra and a third group expressing equivalent levels of both transcription factors. They then show that by experimentally promoting high Sox2 expression cells enter neural tube (NT) fates, whereas high Bra brings cells in the progenitor zone to enter the presomitic mesoderm (PSM). The authors then complement these experiments with evaluation of cell movements within the PZ, NT and PSM to show that cells in the NT are much less motile than those in the PZ and PSM. These data led the authors to propose a fundamental role for Sox2/Bra heterogeneity to maintain a pool of resident progenitors and that it is the high cell motility promoted by high Bra levels what pushes cells to join the PSM, whereas high Sox2 levels inhibit cell movement forcing cells to take NT fates. To validate their hypothesis, the authors generated a mathematical model to show that those expression and motility characteristics can indeed lead to axial extension generating NT and PSM derivatives in the proper positions, while keeping a PZ at the posterior end.

Some specific comments on the manuscript are specified below.

1) Although the description of cells within the PZ containing different Sox2 and Bra expression ratios is more explicit and quantitative in the present manuscript, this has already been previously reported by different methods including immunofluorescence (e.g., Wymeersch et al, 2016). Similarly, that breaking the Sox2/Bra balance towards high Sox2 or Bra is an essential step to bring the progenitors towards NT or PSM fates has also been previously shown in different ways. These observations are, therefore, not totally new. The novel contribution of this paper is the authors' interpretation that "heterogeneity among a population of progenitor cells is fundamental to maintain a pool of resident progenitors". In this work, however, this conclusion is only supported by their mathematical simulation, as the experiments described in this manuscript are not aimed at homogenizing Sox2/Bra expression levels in the progenitor cells (meaning keeping the double positive feature) but, instead, forcing the progenitors to express Sox2 or Bra alone, which permits evaluation of differentiation routes rather than how to maintain the resident progenitor pool. Interestingly, their alternative mathematical model in which the relative Sox2/Bra levels follow an anterior-posterior gradient (which is actually a feature observed in the embryo) was also successful in producing an extending embryo. This model was not favored by the authors (but see my comment below). According to this model, the progenitor zone could be maintained by a cell pool containing equivalent Sox2/Bra levels; when this balance is broken cells eventually enter NT or PSM routes. Therefore, while expression heterogeneity can be observed in the PZ, I am not sure that the work shown in this manuscript is conclusive enough to claim an essential role of such heterogeneity to maintain the progenitor pool.

We acknowledge that regional heterogeneity of Sox2 and Bra has been described in the PZ and we made sure that we cite the bibliography including Wymeersch et al, 2016 and Kawachi,2020. Although these papers described different levels of Sox2 and Bra in the PZ, they did not clearly reported and quantified the fact that direct neighboring cells have very different levels of Sox2 and Bra, therefore we believe that our description of a “random-like” pattern of heterogeneity constitutes a real novelty. In the same lines, we are aware of the several studies independently showing that gain or loss of-function of Sox2 or Bra can act on the progenitor decision to join either the NT or the PSM (these references are cited l.70, l.72). However, we believe that our study is the first to test systematically both overexpression and downregulation of Sox2 and Bra on progenitor distribution in the same biological system and to link Sox2/Bra functions to cellular motility.

Testing the requirements of spatial cell-to-cell heterogeneity to maintain a pool of progenitors is experimentally challenging and even if we were able to homogenize Sox2 and Bra expression, we would have to do it in all progenitors, which is not, so far, technically possible using bird embryo as a model system. We are well aware of these limitations and have toned down claims on the essential role of heterogeneity to maintain progenitor pool. In particular, we have changed the abstract (we removed the last sentence stating that heterogeneity is fundamental to maintain a pool of resident progenitors), as well as the end of the introduction (we removed “while progenitors expressing intermediate/equivalent levels of the two proteins tend to remain resident”). We have pondered our model in the discussion in saying by cell with comparable levels of Sox2 and Bra “could” remain resident (L.370)

To better apprehend the role of cell-to-cell spatial heterogeneity, we have developed a new mathematical model (Figure 5) which integrates both gradient and random heterogeneity in Sox2/Bra values within the PZ and thus fits better to our biological results. In the new version of the manuscript, we compared this model with a model in which the PZ is fully gradient-like and second one in which it is completely random. These comparisons allow us to describe better what properties random and patterned heterogeneities could bring to the system (Figure 6).

2) The other main novelty of this manuscript is the idea that differences in cell motility derived from their Sox2 or Bra contents are a major force driving the generation of NT and PSM from the progenitors in the PZ. While there are clear differences between cell motility in the NT and the other two regions, the differences between what is observed in the PSM and PZ is not that high (actually, from the data presented it is not clear that such differences actually exist). However, independently of motility differences, there is no experimental evidence demonstrating that the essential driver of the cell fate choices is motility itself. Differences in cell motility could be just one of the results of more fundamental (and causal) changes in cell characteristics triggered by Sox2 or Bra activity. Indeed, NT and PSM cells are different in many different ways, including adhesion properties, which are normally a major determinant of tissue morphogenesis. Cell motility could, therefore, be one of the factors but it is not clear that it plays the essential role proposed by the authors. (see also next comment).

Cell motility distributions in the PZ are slightly different from that of the PSM since slower cells were found in the PZ. We agree with the reviewer that this difference might be difficult to see because the average motilities between the two tissues are very similar (Figure 3 and Figure 3-figure Supplement 1). To reveal this difference more clearly we have used a reporter gene for Sox2 and analyze progenitor motility by time lapse imaging. We have specifically tracked GFP positive cells (reporter gene for Sox2) in the PZ and compared them to cells which are not expressing GFP. The result is that Sox2 high progenitors are globally slower than other progenitors clearly revealing heterogeneity in cell movements within the PZ and its relation to Sox2 expression (L.225-232, Figure 3-figure Supplement 1B, video 2).

We agree that there is no experimental evidence that motility itself is the driver of the cell fate choices. To test if the effect on cell motility is taking place downstream of differentiation events, we have analyzed the expression of markers for mesodermal and neural fate (Msgn1 and Pax6) 7hrs after overexpression of Sox2 and Bra. While Sox2 or Bra overexpression triggers changes on cell motility in this short time window, we did not observe any changes in Msgn1 and Pax6 expression (L.267-274, Figure 4-figure Supplement 2) arguing that the effect on motility is an early consequence of the Bra and Sox2 misexpression. Nevertheless, we are aware that this is not a strict demonstration that the effect on fate are coming from the differential motility only. We have therefore toned down our arguments and changed the title of the manuscript (“....guides destiny by controlling their motility “ has been replaced by “...guides motility and destiny”) .

The effects on cell motility we observe could be a consequence of Sox2 and Bra effect on adhesion as suggested by the reviewer, this is an interesting possibility that we cannot and don’t want to rule out. The effect on cell adhesion is taken into account in our model and we discuss this hypothesis in the new version of the manuscript (L. 456-459). Identifying the mechanisms underlying the effects of Sox2 and Bra on cell motility is an extremely interesting project we want to pursue but we consider that this aspect goes beyond the scope of the current manuscript.

3) The authors developed a mathematical model to confirm their hypothesis that Sox2/Bra expression diversity combined with different motility of cells with high, low or intermediate relative levels of Sox2 and Bra expression are the key to guarantee proper axial elongation from the PZ. I am, however, not sure that the model, the way it was designed, actually proves their point. In particular, because it introduces an additional variable that might actually be the essential parameter for the success of the mathematical model: physical boundaries between NT and PSM cells, meaning that cells with high Sox2 or high Bra are unable to mix. As I commented above, this variable reflects a key biological property of the two tissues involved, one epithelial and the other mesenchymal in nature, which might be more relevant that the motility of the cells themselves (e.g. by different cell adhesion properties). How would a model that does not include such physical barriers work? Conversely, how would a model work in which only physical barriers are applied, using similar starting conditions: a prefigured central neural tube (Sox2 high), flanked at both sides by PSM (Brachyury high) and with the PZ (variable Sox2/Bra levels) just posterior to the neural tube?

We agree that adhesion and non-mixing properties are essential to our models. Because it was not clear in the previous version, we have explained them in more details in the new version of the manuscript (l.295-300 and Appendix 1). To assess their roles, we have made two new simulations one without the regulation of non-mixing /adhesion properties and one without motility control by Sox2/Bra. Both simulations show strong defects in morphogenesis arguing that motility on its own is a key component of the system and that the non-mixing and adhesion properties are also important but not sufficient to drive morphogenesis (Figure 5F). Having the same non-mixing/adhesion and motility properties downstream of Sox2 and Bra in all our models allows us to isolate the phenomena we wish to study: the role of the distribution of cell -to cell heterogeneity in the PZ (Figure 6).

4) The authors generate two mathematical models, differing in whether they start with a random distribution of Sox2 and Bra expression throughout the PZ or with prefigured opposing Sox2 and Bra expression gradients, somehow resembling the image observed in the embryo. The two models generated structures resembling the elongating embryo, although with small differences in the extension process and the extension rate. After analyzing the behavior of those models, they concluded that the random model fits better with the expectations from the in vivo characteristics in the embryo. I am however not sure that I agree with the authors' interpretation. First, because the gradient model includes a natural characteristic observed in the embryo, which the random model does not. Second, because one of the deciding characteristics, namely the slower extension rate observed in the gradient model, does not necessarily make it worse than the random model, as it is not possible to properly determine which extension rate actually resembles more accurately axial extension in the embryo. Third, because the observation that in the gradient model the PZ undergoes fewer transient deformations and self-corrective behaviour is in my view an argument to favor, instead of to disfavor the gradient model, both because the final result is at least as good as the one obtained with the random model and it is actually not clear that in the embryo the PZ undergoes such clearly visible deformations and self-corrections during axial extension. In addition, the gradient model generates a "pure" PZ (just yellow cells) in the posterior end of the structure, while in the random model the PZ contains some islands of NT cells, which is not what is observed in the embryo. According to the last features, the gradient model seems better than the random model.

To answer the reviewer’s concern about similarity to the embryo, we have developed a new model that is clearly closer to the biological system because it integrates both the gradient and the random ratio distributions (new Figure 5). Interestingly, by comparing it to the two extreme models (random and gradient), we found that this more “natural” model combines the stability and fluidity brought by the gradient model and the random model, respectively. As pointed out by the reviewer, we found that graded distribution brings more stability to the system with a “purest” PZ. At the opposite, random distribution allows more tissue fluidity and cell rearrangements as well as tissue shape conservation (Figure 6). We want to thank the reviewer for his or her input; we think that the new model and the comparison with the two extreme cases allowed us to reveal more clearly properties that are specific to the two types of spatial distributions and therefore to point out what general morphogenetic properties could emerge from random- like heterogeneity in the embryo.

Reviewer #2:

In this manuscript, Romanos et al show firstly that there is extensive cell-to-cell heterogeneity in the relative levels of Sox2 and Bra in the region containing progenitors for neural and paraxial mesoderm, gradually resolving towards high Bra/low Sox2 in the mesoderm or high Sox2/low Bra in emerging neurectoderm. They then show that overexpression of Sox2/morpholino-based inhibition of Bra or vice versa lead cells to favour neurectoderm or mesoderm respectively. Next they show that cells expressing high Bra are more motile than those expressing Sox2, and show using mathematical modelling that these behaviours can explain many aspects of the eventual segregation of Sox2-high neurectoderm and Bra-high mesoderm.

This interesting and well-presented work leads to the elegant and novel hypothesis that random cell motility induced by Bra and inhibited by Sox2 are sufficient to explain the segregation of NMps towards mesoderm and neurectoderm respectively. The work will be of broad interest to developmental and mathematical biologists interested in the cell biological basis of self-organising cell behaviours. Nevertheless there are some concerns to address in order to solidify the claims in the manuscript.

1) The section where Sox2 and Bra levels are manipulated (line 152 onwards) is somewhat under-analysed. Results are presented as supporting a model where the two proteins mutually repress each other and lead to segregation of neural (high Sox2) and mesodermal (high Bra) cells. However the data presented does not unequivocally support the claims in the manuscript and would require further clarification.

In the new version of our manuscript, we give more details on the analysis of Sox2 Bra levels manipulations. In particular, we provide data showing the tissue localization of manipulated cells on transverse sections (L. 192, Figure 2-figure supplement 3). We have also studied the effects of Sox2 and Bra ovexpression on cell fate maturation in the PZ and provide some evidence that progenitors do not yet express differentiation markers as they acquire specific motile properties in response to Sox2 or Bra overexpression (L. 267-273, Figure 4-figure supplement 1). According to our results and to the literature, we revised the text by removing mentions to Sox2 and Bra mutual repression (L 171, L 386, L389).

2) The mathematical model may be an oversimplification of the role of these two genes in organising a balanced production of neurectoderm and mesoderm.

In the new version of our manuscript, we have made significant efforts to better explain how non- mixing properties are taken into consideration in our models and thus, hopefully, to avoid an impression of oversimplification. We would like to point out that simulations performed to evaluate the impact of non-mixing properties on the elongation process, indicate that adhesion and non- mixing properties alone cannot account for the morphogenetic events we modelled (new Figure 5F), thus reinforcing the view that regulation of cell motility is a key element in the system. Furthermore, we have designed a new mathematical model, which is closer to the biological system because it integrates both graded and random distribution of Sox2/ Bra values (as observed in vivo) (new Figure 5). As explained above in response to reviewer 1, comparison of this model with our previous models, based on either graded or random distribution of the Sox2/ Bra values, points out the importance of random like cell-to-cell heterogeneity in this morphogenetic process.

Reviewer #3:

The manuscript by Romanos and colleagues examines how Sox2 and Brachyury control the behavior and cell fate of neuro-mesodermal progenitors (NMPs) in avian embryos. Using immunohistochemistry, the authors showed that the cells residing in the progenitor zone (PZ) display high variability in Sox2/Bra expression. Manipulation on the levels of the two transcription factors affected NMPs' choice to stay or exit the PZ and their future tissue contributions. This motivated the authors to employ an agent-based computational model and additional functional experiments to explore the importance of Sox2/Bra for cellular motility. The results led the authors to propose that (i) heterogeneity in Sox2/Bra ratio is important for the spatial organization of the PZ and its derivatives and that (ii) Sox2/Bra determine the fate of progenitor cells by controlling cellular movements.

This is a technically sound report that combines single-cell analysis, in vivo functional experiments, and mathematical modeling to explore the link between cell motility and cell identity. While the model proposed by the authors is intriguing, I found that the study should provide evidence placing Sox2/Bra as primary regulators of cell motility in the context of the PZ. Given the extensively-studied role of these transcription factors in NMPs, it is challenging to decouple cellular behavior from cellular identity during tissue formation. The study would benefit from further demonstration that cell fate commitment is regulated by - and not a regulator of - cell migration of NMPs.

We have now tested the effect of Sox2 and Bra overexpression on cell identity. We show that, 7 hrs after electroporation (a time at which we observe an effect on cell movement), no modification of the expression of neural (Pax6) and mesodermal (Msgn1) maturating markers. These data thus indicate that the effect on cell motility happens without a major acceleration of the maturation program (Figure 4 figure supplement 2). However, as mentioned in response to Reviewer 1, these experiments are correlative and do not demonstrate that the effect of Sox2 and Bra on neural and mesodermal differentiation programs are going only thought cell motility, therefore we have accordingly toned down our arguments in the new version of our manuscript.

Strengths and Weaknesses:

• The idea that heterogeneity in cellular behaviors within a progenitor field may act as a driver of morphogenesis is interesting and nicely supported by the agent-based model.

We want to thank the reviewer for this comment. We believe that in the new version of the manuscript we go even further by developing a new model (Figure 5) which is closer to reality and by testing the influence of random versus gradient Sox2/Bra distribution on morphogenesis (Figure 6)

• One of the premises of the model (Fig 4) is that Sox2/Bra ratio determines how much cells move, but this is not clear from the in vivo experiments and seems speculative. A clear demonstration of correlation between Sox2/Bra ratio and cellular motility is necessary for proper support of the model.

The role of the Sox2 to Bra ratio on PZ cell motility is demonstrated in Figure 4. In the new version of the manuscript, these results are presented before the modelling section, we hope that it would help clarifying any doubt the reader can have on the fact that we do demonstrate clearly a role of Sox2 and Bra in controlling PZ cell motility in vivo.

• The authors found that manipulation in the levels of the TFs results in changes in NMP motility, but it is not clear if this the cause or a consequence of commitment to a neural or mesodermal fate. Could Bra-High cell moving more because they have been specified to a mesodermal fate? Conversely, Sox2-High cells might migrate less since they get incorporated into the neural tube. Establishing the timing of cell fate commitment is necessary to resolve this issue

We agree with the reviewer that it is an interesting issue; we have checked for expression of specification markers 7hrs after electroporation of Sox2 and Bra expression vectors, a time point at which electroporated cells did not yet leaved the PZ but have already changed their motility. In these conditions, overexpression of Sox2 and Bra had no discernable effect on expression of the neural marker Pax6 and on the PSM marker Msgn1, respectively (Figure 4 figure supplement 2).

• The study's impact and novelty depend on the demonstration that the primary function of Sox2/Bra in NMPs is to drive cell movement. This is not sufficiently explored in the study, and there are no proposed mechanisms for how Sox2/Bra modulate cellular behavior.

We do have shown that Sox2 and Bra act on progenitor motility in vivo (Figure 4). As a mechanism, we propose that Sox2 and Bra could act directly on motility or indirectly by regulating differential adhesion. Cell adhesion control by Sox2/Bra is part of our modeling assumptions and is therefore a hypothesis that will be the subject of future investigations in the lab. This hypothesis is part of the discussion in the new version of the manuscript (L.457).

Author Response:

Reviewer #1 (Public Review):

[...] Strengths:

1. The loss of ciliary GPR161 has a more robust phenotype in specific tissues (i.e., the limbs and face). As a result, the limb data (in Figure 6) and craniofacial data (in Figure 7) are well presented and clear. In these figures, the authors directly compare and highlight differences between primarily two genotypes (wt and Gpr161mut1/mut1 embryos) and quantify the changes (digit number and distance between nasal pits). Overall, these two figures support the existing GPR161 model, showcasing that a loss of ciliary GPR161 results in a tissue-specific loss of GLI3R (Figure 6D) and consequently the development of additional digits (Figure 6E) and craniofacial defects (Figure 7D and 7E).

Thank you.

Weaknesses:

1. There is no data in the paper showing that Gli3 repressor function is affected preferentially compared to Gli Activator function. In Figure 4C, Gli3 FL/R ratios are not different between wt/wt and mut/mut embryos. The data can be explained by the fact that the mutant Gpr161 is a partial loss of function allele and the resultant weaker phenotypes (compared to the full KO) show some tissue specificity. Linking this allele to a specific biochemical mechanism is not justified by the data.

We have now revised the title of the paper and the discussion emphasizing on these limitations. We have also added a new section in discussion on the limitations of our methods and other optogenetic/chemogenetic methods for generating cAMP in cilia. These limitations arise from the cilioplasm not being strictly restricted from the cytoplasm. Therefore, the second messengers cAMP and Ca2+ are freely diffusible between ciliary and extraciliary compartments (Delling et al., 2016; Truong et al., 2021). A paper published in Cell during revision of this study used optogenetic tools to show that ciliary, but not cytoplasmic, production of cAMP functions through PKA localized in cilia (Truong et al., 2021) to repress sonic hedgehog-mediated somite patterning in zebrafish (Wolff et al., 2003). We have also compared and discussed these results with our study. Our study highlights that the effects of ciliary loss of Gpr161 pools are tissue specific and dependent on the requirements of the tissues on GliR vs GliA in the morpho-phenotypic spectrum. Overall, our results using Gpr161mut1 allele are complementary to the optogenetic study by showing that lack of ciliary Gpr161 pools result in Hh hyperactivation phenotypes arising mainly from lack of GliR, in the limb buds, mid-face and intermediate neural tube.

1. The authors use an endpoint assay based on overexpression in 293T cells to claim that cAMP production is unaffected by the Gpr161mut allele. However, weak effects (very likely given the weak phenotypes) may not be evident this assay. We also do not know if the mutant allele is defective in some other biochemical function or in localization to other places in the cell. One way to address this is to measure ciliary and extraciliary cAMP in their knock-in cells. In Gpr161mut1/mut1 cells, is ciliary cAMP reduced to levels comparable to Gpr161ko/ko cells? Is extraciliary cAMP unchanged compared to WT cells? Or, is cAMP able to diffuse into the cilia from GPR161mut1 localized to vesicles at the ciliary base (Figure 1B)? Many of the conclusions made in the paper equate a loss of ciliary GPR161 to a loss of ciliary cAMP, but this loss of ciliary cAMP is not definitively shown in the paper.

As physiological ligands for Gpr161 are currently not known, we are unable to test extraciliary vs ciliary contribution of Gpr161 in cAMP production in a physiological context. Therefore, we resort to overexpression assays for constitutive cAMP production by Gpr161 and Gpr161mut1. Using these assays, we do not find a difference in constitutive activity among these variants.

As the cilioplasm is not strictly compartmentalized from the cytoplasm, the second messengers cAMP and Ca2+ are freely diffusible between ciliary and extraciliary compartments (Delling et al., 2016; Truong et al., 2021). Thus, in any approach for generating subcellular pools of cAMP, be it genetic, optogenetic or chemogenetic (Guo et al., 2019; Hansen et al., 2020; Truong et al., 2021), extraciliary cAMP could diffuse into ciliary compartments. A recent paper using optogenetic and chemogenetic tools for cAMP production inside cilia or in cytoplasm show that there is free access of cytoplasmic cAMP to intraciliary compartments but is unable to reach critical thresholds in activating PKA (Truong et al., 2021). Thus, we would assume that the extraciliary cAMP produced by extra copies of Gpr161mut1 could diffuse to cilia but is likely to be less effective in activating downstream effectors. In addition, the PKA regulatory subunit-AKAP complexes are fundamentally important in organizing and sustaining PKA catalytic subunit activation to organize localized substrate phosphorylation in restrictive nanodomains (Bock et al., 2020; Zhang et al., 2020). The dual functions of Gpr161 in Gs coupling and as an atypical AKAP (Bachmann et al., 2016) is likely to further restrict cAMP signaling in ciliary or extraciliary microdomains.

1. Compared to Figures 6 and 7, the data presented in Figures 3 and 5 are very confusing and difficult to interpret. On the one hand, this is understandable, the Gpr161mut/mut phenotypes are complex, and some tissues (like the developing spinal cord) are more resistant to change due to a loss of GliR. On the other hand, the data collected from the numerous genotypes analyzed could be easier to interpret by (i) providing a penetrance of the phenotypes and (ii) quantifying the phenotypes.

Thank you for all the suggestions. We have now carried out these quantifications or tabulations, which have considerably improved the presentation of the datasets (Table 2 and Figure 5-figure supplement 1). Some of these experiments required additional experimental animals (Table 1), and we have updated the text accordingly.

Below are a few examples of data that could be improved with quantifications:

— In Figure 3, the authors are trying to convey that the Gpr161mut allele is partially functional and produces a milder phenotype than the Gpr161ko allele. However, the Gpr161ko/ko, Gpr161mut/ko, and Gpr161mut/mut phenotypes showcased in the figure all look quite severe, and it is difficult to appreciate the differences in the defects fully. An accompanying table summarizing the phenotypes and their penetrance in the affected genotypes would help to convey this point.

We have added an accompanying Table 2 summarizing the phenotypes and penetrance for the respective genotypes, when present. Please note that rostral malformations such as exencephaly are similar between Gpr161 ko/ko and Gpr161 ko/mut1, whereas Gpr161 mut1/mut1 embryos have mid face widening. In the same line, Gpr161 ko/ko has no forelimbs, whereas Gpr161 ko/mut1 has smaller fore limb buds, whereas Gpr161 mut1/mut1 embryos have polydactyly.

— In Table 1, the authors note that the Gpr161mut1/mut1 mouse is embryonic lethal by e14.5, but the analysis in Table 1 appears to be incomplete. In the table titled "breeding between Gpr161 mut1/+ parents," the authors indicate that they only assessed one litter of e14.5 and e15.5 embryos. Oddly, the authors note that additional litters were collected, but the embryos were not genotyped because the embryos exhibited no phenotypes. The absence of phenotypes could be due to an absence of viable Gpr161mut1/mut1 embryos; however, the embryos need to be genotyped and a chi-square analysis conducted to verify this. Death can be a measure of phenotype severity, but I think it is important to surmise why the embryos are dying. It is unclear whether the embryos are dying due to the heart defects mentioned in the discussion. If the embryos are dying due to the heart defect, then it would be important to know whether the heart defects are more severe in the Gpr161ko/ko embryos.

Our apologies for the oversight. We have now analyzed additional timed pregnancies at E14.5, E14.75 and E15.5. We find that the embryonic lethality is seen fully by E14.75. Heart defects in Gpr161 ko/ko embryos are not apparent as they are E10.5 lethal. We do see apparent heart defect phenotypes in Gpr161 ko/mut1 vs Gpr161 mut1/mut1. These defects include pericardial effusion, outflow tract defects, A-V cushion abnormalities and smaller ventricles. These phenotypic descriptions are beyond the scope of the current paper. However, we have mentioned about pericardial effusion in the text and Table 2.

— In Figure 5, quantifying the progenitor domains would greatly assist in discerning differences between the various genotypes. For example, a quantification would help readers assess differences in NKX6.1 across the various genotypes.

We have now quantified the differences in Nkx6.1 across genotypes. The data is presented in Figure 5-figure supplement 1.

On an unrelated note, the PAX7 staining of the Gpr161mut1/ko spinal cord looks very strange because the line adjacent to the image does not accurately represent the dorsal-ventral patterning of PAX7 seen in the image. This image would need to be replaced.

Our apologies for the oversight. We have now revised this image.

Reviewer #2 (Public Review):

The premise of the entire study is predicated on GPR161mut1 failing to target to cilia and being WT in every other aspect. The Gs coupling of GPR161mut1 is examined. The ciliary localization ofGPR161mut1 is carefully assessed by conducting staining not just in WT cells but also in INPP5Ecells where GPR161 ciliary levels are known to be elevated. Another prediction is that GPR161mut1is found in an intermediate biosynthetic compartment. Some insights into the compartment whereGPR161mut1 is found would help interpret the phenotype of the GPR161mut1 animals. It would be important to know whether the GPR161mut1 mimics a pre-cilia targeted GPR161 (say at the plasma membrane) or whether it mimics a post-ciliary exit state (say recycling endosomes). In the past few years, work from the von Zastrow lab and others has shown that GPCRs keep activating their downstream partners after endocytosis from the plasma membrane. If GPR161mut1 were to mimic the post-ciliary exit state of GPR161, it may assume some of the signaling functions of ciliaryGPR161.

Thank you for all the suggestions. We have now examined and extensively discussed the plausible source of extraciliary Gpr161 in mediating Hh repression. We already showed that Gpr161 localizes to the periciliary recycling endosomal compartment where it localizes in addition to cilia (Mukhopadhyay et al., 2013) and could activate ACs and PKA in proximity to the centrosome. We now show that Gpr161mut1 also localizes to similar compartments (Figure 1-figure supplement 3). We propose that this compartment could promote Gpr161 activity outside cilia in the in vivo settings in GliR formation (please see model in Figure 8D).

We also compare our results with a recently published paper showing that ciliary, but not cytopasmic, production of cAMP functions through PKA localized in cilia to repress sonic hedgehog-mediated somite patterning in zebrafish (Truong et al., 2021). While this paper is an elegant demonstration of ciliary pools of cAMP in repressing Hh activity despite having no strict compartmentalization exclusively in cilia, it does not capture the roles of ciliary and extraciliary pools of Gpr161-mediated cAMP signaling in different tissues that we show are dependent on the requirements of the tissues on GliR vs GliA in the morpho-phenotypic spectrum.

A second point that the authors may wish to address is whether GPR161mut1 may fail to enrich in cilia because it is hyperactive and undergoes constitutive exit from cilia. The hypothesis here is thatGPR161mut1 couples to beta arrestin better than WT GPR161. Blocking GPR161mut1 exit via depletion of beta arrestin or BBSome is a simple way to test this hypothesis.

As advised by the reviewer, we have tested for Gpr161/Gpr161mut1 levels in cilia upon arrestin1/2 or BBSome loss. These experiments show that Gpr161mut1 is not present in cilia in arrestin1/2 (Arrb1/2) double ko MEFs (Figure 1-figure supplement 1) or upon RNAi of BBS4 (Figure 5-figure supplement 2). We previously also showed that knockdown of the 5’phosphpatase INPP5E that causes accumulation of Gpr161 in cilia does not show any accumulation of Gpr161mut1 in cilia. Based on all these experiments, we surmise that Gpr161mut1 does not transit through cilia.

Finally, it would be good to learn about the levels of expression of GPR161mut1 compared to WTGPR161 using immunoblotting. If GPR161mut1 were to be expressed at much higher levels than WTGPR161, it may compensate for its lack of ciliary localization by elevated total cellular activity.

We were unable to determine protein stability of the mutant receptor in the Gpr161mut1 embryos due to technical constraints in immunoblotting for endogenous levels. However, we note Gpr161mut1 in vesicles surrounding the base of cilia (Figure 1B) and constitutive cAMP signaling activity (Figure 1G, Figure supplements 1-3) in stable cell lines, suggesting that protein levels and activity of the mutant were comparable with wild type Gpr161. As suggested by the reviewer, we also tested LAP-tagged Gpr161mut1protein levels by tandem affinity purification and immunoblotting, with respect to LAP-tagged Gpr161wt in MEFs stably overexpressing these variants. We noted similar immunoblotting pattern from receptor glycosylation in both variants (Figure 2-figure supplement 2).

Reviewer #1 (Public Review):

The authors created a new GPR161 mutant mouse (Gpr161mut/mut) in which GPR161 does not localize to the primary cilium but is still cAMP signaling competent based on an over-expression assay in 293T cells. Through a detailed analysis of the Gpr161mut/mut mouse and its comparison to a previously generated Gpr161 knockout mouse (Gpr161ko/ko), the authors try to discriminate the ciliary and non-ciliary roles of GPR161. The current prevailing model is that GPR161 (localized to the primary cilium in the absence of Hh pathway activation) is constitutively active and elevates cAMP levels within the primary cilium. Elevated ciliary cAMP then activates ciliary (or ciliary adjacent) PKA, driving the processing of bifunctional GLI proteins into transcriptional repressors (GLIR). According to this model, the ciliary pool of GPR161 is critical for suppressing Hh signaling activity, and one would predict that the Gpr161mut/mut embryos would look identical to the Gpr161ko/ko embryos. However, this was not the case. Across multiple developmental tissues, the Gpr161mut/mut phenotype is less severe than the complete knockout, suggesting a role for non-ciliary GPR161 in suppressing Hh signaling activity. The observations made in this paper are interesting, but the data fails to make a clear distinction between the ciliary and non-ciliary roles of GPR161.

Strengths:

1. The loss of ciliary GPR161 has a more robust phenotype in specific tissues (i.e., the limbs and face). As a result, the limb data (in Figure 6) and craniofacial data (in Figure 7) are well presented and clear. In these figures, the authors directly compare and highlight differences between primarily two genotypes (wt and Gpr161mut1/mut1 embryos) and quantify the changes (digit number and distance between nasal pits). Overall, these two figures support the existing GPR161 model, showcasing that a loss of ciliary GPR161 results in a tissue-specific loss of GLI3R (Figure 6D) and consequently the development of additional digits (Figure 6E) and craniofacial defects (Figure 7D and 7E).

Weaknesses:

1. There is no data in the paper showing that Gli3 repressor function is affected preferentially compared to Gli Activator function. In Figure 4C, Gli3 FL/R ratios are not different between wt/wt and mut/mut embryos. The data can be explained by the fact that the mutant Gpr161 is a partial loss of function allele and the resultant weaker phenotypes (compared to the full KO) show some tissue specificity. Linking this allele to a specific biochemical mechanism is not justified by the data.

2. The authors use an endpoint assay based on overexpression in 293T cells to claim that cAMP production is unaffected by the Gpr161mut allele. However, weak effects (very likely given the weak phenotypes) may not be evident this assay. We also do not know if the mutant allele is defective in some other biochemical function or in localization to other places in the cell. One way to address this is to measure ciliary and extraciliary cAMP in their knock-in cells. In Gpr161mut1/mut1 cells, is ciliary cAMP reduced to levels comparable to Gpr161ko/ko cells? Is extraciliary cAMP unchanged compared to WT cells? Or, is cAMP able to diffuse into the cilia from GPR161mut1 localized to vesicles at the ciliary base (Figure 1B)? Many of the conclusions made in the paper equate a loss of ciliary GPR161 to a loss of ciliary cAMP, but this loss of ciliary cAMP is not definitively shown in the paper.

3. Compared to Figures 6 and 7, the data presented in Figures 3 and 5 are very confusing and difficult to interpret. On the one hand, this is understandable, the Gpr161mut/mut phenotypes are complex, and some tissues (like the developing spinal cord) are more resistant to change due to a loss of GliR. On the other hand, the data collected from the numerous genotypes analyzed could be easier to interpret by (i) providing a penetrance of the phenotypes and (ii) quantifying the phenotypes. Below are a few examples of data that could be improved with quantifications:

— In Figure 3, the authors are trying to convey that the Gpr161mut allele is partially functional and produces a milder phenotype than the Gpr161ko allele. However, the Gpr161ko/ko, Gpr161mut/ko, and Gpr161mut/mut phenotypes showcased in the figure all look quite severe, and it is difficult to appreciate the differences in the defects fully. An accompanying table summarizing the phenotypes and their penetrance in the affected genotypes would help to convey this point.

— In Table 1, the authors note that the Gpr161mut1/mut1 mouse is embryonic lethal by e14.5, but the analysis in Table 1 appears to be incomplete. In the table titled "breeding between Gpr161 mut1/+ parents," the authors indicate that they only assessed one litter of e14.5 and e15.5 embryos. Oddly, the authors note that additional litters were collected, but the embryos were not genotyped because the embryos exhibited no phenotypes. The absence of phenotypes could be due to an absence of viable Gpr161mut1/mut1 embryos; however, the embryos need to be genotyped and a chi-square analysis conducted to verify this. Death can be a measure of phenotype severity, but I think it is important to surmise why the embryos are dying. It is unclear whether the embryos are dying due to the heart defects mentioned in the discussion. If the embryos are dying due to the heart defect, then it would be important to know whether the heart defects are more severe in the Gpr161ko/ko embryos.

— In Figure 5, quantifying the progenitor domains would greatly assist in discerning differences between the various genotypes. For example, a quantification would help readers assess differences in NKX6.1 across the various genotypes. On an unrelated note, the PAX7 staining of the Gpr161mut1/ko spinal cord looks very strange because the line adjacent to the image does not accurately represent the dorsal-ventral patterning of PAX7 seen in the image. This image would need to be replaced.

While Franklin has a great suggestion for how the professorship should be setup, I think she makes a great point that, like men, women should be sought after to fill these positions. So, instead of waiting for someone to stop in to claim the position, they should instead seek out the brilliant minds to fill the position.

Reviewer #1 (Public Review):

1) The user manual and tutorial are well documented, although the actual code could do with more explicit documentation and comments throughout. The overall organisation of the code is also a bit messy.

2) My understanding is that this toolbox can take maps from BigBrain to MRI space and vice versa, but the maps that go in the direction BigBrain->MRI seem to be confined to those provided in the toolbox (essentially the density profiles). What if someone wants to do some different analysis on the BigBrain data (e.g. looking at cellular morphology) and wants that mapped onto MRI spaces? Does this tool allow for analyses that involve the raw BigBrain data? If so, then at what resolution and with what scripts? I think this tool will have much more impact if that was possible. Currently, it looks as though the 3 tutorial examples are basically the only thing that can be done (although I may be lacking imagination here).

3) An obvious caveat to bigbrain is that it is a single brain and we know there are sometimes substantial individual variations in e.g. areal definition. This is only slightly touched upon in the discussion. Might be worth commenting on this more. As I see it, there are multiple considerations. For example (i) Surface-to-Surface registration in the presence of morphological idiosyncracies: what parts of the brain can we "trust" and what parts are uncertain? (ii) MRI parcellations mapped onto BigBrain will vary in how accurately they may reflect the BigBrain areal boundaries: if histo boundaries do not correspond with MRI-derived ones, is that because BigBrain is slightly different or is it a genuine divergence between modalities? Of course addressing these questions is out of scope of this manuscript, but some discussion could be useful; I also think this toolbox may be useful for addressing this very concerns!

Worse still. Bush wrote As We May Think in 1939 We? Machines? Yet the Atlantic was quick to spin it with the subtitle "A TOP U.S, SCIENTIST FORESEES A POSSIBLE FUTURE WORLD IN WHICH MAN-MADE MACHINES WILL START TO THINK" We? Machines?

https://www.linkedin.com/in/gyuri-lajos/detail/recent-activity/

A fascinating take on "everyone is dying"

Time in the US, Homelife, Mexican traditions, Holidays, Spanish language, US traditions, Holidays

Return to Mexico, Jobs, Community, Opportunity, Family Relationships, Feelings, Dreams; Reflections, Mexico, The United States

This does not seem to be such a rigid definition to me. I think we use psychology in combination with closely related subjects, such as sociology, and it can become easy to mix the two. I think "a scientific description of the mind, of mental activity, or of mental products" seems like a reasonable definition for psychology.

Peer Reviewed and recommended by Peer Community in Evolutionary Biology

Recommendation<br> Separating adaptation from drift: A cautionary tale from a self-fertilizing plant<br> by Christoph Haag based on reviews by Jon Agren, Pierre Olivier Cheptou and Stefan Laurent.

In recent years many studies have documented shifts in phenology in response to climate change, be it in arrival times in migrating birds, budset in trees, adult emergence in butterflies, or flowering time in annual plants (Coen et al. 2018; Piao et al. 2019). While these changes are, in part, explained by phenotypic plasticity, more and more studies find that they involve also genetic changes, that is, they involve evolutionary change (e.g., Metz et al. 2020). Yet, evolutionary change may occur through genetic drift as well as selection. Therefore, in order to demonstrate adaptive evolutionary change in response to climate change, drift has to be excluded as an alternative explanation (Hansen et al. 2012). A new study by Gay et al. (2021) shows just how difficult this can be.

The authors investigated a recent evolutionary shift in flowering time by in a population an annual plant that reproduces predominantly by self-fertilization. The population has recently been subjected to increased temperatures and reduced rainfalls both of which are believed to select for earlier flowering times. They used a “resurrection” approach (Orsini et al. 2013; Weider et al. 2018): Genotypes from the past (resurrected from seeds) were compared alongside more recent genotypes (from more recently collected seeds) under identical conditions in the greenhouse. Using an experimental design that replicated genotypes, eliminated maternal effects, and controlled for microenvironmental variation, they found said genetic change in flowering times: Genotypes obtained from recently collected seeds flowered significantly (about 2 days) earlier than those obtained 22 generations before. However, neutral markers (microsatellites) also showed strong changes in allele frequencies across the 22 generations, suggesting that effective population size, Ne, was low (i.e., genetic drift was strong), which is typical for highly self-fertilizing populations. In addition, several multilocus genotypes were present at high frequencies and persisted over the 22 generations, almost as in clonal populations (e.g., Schaffner et al. 2019). The challenge was thus to evaluate whether the observed evolutionary change was the result of an adaptive response to selection or may be explained by drift alone.

Here, Gay et al. (2021) took a particularly careful and thorough approach. First, they carried out a selection gradient analysis, finding that earlier-flowering plants produced more seeds than later-flowering plants. This suggests that, under greenhouse conditions, there was indeed selection for earlier flowering times. Second, investigating other populations from the same region (all populations are located on the Mediterranean island of Corsica, France), they found that a concurrent shift to earlier flowering times occurred also in these populations. Under the hypothesis that the populations can be regarded as independent replicates of the evolutionary process, the observation of concurrent shifts rules out genetic drift (under drift, the direction of change is expected to be random).

The study may well have stopped here, concluding that there is good evidence for an adaptive response to selection for earlier flowering times in these self-fertilizing plants, at least under the hypothesis that selection gradients estimated in the greenhouse are relevant to field conditions. However, the authors went one step further. They used the change in the frequencies of the multilocus genotypes across the 22 generations as an estimate of realized fitness in the field and compared them to the phenotypic assays from the greenhouse. The results showed a tendency for high-fitness genotypes (positive frequency changes) to flower earlier and to produce more seeds than low-fitness genotypes. However, a simulation model showed that the observed correlations could be explained by drift alone, as long as Ne is lower than ca. 150 individuals. The findings were thus consistent with an adaptive evolutionary change in response to selection, but drift could only be excluded as the sole explanation if the effective population size was large enough.

The study did provide two estimates of Ne (19 and 136 individuals, based on individual microsatellite loci or multilocus genotypes, respectively), but both are problematic. First, frequency changes over time may be influenced by the presence of a seed bank or by immigration from a genetically dissimilar population, which may lead to an underestimation of Ne (Wang and Whitlock 2003). Indeed, the low effective size inferred from the allele frequency changes at microsatellite loci appears to be inconsistent with levels of genetic diversity found in the population. Moreover, high self-fertilization reduces effective recombination and therefore leads to non-independence among loci. This lowers the precision of the Ne estimates (due to a higher sampling variance) and may also violate the assumption of neutrality due to the possibility of selection (e.g., due to inbreeding depression) at linked loci, which may be anywhere in the genome in case of high degrees of self-fertilization.

There is thus no definite answer to the question of whether or not the observed changes in flowering time in this population were driven by selection. The study sets high standards for other, similar ones, in terms of thoroughness of the analyses and care in interpreting the findings. It also serves as a very instructive reminder to carefully check the assumptions when estimating neutral expectations, especially when working on species with complicated demographies or non-standard life cycles. Indeed the issues encountered here, in particular the difficulty of establishing neutral expectations in species with low effective recombination, may apply to many other species, including partially or fully asexual ones (Hartfield 2016). Furthermore, they may not be limited to estimating Ne but may also apply, for instance, to the establishment of neutral baselines for outlier analyses in genome scans (see e.g, Orsini et al. 2012).

References

Cohen JM, Lajeunesse MJ, Rohr JR (2018) A global synthesis of animal phenological responses to climate change. Nature Climate Change, 8, 224–228. https://doi.org/10.1038/s41558-018-0067-3

Gay L, Dhinaut J, Jullien M, Vitalis R, Navascués M, Ranwez V, Ronfort J (2021) Evolution of flowering time in a selfing annual plant: Roles of adaptation and genetic drift. bioRxiv, 2020.08.21.261230, ver. 4 recommended and peer-reviewed by Peer Community in Evolutionary Biology. https://doi.org/10.1101/2020.08.21.261230

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Orsini L, Schwenk K, De Meester L, Colbourne JK, Pfrender ME, Weider LJ (2013) The evolutionary time machine: using dormant propagules to forecast how populations can adapt to changing environments. Trends in Ecology & Evolution, 28, 274–282. https://doi.org/10.1016/j.tree.2013.01.009

Orsini L, Spanier KI, Meester LD (2012) Genomic signature of natural and anthropogenic stress in wild populations of the waterflea Daphnia magna: validation in space, time and experimental evolution. Molecular Ecology, 21, 2160–2175. https://doi.org/10.1111/j.1365-294X.2011.05429.xISTEX

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Reviews.

Revision round #2.<br> 2021-04-19.<br> Author's Reply.<br> Download author's reply (PDF file) Download tracked changes file.

Dear Dr Haag,

Thanks for handling the review of our manuscript. We agree that the comments of Jon Agren have further improved the quality of this manuscript and we tried to answer to all of them (see the point-by-point reply below). We provide a track-changes version where the changes in the main text and supplementary files are highlighted in bold. The new version is also available online on Biorxiv : https://www.biorxiv.org/content/10.1101/2020.08.21.261230v3.

We hope that you will find this updated version of our manuscript suitable for recommendation by PCIEvolBiol and would be happy to take any further comments if you judge it would improve the manuscript.

Laurène Gay, on behalf of all the coauthors

Decision round #2.

Dear Dr Gay,

Your revised preprint "Evolution of flowering time in a selfing annual plant: Roles of adaptation and genetic drift" has now been reconsidered by two of the original reviewers. As you will see, while one of them is satisfied with the new version, the other is positive but recommends an additional round of minor revision. From my own reading, I agree that the suggestions by the reviewer will likely further strengthen the manuscript. Therefore, before reaching a final decision, I would like to ask you to consider these suggestions, and to revise the manuscript accordingly. When you submit the revised version, please include a letter in which you describe how you have responded to each of the referees comments.

Best wishes, and many thanks for submitting to PCI Evol Biol,

Christoph Haag

Preprint DOI: https://www.biorxiv.org/content/10.1101/2020.08.21.261230v2

Reviewed by Jon Agren, 2021-04-16 11:54.<br> I think the presentation has benefitted from the revisions made by the authors. Below is a list of comments on details regarding terminology and presentation that the authors may want to consider.

p. 1, Abstract first sentence. Resurrection experiments can detect correlations between trait modifications and changes in the environment, but this is not really a test of a causation, is it? Or is the argument here that simultaneous parallel changes in many populations indicate a change in the environment acting over a large area? This could be indicated with a slight rewording.

p. 1, Abstract first sentence. Change “traits modifications” to “trait modifications”.

p. 2, first paragraph. Not fully clear what the important difference is between experimental and natural populations. In both cases, an estimate of effective population size is required.

p. 2, right column, line 3. What does “>0.5” refer to? A broad-sense heritability estimate?

p. 2, right column, line 27. Insert “selfing” after “predominantly”.

p. 2, right column, line 36, “across 22 generations”. Does this species have any seed bank that may affect “effective generation time”?

p. 2, right column, line 47, “taking into account the multilocus genotypic composition…”. Unclear how this should be understood. Reword?

p. 2, right column, line 50, “for neutrality”. I suggest the authors indicate how this is achieved. – By using estimates of genotypic values for flowering time and assuming flowering time is a neutral trait?

p. 3, first paragraph. I still find the procedure for building “families of full sibs” unclear: I suggest the authors state explicitly whether the families multiplied in 2011 each originated from a different pod collected in the field, or whether the families originated from seeds that had been randomly selected from pooled samples of seeds from 1987 and 2009, respectively.

p. 3, right column, paragraph “Temporal changes in sensitivity to vernalization”, “measured as the slope…” This needs some more explanation. Are differences calculated between all possible pairs of plants in the two treatments?

p. 4, third paragraph, “good approximation of the additive genetic covariance”. What about maternal environmental and genetic effects?

p. 4, right column, first paragraph. State explicitly that the individuals analysed represented 145 different families?

p. 5, second paragraph, “As a preliminary step,…”. To me the argument would make more sense in the reverse order, as the changes in flowering time and MLG frequency between 1987 and 2009 are the most direct estimates of evolutionary change. In other words, starting from the observation of the changes in flowering time and MLG frequency, one can examine the strength of the association between flowering time and MLG in the greenhouse, and whether the change is consistent with selection observed in the greenhouse. I see no a priori reason why selection on flowering time in the greenhouse should mirror that at the site of the focal population. To make this order of logic clear, the authors may want to move the description of the selection gradient analyses to after this argument has been formulated.

p. 5, second paragraph, “whether selection in quantified in the greenhouse is likely to mirror selection in the field at present and 22 years ago”. To be strict , it would only need to mirror the predominant selection between 1987 to 2009 to be correlated with the change observed, right? Current selection in the field should matter little?

p. 5, second paragraph, “We then measured…”. I like this approach! The authors should indicate which measure of flowering time was used in this analysis. The legend of Fig. 3 speaks about “average flowering time”. The sensitivity to vernalization treatment varied among genotypes. Are the results of this analysis essentially the same if the analysis is conducted separately for treatment 1 or 2, or separately for estimates of flowering time obtained based on the seed sample from 1987 and from 2009, respectively?

p. 6, first paragraph; Table 3. Since a single line was sampled in each population, it is a bit misleading to call the examined effect a “population effect”. Change to “line effect”?

p. 7, first paragraph, “predict an evolution of towards earlier flowering”. Since estimates of selection and heritabilities are specific to a given environment, this prediction is valid for the greenhouse and not necessarily for other environments.

p. 7. Was there an effect of year of sampling on estimates of flowering time for MLGs sampled in both 1987 and 2009?

p. 7, right column, second paragraph, “were persistent through time”. Change to “were observed in both years” to make the fact that altogether 5 lines were observed in both the 1987 and 2009 sampling more obvious?

p. 7, right column, second paragraph, “Fig. 3A, regression only significant…”. Add sample size (i.e., number of family means included in this regression).

p. 11, second paragraph, “Munguia-Rosas et al.”. Note that selection estimates considered in this meta-analysis largely ignores the effect of variation in number of flowers and plant size, suggesting that many of them rather reflect a correlation between plant condition and fitness.

Finally, I suggest the authors somewhere add a caveat regarding possible G x E interactions for flowering time (greenhouse vs. field), when discussing the possible association between flowering time as expressed in the greenhouse and fitness and evolutionary change in the field.

Reviewed by Stefan Laurent, 2021-03-20 17:00.

I am satisfied with the answers to my comments and with the modifications to the main text. The qqplots should be added to the supplementary figures linked to main figure 3.

Revision round #1.<br> 2020-10-26.

Author's Reply.<br> Download author's reply (PDF file).<br> Download tracked changes file.

Dear Dr Haag, Please find enclosed a revised version of our manuscript. We are very grateful to you and the reviewers for the comments and suggestions that have improved the manuscript substantially. We tried to answer to all of them (see the point-by-point reply below). We provide a track-changes version with line numbers, where the changes in the main text and supplementary files are highlighted in bold. We also added a revised version that you can find after the track-changes (starting page 19). We hope that you will find this updated version of our manuscript suitable for recommendation by PCIEvolBiol and would be happy to take any further comments if you judge it would improve the manuscript.<br> Laurène Gay, on behalf of all the coauthors.

Decision round #1.<br> Dear Dr Gay, Thank you for submitting your preprint "Evolution of flowering time in a selfing annual plant: Roles of adaptation and genetic drift" to PCI Evol Biol. Your work has now been considered by three reviewers, whose comments are enclosed. As you will see, the reviews are largely positive, and, based on these reviews as well as my own reading, I am happy to further consider your preprint for recommendation. However, before reaching a final decision, I would like you to revise your manuscript according to the recommendations by the reviewers. Besides the more minor points (which also should be considered carefully), I think there are two main issues that need particular attention:

• First, the introduction (and perhaps also some other sections) would profit from some streamlining. In my opinion, this does not mean that you should entirely drop the discussion of the effects of selfing on the efficacy of selection. But this section should be reduced in length and care should be taken to clearly state the objective of the study early on without raising issues (e.g., comparison between selfers and outcrossers) that are not subsequently addressed. Incidentally, from my own reading, I also think that the last part of page 1 (where you give some more detail on the different possible approaches to investigate the influence of selection on phenotypic change) would profit from some reformulation: I found this part difficult to follow and its purpose is not entirely clear to me: Do you want to provide details on some of the approaches or do you want to explain why you used only some bot not others in your study? Moreover, the statement that natural populations cannot be replicated may also need to be nuanced (replication might in principle be possible across different populations or using independent samples from the same population).
• Second, the analysis of the frequency changes of the multilocus genotypes needs some clarification, both in terms of potential effects of excluding rare genotypes and in terms of confidence intervals given (likely) non-normal distribution of residuals. If you submit a revised version, please include a letter in which you describe how you have responded to each of the referees’ comments. Best withes, and apologies again for the delayed decision, Christoph Haag

Additional requirements of the managing board:<br> As indicated in the 'How does it work?’ section and in the code of conduct, please make sure that: -Data are available to readers, either in the text or through an open data repository such as Zenodo (free), Dryad or some other institutional repository. Data must be reusable, thus metadata or accompanying text must carefully describe the data. -Details on quantitative analyses (e.g., data treatment and statistical scripts in R, bioinformatic pipeline scripts, etc.) and details concerning simulations (scripts, codes) are available to readers in the text, as appendices, or through an open data repository, such as Zenodo, Dryad or some other institutional repository. The scripts or codes must be carefully described so that they can be reused. -Details on experimental procedures are available to readers in the text or as appendices. -Authors have no financial conflict of interest relating to the article. The article must contain a "Conflict of interest disclosure" paragraph before the reference section containing this sentence: "The authors of this preprint declare that they have no financial conflict of interest with the content of this article." If appropriate, this disclosure may be completed by a sentence indicating that some of the authors are PCI recommenders: “XXX is one of the PCI XXX recommenders.”

Preprint DOI: 10.1101/2020.08.21.261230

Reviewed by Pierre Olivier Cheptou, 2020-10-20 11:18.<br> The study by Gay et al. reports empirical data on the evolution of flowering time in a highly selfing species: Medicago truncatula. The authors used several approach to investigate the question. In particular, they used a resurrection approach with seeds from 1987 and 2009. The aim of the study is to disentangle the role of drift and selection in the shift observed as well as estimating selection gradient of flowering time. The study is interesting and the different experiments (pop centered, regional) is consistent with a shift in flowering time. Below, my comments:

1-The introduction discuss the question of adaptation face to environmental change. While the text is rich and well referenced, I found that the introduction is a bit long. There is a long discussion on whether outcrossing/selfing traits influences adaptation. The logical consequence would be to compare outcrossing/selfing populations. Since the study does not compare outcrossing and selfing populations, I think this part should be greatly reduced. Also, the statement that bottlenecks are more frequent in selfers (if true !!) would be more striking if the references were reporting empirical data. To my knowledge, Schoen and Brown (1991) and Ingvargsson 2002 hypothesize that it is the case but did not demonstrated that selfers suffer from higher bottlenecks. In the following paragraph, I found confusing to assert that “self-fertilization mays have facilitated adaptations to agricultural practices” when discussing the role of mating system on adaptation. Is it because the traits were preadapted or because the genetic architecture of selfers facilitates adaptation? In short, the introduction should be more focused to introduce the question short term adaptation of flowering time in the face of warming.

2-Sum of temperature. The individual flowering time is converted in sum of temperature. The basal temperature is assumed to be 5°C, based on Moreau et al (2007). Would it be possible that Tb has evolved during the two decades? Would the conclusions be different if flowering time were measured as the number of days? At least, the possibility of a shift in Tb should be discussed as I found contradictory to evaluate adaptation to warming but keeping Tb constant.

3-Maternal effects. If I understood well, the results on the studied populations are corrected for maternal effects (one generation to refresh seeds stock) but the results of regional analysis are based on the F1 generation (without correcting for maternal effects). I was interested by the amplitude of the shift: two days in the cape Corsica populations but five days in the regional analysis. This may be a “true result” or an effect of correcting for maternal effects. Did the authors measure the flowering date in the F1 of the cape Corsica populations. I would suggest to mention this result in the discussion. Is it possible that the difference in flowering date reported have changed in Cape Corsica population because of the F1 generation in greenhouse? My feeling is that these results are, as such, interesting. We often see this pattern of a lower amplitude after one generation. If it was only noise, the first generation should exhibit either lower or higher difference than the F2. Epigenetic components of flowering could have played a role in adaptation to warming and these effect cannot be distinguished from true quantitative genetic effects if parts of these effects last more than one generation. Do the same MLG (from 1987 and 2009) have the same fitness? Because the authors have the chance to have the same MLG, it would be interesting to look at this relationship to investigate maternal effects.

4-Genetic analysis. If I understood well, the test for selection versus drift is based only on conserved multilocus genotypes, i.e. a fraction of the population. Why doing this choice? Why not using a Qst/Fst approach that would take into account all the individuals? (the design allows to estimate Qst, doesn’t it?). In addition, I see a potential bias because it assumes that the population behaves as a fully selfing populations, which is not the case. While the authors point the potential differential selective response of outcrossers versus selfers, the results reported are based only on the full selfing fraction of the population, which I found contradictory.

Overall, I found the ms interesting and such long term dataset is rare. However, the ms would benefit from being more focused (particularly the introduction) in order to highlight the results and their biological interpretation.

Reviewed by Jon Agren, 2020-10-19 15:12.<br> This study uses a resurrection experiment and simulations to explore the possible causes of changes in flowering time and genetic composition of a Medicago truncatula population across 22 generations. In the resurrection experiment, plants grown from seeds collected 22 years apart were raised in the greenhouse to produce selfed lines. These lines were then used to document possible changes in flowering time and to quantify selection on flowering time in the greenhouse. Changes in genetic composition were characterized by scoring 20 microsatellite loci (16 kept after filtering) and documenting changes in the frequencies of multilocus genotypes. The paper is well written and addresses interesting problems of wide general interest. However, I think the authors need to (a) motivate their approach to use estimates of selection obtained in the greenhouse to infer selection in the field, (b) provide more detail on the distribution of multi-locus genotypes and the power of their analysis of change in genetic composition, and (c) clarify a few details when it comes to sampling procedure (see below).

Main comments:

The authors appear to assume that selection quantified in the greenhouse is likely to mirror selection in the field at present and 22 years ago. This needs to be motivated.

I suggest the authors provide more detail on the distribution multilocus genotype (MLG) frequencies, and that this information is given already at the start of the third paragraph on p. 7. They report that 60 different MLGs were detected in their sample of 145 individuals. Two MLGs were common, and 12 MLGs were shared between the two sampling years. This suggests that most MLGs were rare and perhaps only represented by a single plant? The authors may want to discuss whether their sample sizes are sufficient to characterize changes in genetic composition of a population with such skewed distributions of MLGs.

I suggest the authors clarify a few details regarding sampling:

(a) For the resurrection experiment, “100 seeds per sampling were replicated” (p. 3, second paragraph). Were these seeds from 100 different pods and thus sampled from 100 plants, or were they a random sample of 100 seeds from a pooled seed sample from each year?

(b) For the genetic analysis, leaves were sampled from “the multiplication generation in the greenhouse” (p. 4, fifth paragraph), and after filtering 145 individuals remained in the data set to be analysed. Please, state explicitly that the “multiplication generation” refers to the plants derived from the 200 field-collected seeds (presumably representing seeds from 200 plants(?); see previous comment). Were seeds from the two sampling occasions equally represented among the 145 individuals included in the analysis?

Minor corrections:

Abstract, line 11 from bottom. Change “population” to “populations”

p. 7, first paragraph, second line from bottom, “in both years”. From this wording, you easily get the impression that selection was quantified in two years. I suggest you add a few words to indicate that this rather refers to a similar negative relationship being observed among lines derived from each of the two years.

To make text in graphs readable, font size should be increased in Figures (in particular in Figs. 3-5).

Reviewed by Stefan Laurent, 2020-10-16 11:05.<br> In this study, the authors test whether flowering time evolved in an experimental population of Medicago truncatula and whether this change could represent an adaptation to varying environmental conditions. For this, they measure changes in flowering time in a natural population over 22 generations (2 timepoints), they quantify the association between flowering time and fitness (as approximated by the number of seeds produced), they track changes in haplotype frequencies characterized by different approximated fitness values, and finally they also measure changes in flowering time in 17 populations from the same geographical region that have been sampled twice over a comparable time range.

The authors report a significant reduction in flowering in the main population and in the regional analysis that appears to be consistent with the specific effects of climate change in the Mediterranean region (i.e. limiting summer drought occurs earlier in the year). They also report a significant association between flowering time and seed production. However, the evidence for the effect of positive selection obtained by analyzing the changes in haplotypes is at best marginal; even if the authors do a good job in describing some of the uncertainty associated with this analysis, I think that one more aspect should be exposed.

Besides my major comment, I find the manuscript clearly written, the analyses carefully conducted and presented, and the intro and discussion very well written and informative, at least for the non-expert.

Major comment

My only major criticism refers to the results presented in Figure 3. The selection gradients measured here seem to be heavily influenced by two outlier points with low seed production and early flowering. As a result, the linear models (especially the one for MLG found in 1987) appear to be a poor fit to the data, as can probably be seen by inspecting the residuals, which are unlikely to be normally distributed. I think that the authors should report the uncertainty around the slopes and that this uncertainty should be further considered in the analyses presented in figure 4, which will likely cause the observed selection gradient to be non-significant under a larger range of Ne values. I am not sure about the best way to obtain confidence intervals for the selection gradients but I imagine that a bootstrap approach should be applicable.

Minor

I agree with the authors that the N_e value estimated from the temporal Fst is very likely underestimated. Comparing the expect heterozygosity under Ne=19 with the observed He would further support the idea that larger Ne values are indeed realistic. How does the observed heterozygosity in the population compares to the theoretical expectations given by Nordborg and Donnelly (1997)? Rescaling the census number (>2000) by 1/(1+F) would lead to a less conservative Ne value for the test for selection and may allow a putative selection signal to be detected even after considering the uncertainty around the observed selection gradient.

Author Response

We are grateful for the thorough and thoughtful comments provided by the reviewers, and we appreciate their support for the design and implications of this study. We have addressed the major points raised by the reviewers as follows.

Major Concerns:

1) Limitations of extrapolation to human health and disease.

From Reviewer 2: Though I found the work largely beyond critique technically, I would have appreciated additional discussion of the limitations of the use of a captive non-human primate to model human dietary response.

From Reviewer 3: However, my major concern is the suitability of these results to explain human relevance and how far they can address the actual evolutionary significance. I think they should tone down a little. For example, is there really any strong reason to assume that macaques will mimic dietary responses in humans? I appreciate the fundamental importance of macaque-specific responses, but I am unclear how captive primates can model human effects─ how do authors factor their (obvious?) fundamental differences between different immune response profiles activated against similar cues and standing microbiome, warranting divergent interactions with the said dietary manipulations. I think these are caveats that need to be carefully discussed to avoid building over expectations among readers.

From Reviewer 3: Could there be more discussion on the relevance of differentially expressed macaque genes in humans?

We appreciate the concern regarding possible overinterpretation of results. There is an extensive body of literature demonstrating the utility of the cynomolgus macaque model to explore influences of diet on numerous phenotypes including atherosclerosis and cardiovascular disease, bone metabolism, breast and uterine biology, and other phenotypes (Adams et al., 1997; Clarkson et al., 2004, 2013; Cline et al., 2001; Cline & Wood, 2006; Haberthur et al., 2010; Lees et al., 1998; Mikkola et al., 2004; Mikkola & Clarkson, 2006; Naftolin et al., 2004; Nagpal, Shively, et al., 2018; Nagpal, Wang, et al., 2018; Register, 2009; Register et al., 2003; Shively & Clarkson, 2009; Sophonsritsuk et al., 2013; Walker et al., 2008; Wood et al., 2007). The cynomolgus model was remarkably accurate in predicting effects of hormone therapies on both cardiovascular disease and breast cancer later demonstrated in the very large Women’s Health Initiative (Adams et al., 1997; Clarkson et al., 2013; Naftolin et al., 2004; Shively & Clarkson, 2009; Wood et al., 2007). Cynomolgus macaque responses to other therapies (tamoxifen, selective estrogen receptor modulators, blood pressure medications, etc.) also have shown great similarities to those in humans (Cline et al., 2001). We have added additional text to the Abstract (lines 51-52), Introduction (lines 136-141), and Discussion (lines 531-542) to situate the current work in the extensive literature that uses cynomolgus macaques as a model to understand human health. We have also included discussion regarding the limitations of extrapolating these results to humans in lines 543-545 of the Discussion

We also tested the overlap of differential gene expression induced by the Western diet with genes implicated in human complex traits (Zhang et al., 2020). Genes implicated in numerous traits associated with cardiometabolic health were enriched in Western genes, while no traits were enriched in Mediterranean genes. We describe these findings in lines 206-215 of the Results section and in Figure 1—figure supplement 1, which depicts traits relevant to human health and disease identified by previous groups where gene expression profiles overlapped with the “Western genes” in the current study. Lines 668-672 of the Materials and Methods detail the statistical approach used.

2) Limitations of this experimental design to test the evolutionary mismatch hypothesis.

From Reviewer 2: My worry is that macaques are so ill-adapted to the Western human diet that the behavioral and inflammation differences seen are explained by this macaque-Western diet mismatch, which dwarfs the human-Western diet mismatch that likely nonetheless exists. This concern can be partially mitigated by careful discussion of this study limitation.

From Reviewer 2: One critique of dietary interventions that attempt to correct the evolutionary mismatch (which would be useful to address when discussing human-macaque differences) is that human evolution continuing to the present day has been marked by putative selection regime changes associated with multiple major dietary shifts, including meat eating and those arising from cooking and domestication of plants and animals. Such selection may have differentiated humans from macaques in key ways that influence macaque suitability as a dietary model.

From Reviewer 2: My recommendations for strengthening the work are minor, besides those outlined above to include caveats concerning the differences between macaques and humans that will hopefully prevent lay readers from over-interpreting the results. Specifically, species-level differences which warrant mention include gross differences in "natural" diet between the species, as well as known recent selection on diet-related genes in humans (reviewed in, e.g., Luca et al. 2010; doi:10.1146/annurev-nutr-080508-141048) and gut microbiome differences between the species (e.g., Chen et al. 2018; doi:10.1038/s41598-018-33950-6).

From Reviewer 2: A simple analysis that begins to address this point analytically would be to compare what results exist for humans (e.g., Camargo et al, 2012; doi:10.1017/S0007114511005812) to those of your study.

From Reviewer 2: Additionally, one could check whether the DE genes you identify are known to be selected in humans.

We appreciate the suggestion to strengthen our discussion of the macaque model of human health. As with early hunter-gatherer humans, macaques are omnivorous in the wild, eating a variety of plants and animals. In addition, the cynomolgus macaque often co-exists with human populations, and in that respect may have co-evolved in many ways. Furthermore, cynomolgus macaques have been used in studies of dietary influences on chronic prevalent human disease for 50 years (Malinow et al., 1972), and nearly 700 papers in a Pubmed literature search support the idea that cynomolgus responses to diet are remarkably similar to those of humans in all systems studied. Some of these studies are identified above. With respect to the microbiome, previous work by others has demonstrated that the gut microbiome of omnivorous nonhuman primates is similar to that of humans living a modern lifestyle (Ley et al., 2008), and we previously reported similarities in patterns of microbiome responses to Mediterranean vs. Western diets between humans and NHPs in the present study (Nagpal, Shively, et al., 2018). We have added discussion of the above and note limitations of extrapolation to humans due to species-level differences in natural diets and the role that selection may plan in responses of humans to Western or Mediterranean dietary patterns (lines 543-545). Similarities between humans in DE genes are noted in responses above. In addition, we already had noted that our studies complement and extend the findings of Camargo (line 399), and we added more detail that we found similar effects of diet on expression of IL6 and NF-kB pathway members (line 397).

3) Lack of control group maintained on a standard chow diet.

From Reviewer 2: In future studies, it would be useful to have samples from proper control monkeys fed a standard primate diet.

From Reviewer 3: Also, this is slightly unfortunate because there is no full control treatment where macaques are maintained in their regular diet (i.e., standard monkey chow) and then compared with groups switched to the Mediterranean vs western diet to estimate the relative deviations from their expected physiological processes and behavioural traits.

We appreciate the concern regarding the lack of a standard monkey chow diet control group. All monkeys ate chow during the baseline phase and were thoroughly phenotyped, exhibiting minimal differences in monocyte gene expression profiles between groups subsequently assigned to the two diets, which involved stratified randomization based on key baseline characteristics while consuming the same diet. Importantly, monkey chow is unlike any historic or current human or nonhuman primate diet as is apparent in Table 1. It is quite low in fat, and rich in soy protein and isoflavones, which are known to alter physiology and immune system function. Therefore, parallel assessments of health measures in monkeys consuming chow long term do not provide data relevant to diet effects on human health. We have added discussion of the strengths of the study (lines 136-141, 531-542), which was designed in order to be able to draw causal inference about the diet manipulation, and we acknowledge limitations to assess directionality of changes (i.e. which experimental diet is driving a particular observed difference) in lines 545-553.

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)): In this manuscript, using in vivo infection of Zebrafish embryos with Mycobacterium marinum and THP1-derived macrophages infected with Mycobacterium tuberculosis, the authors show that these pathogenic mycobacteria trigger an increase of K+ concentration through the expression of OXSR1. The ESX1 secretion system that is essential for the virulence of M. marinum is required for the expression of OXSR1 and SPAK. OXSR1 and SPAK are involved in the WNK signaling pathway and are cytoplasmic serine/threonine protein kinases that regulate the function of a series of sodium, potassium and chloride co-transporters via phosphorylation. Given that K+ efflux is now accepted as the main inducer of NLRP3 inflammasome, the authors report that this infection-induced OXSR1 expression restrains the protective NLRP3 inflammasome response leading to IL-1b maturation and secretion. Il-1b as a very potent pro-inflammatory triggers TNF-a production and the authors demonstrate that infection-induced OXSR1 expression suppressed host protective TNF-a and cell death early in fection. It appears therefore that virulent mycobacteria induce OXSR1 expression to reduce inflammasome activation by maintaining high intracellular K+. The results presented by the authors are convincing and the conclusions raised by the authors are well supported by the data. In zebrafish embryos, OXSR1 knockdown nicely reduces mycobacteria burden. Based on their conclusions that infection-induced OXSR1 expression reduces NLRP3 inflammasome activation, NLRP3 inflammasome activation has therefore a protective effect against bacterial infection. My main concern is that surprisingly, nlrp3 or il1b knockdown has no effect on bacterial burden in comparison to control embryos. Lane 256, as an explanation, the authors wrote "This may have been because we were using mosaic F0 CRISPR knockout, which is not a complete removal". The removal using mosaic F0 CRISPR knockout is nevertheless sufficient to observe a decrease in bacterial burden following OXSR1 knockdown. Would it be possible that OXSR1 also regulates immunity independently of NLRP3 inflammasome?

Yes, we will add text to the discussion to address potential NLRP3-independent mechanisms that connect OXSR1 to immunity against mycobacterial infection.

The lack of effect of il1b knockdown on M. marinum burden has been corroborated by independent laboratories including a publication from the Elks lab in Journal of Immunology: Ogryzko et al 2019. The Ogryzko study found no effect of il1b knockout on M. marinum burden.

**Other comments:** OXSR1 WB in extended Data 3 is really poor quality so that it is hard to see the increased expression of OXSR1 following infection.

The western blot will be repeated for cleaner images.

Figure 2C. It is not shown but I guess that similar results should be obtain using M. tuberculosis.

Material leaving our BSL3 facility must be decontaminated which makes this suggested analysis impossible in our facility.

Figures 5D and 5E. To confirm the involvement of NLRP3, in addition of using MCC950, NLRP3 knock down using siRNA should be also performed. NLRP3-deficient THP-1 cells are also commercially available if the siRNA-mediated knock down of NLRP3 is not convincing enough.

We will purchase NLRP3 deficient THP-1 cells and use our existing shRNA vector to create NLRP3 and OXSR1 deficient cells. We will repeat the experiments in 5D and 5E in these cells to confirm NLRP3 involvement.

**Minor comments:** How do the authors think that mycobacterium induces OXSR1 expression following infection? It has not been investigated and it is not discussed.

In Fig1A we showed upregulation of oxsr1a transcription and in Fig2A we showed upregulation of OXSR1 protein. In line 204 of the discussion we described our hypothesis that oxsr1a transcription is responsive to the mycobacterial ESX1 secretion system.*

*

Reviewer #1 (Significance (Required)): The observations reported in this manuscript are interesting since for the first time, it is described that virulent mycobacteria induce OXSR1 expression to reduce NLRP3 inflammasome activation by maintaining high intracellular K+. This is quite a significant advance in the field. To escape immune control, many successful intracellular pathogens have evolved methods to limit inflammasome activation. While it is known that potassium efflux is a trigger for inflammasome activation, the interaction between mycobacterial infection, potassium efflux and inflammasome activation was not explored. My field of expertise is the regulation of inflammasome activation. As far as I remember, I've never reviewed a paper using zebrafish embryos but here, the explanations and data are clear so that it was easy to understand and to evaluate. Likewise, I did not know the WNK signaling pathway but the literature clearly shows that it is involved in intracellular ionic balance.

Reviewer #2 (Evidence, reproducibility and clarity (Required)): Hortle et al, in this study evaluated the role of WNK kinases SPAK and OXSR1 during M. marinum and M. tuberculosis infection. These two kinases inhibit the KCC channels which have a tendency to export potassium out of the cell. Since potassium efflux is a known stimulator of NLRP3 inflammasome activation, this raises the possible role of these kinases in inflammation and infection. Authors showed that inhibiting OXSR1 genetically and chemically reduced the mycobacterium survival in cells and zebrafish model, thus proposing OXSR1 as a host-directed therapeutic candidate. They showed that knockdown of OXSR1a leads to NLRP3 inflammasome mediated IL1B induction, which results in increase in TNFa and suppression of mycobacterium growth. Furthermore, reduction in mycobacterium growth in OXSR1a KD zebrafish embryos was found to be dependent on ESX1 machinery of Mycobacterium. The role of potassium in regulating Mycobacterium host response is novel. However there are few things which are missing from this interesting work. **Main comments**

1. Since OXSR1 is known to inhibit KCC channels, which will lead to increase intracellular potassium. Why in infected control cells there is no increase potassium, Fig 2C. What would be the role of potassium in OXSR1 mediated control of Mtb growth?

We will perform more experiments with altered levels of extracellular potassium to determine if infected control cells have increased intracellular potassium compared to OXSR1 knockdown cells.

Does addition of extracellular potassium restricts mycobacterium in OXSR1-KD cells?

We will perform additional experiments with the addition of potassium to the cell culture medium to address this concern.

Since OXSR1 is known to inhibit KCC channels, What happens to the activity of these channels in OXSR1 KD cells? This is important, because authors could not find any difference in intracellular potassium between uninfected control and uninfected OXSR1 KD cells (Fig 2C). It will be good to add the flowcytometric histogram or dot plots of potassium staining in the main figure or in extended figures.

We have data showing that although there is minimal difference in basal K+ level in OXSR1 KD cells, there is significantly lower K+ level when the cells are placed in High K+ media, or osmotic shock. We will include this data in the revised manuscript. We will amend the figures to include Flow plots.

Acquisition of potassium stained cells - In methodology it has been mentioned that ion K+ Green stained undifferentiated THP1 cells were acquired using PE channel while differentiated THP1 cells were acquired using FITC channel. Furthermore in methods its mentioned that Leica Sp8 microscope was used to acquire images, however I do not see any of this data in the manuscript.

Ion K+ green emits into both the PE and FITC channels. Our choice to use the FITC or PE channel depended on whether the cells were also infected with red fluorescent bacteria which “contaminates” the PE channel.

Fig 2E and 3D - Meaning of "Normalized CFU/ml"? Each dot represents what? How many times this experiment was performed, please add in the legend.

Normalized CFU/ml means that the CFU at 3 day post infection were normalized to the 0 day post infection intracellular bacterial burden, to adjust for any differences in phagocytosis of bacteria. Each dot represents the CFU from an infected well in a single representative experiment and the experiment was repeated 3 times. This information will be added to the figure legend.

Fig 1D - What could be the reason of no statistical significant difference between wild type and homozygous oxsr1a-KO fish?

This data is from two experimental replicates. We are currently growing more breeding fish to generate embryos for experimental replicates.

Good to have a schematic model showing the finding s of the study

We will add a schematic model to the manuscript.

TNFa is double edge sword and can lead to pathology. Hence treatment of chronically infected animals (say mice) by Compound B, will be needed to confirm the HDT activity of OXSR1.

Yes, we will add discussion of this point as a caveat to our future direction of using OXSR1 inhibition as a HDT.

Reviewer #2 (Significance (Required)): This study showed role of kinases, which regulate trafficking of potassium, in mycobacterium-host interaction. Since kinases are draggable, so this opens a new area for developing host-directed therapies for TB. Reviewer #3 (Evidence, reproducibility and clarity (Required)): In this study, the authors suggest to have evidence for OXSR1 to inhibit NLRP3 inflammasome activation by limiting potassium efflux during mycobacterial infection. To my opinion, the study lacks important results supporting their main conclusions. In many instances, the authors have over-interpreted their data and I therefore do not support publication of this study. **Main comments:** Activation of the NLRP3 inflammasome upon OXSR1 knockdown was not convincingly demonstrated.

We will address the activation state of the NLRP3 inflammasome with NLRP3 KO and OXSR1 KD cells as also suggested by reviewer 1: We will purchase NLRP3 deficient THP-1 cells and use our existing shRNA vector to create NLRP3 and OXSR1 deficient cells. We will repeat the experiments in 5D and 5E in these cells to confirm NLRP3 involvement.

Clearance of bacteria in an organism, herein zebrafish, involves mechanisms in different cell types including downstream of inflammasome activation. Thus, bacterial clearance experiments in THP-1 cells might not necessarily be related to in vivo experiments in an organismal context. Finally, a mechanism as to how mycobacteria enhance OXSR1 expression to block a NLRP3-mediated response has not been addressed.

We are not able to perform in depth analysis of the bacterial side of this host-pathogen interaction as my lab will close in the next 4 months. We have shown that transcriptional upregulation of oxsr1a is ESX1-dependent. We will include data on OXSR1 protein expression with WT and ESX1 mutant bacteria when we repeat the western blots in Extended data 3.

**Specific comments:**

1. The author showed that the M. marinum ESX1 secretion system induced OXSR1 expression to inhibit the NLRP3 inflammasome activation. This is contradictory to another recent study (PMID: 18852239), which showed that the ESX1 secretion system activated the NLRP3 inflammasome. These effects are not mutually exclusive. The ESX1 secretion system has a “deliberate” purpose in exporting mycobacterial effector proteins to subvert cellular immunity while also having an “accidental” role in exposing the host cell cytosol to vacluolar contents that can activate cellular immunity. We do not assert that mycobacteria completely inhibit all NLRP3 activation – rather that attempts to stop full activation via inducing the expression of host OXSR1. This can be seen in the IL-1b data in figure 3E, where infected WT cells release more IL-1b than MCC950 treated cells, but less than OXSR1 KD cells.

In line 102, based on Data shown in Fig 1D, the authors concluded that homozygous, but not heterozygous, oxsr1asyd5 embryos showed reduced bacterial burden. However, in Fig 1D, the difference among the genotypes is not significant.

This concern will be addressed with additional replicates.

In line 196, the authors stated that "We present evidence that pathogenic mycobacteria increase macrophage K+ concentration by inducing expression of OXSR1." However, the authors did not provide evidence for this.

We will soften this phrase in the discussion to replace “by inducing” with “and induce”.

Based on Extended data 3, the authors concluded that infection increases the expression of OXSR1. However, this is not evidenced in the Western Blot. In addition, in panel B, the OXSR1 blot showed many non-specific bands with decreased intensity in OXSR1 knockdown conditions suggesting that there is unequal protein loading making it impossible to interpret these results.

We will repeat the western blots as per Reviewer 1’s comment as well.

The authors concluded that infection-induced OXSR1 expression suppressed inflammasome activity to aid mycobacterial infection. Experiments with Compound B, that inhibits OXSR1 phosphorylation, are used in support of the above conclusion. I do not really see a connection between OXSR1 expression and the inhibitor experiment.

We will reword “expression” to “activity” in regards to the inhibitor experiment.

In line 187, "Knockdown of tnfa reduced the amount of infection-induced tnfa promoter-driven GFP produced around sites of infection ....". How can a knockdown of tnfa affect the GFP expression driven by the tnfa promoter ?

The promoter fragment used in the TgBAC construct contains target sites for two of our guide RNAs. We will also include qPCR validation of the knockdown.

Reviewer #3 (Significance (Required)): Mechanism underlying decreased intracellular potassium level is of great interest in the inflammasome field. However, their observation is not in line with published studies. Audience in the pathogen-host interaction field will be interested. Expertise: dissection of signalling pathway regulation, molecular and cellular mechanism underlying NLRP3 inflammasome activation. We are not using zebrafish model.

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Referee #1

Evidence, reproducibility and clarity

In this manuscript, using in vivo infection of Zebrafish embryos with Mycobacterium marinum and THP1-derived macrophages infected with Mycobacterium tuberculosis, the authors show that these pathogenic mycobacteria trigger an increase of K+ concentration through the expression of OXSR1. The ESX1 secretion system that is essential for the virulence of M. marinum is required for the expression of OXSR1 and SPAK. OXSR1 and SPAK are involved in the WNK signaling pathway and are cytoplasmic serine/threonine protein kinases that regulate the function of a series of sodium, potassium and chloride co-transporters via phosphorylation. Given that K+ efflux is now accepted as the main inducer of NLRP3 inflammasome, the authors report that this infection-induced OXSR1 expression restrains the protective NLRP3 inflammasome response leading to IL-1b maturation and secretion. Il-1b as a very potent pro-inflammatory triggers TNF-a production and the authors demonstrate that infection-induced OXSR1 expression suppressed host protective TNF-a and cell death early in fection. It appears therefore that virulent mycobacteria induce OXSR1 expression to reduce inflammasome activation by maintaining high intracellular K+.

The results presented by the authors are convincing and the conclusions raised by the authors are well supported by the data.

In zebrafish embryos, OXSR1 knockdown nicely reduces mycobacteria burden. Based on their conclusions that infection-induced OXSR1 expression reduces NLRP3 inflammasome activation, NLRP3 inflammasome activation has therefore a protective effect against bacterial infection. My main concern is that surprisingly, nlrp3 or il1b knockdown has no effect on bacterial burden in comparison to control embryos. Lane 256, as an explanation, the authors wrote "This may have been because we were using mosaic F0 CRISPR knockout, which is not a complete removal". The removal using mosaic F0 CRISPR knockout is nevertheless sufficient to observe a decrease in bacterial burden following OXSR1 knockdown. Would it be possible that OXSR1 also regulates immunity independently of NLRP3 inflammasome?

Other comments:

OXSR1 WB in extended Data 3 is really poor quality so that it is hard to see the increased expression of OXSR1 following infection.

Figure 2C. It is not shown but I guess that similar results should be obtain using M. tuberculosis.

Figures 5D and 5E. To confirm the involvement of NLRP3, in addition of using MCC950, NLRP3 knock down using siRNA should be also performed. NLRP3-deficient THP-1 cells are also commercially available if the siRNA-mediated knock down of NLRP3 is not convincing enough.

Minor comments:

How do the authors think that mycobacterium induces OXSR1 expression following infection? It has not been investigated and it is not discussed.

Significance

The observations reported in this manuscript are interesting since for the first time, it is described that virulent mycobacteria induce OXSR1 expression to reduce NLRP3 inflammasome activation by maintaining high intracellular K+. This is quite a significant advance in the field. To escape immune control, many successful intracellular pathogens have evolved methods to limit inflammasome activation. While it is known that potassium efflux is a trigger for inflammasome activation, the interaction between mycobacterial infection, potassium efflux and inflammasome activation was not explored.

My field of expertise is the regulation of inflammasome activation. As far as I remember, I've never reviewed a paper using zebrafish embryos but here, the explanations and data are clear so that it was easy to understand and to evaluate. Likewise, I did not know the WNK signaling pathway but the literature clearly shows that it is involved in intracellular ionic balance.

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

In this project, authors develop a colorimetric and luminescence assay for the detection of SARS-CoV-2 RNA in vitro. They design an RNA based sensor that will be triggered by target RNA then release the ribosome binding site and a translation start site followed by a reporter gene. The released sequence will then trigger the production of reporter protein by transcription-translation coupled assay. Authors also introduce an RNA amplification step in order to increase the sensitivity of this assay.

**Strengths:**

This assay provides a simple, rapid way to detect SARS-CoV2 and it is an elegant way to incorporate transcription-translation coupled assay for SARS-CoV-2 RNA detection and identify SARS-CoV-2 patient samples. It is a nice assay and the performance is comparable with the existing method.

**Weaknesses:**

However, the positioning of this assay is not very clear. The readout of this assay could be recorded by camera whereas it includes several steps such as RNA extraction, amplification, transcription-translation coupled assay and reporter reaction. The limitations of the existing methods (RT-PCR, paper strip) and the advantages of this assay haven't been demonstrated by the experiments. The stability of RNA may also restrict the application of the proposed assay on site.

**Major comments:**

Authors are suggested to design an experiment to show the advantage of this assay compared with the existing method.

Response: We thank the reviewer for pointing this out. In Fig 5, we show a comparison of our assay with the bench mark in COVID-19 diagnostics, which is the RT-qPCR assay. We specifically correlate the Ct- values obtained for RT-qPCRs with the amount of color or luminescence obtained through our assay. From these experiments we note that the sensitivity of our assay is a lttle less than the RT-qPCRs where our assay does not detect Ct-values in the 36 to 38 range (very low viral loads). This comparative experiment highlights that our assay bears clear advantages over the RT-qPCR in terms of ease of assay set up, ease of color detection, amenability to cell-phone imaging and no requirement of sophisticated equipment or technical training to interpret results. The full details of these comparisons are discussed in the manuscript.

This is consistent with the literature on COVID-19 diagnostics where new assays are routinely bench-marked against the “gold-standard” RT-qPCR assay ((Corman et al., 2020; Pearson et al., 2021).

What is the limit of detection of this assay using LacZ and Luciferase reporter respectively?

Response: The limit of detection of the assay as shown in Fig 4B and Fig 4C-D, was found to be 100 copies of RNA, which translates to a concentration of 8 attomolar RNA. In this case, we find the limit of detection to be the same for both LacZ (Fig 4B) and Luciferase (Fig 4C-D) reporter.

The calculations of copy number and sensitivity were made using a commercial source of synthetic CoV-2 RNA (Twist Biosciences) that is used in several studies about COVID-19 diagnostics (Joung et al., 2020; Rabe & Cepko, 2020; Wu et al., 2021). The RNA copy numbers are taken from the product details provided by the manufacturer. These details are now clearly stated in the manuscript. The commercial RNA is provided at 106 copies per ul. From this we take as low as 100 copies per 20ul of NASBA reaction, which we are able to detect using our assay. Hence our sensitivity comes to 8 attoMolar. We have clarified this in the manuscript. We noticed a typo in the original submission where we refer to a sensitivity of 80 attomolar in the Discussion. This is corrected to 8 attomolar. With this sensitivity we are within the range to detect RNA in patient samples, as confirmed by our patient data.

Authors have not examined the selectivity of this assay. What is the specificity, selectivity for each of these variants? Does altering target RNA change the specificity?

Response: We thank the reviewer for raising this point. As recommended by the reviewer, we have now examined the selectivity of this assay through new data (See new Fig S3, new Fig S4 and new Fig S8, also shown below).

We have examined selectivity in 3 different ways.

1. Is our sensor selective to the said region of the SARS-CoV-2 genome? To address this, we generated 19 different Target (Trigger) RNAs spread across the SARS-CoV-2 genome. These were tested against Sensor 12 to examine for their ability to trigger the sensor. We find that our sensor is highly selective for its target RNA and does not show any detectable response to the other regions of SARS-CoV-2 (see new Fig S3).

Next, we asked if our assay is selective to SARS-CoV-2 versus other related human corona viruses. For this, we first examined the sequence of the target RNA (Amplicon RNA 12) that is sensed by Sensor 12. We selected equivalent regions of RNA from a different coronavirus, the HKU1 human coronavirus family. We generated these RNA sequences in vitro and performed IVTT. These new data are shown in new Fig S4 and below. We find that the human coronavirus (HKU1) RNAs are not able to turn on our sensor, whereas the cognate SARS-CoV-2 RNA is able to.

We then asked if our assay can detect a current prominent variant of SARS-CoV-2. A major cause of concern is the ability of SARS-CoV-2 to accumulate mutations in its genome, resulting in different variant strains of SARS-CoV-2. Of these variants, the Delta variant (B.1.617.2) is not only highly contagious but has been noted as a possible vaccine breakthrough mutant of SARS-CoV-2. For this, we obtained RNA from the patient nasopharyngeal swab samples from the NCBS-inStem Covid-19 testing Center, Bangalore, India. RNA was isolated in the BSL-3 facility at the testing center. RNA samples were sequenced and confirmed to be the Delta variant- B.1.617.2 (sequences deposited in GASIAD). RNA extracted from these patient samples were tested against Sensor 12 using NASBA followed by IVTT. We find that our assay can efficiently detect the Delta variant SARS-CoV-2 RNA from patient samples with a build up of color, but no color was observed from control samples. These new data are shown below and in new Fig 5F and new Fig S8. The ability to detect the Delta variant of SARS-CoV-2 is an important feature of our sensor since this variant is now of global concern and extensively found in the population, even becoming the dominant variant in several countries (Callaway, 2021; O’Dowd, 2021; Torjesen, 2021).

In Figure 2C-F, sensor 17 showed higher fold change and sensitivity. Why was sensor 12 selected for further study in Figure 3

Response: The reviewer rightly notes that sensor 17 responds to 1012 copies of RNA and hence appears to be inherently more sensitive than sensor 12, which responds to 1013 copies of RNA. However, neither of these sensitivities are good enough to detect the levels of viral RNA found in patient samples. Hence we coupled these sensors with a step of NASBA amplification. The screen to identify pairs of NASBA primers gave us great hits for sensor 12 right off the bat, where we could detect down to 100 copies of RNA. Hence we moved forward with sensor 12 for further experiments. This has now been clarified in the manuscript.

Authors should show the error bar in all plots. Authors should also indicate what the error bar means (SD, S.E.M. etc.) throughout the manuscript.

Response: This is an important point. We have added the error bars and statistical analyses to all relevant plots. We have included the description of these statistical parameters in the figure legends throughout the manuscript, where relevant. Alternatively, experimental replicates are indicated and shown in the revised manuscript. Specifically in Figures 2 and 3 and 4D we have performed statistical analysis to include p-values to show significance of the data. For the data in Figure 4 B-C we include the experimental replicates as a new Supplementary Figure (see new Fig S5). Data in Figure S5 is now updated to include the experimental replicates. For the patient data in Figure 5, we have included details of specificity and sensitivity analysis for clinical samples (see new Fig 5C).

**Minor comments:**

"This method is relatively faster but may generate false positives due to non-specific amplification and primer interactions." Reference is needed.

Response: We have now added the following references in support of this statement. (Gadkar, Goldfarb, Gantt, & Tilley, 2018; Sahoo, Sethy, Mohapatra, & Panda, 2016)

"using the softwares Primer 3 and NUPACK." Reference is needed.

Response: We have now added the following references (Untergasser et al., 2012; Zadeh et al., 2011)

Reference 15 belongs to CRISPR-CAS based assay but it was cited under RT-LAMP assay.

Response: This has now been corrected. We thank reviewer for this.

Reviewer #1 (Significance (Required)):

This paper will be of interest to scientists interested in developing diagnostic tools for the detection of SARS-CoV2 in viral and host pathogenic sequences; genetic disorders and development of precision medicine.

Reviewer works in the field of Chemical Biology and Nanotechnology including sensor development and the application in diagnosis, cell physiological studies.

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

In this manuscript, Charkravarthy et al. report a new method for detecting SARS-CoV-2 RNA in both in vitro and human saliva and nasal samples. The new detection method, PHANTOM, is capable of detecting as few as 100 copies of the SARS-CoV-2 genome. The method is demonstrated to reproducible over a large range of viral titers and results in a binary report on CoV-2 infection. From my perspective the results are strong and fairly convincing (please see comments below). There is clear, logical, flow to the experiments and engineering of the PHANTOM system. The collaborative work is well organized and logical. The work is clearly of high significance and certainly merits expedited review and publication. I would like to unambiguously state that support publication of this manuscript in its current form in the non-peer reviewed context of this journal, would be more than happy to provide further peer review of this manuscript upon submission to another journal, and would be more than happy to provide further comments if requested by the authors.

My personal background is broad in range, however, I have a long track record of research in RNA folding, structural biology, biosensor development, and bioinformatics. Given this knowledge base, I found the manuscript rather easy to read and digest. The manuscript is well written and clear. In order to expedite the process of review I will not give a detailed review which would include grammatical errors (there are are very few). Rather, I will touch on the most pressing issues I see.

**Major concerns:**

1) There a number of figures that do not show a statistical measure of significance (e.g. error bears, ANOVA, etc.). It is essential that these be included in the final peer reviewed publication. (See Figure 2A, Figure 3D, Figure 4B, Figure 4C, Figure 5A, Figure 5C, Figure 5D).

Response: This is an important point. We have added the error bars and statistical analyses to all relevant plots. We have included the description of these statistical parameters in the figure legends throughout the manuscript, where relevant. Alternately, experimental replicates are indicated.

Specifically in Figures 2 and 3 and 4D we have performed statistical analysis to include p-values to show significance of the data. For the data in Figure 4 B-C we include the experimental replicates as a new Supplementary Figure (see new Fig S5). Data in Figure S5 is now updated to include the experimental replicates. For the data in Figure 5, we have included details of specificity and sensitivity analysis for clinical samples (see new Fig 5C).

2) There are some important points that do not include references within the manuscript. I believe that the authors should reference Abdolahzadeh et al. RNA 2019 in the introduction. This manuscript describes another NASBA viral detection system using fluorescent RNA reporters (also see Trachman et al. Q. Rev. Biophys 2019, for reference on fluorescent aptamers). Also see the ROSALIND method (Jung et al. 2020 Nature Biotechnology) for detecting water contaminants using visual identification by fluorescent aptamers.

Response: We have added the above mentioned references to the manuscript as suggested by the reviewer.

3) The discussion states that "The overall sensitivity in the attomolar range ensures detection of infection in the majority of Covid-positive patients in a population". Please provide a reference to support this and explicitly state the concentration of viral RNA in patient samples. There are a number of times that the copy number of viral genomes and sensitivity of the measurement is stated throughout the manuscript. There should also be a reference and statement about concentration.

Response: The reviewer has raised multiple connected points here, which we address in the revised manuscript.

1. Concentration of RNA in patient samples: We have added the references (Pujadas et al., 2020; Wyllie et al., 2020) where the authors report that the typical concentration of viral RNA in patient nasopharyngeal swab samples lies in the range of 104 to 105 copies of RNA per ml. This translates to a concentration range of 10 to 100 attoMolar. This reference is now added to the manuscript. For the patient samples used on our study, we refer to the Ct- values obtained from the RT-PCR tests and correlate Ct values to the readout from our assay, consistent with other reports on COVID-19 diagnostics ((Joung et al., 2020; Vogels et al. 2020; Wu et al., 2021).

Copy number and sensitivity: As the reviewer notes, we refer to viral genome copy number and sensitivity of our assay in the manuscript. These calculations of copy number and sensitivity were made using a commercial source of synthetic CoV-2 RNA (Twist Biosciences) that is used in several studies about COVID-19 diagnostics (Joung et al., 2020; Rabe & Cepko, 2020; Wu et al., 2021). The RNA copy numbers are taken from the product details provided by the manufacturer. These details are now clearly stated in the manuscript. The commercial RNA is provided at 106 copies per ul. From this, we take as low as 100 copies per 20ul of NASBA reaction, which we are able to detect using our assay. Hence our sensitivity comes to 8 attoMolar. We have clarified this in the manuscript. We noticed a typo in the original submission where we refer to a sensitivity of 80 attomolar in the Discussion. This is corrected to 8 attomolar. With this sensitivity we are within the range to detect RNA in patient samples, as confirmed by our patient data.

Reviewer #3 (Significance (Required)):

I think this is a significant advancement in the field. The introduction of smartphone technology to this robust diagnostic is very attractive. The work is of high significance since the researchers demonstrated robust responses against SARS-CoV-2 variants. As well all now know these are on the rise and cheap robust detection methods are essential for containing this virus.

Response: We thank the reviewers for the positive comments.

This point about where to draw the line between data and artifacts is interesting considering that digital humanities is built upon the very concept of turning artifacts into data. This connects to the concepts in the article by Trevor Owens about the various properties of data. If we view data as an artifact which can also serve as a source of evidence, then we can preserve the integrity and multifaceted nature of the dataset while still using it so serve the purpose of providing a specific source of numerical evidence. It seems to me that this idea is very important to the digital humanities considering the susceptibility to losing the integrity and humanist nature of original data sources when viewing them as sources for discrete data sets.

I completely agree with this statement, and I think that it's become a buzzword (like Alex said) in schools in general, not even just in the field of music education. Our district hired an Equity Officer about 2 years ago, and I was really hoping that they would have a strong presence in our district, at curriculum review meetings, providing PD, etc....I think I have seen them once since I was hired and it was at New Teacher Orientation. We have someone there that could be helping us to fully understand some of these terms/topics instead of assuming we know what it is, its implications, its assumptions, etc. but it feels as if they're not being fully utilized.

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Reply to the reviewers

Responses to reviewers’ comments

We thank the reviewers for their encouraging comments and helpful suggestions.

Reviewer #1

(Evidence, reproducibility and clarity (Required)):

Sanchez et al report several new findings about the adhesive protrusions on Plasmodium falciparum infected erythrocytes. Using super resolution microscopy and correlation analysis, they tracked associations between the knob protein KAHRP and erythrocyte membrane cytoskeleton proteins. They have expanded on and improved previous work on the unusual spiral structure of the knobs, which appears to be a spiral ribbon or blade and have shown a developmental pathway for the association of KAHRP with the cytoskeleton. They have localised KAHRP close to the spiral and determined its abundance in the knobs. They have also used cryo electron tomography and subtomogram averaging to get an improved 3D view of the knob structure.

The work appears to be carefully and thoroughly done, and the paper is clearly written, though non specialists in the optical methods may find it challenging to navigate through the many super resolution images and correlation plots.

Comment 1: The writing needs minor editing to fix a variety of small linguistic errors and typos. For example, line 97 "sideway positions" (they presumably mean lateral location), line 980 typo overlay, line 366 "then could reorganizes", line 435, "a predict volume".

We apologize for the linguistic errors and typos. These have been corrected in the revised manuscript.

(Significance (Required)):

Comment 2: The study provides a distinct advance on the previous state of knowledge of the structure and biochemistry of the knobs. The knobs play a key role in virulence of P. falciparum and they are quite poorly understood. Although this paper does not represent a major breakthrough in determining the molecular structure or mechanistic role of the knobs, e.g. the biochemical identity of the spiral remains unknown, the new information is valuable and likely to be important in understanding the pathogenic actions of P falciparum.

We thank the reviewer for appreciating the importance of our study. We believe that our first-time observations on the dynamics of KAHRP are a very important advance in the field and that revealing the mechanistic basis is a great challenge that at the current stage has to be left to future work.

Comment 3: The interpretation shown in Figure 7 seems fine, except for the proposal that the actin cytoskeleton is reorganised. There is no evidence for that. The cryo tomograms of the cytoskeleton in Watermeyer et al addressed this point and did not find any evidence for reorganisation of the cytoskeleton other than the insertion of the knobs.

In two previous studies we could show that actin is indeed reorganized by the parasite. It is mined from the protofilaments to generate long actin filaments that connect the knobs with the Maurer’s clefts and which are used for trafficking of cargo vesicles from the Maurer’s clefts to the erythrocyte plasma membrane (Cyklaff et al. Hemoglobins S and C interfere with actin remodeling in Plasmodium falciparum-infected erythrocytes. Science. 2011 334:1283-1286; Cyrklaff et al. Oxidative insult can induce malaria-protective trait of sickle and fetal erythrocytes. Nat Commun. 2016 7:13401). Moreover, a life-cycle resolved AFM-study of the cytoplasmic side of iRBCs by the group of CT Lim has demonstrated dramatic coarsening of the spectrin network, which must be accompanied by changes to the actin component of the skeleton (Shi, Hui, et al. "Life cycle-dependent cytoskeletal modifications in Plasmodium falciparum infected erythrocytes." PLoS One 8.4 (2013): e61170). Coarsening of the actin-spectrin network would imply a decrease of the amount of actin in the network, which is consistent with its use in the parasite-derived long actin filaments.

\*Referee Cross-commenting***

I also agree with the other 2.

Reviewer #2

(Evidence, reproducibility and clarity (Required)):

Malaria parasites replicate within circulating red blood cells (RBC). During parasite maturation, the parasite coordinates extensive modification of the host cell, including structural modifications of the RBC cytoskeleton and surface membrane. These host cell alterations play crucial roles in the pathology of malaria, including vascular adhesion by parasitised cells and avoidance of splenic clearance, and so are of great interest. This interesting manuscript describes a detailed examination of the role in these RBC modifications of a well-described parasite protein called KAHRP. Using a combination of cutting-edge super-resolution microscopy, cryo-electron tomography, immuno-EM, SEM and parasite mutagenesis, the authors provide evidence that KHARP localisation alters during parasite maturation but eventually becomes closely associated with the previously-described spiral structures that underlie infected RBC surface membrane protrusions called knobs. The authors provide improved resolution of the spiral formations, generate a quantitative estimate of the number of KAHRP molecules per knob, and provide a model for the role of KAHRP in attaching other proteins to the spirals based on their observations.

In general, this study is thorough and well-performed, and the conclusions drawn are well-supported by the data. Although the work does not advance understanding of knob function or the parasite components that form the bulk of the spirals, it provides an interesting and useful contribution to understanding of the manner in which this important pathogen manipulates its host cell.

We thank the reviewer for appreciating the importance of our study and in acknowledging that it is an important intermediate step towards a complete understanding of skeleton remodelling by the parasite.

I have just a few minor suggestions that should improve the manuscript.

Comment 1: Line 91 (Page 2 paragraph 2). It would be greatly helpful here if the authors could provide a more detailed background on the makeup of the RBC cytoskeleton, and in particular the interactions between beta-spectrin and the actin protofilaments of the junctional complexes. The authors should make it clear that the actin-binding domain of beta-spectrin comprises 2 calponin like domains, and that these are attached to the end of the tandem spectrin repeat domains that make up the bulk of the molecule.

We thank the reviewer for this helpful suggestion and have added a new paragraph to the results section providing detailed background information on the makeup of the RBC membrane skeleton. The new text reads as follows:

“Major components of the red blood cell membrane skeleton are spectrin and actin filaments (Fig. 1B). The spectrin filaments consist of α- and ß-spectrin, which form α2ß2 heterotetramers by head-to-head association of two αß dimers (Lux, 2016; Machnicka et al., 2014). The N-termini of the ß-spectrin subunits are positioned at the tail ends of the heterotetramer and contain two calponin homology (CH) domains for binding to actin protofilaments consisting of 6 to 8 actin monomers in each of the two strands (Lux, 2016; Machnicka et al., 2014). Protein 4.1R strengthens the spectrin actin interaction (Lux, 2016; Machnicka et al., 2014). Groups of up to six spectrin heterotetramers can attach to an actin protofilaments, resulting in a pseudohexagonal meshwork (Lux, 2016). Ankyrin binds to the C-terminal domain of ß-spectrin and connects integral membrane proteins with the actin spectrin network in an ankyrin complex (Lux, 2016; Machnicka et al., 2014).”

Comment 2: Line 97 "These values are slightly larger than the reported physical dimension of the protofilament...". Please provide these reported dimensions here, as well as relevant references.

The requested information is now provided. The sentence now reads as follows:

“These values are slightly larger than the reported physical dimension of the protofilaments of ~37 nm (Lux, 2016) and might be explained by the lateral localization of the spectrin binding sites and the additional sizes of the primary and secondary antibody trees used to detect the two targets.”

Comment 3: Line 366 "reorganize"

The spelling mistake has been corrected.

(Significance (Required)):

Comment 4: This is a useful technical advance in understanding of the structure of the P. falciparum-infected red blood cell, and builds on the work of Watermeyer et al. (2016). The study should certainly be of interest to most malaria researchers, particularly those interested in the pathobiology of the organism.

We thank the reviewer for supporting our study.

\*Referee Cross-commenting***

I fully agree with and endorse the comments of the other 2 reviewers.

Reviewer #3

(Evidence, reproducibility and clarity (Required)):

The binding of P. falciparum infected erythrocyte (iRBCs) to the endothelium is mediated by protuberances (knobs). These knobs are assembled by a multi-protein complex at the iRBC surface. It acts as a scaffold for the presentation of the major virulence antigen, P. falciparum Erythrocyte Membrane Protein-1 (PfEMP1). The knob-associated histidine-rich protein (KAHRP) is an essential component of the knobs and therefore essential for the binding of iRBC to the endothelium under physiological conditions. This manuscript focusses on the knob architecture and KAHRP localization.

Comment 1: It is, at least for this reviewer - hard to assess how the "preparation of exposed membranes by hypotonic shock" and the analysis of the "inverted erythrocyte membrane ghosts" is i) reflective of the physiological architecture within the iRBC and ii) how the authors exclude remnants from Maurers clefts (MCs) in their preparation. The latter appears especially important for the interpretation of dynamic KAHRP repositioning, as MCs are mobile in early stages and non-mobile later on (e.g. McMilian et al. 2013, Grüring et al. 2011) and the authors observed at least some MAHRP1 signal (Figure S8), which is hard to interpret by the single representative image provided.

We understand the reviewer’s concerns, but are convinced that we have done the necessary controls to evaluate our approaches. For example, we evaluated the exposed membrane approach by investigating uninfected erythrocytes and comparing the findings with literature reports (see Figure 1). A high degree of agreement was observed. We further would like to point out that the exposed membrane approach has been successfully used by several other studies referenced in the manuscript (Dearnley et al., 2016; Looker et al., 2019; Shi et al., 2013). Please also allow us to explain why we have used exposed membranes instead of whole cells. The reason is that the hemozoin produced by the parasite interferes with STED microscopy, resulting in a quick and strong build-up of resonance energy in the specimen and, eventually, in the disruption of the cell.

With regard to the question of whether remnants of Maurer’s clefts are present in our preparations, we do not think so, at least we never observed membrane profiles reminiscent of Maurer’s clefts in SEM images of exposed membranes (see figure at the end of the response letter). Irrespectively, we will double check this result using STED imaging of exposed membranes treated with an antibody against the established Maurer’s clefts marker SBP1. These data could be added to a revised manuscript.

Comment 2: line 173: Please provide a detailed description about parasite synchronization (also absent in the methods section).

A detailed description including references are now added to the methods section:

“For synchronization of cultures, schizont-infected erythrocytes were sterile purified using a strong magnet (VarioMACS, Miltenyi Biotec) (Staalsoe et al., 1999) and mixed with fresh erythrocytes to high parasitaemia. 5000 heparin units (Heparin-sodium 25000, Ratiopharm) were added and the cells were returned to culture for 4 hrs (Boyle et al., 2010). Following the treatment with heparin, cells were washed with pre-warmed supplemented RMPI 1640 medium and then returned to culture for 2 hrs to allow for re-invasion of erythrocytes. Subsequently, cells were treated with 5% sorbitol to remove late parasite stages (Lambros and Vanderberg, 1979).”

Comment 3: line 136: Please re-check nomenclature of "PHIS1605w" (mixed nomenclature used throughout the manuscript). I suggest to use either LyMP or the up-to-date ID PF3D7_0532400.

We apologize for the oversight and now consistently use the ID PF3D7_0532400.

Comment 4: Please provide source and references for PfEMP1, MAHRP1 and "PHIS1605w" antibodies that are used. I cannot find them in the methods section or in Table S1.

We apologize for the oversight and now provide the requested information in the amended Table S1.

Comment 5: line 165: Warncke et al. (2016) appears to be misplaced as an appropriate MAHRP1 reference.

We now cite the original MAHRP1 publication by Spycher et al. 2003.

Comment 6: line 159: the sentence "The strong cross-correlation between KAHRP and actin is consistent with previous cryo-electron tomographic analysis showing long actin filaments connecting the knobs with Maurer's clefts in trophozoites (Cyrklaff et al., 2012; Cyrklaff et al., 2011; Cyrklaff et al., 2016)" could be moved to the discussion section.

The sentence was indeed redundant with a section in the discussion and was removed.

Comment 7: line 199: The text refers to Fig. 9AB - but should refer to 4AB or suppl. 11.

We are sorry for this mistake and now refer to the correct figures in the revised manuscript.

Comment 8: Fig. 4: A solid average for the number of subtomograms, but please provide information about what the arrowheads (4E) indicate.

Thank you for this comment. The arrowheads indicate peripheral crown-like densities. We have updated the figure legend to clarify this issue.

Comment 9: The "flexible periphery" is likely a combination of flexibility and occupancy as the average was made from subtomograms with varying number of turns in the spiral. As occupancy is likely a significant contributing factor to the average that should be discussed or at least mentioned.

Thank you for this important comment. Indeed, a significant variation was observed between the individual knobs. The spirals have variable diameter, and the number of peripheral proteins also varied. We added measurements to the supplementary figure 11D. In addition, we update the text and extended the discussion.

Comment 10. On that note, did the authors try and classify based on number of turns prior to averaging and if so did the authors see any differences in structures between few turn and many turn spirals?

We attempted several classifications on the full knobs with variable masks. However due to a limited number of particles in the dataset we could not converge to stable solutions. Instead, we decided to adopt the subboxing strategy where locally ordered segments at the periphery could be analyzed. This showed several structural snapshot at the periphery of the knobs.

Comment 11. What size mask was used? Was it a soft sphere around the core or big enough for the knobs with multiple spiral turns?

While we attempted several alignments and classifications with variable masks, the final refinement and measurement of FSC was performed with a soft contour mask mask. We overlaid it with the structure in Figure S11F and uploaded it as a part of the EMDB deposition. We further show the masks used in this study in a new Figure S14.

Comment 12. It might be useful for readers who are not familiar with Dynamo to provide a little bit more information about how the initial reference was produced. Additionally more information about the sub-boxing strategy ie: spacing etc. would helpful.

Thank you very much for the suggestion. For the initial reference we manually aligned all the particles, summed them up and low-pass filtered them. We now describe it in the methods section.

For the subboxing procedure we added more description to the main text:

“40 segments were extracted at the radius of the 2nd and 3rd spirals followed by their classification into structural classes.”

We further extended and simplified the description in the results section (line ~221).

Comment 13: Fig. 5 Additional (earlier) maturation stages of the iRBC with Ni2+NAT-gold-labelling would be a nice add on - this could help confirm the model and would itself be a control for the later stage labelling.

We thank the reviewer for this insightful suggestion. We are currently performing the proposed experiment and will include it in a revised version of the manuscript.

Comment 14: line 637: DMSI typo and please provide the supplier for DMSI (DSM1).

We corrected the typographic error and now provide the name of the supplier.

Comment 15: Figure 7: Please provide what the purple arrows indicate.

The figure legend has been updated.

Comment 16: Fig S11D: The labels X, Y and Z are confusing, describing the slicing axis as "XZ, YZ and XY" view is more intuitive.

Done as suggested by the reviewer.

Comment 17: Figure S13 B: WBs are cropped. Please provide un-cropped WB.

Uncropped Western blots will be provided in the revised manuscript.

(Significance (Required)):

In general, I highly appreciated the solid data and its thorough analysis of the microscopy data. The authors investigate the structural organization of knobs in iRBCs using high-resolution imaging techniques including STED and PALM super-resolution microscopy-based approaches and electron tomography. The beauty of this paper is that it does nicely re-investigate knob architecture in iRBC (e.g. Watermeyer et al., 2016, Cutts et al., 2017, Looker et al., 2019, McHugh et al., 2020) and provides some intriguing KHARP co-localization with cytoskeleton components. The downside of it is that - by nature - it is descriptive (and the data rather confirmative) and as it stands does not provide us with a deeper molecular dissection of the knob associated structure and its cellular function.

We thank the reviewer for appreciating our study and would like to emphasize the following novelties in our study:

• We show that the association of KAHRP with membrane skeletal components is highly dynamic and changes as the parasite matures. Our results on the dynamics of KAHRP organization reconciles conflicting reports in the literature, and establish for the first time a dynamical model for KAHRP organization.
• We further show that KAHRP finally assembles at remnant actin-junctional complexes devoid of the actin-capping factors adducin and tropomodulin.
• We further quantified the number of KAHRP molecules per knob and show that KAHRP is present as 60 copies per knob, a number one order of magnitude greater than previously thought.
• Last but not least, we provide a 35 Å map of the spiral scaffold underlaying knobs and show that KAHRP associates with the spiral scaffold.

• We conclude by providing a novel model on the biological function of KAHRP by proposing that KAHRP acts as a glue that connects spectrin and parasite-remodeled actin filaments with the knob spiral.

  \*Referee Cross-commenting***

Fully agreed.

Boyle, M.J., Wilson, D.W., Richards, J.S., Riglar, D.T., Tetteh, K.K., Conway, D.J., Ralph, S.A., Baum, J., and Beeson, J.G. (2010). Isolation of viable Plasmodium falciparum merozoites to define erythrocyte invasion events and advance vaccine and drug development. Proc Natl Acad Sci U S A 107, 14378-14383.

Lambros, C., and Vanderberg, J.P. (1979). Synchronization of Plasmodium falciparum erythrocytic stages in culture. J Parasitol 65, 418-420.

Lux, S.E.t. (2016). Anatomy of the red cell membrane skeleton: unanswered questions. Blood 127, 187-199.

Staalsoe, T., Giha, H.A., Dodoo, D., Theander, T.G., and Hviid, L. (1999). Detection of antibodies to variant antigens on Plasmodium falciparum-infected erythrocytes by flow cytometry. Cytometry 35, 329-336.

Author Response:

Reviewer #1 (Public Review):

This paper examines muscle activity at single muscle level during Drosophila ecdysis (adult hatching) behavior. The premise is that quantifying behavior or motor neuron activity is insufficient to understand how the CNS generates behavior - it is also critical to quantify muscle activity. They show that abdominal body wall muscles generate stereotyped patterns of activity during four developmental stages; (phase 0, stochastic activity; phase 1-3, each with different patterns of activity. Co-active groups of muscles form "syllables" which are used in different combinations to generate the stereotyped activity seen in phases 1-3. This analysis was facilitated by use of a convoluted neural network. Interestingly, they found examples where muscle contraction did not match muscle activity (GCaMP elevation), showing the importance of measuring both attributes.

In addition to mapping the stereotyped muscle activity at single muscle resolution in the generation of ecdysis behavior, they find that phase 1 and 3 are quite variable, and speculate that other constraints on the CNS output (e.g. during larval locomotion) may prevent a sharpening up of muscle patterns. They show that the hormone ETH is required for initiating phase 1, and the neuromodulators bursicon and CCAP are required for initiating phase 2. Failure to initiate either phase is lethal. Lastly, they show that in addition to initiating phase 1 or 2, the hormone/neuromodulators result in more coherent muscle activity.

Overall this study sets the stage for a detailed analysis of motor neuron function in driving muscle activity patterns, and then further into the CNS to understand the role of premotor neurons. Ecdysis behavior has the potential to be a powerful system for understanding how the CNS generates behavior at the single muscle /single motor neuron level, as well as for understanding how neuromodulators act to regulate muscle/motor neuron activity.

The figures are almost all too small to see the salient information, and the color scheme is often difficult to resolve. Please enlarge the key aspects of the figures; and try to use more distinctive colors where critical comparisons need to be made. Some examples: left/right colored lines in 1G; panel 3D; lines in 3E; all data in 5G (this is the worst for tiny data); 6C,D,J; all of 7.

Thank you for your thoughtful review and your suggestions on how to improve the manuscript. Some figure panels (e.g. 5G) have been completely replaced. The others mentioned have been divided into multiple figures or panels, which allowed us to enlarge the material in each. Fig. 7 was deleted from the revised manuscript because it was generally found unhelpful. We also felt that the other revisions rendered this figure unnecessary. The revised manuscript now has 11 main figures and 9 figure supplements with more generous layouts for individual panels so that details are more easily resolved. In addition, we attempted to improve the color scheme to facilitate clarity, using the color palette recommended for the color-blind. Other specific changes are referenced in our responses to individual concerns below.

Reviewer #2 (Public Review):

The manuscript by Diao et al. is an important extension of their eLife paper of 2017. Their development of new tools that allow them to follow Ca2+ transients in single muscle fibers over the whole animal through the behavioral sequence and also to independently monitor the Ca2+ transients in the endplates of the motor neurons that innervate these muscles. Their goal is to break down the movements that control the ecdysis sequence into elemental "syllables" and then to defined the role of these syllables in constructing progressively complex behavioral programs and as targets of neuropeptide modulation.

A crucial behavior that occurs during P1 in higher flies is the movement of the gas bubble but this event is largely ignored in the paper. Prior to pupal ecdysis, gas is expelled into the posterior puparial space and then actively translocated, via muscular contractions of the body wall, to the anterior end of the puparium during the latter portion of P1 (shown nicely in the author's 2017 Video). A detailed study by C.G. Chadfield & J.C.Sparrow (1985. Dev. Genetics 5: 103) of pupal ecdysis in Drosophila emphasized the importance of this translocation for head eversion. When they simply removed the operculum at the start of bubble movement, then the gas bubble could not push the animal backwards in the puparial case and head eversion could not occur. However, they saw normal pupation and head eversion if the removed operculum was immediately replaced and sealed down with petroleum jelly.

During translocation, the bubble moves in a fragmented fashion between the pupal cuticle and the puparium. Ignoring this movement leads to statements like on line 378 "Because pupal ecdysis is independent of environmental factors and executed in the absence of competing physiological needs, it is likely that its variability is intrinsic to the ecdysis network." For the pupating animal, its "environment" is the inside of the puparial case and the moving bubble is an unpredictable variable in this environment. The trajectory and route of bubble movement is not fixed, and it is likely that variation in sensory feed-back from the gas movement explains the motor variability and reduced stereotypy during P1. The role for proprioception during this phase is likely to inform the CNS of the progression of the bubble fragments. The author's finding that the blockage of proprioceptors suppresses the behavior progression could mean that this sensory information is needed to signal that an anterior space has been produced, and without this signal, the behavior does not progress to its next phase. This should be addressed in the text if not experimentally.

We very much appreciate the reviewer’s point that the environment within the puparium may affect the pupa’s motor performance. We have now amended our comment on environmental influences to include this point (ll. 479-481 [515-517]), and we elaborate in the Discussion on conditions within the puparium that may influence movement and sensory processing (ll. 457-477 [493-513]). Following the reviewer’s advice, we note that the gas bubble and its dispersion during P1 must be considered a possible determinant of pupal movement. In addition, we mention other possible determinants that we did not previously discuss, namely substrate and surface tension interactions between the body wall, puparium, and residual molting fluid. In line with the Reviewer’s point that understanding the environment of the puparium is critical, we stress the need to account for all external forces acting on the pupal body to achieve a complete understanding of the pupal motor output. In the Discussion, we also now mention the Reviewers’ interesting hypothesis that creation of the anterior space at the end of P1 may provide sensory information necessary for progression of the behavioral sequence (ll. 534-535 [601-602])

Another aspect of the background that is missing is considering earlier studies on the ontogeny of behaviors leading up to ecdysis/hatching. Notable are studies of the progressive construction of the flight motor program during metamorphosis in moths (Kammer & Rheuben 1976 J. Exp. Biol. 65:65.) and a similar feature of assembly of motor programs prior to hatching in Drosophila (Crisp et al., 2008 Development 135:3707). In the moth studies, complex motor programs were gradually assembled during ontogeny with motor neurons firing but without muscle contraction (as the authors see in prepupae during P0 - Fig 2C). A lack of excitation-contraction coupling in the moth prevents muscle movement through most of development. This suppression of contraction is essential because prior to production of adult cuticle, muscle contraction would rip the developing animal apart. The same requirement to suppress muscle contraction would be seen in fly prepupa until sufficient pupal cuticle has been secreted to prevent rupture from actual muscle contractions! This should be addressed in the text.

We thank the reviewer for his comments and for the references on motor program assembly. We agree that this is topic deserved more attention than it was originally given. We have now amended our discussion of P0 to contextualize our observations, pointing to the previous literature on both suppressed muscle activity and latent motor programs observed in other developing animals (ll. 487-500 [523-536]).

Besides not being explicit about how the syllables combine to build the eight basic movements, it is not clear how these basic movements then combine to support the major behaviors of each phase. This is seen in P1, where we see that swing and brace movements can co-occur (e.g., Fig 3D) but is a swing on one side always associated with a brace on the other? What are their phase relationships? Does their temporal association remain stable as the bouts progress? Another example is in Phase 3. There appear to be 5 basic behaviors associated with bouts in Phase 3. The example in Fig 1H shows double peak bouts in phase 3, and the bulk Ca data show a preponderance of double peaks. The different shapes suggest that there are different movements during the two peaks. Their discussion of P3 movements (around line 273), though, does not address this feature of the double peaks. The example in Fig 7A suggests that some movements, like the PostSwing occur at half the frequency of other movements such as the PostCon and AntComp. Is this the basis of the double peaks and how is that reflected in the movements that are finally produced? This should be addressed in the text.

We regret the confusion on these points. As described there, we have made numerous changes to the manuscript to clarify how elements of behavior at one level (e.g. movements) derive from lower-level elements (e.g. syllables) and are used to build higher-level elements (e.g. phases). We describe the phase relationships at all levels for P1 and P2 and summarize the more variable constituents of P3 movements in the text (Figs. Fig. 7D, E and ll. 247-275 [274-302]). The specific questions raised by the reviewer are also now answered in the text. In brief, early P2 bouts (roughly those prior to head eversion) differ from later bouts in containing only a Swing. Later bouts contain in addition to the Swing a Brace performed concomitantly on the contralateral side of the body (l. 182-183 [197-199]). The movements contributing to the peak-double peak motif common to P3 are now more carefully described at ll. 351-360 [383-393])

One approach that I did not find useful was dividing the analysis into compartments - anterior versus posterior and dorsal-lateral-ventral. This may provide a way of generating some statistical analysis, but it did not illuminate anything about the behavior. The line between anterior and posterior segments seems to be arbitrary. Of course, it is important to know if there is directionality of movement [waves going anteriorly versus posteriorly], but beyond that, I am not sure what it adds. [Indeed, it made Fig 7 very confusing!] Also, I could not see a rationale for considering separate dorsal-lateral-ventral compartments. This should be addressed in the text.

We thank the reviewer for this question, which we now address in a revised section of the Discussion on the topic of neuromodulation and compartmentalization (ll. 539-588 [606-655]). To briefly expand upon our explanation there, we think that compartmental activity allows a useful coarse-grained description of the sequential body wall contractions that give rise to movement as indicated by the SequenceMatcher similarity scores (Fig. 6E in the revised manuscript). Second, and more important, we think that how activity flows across compartments provides clues about both the central organization and the neuromodulatory control of ecdysis behavior. Both ETHRB and CCAP neuron suppression exert selective effects on A-P compartments. ETHRB neuron suppression blocks the Lift, a movement of the posterior compartment, while suppressing CCAP neurons prematurely terminates the first (and only) swing-like movement by blocking its progression into the anterior compartment. Additionally, the distribution of CCAP-R appears to reflect mechanisms for selectively regulating distinct D-V compartments. Myotopic maps of larval motor neuron dendrites show that MNs innervating dorsal and ventral muscles are spatially segregated from those innervating lateral muscles and have distinct inputs. This suggests distinct regulation of activity in D-V and L compartments and likely distinct functions. Importantly, CCAP-R is expressed only in motor neurons of the D and V compartments, but in the L compartment it is expressed in muscles. As we suggest, this may allow the different regulatory mechanisms of compartmental regulation to synergize during P2. Finally, our subdivision of the A-P axis at the boundary between segments 5 and 6 has both anatomical and functional importance. At the pupal stage, selective muscle loss imposes differences in muscle composition of segments anterior and posterior to this boundary. Most importantly, anterior segments contain M12, which is a major contributor to behavior only after P1 and is targeted by neuromodulatory Type III terminals containing CCAP and Bursicon. In addition, the A-P boundary also conforms to the functionally and neuroanatomically defined “hinge” region of Tastekin et al. (2018, eLife,), which regulates the switch from forward to backward movement in the larva. Because the compartmental subdivisions we define conform with neuroanatomical differences and appear to underlie functional differences, our working hypothesis is that they will be important landmarks for mapping behaviorally relevant CNS activity as we begin to image it in the next phase of our work.

Author Response:

Reviewer #1 (Public Review):

This manuscript presents new data and a model that extend our understanding of color vision. The data are measurements of activity in human primary visual cortex in response to modulations of activity in the L- and M-cone photoreceptors. The model describes the data with impressive parsimony. This elegant simplification of a complex data set reveals a useful organizing principle of color processing in the visual cortex, and it is an important step towards construction of a model that predicts activity in the visual cortex to more complex visual patterns.

Strengths of the study include the innovative stimulus generation technique (which avoided technical artifacts that would have otherwise complicated data interpretation), the rigor of experimental design, the clear and even-handed data presentation, and the success of the QCM.

The study could be improved by a more thorough vetting of the QCM and additional discussion on the biological substrate of the activation patterns.

We thank the reviewer for the thoughtful summary of our work, for highlighting the strengths of our methodology and analysis, and for noting that our study will make a worthy contribution to understanding the organizing principles of visual cortex.

Reviewer #2 (Public Review):

The goal of this work is to advance knowledge of the neural bases of color perception. Color vision has been a model system for understanding how what we see arises from the coordinated action of neurons; detailed behavioral measurements revealed color vision's dependence upon three types of photoreceptors (trichromacy) and three second stage retinal circuits that compute sums and differences of the cone signals (color opponency). The processing of color at later, cortical stages has remained poorly understood however, and studies of human cortex have been hampered by methodologies that abandoned the detailed approach. Typical past work simply compared neural responses in two conditions, the presentation of colorful (formally, chromatic) vs grayscale (luminance) images. The present work returns to the older tradition that proved so successful.

The project's specific goals were to measure functional MRI responses in human cortex to a large range of colors, and equally importantly, capture the pattern responses with a quantitative model that can be used to predict response to many additional colors with just a few parameters. The reported work achieved these goals, establishing both a comprehensive data set and a modeling framework that together will provide a strong basis for future investigations. I would not hesitate to query the data further or to use the QCM model the paper provides to characterize other data sets.

The strengths of the work include its methodological rigor, which gives high confidence that the goals were achieved. Specifically:

1) The visual presentation equipment was uniquely sophisticated, allowing it to correct for possible confounds due to differences in photoreceptor responses across the retina.

2) The testing of the model was quite rigorous, aided by distinct replications of the experiment planned prior to data collection.

3) The fMRI methods were also state of the art.

The work was well-situated within the literature, comparing its findings to past results. The limitations and assumptions of the present work were also clearly stated, and conclusions were not overstated.

Weaknesses of the current draft are relatively minor, however, I believe:

1) The data could be presented in a way to make them more comparable to prior fMRI work, e.g. by using percent change units in more places, comparing the R^2 of model fits reported here to those reported in other papers, and explaining and exploring how the spatially uniform stimuli, used here but not in other fMRI studies, limited responses in visual areas beyond V1.

2) Comparison between the two models, the GLM and QCM is not quite complete.

3) The present results are not discussed in context with past results using EEG, and Brouwer and Heeger's model of fMRI responses to color.

4) Implications of the basic pattern of response for the cortical neurons producing the data are discussed less than they could be.

We thank the reviewer for this clear summary of the paper, calling to attention our detailed approach to studying cortical color processing, and enthusiasm regarding the impact of our data and computational modeling.

Reviewer #3 (Public Review):

The authors describe a method for fitting a simple, separable function of contrast and cone excitation to a set of fMRI data generated from large, unstructured chromatic flicker stimuli that drive the L- and M- cone photoreceptors across a range of amplitudes and ratios. The function is of the form of a scaled ellipse – hereafter referred to as a 'Quadratic Color Model' (QCM). The QCM fits 6 parameters (ellipse orientation, ellipse elongation, and 4 parameters from a non-linear, saturating (Naka-Rushton) contrast response curve. The QCM fits the dataset well and the authors compare it (favorably) to a 40-parameter GLM that fits each separate combination of chromatic direction and contrast separately.

The authors note three things that 'did not have to be true' (and which are therefore interesting):

1) The data are well-fit by a separable ellipse+contrast transducer - consistent with the idea that the underlying neuronal computations that process these stimuli combine relatively independent L-M and L+M contrast.

2) The short axis of the QCM tends to align with the L-M cone contrast directing (indicating that this direction is one of maximum sensitivity and the L+M direction (long axis) is least sensitive. This finding is qualitatively consistent with psychophysical measurements of chromatic sensitivity.

3) Fit parameters do not change much across the cortical surface – and in particular they are relatively constant with respect to eccentricity.

This is a technically solid paper – the data processing pipeline is meticulous, stimuli are tightly-calibrated (the ability to apply cone-isolating stimuli to fovea and periphery simultaneously is an impressive application of the 56-primary stimulus generator) and the authors have been careful to measure their stimuli before and after each experimental session. I have a few technical questions but I am completely satisfied that the authors are measuring what they think they are measuring.

The analysis, similarly, is exemplary in many ways. Robust fitting procedures are used and model performance and generalizablility are evaluated with a leave-run-out and leave-session-out cross validation procedures. Bootstrapped confidence intervals are generated for all fits and analysis code is available online.

The paper is also useful: it summarises a lot of (similar) previous findings in the fMRI color literature going back to the late 90s and points out that they can, in general, be represented with far fewer parameters than conditions. My main concerns are:

1) Underlying mechanisms: The QCM is a convenient parameterization of low spatial-frequency, high temporal-frequency L-M responses. It will be a useful tool for future color vision researchers but I do not feel that I am learning very much that is new about human color vision. The choice to fit an ellipse to these data must have been motivated at least in part by inspection. It works in this case (possibly because of the particular combination of spatial and temporal frequencies that are probed) but it is not clear that this is a generic parametric model of human color responses in V1. Even very early fMRI data from stimuli with non-zero spatial frequency (for example, Engel, Zhang and Wandell '97) show response envelopes that are ellipse-like but which might well also have additional 'orthogonal' lobes or other oddities at some temporal frequencies.

2) Model comparison: The 40-parameter GLM model provides a 'best possible' linear fit and gives a sense of the noisiness of the data but it feels a little like a strawman. It is possible to reduce the dimensionality of the fit significantly with the QCM but was it ever really plausible that the visual system would generate separate, independent responses for each combination of color direction and contrast? I suspect that given the fact that the response data are not saturating, it would be possible to replace the Naka-Rushton part of the model with a simple power function, reducing the parameter space even further. It would be more interesting to use the data to compare actual models of color processing in retina/V1 and, potentially, beyond V1.

3) Link to perception. As the authors note, there is a rich history of psychophysics in this domain. The stimuli they choose are also, I think, well suited to modelling in the sense that they are likely to drive a very limited class of chromatic cells in V1 (those with almost no spatial frequency tuning). It is a shame therefore that no corresponding psychophysical data are presented to link physiology to perception. The issue is particularly acute because the stimulus differs from those typically used in more recent psychophysical experiments: it flickers relatively quickly and it has no spatial structure. It may, however, be more similar to the types of stimuli used prior to the advent of color CRTs : Maxwellian view systems that presented a single spot of light.

We thank the reviewer for their detailed comments on our paper and for highlighting our careful methodological approach and modeling of the data. We address the specific points.

Author Response:

Evaluation Summary:

This paper compares the properties of UV cone output synapses in different regions of the zebrafish retina using a combination of electron microscopy, quantitative imaging and computational modeling. They relate these differences to ultrastructural differences in synaptic ribbons and evaluate them using a previously-developed biophysical model for the operation of the synapse. The finding of regional differences in ribbon behavior is novel and suggests an under-appreciated degree of control of release by ribbon structure and behavior. The presentation of some of the results, particularly the model, could be strengthened.

We thank the reviewers for their valuable inputs. In response, we have substantially extended and restructured the description of preprocessing steps and modelling to aid clarity. Moreover, we include new analysis of “old” GCaMP6f data to show the similarity of calcium dynamics across retinal regions. Additionally, we worked on the description of the simulation-based inference method and provided more intuitive explanations. Finally, we updated the discussion of the model results. We hope to have addressed the helpful critique of the reviewers and strengthened our conclusions and the whole manuscript.

Reviewer #1 (Public Review):

Preprocessing of glutamate traces. The bulk of the analysis in the paper uses "scaled and denoised" traces. It is important to verify that this process did not either introduce or obscure any differences across regions. This should include some validation of the assumptions that go into the scaling process (such as whether a sufficiently low calcium level is achieved to use that as a standard). An example of a how this concern could impact the conclusions is that the AZ glutamate traces look less rectified than the others, perhaps due to an elevated baseline, as suggested in the text. But the conclusion about the elevated baseline relies on the scaling process creating a proper alignment such that it is accurate to superimpose the traces as in Figure 3a.

Thank you for giving us the opportunity to clarify this point. AZ UV-cones indeed have an elevated baseline, as explicitly shown in our previous publication (Yoshimatsu et al. 2020 Neuron). The scaling process recapitulates this baseline shift, as expected. In this previous work we also show how the lower rectification of AZ cones is directly linked to this baseline shift, and it includes experiments specifically designed to find the “true” minimum calcium levels achievable in UV-cones in different parts of the eye, as suggested by the reviewer.

However, we fully agree that the scaling/denoising process could be described more clearly, and we expanded the explanation in the method section and added a figure (Fig. S3) to visualize all steps explicitly.

Model fitting. Some key aspects of the model fitting were difficult to evaluate and follow. For example, is the loss function the same as the discrepancy defined in the methods (I assumed that is the case - if not the loss function needs to be defined)? The definition of the discrepancy could be clearer (e.g. be careful about using x here and as the offset of the calcium trace). Related, the results would benefit from a more intuitive description of the fitting, rather than just a reference to the methods (which is a bit dense to go through for that intuitive-level explanation of the model development).

We added an overview of the simulation-based inference method to the main section of the manuscript. Additionally, we updated the definition of the loss function and tried to give more intuitive explanations. We hope that these changes will help the reader to better understand the computational methods used.

Some statements seem too strong given the state of current knowledge. E.g. lines 79-80 I think goes too far about the functional role of the ribbon. Similarly lines 97-98 are quite explicit about the connection to prey capture. Lines 276-279 are a particularly important example; I would argue that the statement there requires showing uniqueness of the model.

We agree that the mentioned statements were perhaps quite strong and we have toned them down in the revised manuscript.

Could fixation of the retina for EM change the distribution of vesicles in different compartments? I realize this may not be answerable, but a caution about that possibility might be warranted.

We are not aware of such an effect in previous works. As the reviewer notes it may not be answerable. However, in a way we have an “internal control” for such a possibility, since the different eye regions were treated equally for fixation, yet vesicle distributions differ across eye regions. It seems unlikely that the fixation would have disproportionately distorted vesicle distributions in one eye region without also affecting the others. This is now noted when first discussing the EM approach.

Line 159: it is not clear how similar the calcium signals are. Specifically, could differences in calcium signal get amplified when passed through simple nonlinearity (e.g. due to the calcium dependence of transmitter release) to account for the differences in glutamate output? Maybe rewording here to leave open that possibility unless you have reason to reject it.

We agree that this statement was perhaps too strong at this point of the manuscript. We softened it and included a detailed analysis of additional calcium data later to investigate the regional differences of the calcium signal (Fig. 3k-n)

Can you quantify the fits in Figure 4f,g? For example, can you give a probability of a particular experimental trace or summary parameters for that experimental trace given the parameter probability distributions from the same area and from a different area?

A quantification of the fits is shown in Fig. S4b,c (previously S3b,c). As we perform “likelihood-free inference”, we cannot give probabilities for the model traces, but we show two different loss functions for the model fits as well as for the linear model: the relevant loss, on which the models are optimized (which is based on the summary statistics) and for comparison the MSE to the experimental traces. We apologize if this was not clearly mentioned in the manuscript. We added it more prominently in the revised version.

Reviewer #2 (Public Review):

This study images synaptic calcium and glutamate release from larval zebrafish UV-sensitive cones in vivo. They also study the ultrastructure of ribbon synapses from UV cones in different regions of the retina. They find differences in ribbon dimension and light-evoked glutamate release from cones in different regions of the retina. Cones from dorsal retina show a more pronounced transient component of glutamate release than those from nasal retina. Those in the acute zone in the center of the retina showed intermediate kinetics. Ultrastructural reconstructions of UV-sensitive cones from those regions showed fewer and small ribbons in dorsal cones vs. those in the nasal region or acute zone zone. Light-evoked changes in the kinetics of synaptic calcium were not significantly different suggesting that differences in release kinetics may be related to differences in ribbon behavior in cones from different regions. To relate these different measurements to one another, the authors modified an existing model of cone release to incorporate a simulation-based Bayesian inference approach for estimating best-fit parameters. The model suggested that the differences in glutamate release kinetics could be explained by differences in the rates of transfer between vesicle pools on and off the ribbon. By fixing different parameters, the authors then used the model to explore the parameter space and general properties of ribbon tuning. They also provide a link to the model for others to use.

The main new experimental finding is that glutamate release properties differ among cones in different regions. The finding that kinetics of glutamate release and ribbon ultrastructure vary systematically in different regions of the retina is interesting. They relate these data using a model of ribbon release. While the model is not novel in its general design, the incorporation of Bayesian inference is new. The most interesting finding from the model is that the kinetic differences in release between cones are not due to calcium kinetics but arise primarily from differences in transitions between vesicle pools. Nevertheless, using the model, the authors show that calcium levels and kinetics matter, since if they hold other parameters fixed, calcium levels and kinetics are the most important factors in shaping response detectability and response kinetics. This is consistent with a lot of earlier work that calcium kinetics are important for shaping response kinetics at ribbon synapses.

1) The measured changes in glutamate and calcium are small and noisy and there is considerable overlap in the data from cones in different regions. While the example waveforms show considerable differences, the scatter in the data is less persuasive. If I understand correctly, the imaging data comes from 30 AZ, 16 dorsal, and 9 nasal UV cones. With such noisy data, 9 cones seems like particularly small sample. With imaging data, it should be possible to record from dozens or hundreds of cells and a larger sample would strengthen the conclusions.

We agree that the sample size is quite small, however the dual color experiments are technically extremely challenging. This is part-related to the laser wavelength compromise that needs to be reached for concurrent excitation of red and green fluorescent probes, and the fact that red probes generally give comparatively poor SNR. Notably, to our knowledge concurrent 2P imaging of presynaptic calcium and consequent glutamate release in an in vivo scenario is quite novel, and still very much on the edge of experimental possibilities.

The green glutamate recordings based on iGluSnFR which are particularly central to our work do have a reasonably high SNR, rather the “problem” is more obviously linked to the calcium recordings. For a better understanding of the calcium handling, we therefore now reanalysed an “old” dataset from Yoshimatsu et al., 2020, Neuron (see Fig. 3k-n) that was recorded with SyGCaMP6f, which provides much higher SNR (and is a little faster albeit also more nonlinear). Notably, the SyGCaMP6f calcium dynamics were also analysed in some detail in Yoshimatsu et al., 2020, Neuron, and we built on these conclusions.

We hope that the analysis of the additional calcium dataset which is now included in the manuscript adds to more persuasive conclusions.

2) Calcium and iGluSnfr measurements are both single wavelength measurements and thus sensitive to differences in expression of the indicator. In Fig. 3, the authors show that dorsal cones exhibit larger calcium responses than nasal cones (3c) and that AZ cones show larger glutamate responses than nasal cones (3d). Please address the potential impact of differences in expression on these measurements.

Thank you for this comment. In Yoshimatsu et. al, 2020, Neuron we compared “live 2p” and “fixed confocal” data of the same sample to show that biosensor expression in UV-cones was uniform across regions, and that the different brightness levels were rather a result of variations in calcium levels. We extrapolated this knowledge to the used biosensors in the new experiments. We now note this explicitly in the revised manuscript.

3) Please describe controls performed to assess the potential for spectral overlap between the red and green channels. Is there any bleed-through of one dye into the other channel?

The expression profile of the two indicators is very different, the red fluorescence signal appears in cones, the green in HCs. We illustrated this separation in an additional figure (Fig. S2a,b) which shows that there was no obvious spectral mixing of the two fluorescence channels. We clarified this now in the revised manuscript.

4) I am not a modeler and while I understand the general approach used for the model, I am not competent to critique specific details of the implementation, particularly the Bayesian inference. However, the fact that the linear statistical model seems to perform just as well as the more ornate model is comforting since it says that the Bayesian inference approach didn't lead the model into an unrealistic parameter space. However, while to my eye the linear model appears to perform just as well as the fancier model, the text says otherwise (Figure 4, lines 270-273). Please clarify.

Indeed, the linear model captures the general shape of the glutamate response. However, it fails to recover adaptational processes, more precisely the transient components and adaptation over several steps. The model performances are quantified in Fig. S4 (previously S3), and especially with respect to the relevant loss, which is measuring the relevant features, the biophysical model outperforms the linear model. We expanded the discussion on these points in the manuscript and made a more prominent reference to the quantification figure.

5) Adding a diagram to show where the different regions (dorsal, nasal, acute zone) are located in the eye would be helpful. Is there a difference in the number or size of UV cones from different regions of the retina in larval zebrafish?

A diagram has been added to Figure 1 as requested. Regarding UV-cone numbers, indeed they do vary across the eye to specifically peak in the acute zone, and to a lesser extent also nasally. This relationship was explored in some detail in

Zimmermann et al. 2018 Curr Biol, and also touched upon in Yoshimatsu 2020 Neuron. This known density difference is now noted in the introduction.

6) Are differences in ribbon morphology, glutamate responses or calcium changes retained in adult zebrafish retina? While it may not be feasible to perform similar experiments in adult, some discussion of possible differences and similarities with adult retina would be helpful for putting the results in a more general context.

The reviewer raises an interesting point. Adult zebrafish display a much broader array of visual behaviours than larvae, and moreover have a rather different diet (meaning that the UV-dependence of prey capture - see Yoshimatsu et al., 2020 Neuron - may be different). Unfortunately, the visual ecology of adult zebrafish remains poorly explored so at this point we can only speculate. Notably, unlike larvae, adults also feature a crystalline mosaic of all cones, meaning that at least numerical anisotropies in cones as they occur in larvae (Zimmermann et al. 2018) are not expected. However, this does not preclude the possibility that UV-cones have different properties across the retina, perhaps it would be the most straightforward way to regionally tune outer retinal outputs in adults. Accordingly, we fully agree that this topic would be exciting to explore, however it would go beyond what could be achieved within a reasonable revision cycle.

We now added a summarising note of the above into the discussion section.

Reviewer #3 (Public Review):

The strengths of the manuscript: It contains a thorough characterization of the anatomical and physiological differences of UV cone ribbons at different locations using the state-of-art techniques including Serial-blockface scanning EM reconstruction and dual-color, simultaneous calcium and glutamate imaging. The Bayesian simulation-based inference model captured the key features of the calcium responses and glutamate release dynamics and provided distributions for each biophysical parameters, which gave insights of their interactions and their impacts on ribbon function. The online tool for ribbon synapse modeling is quite useful. Overall, it is a great effort to understand the function of ribbon synapse with a suitable system that allows multi-facet data collection and a new modeling approach.

The weaknesses of the manuscript: 1) Overall the writing/formatting of the manuscript can be much improved - there are many imprecise, hard to understand descriptions in the manuscript; figure legends/descriptions are often inadequate for easy understanding; inconsistencies between description in the main text and methods; and above all, the descriptions of model itself and the results from the model are not communicated in a way that facilitates the understanding of process and implications. In contrast, the previous papers from the same group employing similar modeling approaches are much better explained. 2) Based on the intuitions from the modeling, there has not been a strong connection established between the anatomical data and the functional data to which the model is built to fit. More clearly identifying the consistencies and discrepancies between the data and the model will help the readers to understand the pros and cons of the model and the limitations of the generalizations from the model.

Specific questions and recommendations for the authors:

1) It will be helpful to have a retina diagram indicating the locations of three different regions.

The requested diagram has been added to Figure 1.

2) Fig 1d,e,f (and other figure panels in general) there is no need to mark n.s. On the other hand, in the Statistical Analysis section, GAMs models are mentioned only for Fig 1g, but not other results - needs a clarification.

We find the “n.s.” labels useful, in part because in some panels none of the differences were significant and the label makes this quite explicit. Accordingly, we have opted to retain them. GAMs were indeed only used for Figure 1g - this is motivated by the difference in data structure of this panel compared to others (i.e. a comparison between continuous rather than discrete distributions). We now clarified this in the methods and added a short paragraph on the used testing procedure.

3) Fig 1h is quite confusing, with a mixture of 3D and 2D plot, schematic drawing and statistical marks. What comparisons are these marks for? The legend is not specific and the Suppl Fig S1 doesn't clarify much.

The asterisks are meant to indicate a statistically significant difference in the indicated property (e.g. ribbon size/number) relative to the acute zone. We apologise for not making this clear in the previous version, it is now directly noted in the panel. Regarding the 2D/3D representation, we agree that it may be a little confusing, but we cannot think of a “better” way of summarising all properties analysed by EM in a single panel, so we opted to keep it. We did however expand on the related explanation in the legend to further clarify what is shown.

4) It will be good to discuss the properties of the calcium sensor. Deconvolution of the calcium signal (lines 617-619) notwithstanding, presumably, the sensor has neither the temporal nor spatial resolution to catch the nano-domain calcium peak near the vesicles in RRP, which is critical for the release of RRP.

This point seems to link to the ongoing debate on to what extent release from ribbons is driven by micro- and/or nano-domain calcium signalling. It is our understanding that this debate remains unresolved in a truly general sense. Rather, it seems to be non- mutually exclusive (i.e. both micro and nano-domain signals working together), and moreover quite specific to each ribbon synapse in question. In larval zebrafish cones, the pedicle has a rather small cytoplasmic volume, there is only one invagination from postsynaptic processes, and all ribbons inside the cone are opposed to this single invagination. Accordingly, on a possible “sliding scale” of micro- vs nano-domain dominance, we think it is likely that in larval zebrafish cones microdomains will have a notable impact on release. While we are not aware of any data directly looking at this question in zebrafish larval UV-cones, there is good data available from systems that are perhaps quite similar, such as mammalian rods (which also have a single invagination site). For example, from Thoreson et al., 2004, Neuron, Figure 3.

Already at low micromolar concentrations of calcium that are readily achieved at the level of bulk calcium in the terminal (e.g. 1-2 microM), release is driven to a substantial degree.

However, we fully agree that we cannot detect possible nano-domain calcium signalling with our imaging method (in fact we are unsure that with currently available technology it is technically possible in an in-vivo preparation). We therefore now further emphasise the possibility of nanodomains acting on release in the discussion.

Notably, we do already allow exploring the possible influence of nanodomain-type calcium kinetics in the online model, and we think this usefully adds to our exploration of links between calcium signalling and glutamate release.

5) Likewise, the kinetics of iGluSnFR and of glutamate concentration in the cleft. Admittedly, figs 2a, 3c etc. show that the glutamate signal drops rapidly following the transition from dark to light, however, the rates of vesicle pool replenishment are a topic in the field-some discussion of how glutamate clearance from the cleft and the kinetics of the sensor will influence your estimates of replenishment rates would help future readers better interpret your findings in the context of their own observations.

We agree that there are technical limitations as to what the iGluSnFR signal can tell us about the exact dynamics of glutamate in an unperturbed situation. Likely this will never be fully addressable. Rather, we use the iGluSnFR signals in a comparative fashion across eye regions, where presumably any distortion of the signals as alluded to by the reviewer would be approximately equal. Following the reviewer’s suggestion, we now explain this more directly in the main text.

6) In Fig 2d, the rising phase kinetics of the Glu for that nasal cone is strikingly different from that of the acute zone cone. However, such difference is not seen in Fig 3. Therefore, the one in Fig 2d may not be a good representation?

Thanks, we agree. We have replaced the nasal example with a more representative trace.

7) In Fig 3a, c.u. and v.u. (only defined in Fig 4 in the context of the model) were used here but not S.D. as in Fig 2, any explanation?

After scaling, SD adopts arbitrary units. For consistency with the model later we decided to use c.u. and v.u. Here (i.e. “calcium units”, and “vesicle units”). We agree that this could be explained better, and have now rephrased as follows: “We show the rescaled traces in c.u. (calcium units) and v.u. (vesicle units) respectively, to be consistent with the used units in the model later.”

8) Lines 186-188, how were traces "normalized with respect to the UV-bright stimulus periods"?

The traces were rescaled such that the UV-bright stimulus periods had a mean of zero and a standard deviation of one. We included this missing piece of information and expanded additionally the explanation of the pre-processing.

9) Lines 194-195, "In addition, the glutamate release baseline of AZ UV-cones was increased during 50% contrast at the start of the stimulus" - it is unclear whether higher glutamate baseline occurred during the adaptation step (i.e. it increased during that period) or said increase was the level during adaptation compared to that during bright periods?

Thank you, we meant the former (i.e. glutamate release “is” higher during the adaptation step). This is now clarified in the text.

10) Lines 219-220, "a sigmoidal non-linearity with slope k and offset x0 which drives the final release" - this sentence is not clear, needs to clarify that it is referring to the relationship between calcium and release.

Thanks, this is now clarified in the manuscript.

11) Lines 230-232, "x0 can be understood as the inverted calcium baseline (see Methods)" - Methods don't cover this point, though it is described in the f(Ca) equation, but it isn't obvious how x0 should be the inverted baseline, as if Ca=x0, f(Ca) = 0.5 (i.e., the point of half-release probability). Please clarify this. In general, there are places where explanations of model found in methods don't match those described in the main text (also see some of the points below). Please go over carefully to ensure consistency.

x0 can be seen as an inverted baseline as it shifts the whole linearity to a different operating point: the smaller x0 the less additional calcium is needed to trigger vesicle release. If we assume a fixed calcium affinity this implies an increased baseline level. We apologise for having omitted these explanations in the initial manuscript, we have expanded the explanation in the Methods of the revised manuscript.

12) Fig 4e suggests a 5-10 times difference in RRP size between acute zone and nasal UV cones, which is not in line with the anatomical data (Fig 1h). Some discussions and clarifications will be helpful. As we note in the manuscript, it is difficult to quantitatively link anatomical structures to functional data. However, the small RRP size in the nasal zone inferred by the model (Fig. 4e) matches very well to the low vesicle densities at a small distance from the ribbon in the nasal zone in Fig. 1. Our model thus picks up the right trends for an anatomical structure from pure functional recordings, which is in our opinion already remarkable given the experimental noise and fine-grained differences. We commented on this point in the revised manuscript.

13) From Fig 4h, and Fig S3b,c, the linear model doesn't look too bad (unless I misunderstand the figure panels, which are not explained in great detail). The explanation in lines 272-274 needs some work to make it clearer.

Compared to the “best model”, the linear model clearly lacks in accuracy, perhaps most intuitively visible when looking at adaptation kinetics. This is especially the case for the relevant loss, which is based on the summary statistics. We extended the mentioned lines and hope to clarify it now in the manuscript.

14) Sobol indices and their explanation are lacking. Are they computed using Ca2+ and glutamate signals, or just glutamate? It is hard to parse their relative "contributions" to model behavior as described in the text, when the methods caution against interpreting this analysis as determining the "importance" of parameters (lines 805-806).

The first order Sobol indices measure the direct effect of each parameter on the variance of the model output. More specifically, it tells us the expected reduction in relative variance of the output if we fix one parameter. For the computation, broadly speaking, many parameters were drawn from the posterior distribution and the model was evaluated on these parameters. Afterwards the reduction in variance of the model evaluations was computed if one dimension of the parameter space was fixed. We agree that they are non-intuitive to interpret for a single time point, however its temporal changes give us insight into the time dependent influence on the model output. Often Sobol indices are computed by drawing random samples from a uniform distribution on a high dimensional cuboid [r1,s1] x … x [rn,sn] where each interval [ri,si] is simply defined by the mean+-10% of the parameter fit, where the definition of 10% leaves much room for interpretation and could not be meaningful in the same way for all parameters. We believe that the inferred posterior distributions are a much better suited probability distributions as they encode all parameter combinations which agree with the experimental data.

We expanded our explanation on this point in the manuscript.

15) The sensitivity analysis suggests that vesicle transitions are more important than pool sizes or their calcium dependence. Thus, it appears that one intuition from the model is that ribbon size - the main anatomical difference of the UV cone ribbons from different regions - is not very important for the functional difference observed (also see discussion in lines 438-439). Although, it has been discussed that ribbon size does not necessarily correlate with IP or RRP size, but this appears to be the hallmark of the acute zone.

As the reviewer notes, one potentially interesting hint from our work is that ribbon size does not necessarily translate 1:1 to vesicle pool sizes, or their relative transition rates. One particularly clear example of this might come from comparing Figs. 1d-f and Fig. 1h, between nasal and acute zone. Both have similar ribbon geometry (Fig. 1d-f), but nasal ribbons nevertheless appear to pack fewer vesicles (Fig. 1h). Linking with our functional data and modelling, it then appears that perhaps on top of that, vesicles simply move at different rates between the pools, a property that is impossible to pick up from a static EM reconstruction.

More generally, as mentioned in the manuscript and discussed in the previous point, it is difficult to judge the overall importance of a parameter from the sensitivity analysis. However, we clearly see time dependent effects of the different parameters and especially the RRP size matters for the transient component, which can be seen in Fig. 5. Indeed, the pattern for IP size seems to be different and it may be that case that the used stimulus is not optimal to infer this parameter from functional recordings.

How the ribbon size relates to different vesicle densities and how these densities could potentially influence the changing is however still an open question and cannot be answered in the scope of this manuscript.

16) Lines 460-461, intuitively, a slower RRP refill rate will result in more transient response - after the depletion of RRP, less refilled vesicles to give the sustained component of the response. This is the opposite of what model predicted (a faster RRP). Some explanation and discussion will be helpful.

The RRP refill rate indeed influences the transience in the mentioned way. However, its influence already starts earlier and is also influencing the overall amplitude (if some minimal background activation is assumed). It is therefore especially influencing the sustained component. However, for the nasal model already the inferred RRP size is the smallest and it seems that a small RRP refill rate is sufficient to produce the sustained response behaviour which we see in Fig. 4f. We thank the reviewer for this thoughtful comment and mentioned this behaviour in the discussion.

17) Also, the model simplifies vesicle transition rates by removing their calcium dependence. The Methods section indicates that this choice resulted from early fitting results that essentially "dialed out" the calcium dependence. Given the relative freedom that the model seems to have in finding suitable solutions, how is the lack of calcium dependence justified, and what potential impact might it have on the modeling results?

Identifying model (mis-)specification is a non-trivial task in general. The presented model is complex enough to replicate the recorded data but can easily be extended to more complex dynamics (e.g. more complex calcium handling) in future studies, as it is publicly available online. Further added components could even act as “distractors” to compare the other parameters across zones and we thus decided to use an “as simple as possible” model. Interestingly our previous study (Schröder et al., 2019, Approximate bayesian inference for a mechanistic model of vesicle release at a ribbon synapse, NeurIPS.) showed that even at a temporal resolution of single released glutamate vesicles, it was not necessary to include calcium dependency for the refilling of the vesicle pools. This study thus supports our model choice.

18) Lines 503-508, "In combination with the approximately equal and opposite effects of calcium baseline on the detectability of On- and Off-events (Fig. 7b,f), this suggest(s) that the calcium baseline may present a key variable that enables ribbons to trade-off the transmission of high frequency stimuli against providing an approximately balanced On- and Off- response behaviour." - what will be the physiological relevance for such conditions, perhaps the level of adaptation? Any existing data or predictions?

The reviewer raises an interesting but ultimately perhaps unanswerable point, given the scarcity of available data on temporal natural image statistics in the UV band across the larval zebrafish visual field. It is of course tempting to speculate that the ecological need to tune kinetics and On/Off preferences might be linked (e.g. detecting a “dark looming predator” might disproportionately benefit from a rapid Off response). However, to truly understand this idea at a useful level of detail would likely be a rather involved study in its own right. Accordingly, we here prefer to simply point at the possibility to “tune” the ribbon using calcium baseline, and what effects this might have on kinetics if all else was kept equal.

19) I am slightly skeptical of the predictions that the model might make about the ribbon's frequency tuning (Fig. 7) in light of the fact that the AZ model in particular seems unable to reliably capture the fast transient response to dark flashes (Fig. 4c,f).

The noted effect in the fast transient components in Fig. 4c,f is partially due to the slow calcium recordings which act as an input for the model in Fig. 4. As mentioned, and discussed above, there is an ongoing discussion to what extent nanodomain or more global calcium concentration drives the release. For this reason, we added a simple calcium model for the simulations for Fig. 7 which includes a variable time constant for calcium (nanodomains would presumably have much faster calcium transients than used for the model default). This allows us to explore the influence of different possible calcium handlings. Although this extrapolation to new stimuli is based on the fitted model, it allows for varying all essential parameters. In the online simulation it can be observed that for fast calcium handlings the ribbon is able to also follow higher frequency stimuli. However, we agree that experimentally testing the influence of different ribbon configurations on frequency tuning is an interesting research direction but goes beyond the scope of this manuscript.

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Author Response:

Reviewer #1 (Public Review):

In this manuscript, the authors build off their previous data where they have identified differences in the sst1 locus as responsible for differences in susceptibility of B6 and C3HeB/Fej mice to Mycobacterium tuberculosis infection. The authors have previously shown that this susceptibility is attributed to higher levels of type I IFN signaling and in particular, the ISG IL-1Ra. The sst1 locus contains many genes that could be contributing to the differential susceptibility in C3HeB/Fej mice, and the model in the field was that differences in Sp110 expression was a likely candidate to explain the susceptibility. However, in this manuscript, the authors show that it is not lower expression of Sp110, but instead decreased expression of another gene in the sst1 locus, Sp140, that contributes to the increased susceptibility of mice carrying the sst1S sequence to bacterial infections. This is a very significant and surprising finding, supported by very clear and convincing data from experiments performed with a high level of rigor. Although identification of the gene responsible for differences in susceptibility and outcomes during bacterial infections is an advance for the field, the manuscript stops there in terms of new insight and falls short of providing any additional information beyond what has already been published regarding how this gene or lucus is functioning to regulate immune responses to infection. This limited scope embodies the major concern for this otherwise strong manuscript.

We thank for the reviewer for recognizing the importance of our discovery that loss of Sp140 (not Sp110) confers susceptibility to M. tuberculosis. Our generation of Sp140 deficient mice allows us to demonstrate, for the first time, that Sp140 is a negative regulator of type I IFNs. By generating crosses between Sp140–/– and Ifnar–/– mice, we further demonstrate that type I IFNs mediate the susceptibility of Sp140–/– mice to M. tuberculosis and Legionella. The reviewer appears to believe that because IFNs were previously shown to mediate the phenotype of Sst1S mice that somehow the function of Sp140 was already known. By contrast, we feel that in fact the function of Sp140 was not at all clear prior to our work, and that our work does indeed provide important mechanistic insight into the function of Sp140 as a regulator of type I IFNs. Sst1S mice contain many genetic differences compared to B6 mice. It is only because of our work that we can now go back and reinterpret the prior work on Sst1S mice, but this would not be possible without the work we have reported in this paper. Of course we would love to be able to describe more about the molecular mechanism by which Sp140 represses interferon transcription. This is indeed something we are working on. However, our preliminary experiments indicate this is not likely to be straightforward and will require considerable effort that is certainly beyond the scope of this current paper. It should be noted, for example, that Sp140 is in the same protein family as the well-known transcriptional regulator Aire. The mechanism by which Aire regulates gene expression has been studied for almost two decades and is still not entirely clear (and was certainly not clear in the initial foundational paper on Aire function published by Anderson et al in Science in 2002). We expect the mechanism of Sp140 to be similarly complex. Importantly, we now know for the first time which protein to study mechanistically, i.e., SP140 instead of SP110.

Reviewer #2 (Public Review):

The authors have suggested the importance of SP140 for resistance to Mtb, Legionella infections in mice. They also provide evidence for IFNaR signalling in mediating the increased susceptibility of SP140-/- mice. While they attribute an important function of the transcriptional regulator SP140 to regulation of type I IFN responses by demonstrating the dysregulation of these responses in the SP140-/- mice, more direct evidence for this is needed.

We appreciate the reviewer’s succinct summary of the main conclusions of our manuscript. While we would agree that there is more to learn about the mechanism of SP140 function, it is not entirely clear to us what the reviewer means when they say that more “direct” evidence is needed for our claim that Sp140 regulates the IFN response during bacterial infection. We feel that the genetic experiments we provide are clear on this point. The reviewer may be thinking that we are proposing a specific mechanism, e.g., that our model is that Sp140 regulates IFN production by binding to the IFN beta gene; although that is an appealing possibility, we agree that is not shown in our manuscript, and indeed, we are careful not to make any such claim. Indeed, we explicitly state that a more indirect mechanism is possible (line 390). What is clear, though, is that loss of Sp140 mediates susceptibility to infection via (direct or indirect) increases in type I IFN. We observe increased type I IFN responses in Sp140–/– mice in vivo, and moreover, we find that a cross of Sp140–/– mice to Ifnar–/– mice reverses susceptibility to infection. These results demonstrate that the dysregulation of type 1 IFN in the absence of Sp140 is not merely correlative, but in fact drives susceptibility to bacterial infection in vivo.

Reviewer #3 (Public Review):

In this manuscript Ji et al carefully examine candidate genes driving a previously described susceptibility within the severe susceptibility to tuberculosis (sst1). Surprisingly, mice deficient in the original candidate gene within this locus, SP110, showed no change in susceptibility to infection with M. tuberculosis. In contrast, the authors found that loss of a second gene in this locus, SP140, recapitulated many phenotypes seen in the SST1 mouse, including increased Type I IFN. SP140 susceptibility was reversed by blocking these exacerbated type I IFNs, similar to SST1 mice. RNAseq analysis identify changes in pro-inflammatory cytokines and type I IFNs. The strengths of this paper are the careful and controlled experiments to target and analyze mouse mutants within a notoriously challenging region with homopolymers. Their results are robust, convincing and will be of broad interest to the field of immunology and host-pathogen interactions. Convincingly identifying a single gene within this region that recapitulates many aspects of the SST1 mouse is very important. While a minor weakness is the lack of any mechanistic understanding of how SP140 functions, this is overcome by the impact of the other findings and it is anticipated that this mouse will now be a key resource to dissect the mechanisms of susceptibility in much greater detail.

We thank the reviewer for their generous evaluation. Mechanistically, we do show that Sp140 affects resistance to bacterial infection via regulation of the interferon response, which we think is an important and technically non-trivial advance that provides insight into the function of Sp140. However, we agree that the mechanism for how Sp140 regulates type I IFN is not shown (nor is it claimed to be shown) and addressing this mechanism is now an important and exciting question for future studies.

Author Response:

Reviewer #2 (Public Review (required)):

Using high-speed holographic methodology, the swimming trajectories of two Leishmania life cycle stages are measured. Significant differences between the life stages become apparent. In addition, the authors show in a chemotaxis experiment that the infectious metacyclics respond chemotactically to the presence of macrophages.

The physics part of the study is flawless, and the holography is very impressive, especially in view of the comparatively simple setup. The analysis and presentation of the data is also flawless.

What is not so clear is the biological interpretation of the data. Chemotactic behavior has been repeatedly postulated for Leishmania, trypanosomes, and other parasites. However, there have been no experiments to date that allow conclusions to be drawn about in vivo relevance. Unfortunately, this does not really change with this study.

It has been shown in trypanosomes that the swimming behavior of different species and life stages are influenced by the mechanical conditions of their microenvironments. Viscosity, obstacles, and hydrodynamics can all play a critical role in determining motility. These factors are ignored in the study. Cell culture medium with the viscosity of water cannot image the situation in the vector or body fluids such as blood or lymph. A chemotactic gradient such as the one generated here by rather simple means cannot arise at all in vivo, simply because everything is in flux and parasites and macrophages move continuously. Moreover, one may wonder why Leishmania should actively move chemotactically toward macrophages when they come into contact with target cells much more rapidly by chance due to self-stirring properties of body fluids. I am not questioning the finding at all. I am merely questioning its biological relevance. Perhaps it would be better to describe this aspect of the paper more cautiously and to discuss it quite openly critically. Otherwise, the result might enter our knowledge as evidence for biologically relevant chemotaxis, and that would be problematic.

We thank the reviewer for their perspective and agree that providing formal evidence for chemotaxis in vivo is complicated. The reviewer is right that mechanical stimulus, viscosity, elasticity etc. are present in body tissues, and that they will affect the motion of the flagellum, and that there is evidence that physical obstructions interrupt the flagellar beat (though ‘stirring’ does not play a role in Leishmania’s motion through tissue). At any rate, we contend that an in vitro study such as ours decouples the mechanical heterogeneity of the in vivo environment from the parasite’s cellular response. If a chemotactic response is present in the parasite, then it will be most sensitively and uniquely tested in an isotropic environment such as a bulk Newtonian fluid - indeed, this is what we find. Chemical gradients are known to occur and persist in cutaneous infections, as damage to tissue, sand fly saliva and Leishmania-derived molecules have been shown to recruit immune cells by this mechanism - we have added references and words to this effect on lines 211-214.

Reviewer #3 (Public Review (required)):

The authors describe a clever and powerful assay to show chemotactic behavior in metacyclic Leishmania, which is an important result. The data seem mostly solid, but some results are confusing (perhaps partly an issue with presentation?) and overall conclusions seem like they need to be toned down a little. It is expected that this work will have long-lasting impact on the research community, and the new methods developed will be widely utilized.

Major concerns:

• "Pre-Adaptation", e.g. lines 149-150: A major message of the work is to suggest that motility behavior and chemotaxis is a "pre-adaptation". However, I don't agree that the current studies show that "…flagellar motility is a …preadaptation to infection of human hosts." What are the data to support this? The authors do a very good job of defining motility features of PCF and META forms, including quantitative analysis of motility features in 3D. They find that motility differs in PCF vs META forms. They also demonstrate chemotaxis in META forms. But, I don't see how these combined results demonstrate a "pre-adaptation" to infection of human hosts. As such, the "pre-adaptation" statement should be moved to speculation. Notably, I did not see tests for chemotaxis in PCF. Thus, it is even not formally demonstrated whether or not chemotaxis itself is an "adaptation" specific to META forma, or rather (and quite likely) is a fundamental property of all life cycle stages.

o To test if chemotaxis is an 'adaptation', the authors would need to provide an analysis of PCFs. To be an adaptation, one would expect to find either that PCFs do not exhibit chemotaxis, or that they do not chemotax toward macrophages in the assay used. Without this, the authors cannot say whether chemotaxis is a stage-specific behavior, much less a "pre"-adaptation.

We have moderated the language around claims of ‘pre-adaptation’ (please see next point for locations), and provided additional results from chemotaxis assays in PCF. Consistent with previous studies (e.g. Oliveira et al, Exp. Parasitol. (2000), Leslie et al., Exp. Parasitol. (2002), Barros et al., Exp. Parasitol. (2006)), we find a different chemo/osmotactic response in which PCF cells are drawn towards the agar in the pipette tip even in the absence of an embedded stimulant such as macrophages. We speculate that this result is due to the presence of small carbohydrate molecules from the unrefined agar - and note that the response is distinct to META, which show no such attraction. However, as suggested, this has been made more speculative in the revised discussion.

o Note, I think the work would not be negatively affected if the whole concept of "adaptation" were omitted and the work was framed around the very important results of developing a new and powerful approach to investigate Leishmania motility in 3D; quantitative definition of motility parameters; demonstration of chemotaxis in META forms.

We thank the reviewer for their suggestion (and their positive words), and have modified the language around claims of pre-adaptation. We have rephrased the claims in the abstract, and around lines 188-90 in the summary/conclusions.

• Chemotaxis: The work would benefit from some commentary on chemotaxis in kinetoplastids. A 'suggestion' for a potential advantage provided by chemotaxis (lines153-155) is not unwarranted, but that should be kept to speculation at this point, and implication that this is an 'adaptation' is not supported by the current data. With report of chemotaxis being a major message, the paper would benefit from a brief discussion on what's been demonstrated regarding chemotaxis in trypanosomatids, as this is an important, yet under-represented area of research on these organisms. Without this, the novelty and significance of the author's rigorous, novel and very interesting work are not brought out.

We thank the reviewer for this suggestion, and have added another paragraph to the introduction (lines 53-81), giving additional context to our results by providing an overview of more experiments in the field. We have also changed the word ‘suggest’ to ‘speculate’ in the summary and conclusions (line 243).

• Lines 125 - 129: How is it that tumble frequency decreases, but run duration is unaffacted? I would think that less frequent tumbles would lead to longer runs? This warrants more comment.

We thank the reviewer for pointing out the apparent confusion here. This stems from the fact that (as stated in the subsequent sentence) in the majority of the population, the tumble rate is significantly suppressed, to either one or zero tumbles per track. We require at least two tumbles per track to measure run duration, so the small fraction of the population unaffected by the stimulus contributes the bulk of the measurable runs. We have clarified this section of the text to clarify how we measure run duration.

• Fig 3 and Lines 135-139: How does one reconcile the finding that murine macrophages and human macrophages both induce taxis toward the pipet tip (3A), but there is opposite impact on speed profiles, with murine macrophages causing slower speeds, and human macrophages causing faster speeds (3H,K vs 3I,L)? Perhaps analysis done for human macrophages must also be done for murine macrophages. Some more commentary, and analysis needs to be provided on this point.

We thank the reviewer for this suggestion, and in the light of their comments, we have revised our description of the murine data, highlighting that the results are not statistically significant. To further emphasise this point to the reader, we have recast the error bars in figure 3a in terms of 95% confidence intervals rather than using the standard error on the mean, as in the previous version. Although one may be calculated directly from the other without any further assumptions, the 95% CI representation might be more familiar to the readership. In this light, the fairly modest decrease in average swimming speed (also seen in absolute terms in the DMEM case) reinforces the revised conclusion that the null hypothesis (META are not stimulated by mm\phi) cannot be rejected.

• Regarding replicates: While the number of cells tracked are clearly indicated, I did not see a description of how many different chambers were imaged for each condition, or how many different fields per chamber.

This has been amended in the Methods section, subheading “Chemotaxis Assay”

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Author Response:

Reviewer #1:

This study reports the novel and interesting finding that AKAP220 knockout leads to a dramatic increase in primary cilia in renal collecting ducts. AKAP220 is known to sequester PKA, GSK3, the Rho GTPase effector IQGAP-1 and PP1. Previous work from this group demonstrated that AKAP220-/- mice exhibit reduced accumulation of apical actin in the kidney attributable to less GTP-loading of RhoA. Relatedly, AKAP220-/- mice display mild defects in aquaporin 2 trafficking. In this work, Golpalan et al examine the effects of AKAP220 mutation on cilia. They demonstrate increased numbers of primary cilia decorating AKAP220-/- collecting ducts. This phenotype is striking as little is known about negative regulators of cilium biogenesis.

The authors also provide evidence that interaction of AKAP220 with protein phosphatase 1 (PP1) is critical for its function. Through PP1, AKAP220 may regulate HDAC6, which may in turn inhibit tubulin acetylation, which may in turn control cilia stability. Aberrant cilia function is implicated in autosomal dominant polycystic kidney disease. The authors also speculate that AKAP220 and tubulin acetylation may have clinical relevance for autosomal dominant polycystic disease. However, it remains unclear how increased cilia biogenesis may affect cell or tissue physiology. This work is of interest to cell biologists seeking to understand the biogenesis of the primary cilium, and to others interested in ciliopathies (i.e., disorders of the primary cilium).

We thank the reviewer 1 for their insightful comments and concur with their assessment that “it remains unclear how increased cilia biogenesis may affect cell or tissue physiology”. This is clearly a topic for further study within the field that will include ourselves and other laboratories.

Reviewer #2:

The authors show that AKAP220 knockout in kidney collecting ducts leads to a pronounced increase in primary cilia. They go on to demonstrate that this effect holds true in multiple different preparations, before clearly demonstrating that the PP1 anchoring site is critical for the normal role of AKAP220 is limiting primary cilia formation.

Although the key overall finding is well supported, I did not find the specific mechanism concerning a AKAP220-PP1-HDAC6 signaling complex/axis csufficiently onvincing. The authors propose that AKAP220 interacts with HDAC6 via PP1, and that within the complex HDAC6 is stabilised through phosphorylation. The knock on effect is efficient deacetylation. Although this complicated mechanism is consistent with the data, three supporting observations towards this specific mechanism come with caveats: (i) in figure 2C, they show an increase in acetyl tubulin by immunoblotting, but the densitometry seems to be the ratio of acetyl tubulin to GAPDH - would it not be more appropriate to reference to total tubulin?

We are encouraged that this reviewer considers that our “overall findings are well supported”. In response to their comments, we have bolstered our evidence that AKAP220 interacts with HDAC6 via PP1 by including new biochemical and imaging data showing that recruitment of the histone deacetylase is attenuated in kidney cells engineered to express a PP1-binding defective mutant of the anchoring protein. These new data are incorporated into figure 3D and supplemental figures S3D-L.

The mechanism investigated in this paper is concerned with absolute levels of acetylated tubulin. Since the levels of both control proteins (alpha tubulin and GAPDH) and do not change between wildtype and AKAP220KO, therefore we chose to normalize to GAPDH. It is important to note that normalizing to total tubulin does not change the result.

Reviewer #3:

The authors had previously generated a mouse line with inactivation of AKAP220, which encodes an A-kinase anchoring protein, and observed defects in their collecting ducts (CD) leading to defects in trafficking of aquaporin 2. While further characterizing the samples, they observed that CD epithelia had increased numbers and length of their primary cilia compared to CD cells of control mice. While some AKAP proteins have been localized to the primary cilium, AKAP220 was not one of them so the authors pursued a systematic series of experiments to determine how AKAP220 has these effects. Using a combination of CRISPR-manipulated renal epithelial cell lines (IMCD cells), drugs/compounds, 3D and organ-on-a chip cell culture systems they present compelling data that show that AKAP220 anchors a complex of HDAC6 and Protein Phosphatase-1 (PP1) that controls the polymerization of actin and thereby affects cilia formation and elongation. Genetic or pharmacologic manipulations that disrupt AKAP220 or its ability to bind to PP1, inhibit HDAC6, or affect actin stability result in a similar phenotype of enhanced ciliogenesis and ciliary length. Given that polycystic kidney disease has been described as a ciliopathy, with the gene products of the two most common forms of the disease (polycystin-1 and polycystin-2) localized to the cilia, they tested whether inhibiting HDAC6 activity might affect cyst growth using a human iPSC organoid system. They found that organoids lacking polycystin-2 treated with tubacin had smaller cyst size compared to vehicle-treated mutants, leading them to propose manipulation of HDAC6 as a tentative therapeutic strategy for human autosomal dominant polycystic kidney disease and for ciliopathies characterized by defects in ciliogenesis.

Strengths: These findings will be of interest to the ciliary community. They have identified a new factor and its associated partners that appear to regulate ciliogenesis. The studies follow a logical progression and are generally well-done with suitable controls, rigorous quantitation, and a reasonable level of replication (all done at least three times). They have used complementary methods (ie. Genetic manipulation, pharmacologic inhibition) to support their model, sometimes in combination to show that the underlying factor targeted by either genetics or drugs work through the same mechanism.

Weaknesses: The major weakness of the report is in its attempt to be translational. Here, the report has a number of serious theoretical and experimental limitations. On the theoretical level, the rationale behind using an HDAC6 inhibitor is unclear given their data and their model. On the one hand, a prior study had reported that a non-specific inhibitor of HDACs slowed cyst growth in an orthologous mouse model of ADPKD. The current work could suggest that HDAC6 was the actual target in the prior work and that a specific inhibitor for HDAC6 should confer the same benefits. On the other hand, there are compelling reports that show that genetic inhibition of ciliogenesis actually attenuates cystic disease in orthologous mouse models of human ADPKD. The current paradigm is that preserved ciliary activity in the absence of Polycystin-1 or Polycystin-2 promotes cystic growth. This would suggest that any intervention that boosts ciliary function could actually worsen disease. And while the authors never directly comment on the functional properties of the "mutant" cilia that result from deletion of AKAP220 or inhibition of HDAC6, they imply that these "enhanced" cilia are functional by suggesting the use of HDAC6 inhibitors as therapy for ciliopathies that are the result of defective biogenesis. Their prior work also provides indirect support for the notion that the enhanced cilia are functional. AKAP220 knock-out mice are reported to be generally functional, apparently lacking phenotypes commonly associated with defective cilia structure or function. These contradictory observations suggest that one or more of the following conclusions: the "mutant" cilia are in fact poorly functional, the HDAC inhibitors are working through a different mechanism than that which has been proposed, or that the assay as used in this report is not a good read-out of cyst-modulating effects. The last point is particularly relevant for this report. The investigators scored effectiveness of tubacin based on the relative rate of growth of cysts treated with different concentrations of tubacin vs vehicle. In this assay, cyst growth is principally driven by rates of cellular proliferation. Tubacin is an anti-proliferative agent with some toxicity, and while it might be highly selective for HDAC6, these studies cannot distinguish between effects mediated through the AKAP22-HDAC6 pathway versus others. In sum, while tubacin or a similarly-acting drug may or may not be effective for slowing cyst growth, there are multiple reasons to think it isn't through the mechanism the authors propose.

We are encouraged that reviewer 3 considers “our studies follow a logical progression and are generally well-done with suitable controls, rigorous quantitation, and a reasonable level of replication”. In terms of weaknesses, our reading of the reviewer’s detailed passage has identified two specific points that we can address.

1) Lesions in cilia and polycystins are linked to Autosomal Dominant Polycystic Kidney Disease (Hughes et al., 1995; Mochizuki et al., 1996). Although there is general agreement on this point, the molecular details remain unclear and are inherently paradoxical. For example, loss of morphologically intact cilia favors a less severe cystic phenotype (Ma et al., 2013). In contrast, other investigators report that loss of intact primary cilia results in renal cystogenesis (Kolb and Nauli, 2008; Lin et al., 2003). How primary cilia can be pro-cystogenic in one context yet anti-cystogenic in another context remains an unsolved paradox for the field. We appreciate the need for further clarification on this point as raised by reviewer 3. This conundrum is now noted in the discussion on page 34, line 3.

2) Searching for a therapeutic approach to restore functional primary cilia is the rationale behind our concluding studies. However, the complexity of genetic models for ADPKD and the above mentioned “cilia paradox” limits our ability to accurately predict how pharmacological agents targeting cilia might affect cellular models of cystogenesis. That being said, we realize that HDAC6 inhibitors have been used by other groups to target cyst size (Cebotaru et al., 2016; Yanda et al., 2017). The reviewer is correct in pointing out that the mechanism by which HDAC6 inhibitors act to inhibit cystogenesis could be less than straightforward given the multitude of functions for HDAC6. We have amended the discussion on page 34, line 5to reflect the reviewer’s valid point.

Author Response:

Reviewer #1:

In this paper, the authors study one of the understudied aspects of the evolutionary transition to multicellularity: the evolution of irreversible somatic differentiation of germ cells. Division of labour via functional specialisation of cells to perform different tasks is pervasive across the tree of life. Various studies assume that the differentiation of reproductive cells ("germ-role cells" in this manuscript) into a non-reproducing cell type ("soma-role cells") is irreversible. In reality, the conditions that promote the evolution of this irreversible transition are unclear. Here, the authors set out to fill in this knowledge gap. They model a population of organisms that grow from a single germ-role cell and find the optimal developmental strategy in terms of differentiation probabilities, under different scenarios. Under their model assumptions, they show that irreversible somatic differentiation can evolve when 1) cell differentiation is costly, 2) somatic cells' contribution to growth rate is large, 3) organismal body size is large.

Overall, I think the authors identified an interesting and neglected aspect of cellular differentiation and division of labour. I enjoyed reading the paper; I thought the writing was clear and the modelling approach was adequate to address the authors' question.

Thank you for a detailed and constructive review.

Some aspects that can be improved:

1) Throughout the manuscript, I was somewhat confused about what system the authors have in mind: a colony with division of labour or a multicellular organism? While their model can potentially capture both, their Introduction and Discussion seem to be geared towards colonies at the transition to multicellularity, whereas the Results section gives the impression that the authors have multicellular organisms in mind (e.g. very large body sizes).

We are interested in the transition from a colonial life, where tasks are distributed in time, to multicellular organisms, where tasks are divided between cells. As such, our model covers these scenarios as two limit cases. In the context of our study, we discuss examples from the nature where this transition is observed – e.g. among Volvocales algae. For the purpose of the necessary colony/organism size, we do not need to go further than 2^6 = 64 cells. However, to infer the patterns of the composition effect Fcomp (Fig.3 C,D), we consider organisms doing four more rounds of cell divisions before reproduction, leading to maturity size of 2^10=1024 cells. There, irreversible somatic differentiation can occur at a wide range of differentiation costs (see Fig.4 A). Also, smaller sizes put stronger restrictions on the composition effect Fcomp, so the distribution of parameters presented at Fig.3C,D taken at the n=6 instead of 10, would have much less data points and this could obfuscate the pattern found in this study. Overall, the scale of about 1000 cells, for which we report most of our modeling results, features entities with very diverse complexity: from undifferentiated colonies (ocean algae Phaeocystis antarctica), to intermediary life forms (slime molds slugs), to paradigm multicellular organisms (higher Volvocales and C. elegans). We think that the chosen range of the organism size is adequate to the comparison of entities with undifferentiated and differentiated cells. In the updated manuscript, we extend the exposition of organism size to reflect this aspect.

2) From the point of view of someone who works on topics related to cancer and senescence, I think these fields are very much connected to the evolution of multicellularity. Maybe because I had multicellular organisms in mind rather than colonies with division of labour (above), I thought the manuscript missed this connection. Damage accumulation is key to Weismann and Kirkwood's theories of germ-soma divide and disposable soma, respectively, whereas dysregulated differentiation is one of the important aspects of tumour development (e.g. Aktipis et al. 2015). Making these links could also be relevant to discuss some of the model assumptions. For instance, the authors assume that fast growth comes with no cost in terms of cell damage, which may not always be the case (e.g. Ricklefs 2006) and reversibility of somatic differentiation can come at a cost of increased risk of somatic "cheaters" or cancerous cell lines.

Thank you for this suggestion. Indeed, the aspect of cancer risk has not been considered in the initially submitted manuscript. In the updated manuscript, we introduce a model where differentiation is linked to the risk of an organism for death instead of a delay in development. The results with this model exhibit very similar pattern, see Fig.5. Hence, the term “cost of differentiation” can be interpreted more broadly than just cell division delay suggested by our main model.

3) The authors assume the differentiation strategy (D) does not change over the lifetime (which equates to ontogenesis in their model, i.e. they do not consider mature lifespan). I wonder if this is really the case, or whether organisms/cells can respond to the composition of cells they perceive. For instance, at least in some animal tissues, a small number of stem cells are kept to replenish differentiated tissue cells when needed. I understand that making D plastic can make the model really complicated, but maybe it is worth talking about what strategy would evolve if D was not stable through ontogenesis (and mature lifespan). My initial guess is that if differentiation probabilities can change through life and if one considers cellular damage accumulation, senescence and cancer (as above), the conditions that favour irreversible somatic differentiation would expand.

Indeed, we assume the differentiation strategy to be constant in our model. We do not know whether it is true at the brink of multicellularity and, for sure, once evolution makes a species complex enough, this assumption will become inadequate. Yet, when we consider a dynamic differentiation strategy, we find a very efficient but unrealistic solution: at the very beginning of a life cycle a germ-role cell gives rise to two soma-role cells, then these soma-role cells produce only soma-role cells and finally, at the very last round of cell division, they give rise to as many germ cells as possible. This scenario is the most efficient in terms of the rate of the organism development (100% of useful soma-role cells during growth), amount of offspring produced (every cell becomes a germ at the end of the day), and differentiation costs/risks (differentiation occurs only twice in a life time). Still, it is unrealistic. There must be some constraints on the flexibility of the dynamic differentiation strategy. We think that the exploration of the space of dynamical differentiation strategies and their constraints goes beyond the scope of the current study. Nevertheless, we are very interested to explore this topic further in following projects.

Reviewer #2:

This works seeks to determine the conditions in which simple multicellular groups can evolve irreversibly somatic cells, that is: a replicating cell lineage that provides cooperative benefits as the group grows and cannot de-differentiate into reproductive germ cells.

This question is addressed with a well-constructed model that is easy to understand and provides intuitive results. Groups are composed of germ and soma cells that replicate synchronously until the group has reached a maximal size. When each type of cell divides, they may have different probabilities of producing daughter cells of each type, and the analysis determines the optimal differentiation probabilities for each type of cell depending on a variety of factors. Critically, irreversible somatic differentiation arises when the optimal probability for soma cells is to produce only soma cells.

The elegance of the model means that the predictions are easy to interpret. First, when there is a higher cost for soma cells to produce germ cells, then a dedicated lineage of somatic cells is more favourable. Second, when soma cells produce only soma cells and germ cells can produce both types, the proportion of soma cells in the group will increase with each division. Consequently, for irreversible somatic cells to be optimal, germ cells must produce a small number of soma cells and these few must provide large benefits. Third, larger group sizes are required for a small number of soma cells to arise and provide sufficient benefits to the group.

Inevitably, there is a trade-off between the benefits of a simple model and the costs of idealised assumptions.

Among other assumptions, the model assumes that germ cells and soma cells replicate synchronously and at the same rate, and that soma cells provide benefits throughout the growth of the group, but do not increase the fecundity of germ cells in the last generation. Consequently, it is not clear to what extent the predictions of the model apply to the notable empirical cases where these assumptions do not hold. For instance, in the often-cited Volvocine algae, soma cells do not provide any benefits until the last generation of the group life cycle. This may help to explain why many Volcocine species have a very large number of somatic cells, counter to the second prediction of the model.

Overall, this analysis is targeted and provides clear predictions within the bounds of its assumptions. Thus, these results provide a compelling framework or stepping-stone against which future models of germ-soma differentiation in alternate scenarios can be compared and evaluated.

Thank you for the kind words and the well-thought review. Indeed, our model takes a number of simplifying assumptions. In the revised manuscript, we consider the model, in which the strongest of our simplifications – of simultaneous cell divisions - is violated. This asynchronous cell division model shows that irreversible differentiation may evolve, at least, under asymmetric differentiation costs. However, its evolution is observed less often than in a synchronous model.

We absolutely agree that the design of our model does not replicate the details of Volvocine life cycles. However, our work is not aimed to be a model of germ-soma differentiation in Volvocales. Instead, we developed a simplistic model implementing features from a diverse range of organisms. While in higher Volvocales young colonies develop within a maternal organism, there is a wide range of colonial organisms, which grow from independently living single cell, e.g. colonial diatoms, Haptophytes Phaeocystis antarctica, and amoebazoan Phalansterium. We agree that the protection by maternal organism should play a major role in Volvocales and we are looking forward to investigate a follow-up model taking this factor into account.

Reviewer #3:

This paper provides a theoretical investigation of the evolution of somatic differentiation. While many studies have considered this broad topic, far fewer have specifically modelled the evolutionary dynamics of the reversibility of somatic differentiation. Within this subset, the conditions that select for irreversible somatic differentiation have appeared conspicuously restrictive. This paper suggests that an overly simplified fitness function (mapping the soma-germline composition of an organism to its growth rate) may be partly to blame. By allowing for a more complex fitness function (that captures the effect of upper and lower bounds for the contribution of somatic cells to organism fitness) the authors are able to identify three conditions for the evolution of irreversible somatic differentiation: costly cell differentiation (particularly for the redifferentiaton of soma-cell lineages to germ line); a high/near maximal organismal growth advantage imbued by a small proportion of soma cells; a large maturity size for the organism (typically greater than 64 cells).

The model presented is simple and elegant, and succeeds in its aim of providing biologically feasible conditions for the evolution of irreversible somatic differentiation. Although the observation arising from the first condition (that high costs to reversible somatic differentiation promote the evolution of irreversible somatic differentiation) is perhaps unsurprising, the remaining conditions on the fitness function and the organism maturity size are interesting and initially non-obvious. Particularly tantalising is the prospect of testing these conditions, either against available empirical data, or in an experimental setting.

The model does however make a number of simplifying assumptions, the effects of which may limit the broad applicability of the results.

The first is to assume that cell division is synchronous, so that the costs of cell differentiation can be straight-forwardly averaged across the organism at each division. While the authors present a convincing biological justification for this assumption for algae such as Eudorina illinoiensis and Pleodorina californica, it is not immediately that this assumption should hold more widely.

The second is to assume that the development strategy (i.e. the rates of differentiation between somatic and germ-line cell types) is constant throughout the organism's growth. For instance, there may be a growth advantage in the current model (aside from the advantages with respect to reduced mutation accumulation) of producing more germ cells early in the developmental programme, before transitioning to producing more soma cells in later development.

Exploring such extensions to this model presents a seam of potential avenues for investigation in future theoretical studies.

Thank you for the kind assessment of our findings. In the updated manuscript, we in addition investigated a model with asynchronous cell divisions. However, due to computational limitations, we are unable to fully replicate the investigation protocol of the original synchronous model. The execution time of the synchronous model scales linearly with the number of generations (n) and it still takes about a week to compute a single map like Fig.2A on a 2000-node cluster. The asynchronous model, in turn scales linearly with number of cell divisions, and hence, exponentially with generation time (as 2^n), which results in calculations taking much more time. For instance, the map in Fig.2A requires about 160 times more computer time with the asynchronous model. Nevertheless, we were able to implement this model for smaller organisms, with less statistics. There, we found that asynchronous model allows an evolution of irreversible somatic differentiation. However, it is suppressed comparing with the synchronous model – the fraction of Fcomp profiles promoting irreversible differentiation is much smaller and the organism size restriction is higher.

To study a dynamic differentiation strategy would be wonderful. Early on, we considered studying this scenario. The crucial factor here is how flexible can the strategy be. In a naïve situation with a complete flexibility between every cell generation, the most successful strategy would be all cells of an organism first completely turn into soma-role to gain the maximal benefits, and then at the last step, they all convert back to germ to produce the maximal number of offspring. This is not observed in natural species; hence the flexibility of dynamic differentiation program must be constrained. We are curious to study what kind of constraints can lead to irreversible soma, but this task is beyond the scope of the current study. Our work with a constant differentiation program is the beginning of the future line of research. We are already looking forward to explore the space of dynamic differentiation programs in later projects.

• to : https://ipfs.io/ipfs/bafkreiejmi5bgqwzyaogu2egihryeigiorencib3nt4xykodyizh4en66i?f=As%20We%20May%20Think.pdf

• written : 1939

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Reply to the reviewers

We are grateful to the reviewers for their thoughtful comments and propose the following experiments or clarifications listed below (blue) in a revised manuscript.

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

The authors use a combination of Dsn1-Flag kinetochore purification from yeast extracts and laser trapping experiments (as in a number of previous studies), to study the effect of Mps1-dependent phosphorylation on reconstituted kinetochore-microtubule attachments in vitro. They complement this analysis with genetic experiments characterizing the effects of non-Mps1 phosphorylatable mutants on checkpoint activity and chromosome segregation in yeast.

The authors had previously shown that Mps1 is the major kinase activity that copurifies with Dsn1-Flag in their purification scheme. They now investigate the effect of adding ATP and thereby allowing Mps1 phosphorylation in the reconstituted system. They show that addition of ATP decreases the rupture force of kinetochore-microtubule attachments, meaning it weakens the strength of the attachment. This effect can be negated either by inhibiting Mps1 with reversine, or by providing kinetochores in which the Mps1 phosphorylation sites on Ndc80 (most of them in the N-terminal tail) have been mutated to alanine. Thus, like the activity of Ipl1, Mps1 phosphorylation of the Ndc80 N-tail (which is known to be important for full MT affinity) weakens kinetochore-microtubule attachments.

Cellular experiments demonstrate that non-Mps1 phosphorylatable Ndc80 14-A mutants have a functional mitotic checkpoint (contrary to previous claims by Kemmler et al., 2009), but show synthetic sickness with stu2 alleles that are involved in error correction.

**Major points:**

Within the framework of this experimental setting, the study as presented is logical and clear. The conclusions regarding the effect of Mps1 in this reconstituted system are overall well supported by the data. I have a couple of major and some minor points that can further improve data interpretation and should therefore be considered:

1. In previous publications (e.g. Gutierrez et al., Current Biology 2020), the authors have reported that the Dam1 complex, an established Mps1 substrate, is required for full attachment strength in this system. Are the effects of Mps1-dependent Ndc80 phosphorylation and Dam1 independent from one another? For example would dad1-1 or non Cdk1 phosphorylatable Dam1 complex further reduce the rupture force in ATP? Or does Mps1 phosphorylation affect, for example, Dam1 binding to Ndc80?

Response: To better understand the effects of ATP treatment, we analyzed the levels of Dam1 on the kinetochores after ATP treatment and did not see any change. We will add this data to a supplemental figure. Dam1 clearly makes a major contribution to the strength of the kinetochores because their strength even after ATP-treatment is higher than the rupture force of kinetochores purified from a dad1-1 mutant strain. However, as we report in the paper, blocking the eight Mps1 target sites in the tail of Ndc80 was sufficient to block the effect of ATP, so it is unlikely that phosphorylation of the Dam1 complex by Mps1 makes a major contribution to the ATP-dependent kinetochore weakening in vitro. We think Dam1 phosphorylation by Aurora B probably contributes independently to error correction, because the dam1-3D mutant, carrying phospho-mimetic substitutions in three Aurora B sites, is synthetically lethal when combined with the ndc80-8D phospho-mimetic mutant in eight Mps1 sites. We will add this genetic interaction data to the revised manuscript to provide additional information about the pathways.

What is the effect of ATP on initial binding events? Are there differences in the fraction of beads that spontaneously attach laterally at the start of the experiment? This may allow to draw conclusions whether any kind of binding or specifically force-generating end-on attachments are affected by ATP.

Response: We did measure a reduction in the fraction of free kinetochore-decorated beads capable of binding microtubules upon exposure to ATP (from 20% binding in the absence of adenosine to 11% in the presence of ATP). This observation suggests that the microtubule-binding activity of the kinetochores, like their rupture strength, is reduced upon exposure to ATP, as reported in the methods, in the "rupture force measurements" section. However, because we worked with a low density of kinetochores on the beads, the initial numbers of beads that spontaneously attached was quite low and free beads capable of binding to microtubules were relatively rare. In addition, when we find a bead already attached to the lattice, we cannot distinguish whether it bound initially to the lattice or instead bound to a tip that then grew beyond the bead. For these reasons, we feel it would be very difficult using our current approach to draw statistically significant conclusions about whether there were ATP-dependent changes in the relative affinities of the kinetochores for lateral versus tip attachments.

Ndc80-8D has low attachment strength, consistent with lowered MT affinity of the phospho-mimetic Ndc80 tail. Interestingly, Supplementary Figure S4B shows that the amount of Cse4 in the pull-down western appears substantially reduced in 8D vs 8A or wt. Is the amount of co-purified inner kinetochore affected in this mutant? This may be an alternative explanation for decreased attachment strength, for example if the fraction of "full" or "complete" kinetochores may be reduced. Could this also happen upon inclusion of ATP?

Response: The reviewer is correct that the level of Cse4 and other inner kinetochore components is slightly reduced in the Ndc80-8D kinetochores, for reasons that are not clear to us. However, the incubation of wild type kinetochores with ATP does not affect the levels of these proteins, suggesting that the weakened rupture strength is not due to reduced levels of these inner kinetochore proteins. We will add the data showing that ATP does not affect levels of inner kinetochore proteins into a supplemental figure to clarify this point.

**Minor points:**

page 13 (heading): "Weakening occurs via phosphorylation...". Probably good to mention what is weakened ("Weakening of kinetochore-microtubule attachments occurs via phosphorylation...".

Response: We will alter the heading as suggested.

page 14/Figure5C: Median Rupture Force for Ndc80-8D is 4.8 pN according to the text. In the graph it looks like >5 pN.

Response: We thank the reviewer for noticing this mistake and will correct the median rupture force to 5.6 pN.

page 23: comma missing between T21 S37 and T47 (should be T21, S37 and T47)

Response: We thank the reviewer for noticing this omission and will correct it.

page 24/25: different spelling of G1 (sometimes with subscript)

Response: We thank the reviewer for noticing this inconsistency and will correct all to be G1.

page 24/25: ug instead of µg

Response: Thanks. We will fix this mistake.

page 28: Figure 5B instead of Figure 5A

Response: Thanks for noticing this mistake. We will correct this.

Figure 6A: Lambda-Phosphatase treatment for 20 minutes according to figure legend and 30 minutes according to Material and Methods section.

Response: The material and methods section specified a 20-minute incubation with phosphatase, in agreement with the figure legend. We believe the reviewer might have accidentally confused the time value with the temperature, which was 30 degrees.

Figure 6E: One should not draw any conclusions from the anti-phospho T47 blot here, the quality is simply too poor to allow a statement regarding an mps1-1 effect

Response: While the immunoblots with the T74 phospho-specific antibody are not as clean as many standard antibodies, we have reproduced the results multiple times and therefore feel comfortable concluding that there is a decrease in signal that is Mps1-dependent.

Figure 6: Labelling T47P misleading (Proline substitution?, use pT47 instead)

Response: We will change the labeling on this figure, as suggested, from T74P to pT74. To be consistent, we will also change this nomenclature in the text.

Figure 6F: Make clear in the labelling that a stu2-AID background is used here, makes it easier to understand why Auxin is used here.

Response: We will change the labeling, as suggested, to include the genotype of stu2-AID in the figure.

how specific is reversine for yeast Mps1? I have not seen any data on this in previous publications.

Response: Reversine is not necessarily specific for Mps1. However, the only kinase activity that co-purifies with the isolated kinetochores is from Mps1, so reversine should inhibit only Mps1 in our in vitro experiments. Nevertheless, to further address this concern, we will include optical trapping results using mps1-1 mutant kinetochores in the revised manuscript. We have already performed these additional experiments and found that mps1-1 kinetochores do not undergo ATP-dependent weakening, strongly reinforcing our conclusion that Mps1 is the major kinase involved.

additional genetic interactions might be informative, if Ndc80-8D has weakened attachments, it may have synthetic effects with other mutants (dam1?), conversely, ndc80-8A may show genetic interactions with ipl1 alleles, for example.

Response: We agree that the ndc80 phospho-mutant alleles might have genetic interactions with other mutants. Consistent with this prediction, we have found that ndc80-8D is synthetically lethal when combined with the dam1-3D mutant in three Ipl1 sites. As mentioned above, we will add this data into the revised text. We will also perform additional genetic interaction experiments with ipl1 and mps1 alleles and add any additional interactions we discover into the revised text.

Reviewer #1 (Significance (Required)):

The study adds to the characterization of the effects of Mps1 kinase on kinetochore-microtubule attachments and characterizes the cellular phenotypes of non-Mps1 phosphorylatable Ndc80 mutants. The major conceptual point that Mps1 phosphorylation can weaken kinetochore-microtubule interactions and thereby contributes to error correction in a manner similar to Ipl1 has previously been made in the literature. Maure et al., (Tanaka lab, 2007, Current Biology) have characterized the effects of mps1 mutant alleles on biorientation of authentic chromosomes and on replicated/unreplicated mini-chromosomes. In particular the experiments with unreplicated mini-chromosomes have revealed less frequent detachment in mps1 mutants, demonstrating that Mps1 activity is required to release attachments that are not under tension.

Another benefit of this study is that it puts the Kemmler 2009 EMBO J. paper into perspective and corrects some of it claims. In particular the notion of sustained checkpoint activation in the Mps1 phospho-mimetic Ndc80-14D mutant, whose lethality was claimed to be rescued by checkpoint deletion. It is confirmed here that the allele is lethal but cannot be alleviated by simultaneous checkpoint deletion. Conversely, the Ndc80-14A mutant is shown to have a functional checkpoint. One could argue that since the publication of the Kemmler paper, the idea of requirement of Mps1 phosphorylation on Ndc80 for checkpoint activity has not gained any traction in the field, but it's still useful for the field to put some of these earlier claims into perspective. The paper will therefore be interesting to researchers working on mechanisms of chromosome segregation and error correction.

From my background I cannot comment on technical details of the biophysical force spectroscopy experiments (laser trapping), but I have no reason to doubt that the authors accurately report their findings.

Response: We sincerely thank the reviewer for their careful reading, helpful comments, and enthusiasm for our manuscript.

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

This paper focusses on the mechanisms underlying chromosome biorientation in mitosis, an essential process that warrants equal chromosome segregation to the dividing cells. Correction of improper kinetochore-microtubule attachments relies on two conserved protein kinases, Aurora B and Mps1, that detach kinetochores that are not under tension in order to provide them with a second opportunity to establish bipolar connections. In vivo, Aurora B and Mps1 have intertwined functions and share some common targets. For this reason, despite the large body of literature on the subject, their precise roles in chromosome biorientation have been difficult to tease apart.

The authors take advantage of an in vitro reconstitution assay that they previously published (Akyioshi et al., 2010) to identify the critical target(s) of Mps1 in weakening kinetochore-microtubule connections. The assay uses kinetochore particles purified from budding yeast cells that bear Mps1 but are notably deprived of Aurora B. Upon addition of ATP to activate the co-purified kinases (e.g. Mps1), kinetochores are added to coverslip-anchored microtubules to which they attach laterally. Through a laser trap, kinetochores are brought to the microtubule plus-end and pulled with increasing force until the kinetochore detaches, which allows measurements of the average rupture forces that reflect the strength of the attachments. The approach is straightforward and potentially very powerful, first because it provides a simplified experimental set-up in comparison to the cellular context, and second because it directly measures the impact of protein phosphorylation on the strength of attachments.

The authors convincingly show that Mps1-dependent phosphorylation of the N-terminal part of Ndc80 significantly weakens the strength of kinetochore-microtubule attachments in vitro, while phosphorylation of other known Mps1 targets, such as Spc105, does not seem to have an effect. Eight phosphorylation sites in Ndc80, which were previously identified as Mps1-dependent phosphorylation sites (Kemmler et al., 2009), are shown to be critical to destabilise kinetochore-microtubule attachments in the in vitro reconstitution assays. The authors also present evidence for a moderate involvement of Ndc80 phosphorylation by Mps1 in correcting improper attachments in vivo, suggesting that additional mechanisms are physiologically relevant for error correction.

The experiments are mostly well designed, the data are solid and support the main conclusions. However, to my opinion additional experiments could be performed, as outlined below, to strengthen the physiological relevance of the main findings and corroborate some of the conclusions.

**Major points:**

1. Given the partially overlapping function of Mps1 and Ipl1 (Aurora B) in error correction, the ndc80-8A mutant should display synthetic growth and chromosome mis-segregation defects with ipl1 temperature-sensitive alleles. Conversely, the ndc80-8D mutant should suppress the lethality at high temperatures of mps1-3 mutant cells, which were recently shown to be defective in chromosome biorientation (Benzi et al., 2020). Finally, chromosome mono-orientation could become apparent in ndc80-8A cells upon a transient treatment with microtubule-depolymerising drugs, which should amplify the cellular need for error correction.

Response: We agree that further exploration of the possible genetic interactions might help to reinforce the physiological relevance of our main findings. Toward this goal, we will obtain the mps1-3 mutant to determine whether ndc80-8D can suppress its lethality and will add this to the revised manuscript if there is a positive result. As mentioned in response to Reviewer 1, we will add a synthetic lethal interaction between ndc80-8D and a dam1-3D mutant where the Aurora B sites are altered to the revised text. We will also perform additional genetic interactions with ipl1 and mps1 mutants and add any we find into the revision. As requested, we will perform a nocodazole wash out experiment, to determine if ndc80-8A cells show a defect in error correction and add this data to the revision if there is a defect.

The authors show that Mps1-dependent phosphorylation of Ndc80 is not involved in the spindle assembly checkpoint, a conclusion that contradicts a previous report (Kemmler et al., 2009). They also find, in contrast with the same report, that the lethal phenotype of the ndc80-14D phospho-mimetic mutant cannot be rescued by disabling the spindle checkpoint. In my opinion, Kemmler et al. convincingly showed, through a number of different experimental approaches, that ndc80-14D cells die because of spindle checkpoint hyperactivation. Not only deletion of checkpoint genes was shown to rescue the lethality, but re-introduction of a wild type copy of the deleted checkpoint gene reinstated lethality. Thus, the explanation invoked here that spontaneous suppressing mutations could underlie the viability of ndc80-14D SAC-deficient mutants is not consistent with the published observations. A thorough examination by the authors of the phenotype of ndc80-14D cells in their hands should be carried out to support these conflicting conclusions. If authors find that ndc80-14D cells actually die because of chromosome mono-orientation, then this would highlight an important function for some or all the six additional phosphorylation sites, relative to the ndc80-8D mutant, for chromosome biorientation in vivo.

Response: We were unable to reproduce the data that deletion of the spindle checkpoint suppresses lethality of the ndc80-14Dmutant, so it remains unclear why our results differ from those of the Kemmler paper. However, we note that re-introducing a wild-type checkpoint gene via transformation and restoring lethality to the ndc80-14D cells does not necessarily mean there were no suppressors. While that is one possible interpretation, another possibility is that there was a suppressor mutation in the viable ndc80-14D cells that also required the lack of the checkpoint to live. Kemmler and co-workers selected for viability on FOA media and never backcrossed those viable strains to show that they could regenerate the double mutant through a cross with the expected segregation pattern of two mutations, which would have been a more rigorous demonstration that the viability was specifically due to ndc80-14D and the checkpoint mutation. Instead, they transformed a wild-type copy of the checkpoint gene back into the strain that was selected for growth on FOA and showed that it reverted the phenotype. This approach cannot rule out a suppressor mutation that fails to suppress in the presence of an active checkpoint. Therefore, in our opinion, the Kemmler paper does not make an entirely convincing case that the ndc80-14D cells die because of spindle checkpoint hyperactivation.

To further analyze the phenotype of ndc80-14D cells, we have constructed an Ndc80-AID ndc80-14D strain and added auxin, to deplete the wild-type copy of Ndc80. In agreement with the findings of Kemmler et al., this did trigger the spindle assembly checkpoint. However, when we made an Ndc80-AID ndc80-14D mad2 strain and analyzed segregation, we found that chromosome 8 missegregated in 28% of the cells compared to 2% of control cells. This observation suggests that there is a kinetochore defect in these cells that may have triggered the checkpoint and is inconsistent with the mutant solely activating the checkpoint in the absence of any other kinetochore defect. In addition, the levels of Ndc80-14D as well as Mps1 were altered on the mutant kinetochores. The combination of these defects strongly suggests that the ndc80-14D mutant alters kinetochore function in addition to leading to constitutive checkpoint signaling. Because our manuscript is mainly focused on phosphorylation of the Mps1 target sites within the N-terminal tail, we do not plan to add this data involving many additional sites, including Ipl1 target sites and sites on the CH domains of Ndc80, into the current manuscript. We will further pursue the other phosphorylation sites in the future.

The conclusion that Spc105 phosphorylation by Mps1 is not required for the Mps1-mediated weakening of kinetochore attachments in vitro is based on the comparison between kinetochore particles bearing wild type, untagged Spc105 and particles bearing non-phosphorylatable Spc105-6A tagged at the C-terminus with twelve myc epitopes. Thus, the presence of the tag could obliterate the effects of the mutations in the phosphorylation sites by destabilising kinetochore-microtubule attachments in the presence of ATP. Consistent with this conclusion, Spc105-6A-12myc-bearing kinetochores withstand lower rupture forces than Spc105-bearing kinetochores upon ATP addition. Furthermore, Spc105-6A-12myc kinetochore particles show an interacting protein at MW above 150 KD that is not present in wild type particles (Fig. S2A), suggesting that either the tag or the mutations might affect kinetochore composition. Thus, this set of experiments should be repeated using Spc105-6A kinetochore particles lacking the tag.

Response: If we understand correctly, the reviewer is suggesting that the myc tag on Spc105-6A could cause an ATP-dependent effect on kinetochore strength. While this is formally possible, it seems highly unlikely to us, for two reasons: First, a myc tag is not expected to bind nucleotides, and while it can sometimes have a general effect on protein stability or interfere with protein-protein interactions, we are not aware of any evidence for a myc tag directly causing an ATP-dependent effect in vitro. Second, when we measured Spc105-6A kinetochores in control experiments, without adenosine or with ADP, their rupture strengths were high like wild-type kinetochores. The strength of ADP-treated Spc105-6A kinetochores (8.7 pN), for example, was statistically indistinguishable from that of ADP-treated wild-type kinetochores (8.7 pN, p = 0.27 based on a log-rank test). The wild-type-like behavior of untreated and mock-treated Spc105-6A kinetochores indicates that their composition is not affected in a manner that significantly impacts kinetochore-microtubule strength.

In general, it would have been informative to complement the data presented here with a mass spec analysis of the composition of kinetochore particles, at least for the experiments that are most relevant to the conclusions. For instance, the composition of the Ndc80-8A kinetochore particles is assumed to be similar to that of wild type kinetochores based on gel silver staining (Fig. S4A; note also that ndc80-8A particles are compared to ndc80-8D particles and not to wild type particles). However, the authors previously showed that kinetochore particles purified from dad1-1 mutant cells (affecting the Dam1 complex) have an apparently identical composition to particles purified from wild type cells by silver staining, yet they display significantly lower resistance to the rupture strength in vitro (Akyioshi et al., 2010). What is the status of the Dam1 complex (or other kinetochore subunits) in kinetochores purified from ndc80-8A/-8D or spc105-6A cells relative to wild type kinetochore particles?

Response: We agree that further characterization of the kinetochore particle composition would be valuable and propose to further analyze the composition by purifying wild-type, Ndc80-8A, Ndc80-8D and Spc105-6A kinetochores and performing immunoblotting against the Dam1 complex. In addition, we will analyze the Ndc80-8A and Ndc80-8D kinetochores by mass spectrometry and report a qualitative analysis of the relative amounts of each kinetochore subcomplex in the revised manuscript supplementary data.

**Minor comment:**

I believe that the right reference for the sentence in the Discussion "If Aurora B is defective, for example, the opposing phosphatase PP1 prematurely localizes to kinetochores" is Liu et al. 2010.

Response: We had cited the reference showing this effect in yeast, since our work was performed in yeast. We will also add the Liu et al paper, which showed the same result in human cells.

Reviewer #2 (Significance (Required)):

Although the experiments are well designed and the conclusions are mainly supported by the data, the question arises as to what extent the in vitro assays recapitulate, at least partly, what happens in vivo. An emblematic example is the involvement of Spc105 in the error correction pathway. The Biggins lab previously showed that Spc105 phosphorylation by Mps1 and subsequent Bub1 recruitment is not only essential for the spindle assembly checkpoint, but is also crucial for chromosome segregation in vivo, as shown by slow-growth phenotype and aneuploidy of the spc105-6A non-phosphorylatable mutant (London et al., 2012). Additionally, a recent paper showed that Spc105 is a crucial Mps1 target in chromosome biorientation (Benzi et al., 2020).

In sharp contrast, the ndc80-8A mutant, which in vitro completely erases the ability of Mps1 to destabilise kinetochore-microtubule attachments, displays no growth defects in otherwise wild type cells and only modestly enhances chromosome mis-segregation in a mutant affecting an intrinsic correction pathway (stu2ccΔ). The N-terminal part of Ndc80 (aa 1-116) containing the aforementioned eight phosphorylation sites can even be deleted altogether without any consequence on cell viability (Kemmler et al., 2009). Thus, although the in vitro assays presented here produced clear-cut and reproducible results, their physiological relevance in vivo remains unclear.

Left apart this criticism, the manuscript has several merits outlined above and will be of interest for people working in the fields of chromosome segregation, kinetochore assembly, spindle assembly checkpoint, etc.

Expertise of this reviewer: mitosis and related checkpoints

Response: We are grateful to the reviewer for carefully reading our manuscript and detailing their concerns. We agree that it can be challenging to establish the physiological relevance of experiments performed in vitro. However, our in vitro approach allowed the effects of Mps1 specifically on kinetochore-microtubule attachment strength to be disentangled from its numerous other effects in vivo. In our view, the relatively mild phenotypes associated with mutants in the Mps1 phosphorylation sites on the Ndc80 tail are consistent with similarly mild phenotypes of mutants in the Aurora B phosphorylation sites on the Ndc80 tail. In both cases, this appears to be due to additional error correction pathways that compensate in vivo.

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

Sarangapani, Koch, Nelson et al. applied a combination of in vitro biophysical assays with purified kinetochore particles and in vivo analyses to investigate the contribution of Mps1 kinase to kinetochore-microtubule (KT-MT) attachment stability and error correction.

The manuscript is well written and the authors nicely highlight the facts that 1) the focus of the field has long been on the contribution of Aurora kinases (Ipl1 in budding yeast) to attachment stability and error correction, and 2) it has been difficult to assess the relative contributions of Aurora versus Mps1 kinases in cell-based experiments. The authors note that their KT particle assay is uniquely positioned to address this gap in our understanding and to specifically isolate the contribution of Mps1 to attachment stability in vitro. The findings are well-presented and quite convincing although I have several comments that should be addressed to strengthen the central conclusion that this work has isolated the contribution of Mps1 in their assays.

**Major points:**

1) I think it is important to note that reversine is not specific for Mps1 kinase - although it is typically presented as such in the field. It was initially identified as an Aurora kinase inhibitor (IC50: ~25nM (Aurora B) - 900nM (Aurora A)) that turned out be an even more potent Mps1 inhibitor (IC50 ~6nM). I have concerns that the in vitro assays were done with 5 uM reversine - a concentration so high that it could certainly inhibit any Ipl1 that is present (see comment 3 below) and possibly even inhibit Bub1 activity as Santaguida et al. (JCB, 2010) measured an IC50 >1uM for Bub1 inhibition. It is important to complement/confirm the chemical inhibitor experiment by repeating the rupture assays +/- ATP in KT particles purified from the mps1-1 strain (shown in Figure 6).

Response: We agree that reversine is not necessarily specific for Mps1 and this concern was also brought up by Reviewer 1. Because Mps1 is the only kinase activity that co-purifies with the isolated kinetochore particles, we expect reversine to inhibit only Mps1 in our in vitro assays. However, to further address this point, we will add rupture force assays using kinetochores purified from mps1-1 mutant cells to the revised manuscript. We have already performed these experiments and they confirm that kinetochores lacking Mps1 do not undergo ATP-dependent weakening. We did not put this data into the original submission because the experiment needs to be performed differently due to altered Dam1 levels. But we will clarify the changes in the materials and methods and add the data to a supplementary figure.

2) If the ATP-mediated reduction on rupture force is lost in the mps1-1 KT particles, which will also lack Bub1 kinase, then preserving the ATP-dependent reduction in rupture force from KT particles purified from the Bub1delta mutant strain would be strong evidence that the contribution of Mps1 kinase has been disentangled from other kinases in this assay.

Response: Although Mps1 recruits Bub1, we think it is unlikely that we are assaying Bub1 kinase activity in our in vitroexperiments. We cannot detect Bub1 activity on the purified kinetochores using a sensitive radioactive kinase assay (London et al, Curr Bio 2011), and the levels of Bub1 in our kinetochore purifications are very low (for example, see Akiyoshi et al, Nature, 2010). However, we agree with the reviewer that this caveat should be mentioned and will add this point to the revised text for clarity.

3) Recent work has shown that Sli15-Ipl1 interacts with and is recruited to KTs by the COMA complex (Rodriguez et al., Curr Biol, 2019 and Fischbock-Halwachs et al., eLife 2019) and that this population of Ipl1 is important for accurate chromosome segregation as also shown 10 years prior by Knockleby and Vogel (Cell Cycle, 2009). I realize that this group previously showed (London et al., Curr Biol, 2012) that phosphorylation of KT particles was not affected when purified from the ipl1-321 mutants, but in light of the recent findings how sure are the authors that there is not any Sli15-Ipl1 in the preparations? I think commenting on this would be worthwhile.

Response: We have not detected Ipl1 or Sli15 in the numerous mass spectrometry experiments we have performed on the kinetochore purifications. In addition, we have been separately assaying the effects of Ipl1 phosphorylation on kinetochores for another project (de Regt, https://doi.org/10.1101/415992), which independently confirmed that the only detectable kinase activity in our kinetochore purifications is Mps1. We will add this additional reference to the manuscript.

4) Since the interplay between Mps1 and Aurora B are central to this story, the authors should expand upon the sentence on page 5 reading "While there is some evidence that Mps1 regulates Aurora B activity (Jelluma et al., 2010; Saurin et al., 2011; Tighe et al., 2008), significant data suggests it has an independent role in error correction and acts downstream of Aurora B (Hewitt et al., 2010; Maciejowski et al., 2010; Maure et al., 2007; Meyer et al., 2013; Santaguida et al., 2010)." I am not entirely convinced that the in vivo experiments presented here differentiate as to whether Mps1 is upstream from Ipl1 or whether they are acting independently? For example, phosphorylation of T74 looks to be completely lost in figure 6E (although it's difficult to tell since the blot for T74P is very smeary). If they are acting independently in error correction then Ipl1 should still be able to phosphorylate T74 in this condition. However, if the P-T74 really is lost completely in the mcd1-1 cells then this suggests to me that Ipl1 is downstream of Mps1 in this live cell error correction assay.

Response: We thank the reviewer for bringing this to our attention. We did not mean to imply that Mps1 is downstream from Aurora B in budding yeast and were intending only to summarize findings from the literature regarding other organisms. We will revise this section of the text to make that point clearer, and we agree that the order of events remains unresolved. In addition, we will note that Mps1 does not eliminate the phosphorylation detected by the T74 antibody in the revision, to avoid misconceptions about the order of events.

**Other points:**

1) On p.8 "a median strength of 7.5 pN, similar to untreated and ADP-treated kinetochores". Similar is vague so I'm curious as to whether there a statistically significant difference between this and the 9.8 pN and 8.7 pN measured in the other conditions. If so this could be explained by partial dephosphorylation with the phosphatase.

Response: The quoted phrase refers to the 7.5-pN strength measured when λ-phosphatase was included together with ATP (data from Fig. 1D and Supp. Fig. S1B). P-values computed from comparisons of survival plots using the log-rank test show that this strength was not significantly different from the ADP-treated wild-type (8.7 pN, p = 0.06), nor was it significantly different from the ADP- and MnCl2-treated wild-type (8.1 pN, p = 0.35). However, it was barely significantly different from MnCl2-treated wild-type (8.6 pN, p = 0.03), and it was more significantly different from untreated wild-type (9.8 pN, p = 0.0007). With the revised manuscript, we will include a supplemental table with p-values computed from log-rank tests for all the key statistical comparisons, including those mentioned here.

2) On p.19 the authors note that Aurora A phosphorylates Ndc80 tail during mitosis. Ye et al. (Curr Biol, 2015) also showed that Aurora A can phosphorylate Aurora B sites and that this activity "converges" at the tail to weaken attachments during error correction.

Response: We will add the reference and thank the reviewer for pointing out this omission.

3) Optional: I am curious as to whether the addition of ATP to the Ndc80-8D particles further reduces the rupture force. If so then other sites may also be in play.

Response: We agree this is an interesting question but we have not yet performed those assays and agree it might be worthwhile for a future study.

4) Please comment on why MnCl2 is used in the rupture assays in Figure S1. I saw no mention of this in the main text.

Response: We include MnCl2 in the assay because it is required for phosphatase activity and will add this point to the legend of supplementary Figure S1.

5) Consider moving S2 A and B to Figure 3 C and D. This is an interesting result and would go well in the main figure next to the significantly reduced rupture force measurements for the 6A mutant so the reader doesn't have to dig into the supplemental for the data providing this reasonable explanation for the rupture force result.

Response: We thank the reviewer for this suggestion and will move S2A and S2B into Figure 3.

Reviewer #3 (Significance (Required)):

The significance of this relates to focusing on an important phenomenon - error correction - and in looking beyond the traditional focus of the field on Aurora kinases to Mps1 kinase, which is largely implicated in checkpoint signaling. Disentangling the contributions of these two players is an important advance.

The work will be of interest to audiences interested in: kinases, cell division, checkpoints, kinetochore biology, biophysics

The above areas of interest overlap with my expertise.

Response: We thank the reviewer for their enthusiasm for our experiments that help distinguish kinase activities and thus contribute to understanding the process of error correction.

Kunal - This is what we are trying to do. They have proposed a solution a few paragraphs below.

Author Response:

Reviewer #1 (Public Review):

The authors have studied mutations in the K13 gene that is linked to Artemisinin resistance in a range of African parasites. They show that these mutations can confer resistance in a in vitro survival assay but that they are often linked to reduced fitness. The authors also show that different parasites have less of an impact on fitness when the K13 mutations are introduced in line with the suggestion that the overall genetic background is critical for transmission of K13 mutations. The paper also shows evidence that genes potentially contributing to the genetic background are not involved.

The overall work involves a significant amount of work that to generate a wide range of different parasite lines that allow a detailed assessment of how different mutations interact with the genetic background of the parasite. This provides a significant amount of new insights. A key conclusion the authors draw from this work relates to the relationship between fitness and resistance and by inference on why artemisinin resistance has occurred in SE Asia. While this indeed would be a striking conclusion I think the data at this stage is not strong enough to make this claim. The claim is mainly based on Figure 3 E and F as well as 5 C and D. While indeed, initially it looks like RSA has much less of a survival impact in Dd2 there is some concern that the data is generated using different baselines (isogenic WT parasite in Figure 3 and Dd2eGFP in Figure 5 D). This is noteworthy as in Figure 5C the Dd2wt parasite is used and the fitness cost appears to be different.

Please see our reply below to Reviewer 1 Comment #2.

A striking finding is that the UG659C560Y line appears to have a relatively small fitness cost - especially if looked at for the whole 40 generations rather than the somewhat arbitrarily picked 38 days. This data could suggest that there are parasites in Africa that have the capacity to acquire resistance with minimal cost to fitness.

We thank the Reviewer for this suggestion and have now recalculated our fitness data using a 36-day period, which we have adopted as a standardized timeline and which allows us to compare across all prior and newly acquired fitness assays. We note that this is already relatively lengthy compared to a number of other reports in the literature. For example, Baragana et al. (2015, Nature) measured competitive growth rates over a 14-day period. Gabryszweski et al. (2016, Mol Biol Evol) used 20-day assays. Siddiqui et al. (2020, mBio) used longer 48-day assays. We agree with the Reviewer that our data suggest that some African strains can achieve in vitro ART resistance with a minimal cost to fitness. In support of this, our new data presented in the revised Figure 3 provide evidence for the R561H mutation having little to no fitness cost in 3D7 parasites that are closely related to Rwandan isolates (see our response above to Comment #2 from the Editors).

As pointed out above, we now include new fitness data on the R561H variant in African parasites, based on competition assays with an eGFP reporter line. To standardize our fitness data, we now have analyzed our data to day 36 across assays, as follows:

Methods lines 538-539: “Cultures were maintained in 12-well plates and monitored every four days over a period of 36 days (18 generations) by harvesting at each time point a fraction of each co-culture for saponin lysis.”

Figure 3 Legend lines 920-921: “K13 mutant clones were co-cultured at 1:1 starting ratios with isogenic K13 wild-type controls over a period of 36 days.”

The selective sweep to C560Y in SE Asia is something that has been known for a while. It is striking that it has been selected as based on the data presented here P563L has a similar fitness and RSA profile. The authors could explore this further.

The Reviewer highlights the important point that RSA values and fitness were comparable for C580Y and P553L, yet only the former swept across Southeast Asia. This would argue for additional factors that contribute to the successful dissemination of C580Y. These could include favorable genetic backgrounds that help propagate C580Y mutant parasites, or increased transmission rates, relative to P553L. To date, reasons for C580Y’s success beyond its moderate resistance and relatively minor fitness cost have not been firmly established. One possibility might be related to piperaquine pressure that selected for amplification in plasmepsins II and III as well as novel mutations in PfCRT, which emerged in parasites harboring K13 C580Y and which have been shown to spread as a series of genetically closely related sublineages (referred to as KEL1/PLA1; Hamilton et al. 2019, Lancet Infect Dis; Imwong et al. 2020, Lancet Infect Dis). These points are discussed as follows:

Discussion lines 361-369: “Our studies into the impact of K13 mutations on in vitro growth in Asian Dd2 parasites provide evidence that that the C580Y mutation generally exerts less of a fitness cost relative to other K13 variants, as measured in K13-edited parasites co-cultured with an eGFP reporter line. A notable exception was P553L, which compared with C580Y was similarly fitness neutral and showed similar RSA values. P553L has nonetheless proven far less successful in its regional dissemination compared with C580Y (Menard et al., 2016). These data suggest that additional factors have contributed to the success of C580Y in sweeping across SE Asia. These might include specific genetic backgrounds that have favored the dissemination of C580Y parasites, possibly resulting in enhanced transmission potential (Witmer et al., 2020), or ACT use that favored the selection of partner drug resistance in these parasite backgrounds (van der Pluijm et al., 2019).”

Overall, the main conclusion that there are K13 mutations that can confirm resistance to Art in the context of African parasites is clearly presented and convincing and this highlights the risk that exists for public health officials in African nations. What would be interesting from a readers perspective is how likely it is that this loss of fitness hurdle is going to be overcome in Africa and whether the risk of resistance development will increase as transmission rates drop.

We appreciate this suggestion from the Reviewer. Our revised manuscript now addresses this topic as follows:

Discussion lines 393-399: “It is nonetheless possible that secondary determinants will allow some African strains to offset fitness costs associated with mutant K13, or otherwise augment K13-mediated ART resistance. Identifying such determinants could be possible using genome-wide association studies or genetic crosses between ART-resistant and sensitive African parasites in the human liver-chimeric mouse model of P. falciparum infection (Vaughan et al., 2015; Amambua-Ngwa et al., 2019). Reduced transmission rates in areas of Africa where malaria is declining, leading to lower levels of immunity, may also benefit the emergence and dissemination of mutant K13 (Conrad and Rosenthal, 2019).”

Reviewer #2 (Public Review):

In this paper, the investigators performed two large-scale surveys of the propeller domain mutations in the K13 gene, a marker of artemisinin (ART) resistance, in African (3299 samples) and Cambodian (3327 samples) Plasmodium falciparum populations. In the African parasite population, they identified the K13 R561H variant in Rwanda, while parasites from other areas had the wild-type K13. In Cambodia, however, they documented a hard genetic sweep of C580Y mutation that occurred rapidly. They generated the C580Y and M579I mutations in four different parasite strains with different genetic backgrounds and found that these mutations conferred varying degrees of in vitro ART resistance. They further edited the SE Asian parasite strains Dd2 and Cam3.II with 7 K13 mutations and found that all the propeller domain mutations conferred ART resistance in the Dd2 parasite, whereas three of the mutations did so in the Cam3.II background. The R561H and C580Y mutations were also evaluated in several parasites collected from Thailand. In vitro growth competition analysis showed that K13 mutations caused substantial fitness costs in the African parasite background, but much less fitness costs in the SE Asian parasites. This study demonstrated the potential emergence of ART resistance in African parasite populations and offered insights into the importance of the parasite's genetic background in the emergence of ART resistance.

We thank the Reviewer for this thorough summary and favorable assessment of our work.

Reviewer #3 (Public Review):

Stokes et al address the question: Why have mutations in the K13 gene spread rapidly across South East Asia and led to widespread treatment failure with artemisinin-based antimalarials? In contrast, why do K13 mutations remain quite rare in Africa, and artemisinin-based antimalarials remain effective?

The work combines a number of different studies on different parasites of different origins. Gene editing has been used to assess the effects of K13 mutations in different parasite backgrounds, leading to a very complex view of the competing factors of level of resistance conferred and fitness cost.

The authors put forward the hypothesis that fitness costs associated with K13 mutations select against their dissemination in the high malaria transmission settings in Africa. However, the complexity of the genetic backgrounds of the parasites makes it difficult to tease out the contributing factors.

We agree that these are complex and multifactorial areas of investigation and appreciate the Reviewer’s summary.

Author Response:

Reviewer #1 (Public Review):

This work described a novel approach, host-associated microbe PCR (hamPCR), to both quantify microbial load compared to the host and describe interkingdom microbial community composition with the same amplicon library preparation. The authors used the host single (low-copy) genes as PCR targets to set the host reference for microbial amplicons. To handle the problem that in many cases, the host DNA is excessive compared to the microbiome DNA, the authors adjusted the host-to-microbe amplicon ratio before sequencing. To prove the concept, hamPCR was tested with the synthetic communities, was compared to the shotgun metagenomics results, was applied in the biological systems involving the interkingdom microbial communities (oomycetes and bacteria), or diverse hosts, or crop hosts with large genomes. Substantial data from diverse biological systems confirmed the hamPCR approach is accurate, versatile, easy-to-setup, low-in-cost, improving the sample capacity and revealing the invisible phenomena using regular microbial amplicon sequencing approaches.

Since the amplification of host genes would be the key step for this hamPCR approach, the authors might also include more strategy discussions about the selection of single (low copy) genes for a specific host and the primer design for the host genes to guarantee the hamPCR usage in the biological systems other than those mentioned in the manuscript.

A deeper discussion about the design of suitable host primers has been added to the Supplementary Information as Supplementary Discussion 3, and is now mentioned in the main text in the first section of the Methods.

Reviewer #2 (Public Review):

Lundberg and colleagues provide a detailed set of data showing the utility of host-associated microbe PCR. By simultaneously amplifying microbial community and host DNA, hamPCR provides an opportunity to measure the microbial load of a sample. I was largely convinced about the robustness of this approach after seeing the many different optimization datasets that were presented in the paper. I also appreciated the various applications of hamPCR that were demonstrated and compared to other standard approaches (CFU counting and shotgun metagenomics, for example). As clearly illustrated in Figure 6f, hamPCR could dramatically improve our understanding of interactions within microbiomes as it helps remove issues of relative abundance data.

One challenge about the approach presented is that it cannot be quickly adapted to a new system. Unlike most primers for 'standard' microbial amplicon sequencing, considerable time will be required to determine which host gene to target, how to make that host gene size larger than the size of the microbial amplicon, etc. This may limit wide adoption of hamPCR in the field. I do appreciate the authors providing some details in the Supplement on how they developed hamPCR for the several different systems described in this paper. The helpful tips may make it easier for others to develop hamPCR for their own systems.

Additional strategy of primer design was addressed in the response to Reviewer #1 Public Review.

An issue that repeatedly came up is that at high and low ends of host:microbe ratios, inaccurate estimates can occur. For example, with high levels of microbial infection, the authors note that hamPCR has reduced accuracy. The authors propose three solutions to this problem (1. altering host:microbe amplicon ratio, 2. use a host gene with higher copy number, 3. and adjust concentrations of host primers), but only present data for #1 and 3. Do they have any data to show that #2 would actually work?

One instance of potential unreliable load that sticks out in the paper is in Figure 5b. The authors note that this is likely due to unreliable load calculation. Is this just one of 4 replicates? What are other potential reasons this would be an outlier and how can the authors rule this out? Did they repeat the hamPCR for this outlier to confirm the striking difference from the other three samples in the eds1-1 Hpa + Pto sample?

Both qPCR and amplicon sequencing can be used to detect copy number variation in genomes [1]. Because amplicon-based methods are known to be sensitive to small differences in gene copy number, we are confident, without generating additional data on the topic, that #2 would work.

Furthermore, bacterial genomes from different taxa are known to vary slightly in their copy number of 16S rDNA, usually from between 1 to about 15 copies [2]. These variations are reflected in sequence counts from amplicon sequencing, biasing the counts towards taxa with more 16S rDNA gene copies [2, 3, 4]. This phenomenon has been well documented, distorts the accurate description of microbial communities, and therefore has led to some efforts to correct 16S rDNA gene amplicon data by dividing the counts from each taxon by the (estimated) 16S rDNA copy number of that taxon, so that the counts better reflect the numbers of bacterial cells.

Because amplicon methods are sensitive to copy number variation (whether those copies are from inside the same cell, or coming from different cells), we reasoned that choosing a host gene with a higher copy number, similar to the effects of copy number variation on 16S rDNA gene counts, will increase the representation of that host amplicon in the final library (because there will be more template host DNA molecules available to amplify). We did not test this explicitly - we think the evidence from literature is strong support on its own. We have added to the paper a statement that now references the Kembel 2012 paper, which we hope adequately supports our claim:

“Second, a host gene with a higher copy number could be chosen for HM-tagging throughout the entire project, which would increase host representation by a factor of that copy number (Kembel et al., 2012).”

1) Martins, W.F.S., Subramaniam, K., Steen, K. et al. Detection and quantitation of copy number variation in the voltage-gated sodium channel gene of the mosquito Culex quinquefasciatus . Sci Rep 7, 5821 (2017). https://doi.org/10.1038/s41598-017-06080-8

2) Kembel, S. W., Wu, M., Eisen, J. A., & Green, J. L. (2012). Incorporating 16S gene copy number information improves estimates of microbial diversity and abundance. PLoS Computational Biology, 8(10), e1002743. https://doi.org/10.1371/journal.pcbi.1002743

3) Starke, R., Pylro, V. S., & Morais, D. K. (2021). 16S rRNA Gene Copy Number Normalization Does Not Provide More Reliable Conclusions in Metataxonomic Surveys. Microbial Ecology, 81(2), 535–539. https://doi.org/10.1007/s00248-020-01586-7

4) Louca, S., Doebeli, M., & Parfrey, L. W. (2018). Correcting for 16S rRNA gene copy numbers in microbiome surveys remains an unsolved problem. Microbiome, 6(1), 41. https://doi.org/10.1186/s40168-018-0420-9

Could the DNA extraction method used cause biases in hamPCR for/against either the host or the microbiome? If two different labs study the same system (let's say bacterial communities growing on Arabidopsis leaves) but use different DNA extraction approaches, would we expect them to obtain different answers using hamPCR? Did the authors try several different DNA extraction methods to see if this is an issue? Or has another team of researchers considered this and addressed it in a separate paper? I would appreciate seeing either data to address this or a discussion paragraph that reasons through this.

Differences in DNA extraction method will certainly change the results, not only of the microbe-to-plant ratio, but also in the representation of microbes, because microbes differ in their sensitivity to different lysis methods. This is a well-documented concern in microbiome studies and has been demonstrated by using different methods on the same mock community in papers such as the following:

Yuan, S., Cohen, D. B., Ravel, J., Abdo, Z., & Forney, L. J. (2012). Evaluation of methods for the extraction and purification of DNA from the human microbiome. PloS One, 7(3), e33865. https://doi.org/10.1371/journal.pone.0033865

Albertsen, M., Karst, S. M., Ziegler, A. S., Kirkegaard, R. H., & Nielsen, P. H. (2015). Back to Basics--The Influence of DNA Extraction and Primer Choice on Phylogenetic Analysis of Activated Sludge Communities. PloS One, 10(7), e0132783. https://doi.org/10.1371/journal.pone.0132783

In short, if the DNA is not extracted because plant or microbial cells are not lysed, it cannot be amplified in PCR. However, there is a good overall strategy to minimize the problem, as also proposed in the above papers, and that is to err on the side of a harsher lysis (using strong bead beating, as we have done), since this will leave fewer cells unlysed (and thus less information will be hidden). We note that similar concerns about lysis methods changing results also apply to DNA extraction for qPCR and live bacterial isolation for CFU counting (for which too harsh a lysis will kill bacteria, but too gentle a lysis will not release them from host tissue).

We addressed this in two places. First, in the results section we mention briefly the following:

“All DNA preps employed heavy bead beating to ensure thorough lysis of both host and microbes, as an incomplete DNA extraction can lead to underrepresentation of hard-to-lyse cells (Albertsen et al., 2015; Yuan et al., 2012).”

Second, we added a paragraph to the discussion about sample selection and DNA extraction as follows:

“Because hamPCR can only quantify the DNA available in the template, choice of sample and appropriate DNA extraction methods are very important. In particular, the sample must in the first place include a meaningful quantity of host DNA. For example, although there is some host DNA in mammalian fecal samples or in plant rhizosphere soil samples, this host DNA does not accurately represent the sample volume, and therefore relating microbial abundance to host abundance probably has less value in these cases. Further, the DNA extraction method chosen must lyse both the host and microbial cell types. An enzymatic lysis suitable for DNA extraction from pure cultures of E. coli may not lyse host cells or even other microbes. Appropriate DNA preparation methods for metagenomics have been thoroughly evaluated elsewhere (Albertsen et al., 2015; Yuan et al., 2012), and a common point of agreement is that strong bead-beating increases the yield and completeness of the DNA extraction, but comes at the cost of some DNA fragmentation. Especially for short reads, as we have used here, this fragmentation is not a problem, and we recommend to err on the side of a harsher lysis, using strong bead beating potentially preceded by grinding steps using a mortar and pestle as necessary for tougher tissue.”

One emerging theme in microbiome science is to have consistent methodologies that are used across studies/labs to allow direct comparisons of microbiome datasets. Standardization of approaches may make microbiome science more robust in the long-term. Given much of the nuance in developing hamPCR for different systems, my impression is that this method is best for comparing samples within a particular host-microbe system and not across systems. For example, it may be challenging to directly compare my bacterial load hamPCR data from Arabidopsis to another lab's if we used different Arabidopsis host genes or if we used different 16S gene regions. Can the authors unpack this a bit in a discussion paragraph? If it is widely adopted, is there a way to standardized hamPCR so that it can be consistently used and compared across datasets? Or should that not be the goal?

There appears to be considerable non-specific amplification or dimers in the gels presented throughout the manuscript. Could this non-specific amplification vary across host-microbe primer combinations? Would this impact quantification of host and microbial amplicons?

Non-specific amplification / dimers do vary across host-microbe primer combinations. Indeed, they also vary between common 16S rRNA primer pairs used on their own (not shown). Fortunately non-specific amplicons amplified during the exponential PCR step do not, at least with our method, seem to impact quantification of host and microbial amplicons.

One reason is that non-specific amplicons can be recognized by their sequence and ignored. After the sequences of the amplicons have been extracted from the short read data, only those that match expected length and sequence patterns of the targeted amplicons need to be counted. Non-specific amplicons are certainly a nuisance because they represent wasted sequencing resources, but they can be excluded bioinformatically and therefore do not change the accuracy of the microbial load measurement. This is in contrast to ddPCR/qPCR, for which any off-target amplicons are also quantified!

A second reason is that the sensitive exponential amplicon step of hamPCR is done with a single primer pair. Off-target sequences do squander PCR reagents including primers and dNTPs, such that they become limiting at earlier cycles than without off-target sequences, but because the exponential PCR step is done with a single primer pair, such inferior amplification conditions are shared by all molecules, and therefore do not differentially affect the host or microbial amplicon. Any off-target binding occurring in the initial tagging reaction (before the PCR step) would certainly be a concern if the reaction was carried on long enough, because for example the microbial primer pair might become limiting at an earlier cycle number, leading to underestimates of microbial load. However, limiting the tagging cycle to a low number of cycles ensures that – should primers targeting a particular host or microbial amplicon be non-specific – the fraction still available to bind the correct sequence remains in excess.

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Reply to the reviewers

General comments:

We thank the reviewers for their constructive critique and are pleased they see the results as interesting and of general relevance. We also acknowledge their concerns on the issue of whether all claims are supported by sufficiently strong data. Our careful reading and analysis of the points that are raised suggest there are different reasons for the different cases that are brought up:

1. Misunderstandings, due to lack of clarity on our side. Example: When talking about ‘reduced actin’, our wording focussed on the endosome-associated actin (partly out of consideration for the fact the actual measurements we show come from the area around the endosomes, so we did not want to make any stronger claims), even though we should have made it clear that other areas of the cell tip are also affected. This will be addressed by clearer explanations.

Ill-advised wording we chose that is or can be seen as overinterpretation.

Example: ‘anchoring’ of actin at endosomes. We had not intended to infer anything about specific anchoring sites or mechanisms. We should have used a more neutral term, such as ‘associate with’ or accumulate around’ for the description. This and other cases can also be resolved by rewriting and better wording.

Anecdotal evidence or insufficient data.

Example: Images of phalloidin stainings depicting how actin is organized around late endosomes in control embryos. These and other cases will be addressed by adding further examples and additional quantification.

Finally, one suggestion was made for obtaining additional experimental data, which would involve laser ablation. While the experiment would provide an interesting extension of our findings, we will sadly not be in a position to carry it out in the foreseeable future, as explained below. We hope the referees will agree that our now extended discussion addresses the point in question sufficiently to support the conclusions from the experiments we do present.

These and all other points are addressed individually below. We highlighted the corresponding text changes in the manuscript file for the reviewers to identify them more easily.

Detailed responses:

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

**Summary**

Rios-Barrera and Leptin investigate the formation and guidance of the subcellular tube that forms in the terminal cell of the dorsal branches of the Drosophila tracheal system. In previous work the authors documented the presence of late endosomes at the tip of the growing terminal cell, ahead of the forming subcellular tube, which are involved in membrane recycling {Mathew, 2020 #1407}. In this present work they analyze the organization of the actin cytoskeleton in the tip terminal cell, in relation with the late endosomes, and assess the guidance of the subcellular tube. They find that the presence and localization of late endosomes play a role in tube guidance. They also find that late endosomes recruit actin around them, mediated by the activity of the actin polymerization regulator Wash, which is recruited to maturing late endosomes. When Wash activity is decreased, actin around late endosomes is decreased and tube guidance is compromised. Based on this observation, laser ablation experiments of actin ahead of the tube and actin staining at the tip of the terminal cell, the authors propose an exciting model: late endosomes recruit actin, which connects the actin pool of the basal membrane and the actin pool of the apical (subcellular tube) membrane thereby directing tube growth and guidance.

The manuscript is well-written and well-presented, the images and movies are of high quality and the experimental data, which is technically challenging, is very good and sufficiently replicated.

**Major comments:**

1. A critical point in the model that the authors put forward (which is also contained in the title and abstract) is that actin organized at late endosomes anchors apical and basal actin cortices. However, there is no clear and conclusive evidence for this. Clear evidence in this direction should be provided to propose it as a mechanism (as it is in the text, particularly in the first sentences of the discussion) and imply it in the title. The authors show endogenous actin around late endosomes and actin fibers at the tip of the terminal branch. However, at the level of resolution presented (Fig 3A,B), it is not possible to determine whether the different actin populations are actually "anchored". I suggest to present stronger data supporting this important conclusion.

In the same direction, it would be critical to show that this anchoring of actin fibers is disturbed when actin enrichment at the late endosome is perturbed (see also point 5).

Actually, the authors show that when Vha or Wash activity are downregulated actin accumulation around the CD4 vesicles decrease. However, this experiment has a few inconveniences. First, it is difficult to determine levels of a construct that is overexpressed (UAS-utr::GFP). Could the authors use phalloidin or an actin antibody to confirm the result?

Second, I find the result difficult to interpret. In the images provided I see a general decrease of actin (UtrGFP) at the tip, not only around the CD4 vesicles (Fig 6D,F) . Are these mutant conditions also affecting the rest of actin pools? If this is the case, can the authors attribute the defects exclusively to the abnormal recruitment of actin to the late endosomes?

Most importantly, the authors should analyze the pattern of actin distribution (labelling endogenous actin) and determine a possible loss of "anchoring" of fibers when late endosome maturation is perturbed.

We understand the referee addresses three issues here, to which we will respond in turn below:

• As already mentioned above, the referee interprets the term ‘anchoring’ in a more specific meaning than we had intended it to have. We obviously have to rephrase.
• A technical critique of the use of an overexpressed construct to visualize actin, which in turn has two sub-points: potential physiological effects on actin, and potentially inaccurate localisation. Both are valid points, but in our view do not undermine our conclusions. We will raise and discuss these concerns in our revised text.
• The specificity of the effects of reducing Vha and Wash of function on actin associated with endosomes versus throughout the growth cone of the cell – a very good point, about which we should have been much clearer and now will be. (a) Considering the use of the term ‘anchoring’, and the referee’s concern over whether we provide the appropriate evidence gave us with a good starting point to re-think what we actually show and how it can be interpreted.

Put in neutral terms, what [we felt] we had shown was an accumulation or enrichment of actin around endosomes that was dependent on proper functioning of Vha and Wash.

We agree that the term ‘anchoring’ cannot be justified by the description of actin localisation alone. The term implies a physical (and perhaps strong or long term) interaction between the endosome and the surrounding actin.

We see a strong enrichment of actin around endosomes, including in experiments in which we use phalloidin to visualize actin (Fig. 3B). The resolution of our images is approximately 200nm so they are able to reveal the very close association. The question is what the mechanistic basis for this closeness is. It is unlikely to be random, as shown by a quantification we have now included (Figure S3C). It is difficult to imagine how it could persist without at least transient physical interaction between the two components. The association is indeed highly dynamic and is constantly being re-established. This must mean that something ‘attracts’ actin to endosomes, most likely a molecule that is itself associated with endosomes. The presence or accessibility of such a molecule depends on the proper maturation of endosomes, as shown by the results of reducing Vha activity. And the ability of actin to associate depends on Wash. Together these findings suggest to us the existence of a (dynamic) molecular link between the endosomes and the actin network.

In order not to give the impression that we are claiming a permanent ‘anchor’, we now use more general terms such as ‘associates’ or ‘accumulates’, but also include the clarification on our thinking in the text. Furthermore, to illustrate a representative range of cases, we will add more examples of late endosomes and the actin meshwork surrounding them (Figure S3A, B). These images should give a broader reflection of the actin populations and their dynamism during tube growth.

(b) A major reason why we use live imaging with actin reporters is that the distribution of actin around late endosomes and the tip compartment in general is very dynamic, so capturing cells at the right time point can be challenging from fixed samples. This problem is exacerbated by a technical limitation: For the actin cytoskeleton to be well preserved during fixation, embryos have to be manually dechorionated which limits the throughput of the experiment. We therefore found that analysing cells over time is more informative than analysing cells fixed at a given time point.

As the reviewer points out, using an overexpressed reporter can have drawbacks. With regard to the problem of not representing the endogenous distribution faithfully, this can be the case when making statements about the absolute distribution. However, what we are looking at here is not absolute quantities of actin but relative changes in the area of interest with respect to other, unaffected regions of the cells, and then comparing these between mutant conditions and the control. We do this by normalizing the signal to the levels seen in the subcellular tube, using it as an internal control that allows us to adjust for variation in expression levels.

There is on case where such a normalization could be problematic, and that is when comparing actin levels in cells expressing bitesize RNAi, because Bitesize is itself involved in organizing the actin cytoskeleton in the tube membrane (JayaNandanan et al., 2014). However, in this experiment, the analysis still shows that actin levels at late endosomes do not correlate with the tube misguidance phenotype.

With regard to potential physiological effects of an over-expressed construct, some of the commonly used actin reporters have subtle effects on actin physiology, whereas Utr-ABD has negligible or no effects on the actin cytoskeleton, and it also reproduces actin dynamics faithfully (Spracklen et al., 2014). It is therefore generally considered the most reliable tool for live imaging of actin in Drosophila.

We have adapted the text and commented on these issues and hoped we have achieved more clarity.

(c) We agree that when Vha or Wash are downregulated, actin levels are overall reduced in the growth cone of the cells, while this is not the case in other regions, for example at the base of the cell. Although we had not explicitly stated this (but now will), this is a further indication that the different actin populations in the growing tip interact with each other.

For the downregulation of Wash, this could potentially have been due to a direct effect of Wash on the apical and basal actin, but then we would have expected a similar result in other parts of the cell, including the cell body and the proximal part of the branch, but we do not see that. Even more importantly, the expression of Vha100-DN has the same effect and this cannot be easily explained by a direction action on actin. Together, these findings therefore indicate that depletion of actin around endosomes has a knock-on effect on the basal and apical actin cortex in the vicinity. We have included this reasoning in the paper now.

Another critical point in the model put forward by the authors is that late endosomes drive tube guidance. To test this point the authors use an elegant system to mislocalize Rab7 late endosomes.

However, the effects are not strong (1G), and only a proportion of branches show misguided tubes. Do the cases with a ventrally-guided tube in the experiment Rab7:YFP+/+ (Fig. 1G) have a CD4 endosome (with Rab7YFP) at the tip? This would help to explain the weak effect.

This is an excellent point, and it is indeed what we observe: all cells with ventrally guided tubes have a late endosome that is positive for the YRab7-nanobody-membrane complex at the tip of the cell (n=42), whereas only 2/3 of misguided tubes do (n = 12), and those always have the additional endosome at the tip of the misguide tube. As the reviewer suggests, this provides an obvious explanation for why these cells do not have a tube misguidance phenotype. We have added a representative image of this condition (Rab7::YFP+/+, ventrally-guided tube) in Figure 2 to illustrate the phenotype.

What is the cause that preventing proper endosome maturation and acidification leads to misguided tubes (rather than missing ones)?

A complete loss of late endosome activity would indeed result in the absence of the subcellular tube. However, we and others have shown that partial loss of function (as caused by RNAi) can have more subtle effects. For instance, fully blocking endocytosis using the shibire**ts line completely prevents proper tube extension (Mathew et al., 2020), but expression of a shibire RNAi still allows tube formation to proceed, albeit in a defective manner (Schottenfeld-Roames et al., 2014). Similarly, the misguidance phenotypes resulting from Vha downregulation likely reflect weaker loss of late endosome function. These perturbations would allow initial tube growth to proceed, but later on they would uncover this later function of the endocytic pathway in regulating tube guidance.

We believe that what we see as this weaker defect is an uncoupling of direction from growth per se. The cells still receive their growth-inducing signals from the FGF-receptor, and this leads to directed cell growth in the direction of the chemotactic signal. The normal trafficking of membrane material from the apical to the basal domain is also not disrupted. Thus, membrane keeps being added to both domains and both the tube and the basal domain continue to growth. However, the growing tube has been disconnected from its guiding structure at the tip of the cell (our speculation: because failed endosome maturation no longer allows proper actin coordination) and therefore follows a random path. We had not been sufficiently clear about this but have now hopefully remedied this in the text.

The authors indicate that downregulating Vha activity leads to defects in acidification, but late endosome-MVB normally form. It is intriguing to see extra CD4 vesicles (like in 1C or 6C).

Wouldn't we expect to see "normal" tip accumulation of CD4 vesicles only, and not extra ones? How relevant are these extra CD4 vesicles?

Wouldn't we expect to see "non functional" CD4 vesicles, unable to recruit actin and lead intracellular tube formation (i.e. no tube) rather than missguidances? (1D shows higher proportion of misguided tubes than no tubes)

Similarly, is Wash-RNAi producing extra CD4 vesicles (as observed in movie 5, fig 6E)?

We do not postulate that the late endosomes are morphologically normal – there are vesicles carrying the CD4 marker (which is only a membrane marker, not specific for endosomes), but the literature indicates that the endosomes do not undergo their normal maturations, and we would have no reason to claim otherwise. So we agree that the ones we see in the Vha-downregulated cells are not fully functional, and this is indeed confirmed by their inability to recruit actin.

With regard to the number of large CD4 vesicles at the tip, terminal cells can normally have from 1 to 3 in the growth cone, and the fact that the experimental cells we showed were at the upper range whereas the control at the lower end was pure chance. We have now quantified the number of vesicles in the abnormal conditions and see that there is no increase (Figure S5F).

Actin recruitment to late endosomes was already documented, where it plays a role in cargo trafficking.

The authors propose that Wash is recruited to late endosomes upon acidification where it would prime actin nucleation around the endosome. The authors indicate a decrease in Wash accumulation upon expression of Vha dominant negative. However, this decrease is not quantified. In addition, it is difficult to determine levels of a construct when this is overexpressed (UAS-Wash::GFP). It would be desirable to use antibodies against the endogenous protein (Wash in this case) to claim differences in accumulation in mutant conditions.

We have quantified the amount of Wash::GFP in CD4 vesicles. As mentioned, the vesicles are very dynamic, and so is their recruitment of Wash::GFP, and doing the analysis in the live cells is therefore more meaningful than extracting information from fixed samples, but we will also try to obtain the antibody for confirmation in fixed material. We appreciate that as discussed above for actin, results using overexpressed constructs have to be interpreted with care, but here again, we mitigate against this by assessing relative changes rather than absolute amounts and mitigate against misinterpretation by normalizing the signal to the one seen in the cytoplasm.

The results presented do not rule out a requirement of Wash in terminal branching which is not associated with the enrichment in the late endosomes. The genetic interaction observed with Shrub is also compatible with both proteins acting on terminal branching but in different/parallel mechanisms.

While the fact that downregulation of Vha has the same effect cannot be explained in this manner, we agree with the reviewer and will rephrase this section in the paper.

Laser ablation experiments

The laser ablation experiments are difficult to interpret.

First, it is unclear to me what the results exactly indicate. What does the recoil observed suggest? Does it fit with the expected tension exerted by a link of the actin cytoskeleton relayed by late endosomes?.

The observed recoil suggests that there was tension across the ablated area. The laser ablation experiments were one way to evaluate whether the actin cytoskeleton within the tip of the cell was continuous between the subcellular tube and the leading edge of the cell. Tension along this axis would support such a model. We assumed that if the actin cytoskeleton at the tip is continuous with both membrane compartments it was likely to be under tension, and our laser ablation experiments showed that is indeed the case. We have rewritten this section to make it clearer.

From the text and figure I don't understand how is the recoil calculated: retraction of the subcellular tube backwards? "enlargement" of the bleached area?

Briefly, we had used three measuring points: the backward displacement of (i) the subcellular tube and (ii) of the plasma membrane adjacent to the ablated area, which both retract towards the cell body, and we also measured (iii) the forward displacement of plasma membrane on the other side of the ablated area. We then calculated the average of these for each experiment.

However, we have now redone the evaluations of these experiments using PIV, an established method that is commonly used to calculate initial recoil after ablation and have explained this in the text.

Second, it is unclear to me what laser ablation actually ablates. Does it only affect actin? Or are also CD4-late endosomes and other tip structures affected?

The laser ablations with the conditions we use have in the past been shown to temporarily disrupt the actin cytoskeleton without otherwise damaging the cell (Rauzzi et al., 2015).

The ablations were done in cells that express the actin reporter Utr::GFP together with the membrane marker CD4::mIFP but we have no reason to believe that CD4 containing structures were damaged. For example, upon ablation, the CD4 vesicles in the ablated area are bleached, but in the recovery phase, we observe actin puncta in the positions where CD4 vesicles were originally located, suggesting that the vesicles themselves persist. Our interpretation of these observations is that the bleached CD4 vesicles do not recover their fluorescence (CD4::mIFP is an integral membrane protein and cannot simply be re-inserted within short periods), but they are still capable of recruiting actin. We have added a representative image of this to better describe the experiment (Fig. S4).

Third, is the recovery observed after ablation correlated with new actin recruitment around old or new late endosomes?

Actin rapidly reappears in the bleached area and the region that recoiled, where it is first seen in the basal cortex and filopodia. The tube re-extends towards the ablated area, and actin reassembles around the tube within seconds. During further recovery, actin reappears in puncta ahead of the tube and we assume that this is partly de novo assembly around the existing vesicles (Fig. S4A, B). At the same time, we also see new CD4 vesicles reaching the tip, so it is likely that both populations (old and new vesicles) mediate the recovery phase. We have added images of additional examples that illustrate these points.

Forth, I find the experiments in cells with secondary subcellular tubes very confusing and the explanations very speculative

The data on cuts in cells with tube duplications are indeed difficult to interpret, and because the emergence of secondary branches is unpredictable, it is not easy to obtain large numbers of observations. Figure S4 is another example of the response of these cells to the laser cut, and we will make clear that our interpretations are merely speculative.

Finally, and most importantly. I think that performing laser ablation experiments in mutant conditions that affect actin recruitment (VhaDN and Wash RNAi,....) would be very informative. One would expect to find a decrease in recoil. If this was the case, it would validate, on the one hand, that in control conditions there is a tension that depends (at least in part) on actin organization, and on the other hand it would show that when actin recruitment is affected tension decreases, supporting the "anchoring" model. I understand that laser ablation experiments are not easy to perform, but I think this would be a useful experiment.

To my understanding, as it stands, the laser ablation experiments "....support the notion that adequate cytoskeletal organization at the tip is required for tube guidance and stability" as the authors acknowledge, but they do not convincingly support their "anchoring" model

Laser cuts on cells that express Vha100-DN or wash-RNAi would be a nice addition that would take the work to the next level. But sadly, these are among the experiments that right now are impossible to carry out because of all the logistical and other problems resulting from the Covid pandemic, as explained in the cover letter.

**Other comments:**

• From the images presented, it is often difficult to figure out where the subcellular tube forms, the presence of vesicles, the cell morphologies,... and to determine the correlation between the CD4 vesicles and tube guidance.

This is the result of a frustrating technical limitation. In experiments in the past we have used markers for the outline of the cell, as we do here, too. Thus, where CD4 is expressed under the btl-gal4 driver it marks the entire outline of the cell against a completely negative background. Even for other markers, if expressed under btl-gal4, the outline of the cell is visible against the dark background. However, for endogenously marked proteins that are expressed ubiquitously, this is no longer true, and as we add more markers to follow different structures, we run out of fluorescent colours for everything we would like to highlight (and genetically, out of chromosomes to accommodate the necessary transgenic or endogenously modified constructs). We will provide tracings of the outlines of the cells to make the images clearer.

For instance, in Fig 1H and 1J, is there a "lateral" CD4 vesicle? Why it does not generate a missguided tube?

Yes, there are also CD4 vesicles closer to the proximal part of the cell. They are enriched at but not restricted to the tip of the cell. As we have shown previously (Mathew et al., 2020), they emerge along the subcellular tube, and most are transported towards the tip (also seen in Fig. 1A, for example). Why the remaining ones do not affect the guidance of the tube is unclear, but it is almost certain that the growth of the tip of the cells towards the chemotactic FGF signal plays a role: the basal membrane is constantly moving away from the tip of the tube at this location, but not at the sides further down the branch.

Fig 1I, are there 2 subcellular tubes? Can the authors mark them? I cannot really visualize them with the CD4 marker, they seem stalled or short or missing.

In Fig 1I, the tube is curled up inside of the cell, a phenotype often seen in larval terminal cells with excessive FGF signaling (for instance see Ukken et al., 2014). We added diagrams that explain the morphology of the tubes in this figure.

Fig 1L: what do the authors mean by "corrected" tube sprouts?

This is not well phrased, and we will also improve the figure to make the point clearer.

Panels 1K-M (now 1H-J) show snapshots from a movie of a cell that originally had only a misguided tube (at the top left) and is here in the process of forming its ‘correct’ tube growing in the ventral direction. In 1L (now 1I) this second tube is showing first signs of emerging, in 1M (now 1J) it is clearly visible. We have changed the wording in the figure and add an explanation in the legend, and we added a second example of this process in Figure S1.

It is difficult to identify the cell in Fig 2D-F

We added a dotted line in one of the channels showing the general morphology of the cell.

• Movie S3: I find it difficult to spot the association of CD4 and utrGFP that the authors point. Can the authors label in the movie the vesicles and the association?

We added pauses in the movie and arrows to the frames where actin is seen surrounding late endosomes.

• The results with the Rab7 downregulation and upregulation are not very clear.

Does the downregulation of Rab 7 (Rab7 DN construct) have any effect on tube guidance?

Does it decrease or eliminate actin association with CD4 vesicles in the embryo? The authors show that in the larvae expression of Rab7 DN leads to loss of actin enrichment in Rab7 vesicles. Does this have an effect on terminal branching?

Rab7DN is not visible in the embryo so we did not pursue further experiments in those stages and we previously showed that loss of Rab7 does not affect branching in larvae (Best and Leptin 2019). However, as the reviewer rightfully pointed out, expression of Rab7DN prevents actin nucleation at late endosomes in larval stages, so having the phenotypic consequence of this experiment would be informative and we are grateful for the observation. We had done the experiment, and we found no difference in the number of branches compared to controls. This suggests either that at larval stages actin recruitment at late endosomes is no longer required for branching or that there are redundant mechanisms that can balance the lack of actin nucleation. We favour the second model, because it has been shown that microtubules also play a role in tube branching and in coordinating the actin cytoskeleton (Araujo lab, 2021), so it is possible that actin nucleation can be bypassed. This is also consistent the fact that the phenotypes we describe are not all fully penetrant, again pointing to redundant mechanisms ensuring consistent directed growth.

We added the data regarding Rab7DN to the manuscript (Figure S2).

The Rab7 active construct produce effects at larval stages but not in the embryo. Is terminal cell branching in the larvae also dependent on late endosomes? Can the authors show "excess" of late endosomes in the larvae that lead to extra terminal branches? Even that the authors indicate that they cannot detect Rab7Q67L, can they find any effect at embryonic stages (e.g. presence and position of CD4 vesicles, other unrelated effects,...)?

Expression of Rab7CA in the embryo generates similar defects as nanobody-mediated mislocalisation of Rab7. We include below an example for the reviewer, but we did not feel comfortable including these data in the paper because some technical complications made them impossible to document and interpret with the certainty that we would wish. Most importantly, the YFP fusion protein is not detectable at embryonic stages, even with the most sensitive microscopes and detectors available to us. This means that we cannot correlate the observed phenotypes with the presence or absence of Rab7CA, which in our view makes them too weak for publication. At face value, these results suggest that Rab7CA begins to trigger branching during embryonic development, which eventually leads to the excess number of branches we see in the larva, but alas, we think this is too speculative to include in the paper.

• In some examples in the movies there seem to be a correlation between CD4 vesicles presence/positioning and basal lamellipodia/filopodia or actin enrichment, and also in -btl experiments. Have the authors explored this? They may want to comment on this in the discussion section.

That is a very pertinent point, and we should indeed have commented on it. If we assume the reviewer is looking at examples such as the one in Fig. 1I (currently S1C), then the explanation is the following. The terminal cells in the embryo often form transient side-branches, presumably in response to a low level FGF signals from the environment. In those cases, the basal actin cytoskeleton rearranges in the branching area to form the filopodia that lead the outgrowth of the branch, and what the reviewer observed is that this transient branch also forms the late endosome structure that we see in the main or proper growth cone. Thus, the guiding FGF-signal leads to a reorganisation of the entire actin cytoskeleton in the growth cone, and the formation of the actin-covered endosome is part of that process. We have included this in the discussion.

Reviewer #2 (Significance (Required)):

This work is relevant for the morphogenesis field and deals with the important issue of how the cytoskeleton regulates shape and cellular events. The work represents a deep analysis of a specific issue in the specialized field of tracheal development, but the results may be relevant for other types of cells forming subcellular tubes. Describing a function of trafficking vesicles (late endosome in this case) in cell morphogenesis (in addition to cargo trafficking) in an in vivo system is also relevant to advance in the cell biology field.

**Referees cross-commenting**

I agree with the comments of reviewer #1. I find relevant the points raised in "major comments number 2 and 4".

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

**Summary:**

The authors investigate the role of late endosomes in

the context of actin organization during cell morphogenesis. They use as experimental model the polarized terminal cells in the Drosophila tracheal system that forms a sub-cellular projection containing a tube. The authors show that disruption of the sub-cellular localization or maturation of late endosomes leads to increased proportion of terminal cells with mis-guided tubes. Their analysis indicated that endosomal F-actin recruitment is crucial for the directionality of the tube growth. The authors propose a model where, late endosomes control a coordinated crosstalk between endosomal and cortical actin pools to drive subcellular tube-guidance.

**Major comments:**

1. The conclusion about how WASH functions in the tube-guidance, is not clearly shown and it should be better explained and documented. It is known that loss of function of the WASH leads to dysregulation of endosomal tubulation inducing enlarged endosomes, which in turn affects the endosome-to-plasma-membrane recycling of various cargos (including luminal cargos like Serp) (Gomez, et al., Mol. Biol. Cell, 2012; Dong et al, Nat. Comm. 2013). The authors should clarify if there is a defect in the integrity of endosomes located close to the cell tip in the btl>WASHIR knock down. In the cartoon panel C' (Figure 7), the endosomes in the cell tip are shown intact (Including their relative position to the tip) but no experimental data support this conclusion.

Given the fact that Wash contributes to proper late endosome morphology we do not necessarily expect the endosomes to look normal. We had not shown this in the diagram because our own data do not directly address this point, but the literature is of course clear enough about this, so we have modified our diagrams so that they better reflect the expected phenotypes and included a reference to the relevant literature.

We and others have shown the important role of late endosomes in plasma membrane and luminal cargo delivery, and as elaborated in the response to referee 2’s point 3, complete loss of endosomal function blocks these processes. Here, at reduced but not abolished function plasma membrane delivery is clearly still functional.

-The functional analysis of WASH was based on RNAi knock down. The authors express a single RNAi construct against WASH. The expression of this RNAi line gave a low penetrance phenotype. A well-known caveat of RNAi is off-targeting. Hence, phenotypic analysis needs to include a verification by a second independent RNAi construct or a rescue of the RNAi phenotype with an overexpressed cDNA of WASH. Ideally, the null wash mutant (Nagel et al. 2017) can be used to confirm the phenotype.

Analysing wash mutants would provide a welcome additional confirmation of the knockdown results, and it is true in general that poorly characterised RNAi lines can have off target effects. However, this is a well validated line: Nagel et al. (2017) showed that the same RNAi line that we used fully recapitulates the phenotype seen in wash mutants: In both cases, actin fails to localize to late endosomes, and this is what we also found in terminal cells.

Whereas we believe therefore that the experiment is not essential to support our conclusions, we agree it is desirable and have ordered these flies. However, progress is being hampered by import restrictions at the first author’s lab: the necessary paperwork for flies to be imported for his work is still under revision by officials. The experiment thus cannot be done at the moment.

The authors claim a role of the late endosomes in subcellular tube growth and guidance. But show no data on lumen formation to prove tube presence in the tracheal terminal cells of V100R755A, btl>WASHIR, shrb mutants or in GrabFP-Bint treated terminal cells. The interpretation and quantification of the phenotypic classes "miss-tube-guided" and "ventrally-tube-guided" are based on membrane markers and not on luminal markers. The presented data with the provided resolution does not prove if the mCD4-mIFP or PH-GFP markers define apical membrane protrusions/extensions or tubular structures. Therefore, the classifications of the tube-guidance phenotype and the quantification of "distance from tube to tip" may be suggestive. The authors need to provide additional confocal data of co.stainings of the endosomal compartments with luminal antigens (i.e. GASP or Serp or Verm).

We are very unsure as to what this would add and in what context it would be necessary. Membrane and actin markers have been widely used to follow the formation of the subcellular tube by all groups working in this field. There is ample documentation in the literature that the subcellular tube, as defined by luminal content (Serp, Verm, Gasp, CBP-GFP, ANF-GFP) is surrounded by apical plasma membrane which carries apical transmembrane proteins (Crb, Uif) and their associated apical cytoplasmic complexes (Par3, Par6, aPKC, pMoesin), and apical phospholipids which can be visualized by specific PIP-binding markers, e. g. The PLC-d PH-domain that binds to PIP2 (see, e.g., Kato et al., 2004; Oshima et al., 2006; Okenve-Ramos & Llimargas, 2014 (here they use both luminal and actin reporters); Ochoa-Espinosa et al., 2017; and from our lab: JayaNandanan et al., 2014; Mathew et al., 2020. Therefore, all labs in this field have used these markers interchangeably to visualize the subcellular tube and we are not aware of a single case where luminal shape and apical membrane shape were not exactly congruent.

We have in fact used luminal markers in some experiments here, but we believe there is no reason to assume that luminal markers would have a different distribution compared to membrane, apical or actin reporters in any the experiments described here. Finally, the focus of the paper is on the behaviour of the early out-growing membrane rather than the mature tube, and on how membrane is remodelled in this process by modifications in the actin cytoskeleton. Including confirmation of the presence of luminal material would not add to the paper.

page 8 line 248, the authors interpret that reducing the dose of Shrb by half strongly enhances the wash-RNAi phenotype and suggest that WASH and Shrb act in the same pathway. Shrub is a subunit of the ESCRT-III complex involved in inward membrane budding of endosomes and WASH functions in outward endosomal membrane budding.

The Shrb and WASH form discrete molecular complexes in endosomes. The authors should consider that Shrb and WASH may well act in parallel to control directional tube growth.

This is a good point and we will rephrase our conclusions from this experiment.

The authors use nanobody-based GFP trap construct to investigate the effect of Rab7YFP localization. This is an excellent way to provide novel information for protein miss-localization in vivo. Using this method the authors concluded that ... "the correct distribution of late endosomes is required for proper tube guidance" (page 5, lines 157-158). The authors obviously consider that GrabFP-B-Int construct affected the distribution of late endosomes. However, this is unclear and additional control experiments are needed to support the author's claims. For instance, did expression of GrabFP-B-Int, target the Rab7-YFP protein or the Rab7-associated endosomes? With the presented data, it is not clear if the Rab7-YFP positive vesicles are endosomes? or aggregates formed by the trapped Rab7-YFP protein? Co-stainings using GFP in Rab7-YFP terminal cells with another endosomal markers i.e. Avl, or hrs, should be provided. It is also not clear if endocytosis of apical/basal membrane or luminal cargos was affected in GrabFP-B-Int treated terminal cells. The loss of endocytic components has been associated with defects in subcellular tube shape and morphology (Schottenfeld-Roames et al, Cur Biol. 2014). The authors should clarify these issues.

The nanobody would of course bind both to free Rab7::YFP (if there is any available) and to endosome-associated Rab7::YFP. However, in addition to Rab7::YFP we also assayed the distribution of CD4::mIFP, a membrane-associated protein that is seen at very low levels in all membranes (Mathew et al., 2020), but highly enriched in cytoplasmic vesicles, which we showed by co-expressed markers to correspond to endosomes (Mathew et al., 2020). If the nanobody sequestered free Rab7::YFP, we would expect little overlap between Rab7::YFP and CD4::mIFP puncta. Instead, we see that the large Rab7::YFP/nanobody puncta have membrane associated with them (63% of vesicles are triple positive, vs 8% of Rab7::YFP-GrabFP vesicles) indicating that they are not merely Rab7 aggregates. We will include a quantification of the degree of overlap between these components.

Regarding the question of whether endocytosis is affected, we believe this is unlikely, or if it is at all, only to a minimal extent, since growth of the outer membrane, which crucially depends on endocytosis, continues in these cells. We have added a comment to this effect in the text. The cells look very different from cells in which endocytosis has been inhibited.

In the legends of Figure 7 (C'), the authors stated that.... "lack of actin regulators at the basal cortex prevents the connection of the actin meshwork at the tip to the basal plasma membrane".... by depicting the singed mutant phenotype. singed mutant analysis is not shown in the manuscript.

Singed/Fascin has previously been shown to be required for actin organization in fillopodia (Okenve-Ramos & Llimargas, 2014). We have now included new data that show that cells expressing singed RNAi also have reducedamounts of actin at late endosomes, and that reduced actin correlates strongly with tube misguidance. This shows that an actin bundling protein that has previously been shown to be needed for actin bundles in filopodia again affects actin around endosomes, providing another illustration that these compartments interact with each other.

Our quantifications on actin around late endosomes show that interfering with endosome maturation, actin nucleation via Wash and basal/filopodial actin all lead to loss of actin around endosomes, and the misguidance phenotype correlates with actin loss (Figure 6J). By contrast, disruption of the apical actin cortex does not affect endosomal actin but does lead to misguidance. This establishes a hierarchy of actin organisation in the tip of the cell: basal actin affects endosomal actin, loss endosomal actin affects both apical and basal actin, but apical actin does not feed back on endosomal. All three pools are nevertheless required for tube guidance.

The authors consider the late endosomes nucleate actin ahead of the tube (i.e. page3, line 87-88, page 9, line 285). This is not very convincing from the presented data. The authors should provide some quantitative data showing that lack of WASH (and endosomal F-actin network) effects the apical and basal F-actin pools in the tip of the cell.

If we understand the reviewer correctly, there are two comments included in this point: (i) whether actin is nucleated at late endosomes, and (ii), whether reducing endosomal F-actin affects apical-basal actin pools in the tip of the cell.

(i) As stated above in the response to reviewer #2, we have added quantitative data illustrating actin recruitment at late endosomes with phalloidin stainings. Actin association with endosomes is also confirmed by the Rab7 stainings in larval terminal cells in Fig. 3G-H that show actin puncta associated with endosomes.

(ii) Again, as mentioned in the response to reviewer #2, we do think that all actin pools in the growth cone are affected. We are glad that the reviewers encouraged us to make this more explicit and will now discuss more clearlyhow endosomal F-actin could affect apical and basal F-actin pools.

**Minor concerns:**

1. The authors concluded (page 9, line 285) that "endosomes serve as actin nucleating centres that propagate forces within the cell by physically linking different subcellular compartments".

We agree with the reviewer, this is a good way of phrasing it, and we will rewrite this conclusion accordingly.

The authors should depict in the panels the Ventral/Dorsal axis.

All images are positioned in the same orientation, but we will ensure that the D/V axis orientation is stated in the manuscript.

Numerous omissions need to be corrected. Labeling is missing in the panels J-M' (Figure 1). The statistical significance and the p values levels are not indicated in Figure 2 (G). The panel figure 5 (D) is miss-labelled. The panels C-C' in f igure 7 are not very informative. They do not reflect the general model of the study. How the prevention of actin nucleation at late endosomes, or apical or basal cortex affects tube directionality is not graphically shown.

We thank the reviewer for noticing these omissions, we will fix them for resubmission. Having added more discussion about the general organization of actin at the tip of the cell, we think the relevance of panels 7C is justified.

In the section "Crosstalk between cytoskeletal compartments" (Lines 359- 400, discussion) the argument about the involvement of microtubules in tube-guidance is a likely scenario. But I found this argument over-extended. WASH interacts with tubulin Derivery et al. Dev Cell (2009) and WASH activity balances the endosomal and cortical F-actin networks during epithelial tube maturation in multicellular tracheal tubes (Tsarouhas et al., Nat. Comm 2019). These results should be considered in the discussion section.

We will incorporate these references to the discussion, they will for sure enrich it.

Reviewer #1 (Significance (Required)):

The important role of actin cytoskeleton in the initiation of endocytosis is well established. Actin structures in the plasma membrane are dynamically organized to assist the remodeling of the cell surface and to facilitate the inward movement of vesicles. Similarly actin networks in endosomes are critical for endosomal fusion and fission. In this work, the authors identified an opposing but interesting scenario. They propose a role for the late endocytic pathway in organizing actin networks for proper cell morphogenesis and point out an intracellular crosstalk and coordination between distinct cytoskeletal pools within a cell.

Although the mechanism about how the separate F-actin pools communicate is not shown, the paper is interesting and shows an original contribution in the area of cell morphogenesis. In addition it represents a conceptual advance as it proposes a mechanism through which actin cytoskeleton is coordinated to regulate tube morphogenesis. The proposed mechanism may be relevant for tracheal terminal cells, but could represent a general mechanism in the field of cell biology. The methodology is appropriate and the text flow is well organized. However, as explained, there are few inconsistencies in the manuscript. I believe the above additions would strengthen the conclusion of the paper.

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Referee #2

Evidence, reproducibility and clarity

Summary

Rios-Barrera and Leptin investigate the formation and guidance of the subcellular tube that forms in the terminal cell of the dorsal branches of the Drosophila tracheal system. In previous work the authors documented the presence of late endosomes at the tip of the growing terminal cell, ahead of the forming subcellular tube, which are involved in membrane recycling {Mathew, 2020 #1407}. In this present work they analyze the organization of the actin cytoskeleton in the tip terminal cell, in relation with the late endosomes, and assess the guidance of the subcellular tube. They find that the presence and localization of late endosomes play a role in tube guidance. They also find that late endosomes recruit actin around them, mediated by the activity of the actin polymerization regulator Wash, which is recruited to maturing late endosomes. When Wash activity is decreased, actin around late endosomes is decreased and tube guidance is compromised. Based on this observation, laser ablation experiments of actin ahead of the tube and actin staining at the tip of the terminal cell, the authors propose an exciting model: late endosomes recruit actin, which connects the actin pool of the basal membrane and the actin pool of the apical (subcellular tube) membrane thereby directing tube growth and guidance. The manuscript is well-written and well-presented, the images and movies are of high quality and the experimental data, which is technically challenging, is very good and sufficiently replicated.

Major comments:

1. A critical point in the model that the authors put forward (which is also contained in the title and abstract) is that actin organized at late endosomes anchors apical and basal actin cortices. However, there is no clear and conclusive evidence for this. Clear evidence in this direction should be provided to propose it as a mechanism (as it is in the text, particularly in the first sentences of the discussion) and imply it in the title.

The authors show endogenous actin around late endosomes and actin fibers at the tip of the terminal branch. However, at the level of resolution presented (Fig 3A,B), it is not possible to determine whether the different actin populations are actually "anchored". I suggest to present stronger data supporting this important conclusion.

In the same direction, it would be critical to show that this anchoring of actin fibers is disturbed when actin enrichment at the late endosome is perturbed (see also point 5). Actually, the authors show that when Vha or Wash activity are downregulated actin accumulation around the CD4 vesicles decrease. However, this experiment has a few inconveniences. First, it is difficult to determine levels of a construct that is overexpressed (UAS-utr::GFP). Could the authors use phalloidin or an actin antibody to confirm the result? Second, I find the result difficult to interpret. In the images provided I see a general decrease of actin (UtrGFP) at the tip, not only around the CD4 vesicles (Fig 6D,F) . Are these mutant conditions also affecting the rest of actin pools? If this is the case, can the authors attribute the defects exclusively to the abnormal recruitment of actin to the late endosomes? Most importantly, the authors should analyze the pattern of actin distribution (labelling endogenous actin) and determine a possible loss of "anchoring" of fibers when late endosome maturation is perturbed.

1. Another critical point in the model put forward by the authors is that late endosomes drive tube guidance. To test this point the authors use an elegant system to mislocalize Rab7 late endosomes. However, the effects are not strong (1G), and only a proportion of branches show misguided tubes. Do the cases with a ventrally-guided tube in the experiment Rab7:YFP+/+ (Fig. 1G) have a CD4 endosome (with Rab7YFP) at the tip? This would help to explain the weak effect.
2. What is the cause that preventing proper endosome maturation and acidification leads to misguided tubes (rather than missing ones)? The authors indicate that downregulating Vha activity leads to defects in acidification, but late endosome-MVB normally form. It is intriguing to see extra CD4 vesicles (like in 1C or 6C). Wouldn't we expect to see "normal" tip accumulation of CD4 vesicles only, and not extra ones? How relevant are these extra CD4 vesicles? Wouldn't we expect to see "non functional" CD4 vesicles, unable to recruit actin and lead intracellular tube formation (i.e. no tube) rather than missguidances? (1D shows higher proportion of misguided tubes than no tubes) Similarly, is Wash-RNAi producing extra CD4 vesicles (as observed in movie 5, fig 6E)?
3. Actin recruitment to late endosomes was already documented, where it plays a role in cargo trafficking. The authors propose that Wash is recruited to late endosomes upon acidification where it would prime actin nucleation around the endosome. The authors indicate a decrease in Wash accumulation upon expression of Vha dominant negative. However, this decrease is not quantified. In addition, it is difficult to determine levels of a construct when this is overexpressed (UAS-Wash::GFP). It would be desirable to use antibodies against the endogenous protein (Wash in this case) to claim differences in accumulation in mutant conditions.

The results presented do not rule out a requirement of Wash in terminal branching which is not associated with the enrichment in the late endosomes. The genetic interaction observed with Shrub is also compatible with both proteins acting on terminal branching but in different/parallel mechanisms.

1. Laser ablation experiments The laser ablation experiments are difficult to interpret. First, it is unclear to me what the results exactly indicate. What does the recoil observed suggest? Does it fit with the expected tension exerted by a link of the actin cytoskeleton relayed by late endosomes?. From the text and figure I don't understand how is the recoil calculated: retraction of the subcellular tube backwards? "enlargement" of the bleached area? Second, it is unclear to me what laser ablation actually ablates. Does it only affect actin? Or are also CD4-late endosomes and other tip structures affected? Third, is the recovery observed after ablation correlated with new actin recruitment around old or new late endosomes? Forth, I find the experiments in cells with secondary subcellular tubes very confusing and the explanations very speculative Finally, and most importantly. I think that performing laser ablation experiments in mutant conditions that affect actin recruitment (VhaDN and Wash RNAi,....) would be very informative. One would expect to find a decrease in recoil. If this was the case, it would validate, on the one hand, that in control conditions there is a tension that depends (at least in part) on actin organization, and on the other hand it would show that when actin recruitment is affected tension decreases, supporting the "anchoring" model. I understand that laser ablation experiments are not easy to perform, but I think this would be a useful experiment. To my understanding, as it stands, the laser ablation experiments "....support the notion that adequate cytoskeletal organization at the tip is required for tube guidance and stability" as the authors acknowledge, but they do not convincingly support their "anchoring" model

Other comments:

• From the images presented, it is often difficult to figure out where the subcellular tube forms, the presence of vesicles, the cell morphologies,... and to determine the correlation between the CD4 vesicles and tube guidance. For instance, in Fig 1H and 1J, is there a "lateral" CD4 vesicle? Why it does not generate a missguided tube? Fig 1I, are there 2 subcellular tubes? Can the authors mark them? I cannot really visualize them with the CD4 marker, they seem stalled or short or missing. Fig 1L: what do the authors mean by "corrected" tube sprouts? It is difficult to identify the cell in Fig 2D-F
• Movie S3: I find it difficult to spot the association of CD4 and utrGFP that the authors point. Can the authors label in the movie the vesicles and the association?
• The results with the Rab7 downregulation and upregulation are not very clear. Does the downregulation of Rab 7 (Rab7 DN construct) have any effect on tube guidance? Does it decrease or eliminate actin association with CD4 vesicles in the embryo? The authors show that in the larvae expression of Rab7 DN leads to loss of actin enrichment in Rab7 vesicles. Does this have an effect on terminal branching? The Rab7 active construct produce effects at larval stages but not in the embryo. Is terminal cell branching in the larvae also dependent on late endosomes? Can the authors show "excess" of late endosomes in the larvae that lead to extra terminal branches? Even that the authors indicate that they cannot detect Rab7Q67L, can they find any effect at embryonic stages (e.g. presence and position of CD4 vesicles, other unrelated effects,...)?
• In some examples in the movies there seem to be a correlation between CD4 vesicles presence/positioning and basal lamellipodia/filopodia or actin enrichment, and also in -btl experiments. Have the authors explored this? They may want to comment on this in the discussion section.

Significance

This work is relevant for the morphogenesis field and deals with the important issue of how the cytoskeleton regulates shape and cellular events. The work represents a deep analysis of a specific issue in the specialized field of tracheal development, but the results may be relevant for other types of cells forming subcellular tubes. Describing a function of trafficking vesicles (late endosome in this case) in cell morphogenesis (in addition to cargo trafficking) in an in vivo system is also relevant to advance in the cell biology field.

Referees cross-commenting

I agree with the comments of reviewer #3. I find relevant the points raised in "major comments number 2 and 4".

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Reviewer #1 (Evidence, reproducibility and clarity (Required)): Very high evidence and clarity. Excellent scientific rigor. The findings are important and reported clearly. The experiments are conducted in a rigorous way by numerous participating laboratories. Reviewer #1 (Significance (Required)): Very high significance, both from a molecular biology and clinical standpoints. This is an important manuscript that challenges the findings and conclusions of a prior high-profile paper in Science by Ma et al 2016, claiming that LAG3 is a receptor for aggregation-prone species of alpha-synuclein and that deletion of LAG3 results in reduced cell to cell propagation of alpha-synuclein aggregates. The experiments in this paper are numerous and employ a variety of techniques. The overall conclusions are that LAG3 is not expressed by the relevant neurons and that LAG3 is not a receptor for alpha-synuclein fibrils (of different sizes). Therefore, the authors conclude that LAG3 is unlikely to play a role in the spread of alpha-synuclein pathology in Parkinson's disease and related disorders. There are, however, some weaknesses. For example, the Introduction contains passages that are not written in a stringent way: 1. "Histologically, PD is characterized by α-synuclein aggregates known as Lewy Bodies in neurons of the substantia nigra," That is not a good description of PD neuropathology. Lewy pathology is present in numerous areas of the CNS and PNS, and is not restricted to the substantia nigra.

We have added a more detailed account:

“Histologically, PD is characterized by α-synuclein inclusions known as Lewy Bodies whose accumulation is associated with neurodegeneration (Dickson, 2012; Mullin and Schapira, 2015; Corbillé et al., 2016). These inclusions affect the Substantia nigra and other mesencephalic regions as well as, in some cases, the amygdala and neocortex (Dickson, 2018).”

1. "Growing evidence suggests that α-synuclein fibrils spread from cell to cell". While alpha-synuclein pathology can spread from cell to cell, it is not known if the fibrils are the species (alone or combined with other conformers) that cause the spreading of the pathology in a seeding fashion, or if smaller alpha-synuclein assemblies play that role.

We have reformulated the sentence to credit the fact that we do not know which synuclein species is the one that is transmitted:

“Growing evidence suggests that α-synuclein aggregates spread from cell to cell (Volpicelli-Daley et al., 2011; Volpicelli-Daley, Luk and Lee, 2014)… “

1. "...by a "prionoid" process of templated conversion (Aguzzi, 2009; Aguzzi and Lakkaraju, 2016; Jucker and Walker, 2018; Kara, Marks and Aguzzi, 2018; Scheckel and Aguzzi, 2018; Uemura et al., 2020)." This sentence gives the impression that the corresponding author has led the field when it comes to alpha-synuclein's prionid properties. That is not really the case, and it would be appropriate to cite the literature in a more scholarly fashion that reflects how this part of the alpha-synuclein research field developed.

I cannot disagree, and in fact I suspect that the present paper may be my second and possibly last experimental contribution to the synuclein field! However, I do claim intellectual parenthood of the prionoid (not “prionid”) concept, which I first expounded in a 2009 Nature paper. Anyway, we now provide a more balanced citation:

“…by a “prionoid” process of templated conversion (Aguzzi, 2009; Jucker and Walker, 2018; Kara, Marks and Aguzzi, 2018; Henderson, Trojanowski and Lee, 2019; Karpowicz, Trojanowski and Lee, 2019; Uemura et al., 2020; Kara et al., 2021).“

1. "Interrupting transmission of a-synuclein may slow down or abrogate the disease course." This is a bold statement and far from certain. While one might propose that this is the case, it is still just a hypothesis and the Introduction should reflect that.

We have rewritten the sentence in a more subdued manner:

“It is thought that interrupting transmission of a-synuclein may slow down or abrogate the disease course.”

**Referee Cross-commenting** I concur with reviewers 2 and 3, and the new comment from reviewer 2. This paper should be published as soon as possible.

*********************************************

Reviewer #2 (Evidence, reproducibility and clarity (Required)): This study conclusively shows that LAG3 is not the receptor for a-synuclein that underlies the spread of synucleinopathic damage in various PD-related conditions. The paper is done extremely carefully and comprehensively. My only suggestion is to indicate the significance level in Figure 5a, as it may turn out that LAG3 is actually protective.

We have added the significance level in Fig. 5A, in the legend: “The survivals of ASYNA53T LAG3-/-, LAG3+/- and LAG3+/+ mice were similar (Mantel-Cox log-rank test, p-value = 0.165).”

Reviewer #2 (Significance (Required)): This study is of extremely high significance - we need mechanisms to deal with spectacular results in the literature that should not have been published because they are were uncompelling to begin with, but were published for various sociological/political reasons. Science won't progress if we don't find correction mechanisms for wrong conclusions. **Referee Cross-commenting** I agree with reviewers 1 and 3, especially with the suggestions made by reviewer 1, which should be instituted. I think we all concur that the paper should be published without new experiments. I believe testing a-synuclein propagation in vivo in LAG3 KO mice would be useful, but given the complete lack of replication of LAG3 expression in brain and of a-synuclein binding to LAG3, this is not necessary.

We considered running experiments in addition to those performed in vivo in ASYNA53T transgenic mice (including LAG3 KO) and ex vivo in organotypic slices, the latter using pre-formed fibrils. However, the outcome of these experiments, along with the absence of LAG3 expression in neurons and its unclear binding, convinced us that the usage of further animals and reagents would be unwarranted.

*****************************************

Reviewer #3 (Evidence, reproducibility and clarity (Required)): It was proposed that LAG3 is important in the treatment of PD and related disorders, because it functions as a receptor of pathogenic α-synuclein and the treatment with anti-LAG3 antibodies attenuated the spread of pathological α-synuclein and drastically lowered the aggregation in vitro (Mao et al, Science 2016). In this study, authors characterized 8 antibodies to LAG3 and investigated the presence of LAG3 in cultured cell lines, NSC-derived neural cultures, or organ homogenates for the presence of human or murine LAG3. But it was not detected in any of the neuronal samples tested. In addition, single cell (sc) RNAseq yielded only minimal counts for the LAG3 transcript in neurons, astrocytes, and mixed glial cells, and single-nucleus (sn) RNAseq human brain dataset for LAG3 expression across different cell types confirmed no LAG3 signals for any of 34 identified cell clusters, including 13 clusters of excitatory and 11 subtypes of inhibitory neurons, oligodendrocytes, oligodendrocyte precursor cells, microglia, astrocytes, and endothelial cells. Authors also analyzed the binding of LAG3 with α-synuclein in mouse and human model systems, and concluded that the affinity of LAG3 for α-synuclein fibrils, if any, is micromolar or less. Furthermore, authors studied the propagation of pre-formed fibrils (PFFs) of α-synuclein in neural stem cell (NSC)-derived neural cultures in the presence or absence of LAG3, and the impact of LAG3 on survival in ASYNA53T transgenic mice expressing wild-type LAG3 as well as hemizygous or homozygous deletions thereof. However, they were unable to see any significant role for LAG3 in these in vitro and in vivo models of α-synucleinopathies. In this connection, the reviewer would like to ask one question: Have you conducted any experiments of the propagation of PFFs of α-synuclein in LAG3-KO mice ? If they did, what were the results ?

We did consider the possibility of replicating the experiments using PFFs in LAG3 KO mice. However, as stated above, we felt that our experiments – including the survival study in vivo in ASYNA53T transgenic mice – were unambiguous. After critical consideration, we remained unconvinced that this additional experiment would change the weight of our evidence in a substantial manner that would justify the inoculation of other animals and the utilisation of more resources.

**Minor point** In Page 10, I think it's a typo: ASYYN mice must be ASYN mice.

Thank you for pointing this out. We corrected it.

Reviewer #3 (Significance (Required)): These negative findings about the LAG in α-synucleinopathies shown in this manuscript do not provide any new insight into the mechanisms of α-synuclein propagation. However, it is clear that LAG3 is not expressed in neuronal cells and the binding of LAG3 to α-synuclein fibrils appears limited. Overexpression of LAG3 in cultured human neural cells did not cause any worsening of α-synuclein pathology ex vivo. The overall survival of A53T α- synuclein transgenic mice was unaffected by LAG3 depletion and the seeded induction of α-synuclein lesions in hippocampal slice cultures was unaffected by LAG3 knockout. These data shown in this manuscript are convincing and the information is very important in terms of correcting the direction of disease treatment and research. **Referee Cross-commenting** I agree with reviewers 1 and 2. This paper should be published as soon as possible.

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Referee #2

Evidence, reproducibility and clarity

This study conclusively shows that LAG3 is not the receptor for a-synuclein that underlies the spread of synucleinopathic damage in various PD-related conditions. The paper is done extremely carefully and comprehensively. My only suggestion is to indicate the significance level in Figure 5a, as it may turn out that LAG3 is actually protective.

Significance

This study is of extremely high significance - we need mechanisms to deal with spectacular results in the literature that should not have been published because they are were uncompelling to begin with, but were published for various sociological/political reasons. Science won't progress if we don't find correction mechanisms for wrong conclusions.

Referee Cross-commenting

I agree with reviewers 1 and 3, especially with the suggestions made by reviewer 1, which should be instituted. I think we all concur that the paper should be published without new experiments. I believe testing a-synuclein propagation in vivo in LAG3 KO mice would be useful, but given the complete lack of replication of LAG3 expression in brain and of a-synuclein binding to LAG3, this is not necessary.

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Rebuttal letter – Response to Reviewers

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

This study focused on P. vivax, which is an important neglected human malaria killer. The reported evidence will have a significant impact on diagnosing infectious diseases. The language in the manuscript is very good. However, some typos were reported. Some paragraphs might need particular attention to punctuation. Overall, the work is very good. The statistics are straight forward. However, there are a couple of major points that must be addressed before publication. Some of my comments are just recommendations to clarify some sections of the text.

**Major comments:**

The statistical methods can be improved by using generalised mixed models (GLMM).

1- PCA graphs need to be organised in more descriptive ways. Dim1 and Dim2 in each axis need to be defined clearly in the figures. PCA in Fig2 c is very difficult to follow, and it needs to be organised.

Answer: Figures have been amended to be more self-explanatory and clearer to the reader.

2- In this study, patients were male and female, and we know already male and female haematological parameters are hugely different, specially Hb level, and so on. My question is how the sex variable is treated in this study? Did your control group were from both sexes? Sex could be treated as a random variable in all studies if GLMM models were used.

Answer: Information in how the sex variable was treated in the study has been added to the methods section. In our cross-sectional study with uncomplicated P. vivax malaria patients seen at FMT-HVD in Manaus, Brazil, patients and healthy donors (controls) were matched by age and sex. In both groups, frequency of female individuals was 30% and male individuals 70%.

We think sex is better fitted as fixed effect since only two levels for this factor are possible. Thus, we used linear models with age and sex as fixed variables for statistical testing and to ensure that the differences observed between P.vivax- infected patients and controls, as well as between the clusters, were only due to disease status. This analysis showed that red blood cells count, hemoglobin, hematocrit, MXD and neutrophils counts (this parameter only when comparing the clusters) needed to be corrected only due to sex influence. For these parameters, estimates of predicted sex influence were subtracted from the raw parameter values and residuals were used for statistical testing. We have added this information in the Methods section as indicated below:

Page 6, line 128: Patients and healthy donors were age and sex-matched, with a frequency of 30% female and 70% male individuals in both groups.

Page 14, line 336:

To ensure that differences observed between P. vivax - infected patients and controls, as well as between the clusters, were due to disease status and not confounded by age or sex, the clinical parameters were fitted as response variables in a linear model with sex and/or age fitted as explanatory variables. Age and sex were included in the model if their coefficients were estimated as different from zero with p-value The residuals from the linear model were then used as age and/or sex corrected parameters in subsequent analyses.

3- Why 6h and 18h used for the HUVEC evaluation?

Answer. We ran several optimization experiments with individual plasma samples where we observed maximal mRNA expression changes after 6h of stimulation. For experiments detecting protein expression (IFA and flow cytometry), we increased the stimulation time to 18h. Preliminary experiments suggested this to be the optimal duration without compromising cellular viability.

4- It is mentioned only neutrophil enriched in this study, if myelopoiesis is affected, why the other granulocytes were not showed significant enhancement?

Answer: Our data reveal no change in the number of circulating neutrophils in the different clusters of individuals. However, mixed cell counts (MXD), a parameter representing monocytes, basophils and eosinophils numbers, was significantly reduced in Vivaxhigh patients. As a result, there was a significant enrichment of neutrophils in the leukocyte fraction in the blood of Vivaxhigh patients as well as a higher Neutrophil:Lymphocyte count ratio (NLCR) (Figure 4). In hematopoietic progenitors, stochastic changes in each factor’s concentration could result in one factor’s becoming more abundant and committing a hematopoietic progenitor to a particular lineage. To generate each mature granulocyte population (e.g. basophils, eosinophils and neutrophils), common myeloid precursor cells (CMPs) and later precursors for granulocytic and monocytic lineages (GMPs) follow in the BM different lineage commitment programs, tightly-regulated or instructed by a specific set of soluble factors, cell-cell interactions and transcription factors, that define cell fate decisions and lineage restrictions. For instance, differential PU.1 activity can specify different cell fates during haematopoiesis regulating monocyte and neutrophils differentiation. Genetic and biochemical analyses have shown that G-CSF can direct granulocyte differentiation by changing the ratio of C/EBPα to PU.1 (Zhu et al., Oncogene 2002; Friedman Oncogene 2002; Dahl et al., Nat Immunol 2003). High expression levels of PU.1 and C/EBPa, stimulated by G-CSF, promote GMP differentiation to neutrophils and inhibits monocyte differentiation, while only PU.1 expression, IRF-8 and lower expression/activity of C/EBPs induce GMP differentiation to monocytes (Zhu et al., Oncogene 2002; Friedman Oncogene 2002; Dahl et al., Nat Immunol 2003). Meanwhile, a combination of PU.1, C/EBPb and low levels of GATA-1 differentiates GMPs to eosinophil lineage (Kulessa et al., 1995; McDevitt et al., 1997; Yamaguchi et al., 1999) and PU.1 must also cooperate with GATA2 to direct mast cell differentiation (Walsh et al., Immunity 2002). In addition, eosinophil and basophil differentiation are induced by a different set of cytokines, usually produced in prevalent T-helper 2 response, such as IL-5, which should be inhibited in the strong Th1 environment evidenced by our and previous Luminex data in Pv patients. The enrichment of activated neutrophils in the peripheral circulation of P. vivax patients could be due to a response that specifically enhances neutrophil production and release from the bone marrow (BM). This hypothesis is supported by emerging evidence for enrichment of P. vivax parasites in the hematopoietic niche of BM, our Luminex data showing significant increase in pro-inflammatory cytokines associated with emergency myelopoiesis (e.g., TNF-a, IL-1a, IL-1b, IL-6, IL-8), and increased circulating levels of G-CSF, the major inducer of neutrophils production in the BM. Likewise, increased activation-induced cell death (AICD) in T cells, splenic T-cell and platelet accumulation or decreased lymphopoiesis due to myeloid-biased HSC differentiation induced by inflammatory cytokines and EC activation in the BM (refs 36,37,39) might explain the neutrophil enrichment in vivax patients.

5- I would also ask the authors to speculate a bit on, What could be the mechanism behind the different function of P. vivax compared to P. falciparum? From an evolutionary perspective, the parasite should rather become softer and keep the host alive for its own benefit.

Answer: One of the characteristics of P. vivax that could play an important role in immunity is its restriction to invade immature reticulocytes. For example, the infected reticulocyte could play a role in the presentation of parasite antigens as reticulocytes (but not mature RBCs) express MHC-I and are capable to process and present antigens on their surface for recognition by T cells. Indeed, it has been shown that reticulocytes act directly as an antigen-presenting cell, emphasizing the importance of erythrocyte surface antigens both in the induction as well as the target of a protective immune response (Burel et al 2016, Junqueira et al 2018). Recent investigations comparing P. vivax and P. falciparum controlled human infection models (CHMIs) also revealed marked differences in the immune profiles generated following infection with the two species and postulated that protective immune responses to Plasmodium are species-specific. It has been hypothesized that this difference is due to strict P. vivax tropism for MHC-I-expressing reticulocytes that, unlike mature red blood cells, can present antigen directly to CD8+T cells. Specifically, P. vivax but not P. falciparum infection led to the expansion of a specific subset of CD38+CD8+ T cells which were associated with an activated phenotype and cytotoxic potential. Corroborating Burel et al findings in the CHMI model, Junqueira et al showed that P. vivax–infected reticulocytes express HLA-I. In P. vivax-infected patients, CD8+ T cells in the peripheral blood express high levels of cytotoxic proteins, recognize and form immunological synapses with P. vivax–infected reticulocytes in HLA–dependent manner. Next, it was showed that P. vivax-specific CD8+ T cells release their cytotoxic granules to kill both host cell and intracellular parasite, which prevented reinvasion (Junqueira et al 2018). Although these data indicate a protective role of cytotoxic CD8+ T cells during P. vivax blood-stage malaria, it is not clear whether these lymphocytes would always be beneficial because they might contribute to anemia, inflammation or other pathological sequelae of infection, which needs to be further investigated.

**Minor comments:**

• It is important to have a reference, version, and date for the R software, packages and GraphPad.

Answer: We have added version and date for the R and GraphPad software.

2- In Fig 5, E missed to report. This figure can be better organised. It is very hard to read and follow.

Answer: There is no E in Figure 5. We will organize the figure to make it easier to read and follow.

Reviewer #2 (Significance (Required)):

1. • vivax remains endemic in 51 countries across Central and South Americas, the Horn of Africa, Asia and the Pacific islands. In most areas it is co-endemic with P. falciparum, which has been the priority species to address for national malaria control programmes. Malaria related deaths are mostly attributable to the more pathogenic P. falciparum, but over the last decade these have declined, however there has been a consistent rise in the proportion of malaria cases due to P. vivax. However, because it is difficult to diagnose resistant strains, strategies to detect and track drug resistant P. vivax* are limited. In this context it is vital to develop better tools to assess diagnostic, antimalarial efficacy and drug susceptibility so that emerging drug resistance can be tracked, and novel treatment strategies explored. From my viewpoint, despite some statistical problems to understand the complex nature of data (mixed interactions among multiple variables), these findings seem to be very interesting and (after a major revision) worth to be published. As said before, the story told by the authors could become interesting.

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

The manuscript titled: "Total parasite biomass but not peripheral parasitaemia is associated with endothelial and haematological perturbations in Plasmodium vivax patients" by Silva-Filho et al., reinforce the original observation and data by the group of Nicholas Anstey and coworkers, who first proposed the use of plasma parasite lactate dehydrogenase and PvLDH as a marker of parasite biomass. In that work, it was already demonstrated that P. vivax biomass is related to plasma concentration of LDH levels. As such, the present work cannot be considered of high novelty. Yet, through a meticulous approach including clinical data, computational approaches, machine learning, LDH measurement, multiplex analysis and quantitave RT-PCR, the authors here have extended the original observations that a large biomass of P. vivax parasites is out of blood circulation. In contrast, unlike the original observations of Anstey´s group, a correlation between total parasite biomass and systemic levels of markers of endothelial cells activation, was observed. The manuscript is very well written and the discussion brings new knowledge in this key topic for elimination of malaria. This manuscript is therefore recommended for publication after the following comments are addressed.

**Major comments:**

1. The vascular endothelium plays a pivotal role in malaria. Therefore, to test whether cell and/or parasite factors affect the vascular endothelium, HUVEC cells were used in this study. This is of major concern as endothelial cells from the bone marrow, where most hematological disturbances, notoriously thrombocytopenia, occur, were not used instead. HUVEC cells seems the only endothelial cell that does not express ABO blood group antigens, thus suggesting that surface expression on these cells is highly altered (O´Donnell et al., 2000 J Vasc Res). Moreover, significant functional differences between HUVEC cells and adult vascular endothelium have been reported (Chan et al., 2004). Together, this indicates that results obtained with HUVEC cells might not reflect responses of the bone marrow vascular endothelium. As one of the corresponding authors have ample experience with working with human bone marrow endothelial cells (Mantel et al., 2016 Nat Comm), it is suggested to perform some experiments with these cells to assure extrapolation of the results obtained with HUVEC cells.

Answer: We agree with the reviewer that performing ex vivo assays with primary human bone marrow endothelial cells would be an excellent alternative. However, we would like to argue that HUVECs are also suitable for our purposes. HUVECs are widely used to study endothelial barrier function, for example in the context of angiogenesis and inflammatory responses/barrier disruption. To emphasise this point, we have now referenced examples where HUVECs were used in the context of endothelial barrier biology and in different inflammatory conditions (see also lists a, b, c below).

1. Papers showing the use of HUVECs in studies yielding important insights about endothelial barrier function

• Krispin S et al. Growth Differentiation Factor 6 Promotes Vascular Stability by Restraining Vascular Endothelial Growth Factor Signaling. Arterioscler Thromb Vasc Biol. 2018.
• Aranda JF et al. MYADM controls endothelial barrier function through ERM-dependent regulation of ICAM-1 expression. Mol Biol Cell. 2013.
• Orsenigo F et al. Phosphorylation of VE-cadherin is modulated by haemodynamic forces and contributes to the regulation of vascular permeability in vivo. Nat Commun. 2012.
• *

1. Papers that used HUVECs in studies about endothelial barrier function in inflammatory conditions

• Dickinson CM et al. Leukadherin-1 ameliorates endothelial barrier damage mediated by neutrophils from critically ill patients. J Intensive Care. 2018.
• Kuck JL et al. Ascorbic acid attenuates endothelial permeability triggered by cell-free hemoglobin. Biochem Biophys Res Commun. 2018.
• Tramontini Gomes de Sousa Cardozo F et al. **Serum from dengue virus-infected patients with and without plasma leakage differentially affects endothelial cells barrier function in vitro. PLoS One. 2017.
• Fox ED et al. Neutrophils from critically ill septic patients mediate profound loss of endothelial barrier integrity. Crit Care. 2013.
• Rahbar E et al. Endothelial glycocalyx shedding and vascular permeability in severely injured trauma patients. J Transl Med. 2015.
• *

1. Papers showing that HUVECs behave similarly to other endothelial cell types in regard to barrier function, except when the comparison is with blood brain barrier models

• *

• Totani L et al. Mechanisms of endothelial cell dysfunction in cystic fibrosis. Biochim Biophys Acta Mol Basis Dis. 2017, Dec;1863(12):3243-3253.

• Gündüz D et al. Effect of ticagrelor on endothelial calcium signalling and barrier function. **Thromb Haemost. 2017 Jan 26;117(2):371-381.
• Deitch EA et al. Mesenteric lymph from rats subjected to trauma-hemorrhagic shock are injurious to rat pulmonary microvascular endothelial cells as well as human umbilical vein endothelial cells. ** 2001 Oct;16(4):290-3. Importantly, we were able to reproduce in the HUVEC ex vivo assays a phenotype of endothelial perturbations that is inferred based on the in vivo Luminex data using the same plasma sample. These data also support our hypothesis that patients with higher parasite biomass present higher endothelial cell perturbations, corroborating the associations between parasite accumulation in deep tissues (total parasite biomass represented by PvLDH levels) and endothelial cell activation as demonstrated in the Figure 6.

Strikingly, the authors stated that "P. vivax infection results in different ranges of EC alterations without massive cytoadhesion". This statement has no data supporting it. In fact, their own flow cytometry data convincingly demonstrated that exposure of HUVEC cells to plasma of vivax-high patients significantly increased the surface expression of ICAM-1 and VCAM. ICAM-1 expression is a well know receptor for cytoadhesion in malaria and Dr. Costa first demonstrated the importance of this receptor in cytoadherence of P. vivax (Carvalho et al., 2010). Moreover, these data are in some contradiction with the original observations of Anstey and collaborators who demonstrated that parasite LDH concentration did not correlate with markers of endothelial activation (Barber et al., 2015 PLoS Path). Therefore, this sentence should be modified to accommodate the alternative possibility of cytoadherence, deleted from the manuscript or binding functional assays should be performed to sustain it.

Answer: We agree with the reviewer and have removed this statement.

Page 22, line 543: The association between endothelial activation, Syndecan-1 and parasite biomass (PvLDH) indicates a positive feedback loop between glycocalyx breakdown, activation of endothelial receptors such as ICAM-1and VCAM-1 and parasite accumulation in deep tissues9,12.

Extracellular vesicles are key players in pathology of malaria and this includes P. vivax where concentration of circulating microparticles were associated with acute infections (Campos et al., 2010 Mal J). Moreover, Dr. Marti has pioneered this field since the original manuscript describing the role of EVs in malaria as intercellular communicators (Mantel et al., 2013 Cell). More recently, his group also demonstrated that interaction of EVs with bone marrow endothelial cells induce expression of IL-6 and IL-1 as well as vascular endothelium perturbations after trans-endothelial electrical resistance experiments (Mantel et al., 2016 Nat Comm). Furthermore, another recent report showed the physiological role of EVs in vivax malaria by demonstrating that EV uptake by human spleen fibroblast induced nuclear translocation of the NF-kB transcriptional factor, concomitant with surface expression of ICAM-1, thus facilitating cytoadherence of infected reticulocytes from P. vivax patients (Toda et al., 2020 Nat Comm). This growing evidence indicates that plasma circulating EVs are key communicators in malaria infections potentially explaining some of the findings reported in this work. Neglecting the importance of EVs in the discussion of this article is not reasonable and weakens this manuscript. Including a paragraph on EVs and accurate references in the discussion is thus strongly recommended.

Answer: We agree with the reviewer that extracellular vesicles are key communicators in malaria infection. We have not measured them in our study, however, and therefore can only speculate about their impact on our observations. We have added a phrase in the discussion:

Page 27, line 661: It is likely that other circulating factors that we have not directly measured in our study are also contributing to EC activation and vascular permeability. In particular, extracellular vesicles (EV) originating from ECs, platelets, and RBCs are present during malaria infection and are known to modulate the host immune response to the parasite54-56 . In P. falciparum, infected RBCs release EVs containing immunogenic parasite antigens, that activate macrophages, induce neutrophil migration and alter endothelial barrier function54,55. In P. vivax, plasma-derived EVs from iRBCs are taken up by human spleen fibroblasts (hSFs). This event signals NF-kB translocation and upregulation of ICAM-1 expression, facilitating cytoadherence of P. vivax-infected reticulocytes56.

**Minor comments:**

1. The lack of a group including severe vivax malaria patients is a drawback of this article as this group would have firmly validated the predictor of severe disease.

Answer: This study was investigating a cohort of uncomplicated P. vivax malaria compared to controls. We agree that it will be important to extent our analysis to severe vivax malaria in future studies.

In the selection criteria of the patients to be included in the study, no information on other co-infections were mentioned. Is this information available? If so, this should be mentioned.

Answer: As described in the Methods sections, Page 6, line 132, mono infection by P. vivax was confirmed by analysis of blood smears and quantitative PCR (qPCR) for both P. vivax and P. falciparum. We agree that excluding other coinfections could have been of interest. However, the differential diagnosis for an acute febrile illness is very broad and it would be impractical to track all other possible diseases. In addition, the patients included in the present work had mild disease, and therefore were discharged from hospital after a positive malaria diagnosis. No further investigation on other infections was done.

The main coinfection to be considered for an acute febrile illness with no localizing signs in our context is Dengue Fever. Although Dengue coinfection in our cohort is possible, the incidence at the Hospital is only 2.8% (P. vivax/Dengue coinfection) (Magalhães et al, Plos NTD 2014). Thus, it is unlikely that such a coinfection would have a major impact on our findings.

This work determined the levels of PvLDH in a cohort of uncomplicated P. vivax patients as well as healthy volunteers using a double-sandwich ELISA assay: (i) are the clones to determine PvLDH values freely available to facilitate similar studies by independent groups? (ii) How was the cut-off of positivity defined? This is not evident, neither in the materials and methods, nor in the results.

Answer: Clones are commercially available and were purchased from Vista Diagnostics International LLC, WA, USA. Information has been amended to the text in the Methods section.

Page 8, line 186: “Cut-off of positivity was defined by correcting absorbance values generated in the plasma samples from healthy donors (controls) by blank values (plate controls), with both values being in the same range. Absorbance values higher than controls were considered positive. In parallel, we used schizont extracts to perform standard curves and lower absorbance values were in the range of O.D = 0.03-0.04. All positive patient samples gave O.D. values equal or higher than 0.05. This information has also been added in the Methods section.:

It is not clear why varying percentages of pooled plasma (30% for imaging and flow cytometry, and 20% for impedance changes) from the different clusters were used for the functional EC assays. Moreover, no information about the concentration of plasma used for transcriptional analysis is available. Please clarify.

Answer: The concentration of 30% pooled plasma was also used for transcriptional analysis, as indicated in the Methods section, page 11, line 250. This information was also added in the legend of Figure 5B. We had run several optimisation time-course and titration experiments with individual plasma samples, testing concentrations of plasma varying from 10% up to 30% v/v and we did not observe differences in mRNA expression between 20% and 30% v/v plasma conditions.

As for the ECIS, our collaborators (Erich V de Paula group) have optimised this assay and they use a range of 15 to 20% (Santaterra et al 2020). Higher concentrations of plasma reduces the reproducibility, probably to fibrin formation.

Reference 9 is a nonhuman primate study where no LDH is used. Please remove it.

Answer: Reference 9 has been removed following the reviewer suggestion.

Reference 39 is a review on the subject and cannot be included in the sentence on line 556 "In agreement with a previous study8,39, where reference 8 is accurate. Please remove reference 39 from here.

Answer: The text has been amended as suggested.

Reviewer #3 (Significance (Required)):

This paper further contributes to explain the conundrum of low peripheral blood parasitemia and clinical severity in P. vivax. Moreover, by including new human markers and solidly applying computational tools, this paper further contributes to advance clinical research in P. vivax.

Clinical diagnosis of hematological disorders including anemia, lymphopenia and thrombocytopenia, are routinely obtained from a complete blood count. Therefore, I believe the major significance of this work is to raise public health awareness of including in these clinical examinations, the determination of PvLDH levels. They might prognose, as suggested by the authors, better diagnosis and treatment of P. vivax,

My main expertise is the biology of host-pathogen interactions with a focus on P. vivax.

Reviewer #4 (Evidence, reproducibility and clarity (Required)):

The study evaluates P. vivax biomass (serum LDH) versus peripheral parasitemia with multiple variables. From the biomass Vivax high vs. Vivax low, they compare multiple determination in patients with uncomplicated P. vivax. This raises questions about disease and the presence of parasites in various organs. The question is if P. vivax sequesters and the answer is yes in the bone marrow and spleen. Does it sequester like P. falciparum that causes disease by sequestration by binding endothelium in various organs. That is less clear. As P. vivax is rarely fatal, the sequestration has not been studied. The presence of parasites in organs of P. vivax infected splenectomized squirrel and Aotus monkeys has been found in bone marrow and liver (note: splenecotomized monkeys so parasitemia can rise to higher levels than in non-splenectomized monkeys). There are studies of binding of schizonts infected red cells to lung endothelium in vitro does not answer the question of whether sequestration occurs in vivo.

The most important complication of P. vivax is generally anemia. This did not correlate with vivax biomass, but this raises the question of the length of infection and the possibility that parasite biomass may vary at different times of infection. Anemia was seen in P. vivax infected patients, but it did not relate to biomass at the time of study. Note the caveat mentioned in the previous sentence on long term effects of infection on anemia.

The finding of biomass with reduced platelet counts and endothelial effects that may be related to a serum factor and not sequestration. This is the main limitation of the paper besides the unknown long term effect infection. If one could identify an effect of P. vivax infected human serum, this may be worth a study in the future on what is in serum causing the effects.

Reviewer #4 (Significance (Required)):

This study is unique with the caveats mentioned above. It has a good review of the literature.

Answer: We appreciate the reviewer comments. In our cohort, the frequency of anaemia was not as high or severe as the frequency of thrombocytopenia and lymphopenia. However, we still find associations between endothelial cell activation marker Ang-2 and the pro-inflammatory cytokine IL-1 IL-1 negatively associated with several markers of anaemia, such as haemoglobin, haematocrit and RBC numbers. Although we did not further investigate this association, it may indicate indirect effects of parasite biomass on anaemia mediated by inflammation and EC activation, which will be further investigated in other current longitudinal cohort studies.

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Reply to the reviewers

Response/revision plan

(Point-by-point response)

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

The manuscript by Pennauer et al is the first to systematically investigate the role of class I&II Arfs using a knockout approach. It builds on earlier work by the Kahn lab who used an RNAi approach (Volpicelli-Daley et al. 2005) and is complementary to the overexpression approach used by the Hauri lab (Ben-Tekaya et al, 2010). The work is elegant and the data are strong. I am strongly in favor of publishing this work and my comments are technical in nature (2-5) and a request for some text changes (1). have the following comments for improvements:

1- When it comes to evaluating the role of depletions of Arfs on cell fitness, it would be better to use a non-transformed cell line. I am not asking the authors to go through the painstaking process of generating knockout cell lines in RPE1 cells for instance. Rather, I suggest that the authors make the reader aware that conclusions about cell survival have to be taken with care due to the use of a transformed cell line.

We will add this valid point to the Discussion.

2- Why do Arf1 and Arf4 ko cells grow more slowly. Is it a higher rate of cell death? Is it a block in a certain phase of the cell cycle. Given the link of the Golgi to G2-M entry, I think that an analysis of the cell cycle distribution would add more depth to these data. If the cell cycle distribution is unaffected, then I would conclude that that the difference in doubling time are due to reduced cell survival. If there is an effect on the cell cycle distribution, then the conclusion of the authors is safe that no single Arf is required for survival

We plan to analyze cell cycle distribution.

3- It is not clear to me how many cells were quantified in Figure 2D-F. I suppose that each dot represents a cell. In this case, the number of cells quantified is a bit low. Such a quantification of fluorescence intensities in two channels in the same region is a simple task and I think it should be no problem obtaining at least 100 cells per condition.

We will add the number of cells analyzed to the figure legends: At least 40 Golgis were quantified in each experiment. thus >100 in total.

4- Is the drop in the ratio of beta-COP/GM130 in Arf1 depleted cells reflecting reduced recruitment to the Golgi? Because the Golgi is bigger, it might be reflecting a reduced density in the number of coatomer molecules per surface area. If it is due to reduced recruitment, then the ratio of membrane/cytosolic betaCOP should be altered. This of course requires to show that the knockout does not affect total levels of coatomer. I think that such fractionation experiments would be a valuable addition to the manuscript and increase the depth of the data.

We are currently performing immunoblot analysis to determine bCOP levels.

In the Figure below, we have plotted the total intensity of GM130 or bCOP per Golgi from our immunoflurescence data. Total intensity of GM130 significantly increased in the cell lines lacking Arf1, consistent with the increase in Golgi volume. The amount of bCOP at the Golgi remained constant, resulting in reduced bCOP/GM130 ratio. Deletion of Arf1 thus results in reduced rate of coat recruitment that is compensated by an increase in Golgi mass. In the simplest model, reduced formation of Golgi-exit carriers causes Golgi growth until exit carrier formation allows for the required flux.

We propose to include this data in the revised manuscript.

FIGURE

5- The finding that Arf4-ko cells exhibit a defect on retrieval of ER-resident proteins is exciting, and in my opinion, it is the most significant finding in this manuscript. How can this be reconciled with the lack of an ARf4 ko effect on coatomer recruitment to the Golgi. Looking carefully at the data, I see that in 2 out of 3 experiments, Arf4 ko reduced the betaCOP/GM130 ratio. This is why I think it is crucial to perform more experiments and add more cells to increase the confidence in the data. Reduced retrieval of ER chaperones is frequently found in tumors and we still don't understand the reason behind this. Therefore, this finding is of significance beyond the community of cell biologists.

We plan to repeat quantitation with COPI for better statistical validity.

6- I find Figure 6A confusing. Why do Arf1 overexpressing parental HeLa cells exhibit less Arf1 than control cells?

In order not to overload the immunoblot of Arf overexpressing lysates, a smaller aliquot (1/20) was loaded. We will indicate this directly below the blots to make this more obvious in the revised figure.

7- Why was the following condition not tested: Arf4ko cells with Arf1 overexpression. Given the importance of Arf1 in retrograde (Golgi-to-ER) trafficking, I would expect a partial rescue of the retrieval of ER chaperones.

We will to do this experiment.

Reviewer #1 (Significance (Required)):

**Significance of the work:**

The paper is important because it is the first to examine the role of Arfs using a knockout approach. Another very important finding is that Arf4 depleted cells exhibit problems with retrieval of ER chaperones. This is a very novel finding and to the best of my knowledge

**Audience:**

The primary audience is of course the community working on membrane trafficking, organelle biology and proteostasis. However, I think that the data on the role of Arf4 in retrieval of ER chaperones might be of relevance for cancer biologists. Secretion of ER chaperones is frequently found in many tumors and we still do not understand why this is happening and what the significance thereof is.

**My own expertise:**

Export from the endoplasmic reticulum Golgi fragmentation in cancer cell migration Rho GTPases Kinase signaling Pseudoenzymes Cell migration of breast cancer cells Proteostasis in multiple myeloma

**Referee Cross-commenting**

Just a follow-up comment from my side:

I agree that it has not been unequivocally established that Arf1 is the main/sole of retrograde transport. However, even less established is the role of Arf4 in this process. The authors show that it is mainly Arf1 depletion that reduces the amount of COPI at the Golgi (ratio of COPI/GM130). Thus, I remain very surprised that it is actually the Arf4 depletion that results in reduced retrieval.

What is the significance of having less COPI at the Golgi in Arf1-ko cells? Certainly, the Golgi is not more "leaky". Does the level of COPI at the Golgi not reflect the strength of retrograde trafficking? Maybe there is no less COPI at the Golgi, and it only appears to be less, because the Golgi is bigger. This is why a simple fractionation experiment would be good. Something like making a cytosol and a microsome fraction and looking at the ratio of COPI (Cyt/Mem).

If both reviewers think it is too much, or unlikely to work, then I am happy to drop this point.

Below are my comments to the evaluations by the other two reviewers:

1- I agree with most comments that the two other reviewers made. Some of them are actually overlapping with mine (e.g. the use of a cell line other than HeLa).

2- I am not sure whether the impact of the paper would improve by adding data on Arf6.

3- To the comment on Golgi polarity. Maybe we could be more specific here and say that it would be sufficient to show that a trans-Golgi protein and a cis-Golgi protein can be separated by fluorescence microscopy, or whether we alternatively want them to actually do it by immunogold labeling for EM (which is more difficult).

4- I agree with reviewer 2 that the work proposed needs 1-3 months. I think reviewer 3 is a bit too optimistic with 1 month, because her/his comment on using a cell line other than HeLa cannot be addressed in just a month.

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

Pennauer uses HeLa cells and CRISPR/Cas9 to delete the 5 members of the class I and class II ARF family of small regulatory GTPases either individually or in combinations. The characterization of the KO cells is excellent and convincingly demonstrates that true KOs were generated. The quality of the data presented is high. Using the KO cells she documents minor alterations in Golgi architecture and the recruitment of vesicular coats in cells deleted of all ARFs except ARF4. In contrast, there is a significant lack of retention/recycling to the ER of KDEL-containing ER proteins in ARF4 KO cells, with numerous ER chaperones now released into the medium (the ARF4 KO secretome). This is a well-done study that showcases the ability of ARF4 alone to sustain cellular life (quite a surprise to this reviewer). Yet, the characterization of the phenotypes is somewhat minimal and the conclusions would be more robustly supported by additional experiments. Specifically:

1. The authors completely ignore class III ARF6 and this paper would be much more comprehensive and informative if analysis of that ARF was also included (ARF6 has been seen at the Golgi and also mediates endosomal trafficking that intersects with the TGN).

In agreement with the reviewers' consensus in cross-commenting, we consider Arf6ko to be beyond the scope of this study.

Although the overall Golgi architecture seems to be largely conserved, it remains essential to test whether Golgi polarity is similarly maintained, and such data would significantly expand the significance of the reported findings

We have performed super-resolution microscopy of wild-type and Arf1ko Golgis for GM130 and TGN46 as cis- and trans-Golgi markers, respectively, showing that polarity is still intact for Arf1ko, the morphologically most affected knockout cell line. We plan to include the following Figure in the revised manuscript.

FIGURE

Golgi complexes were imaged by superresolution microscopy for GM130 (green) and TGN46 (red), and displayed as maximum intensity projections, or tomographic 2D slices. Scale bar, 3 μm.

Since there is a defect in retrieval of KDEL-proteins, it would be important to show the intracellular localization of the KDEL-R in the cells (especially in the ARF4 KO cells that don't retrieve KDEL-GFP) - is the receptor degraded, stuck in some specific place - knowing that would increase the impact of this study and provide a mechanistic explanation for the observed phenotype

We plan to perform immunoblot analysis for KDELR to test for changes in levels in Arf deletion cells, and immunofluorescence microscopy to analyze changes in KDELR localization.

The rescue experiments in Figure 6 are good as far as they go, but this experiment would be much more informative if in addition to the same class rescue, the other class ARFs (at least one!) were also characterized.

We will to do this experiment.

This is maybe a little too much to ask, but since the authors propose a mechanistic explanation for the ARF4 KO KDEL phenotype as being due to different effectors recruited by this ARF (in this case different COPI isotypes - this study would increase in impact by actually testing this mechanisms by assessing whether ARF4Q71L mutant preferentially bound any particular isotype of COPI or even try to do mass spec to identify relevant effectors for this extremely interesting ARF.

We also think that this additional analysis is beyond the scope of this study.

The Discussion is a very limited and would be more impactful by adding some discussion of organismal effects of ARF deletions (many are embryonic lethal while cells seems to live quite happily) or mutations (links to cancer come to mind here), as well as some mention of data from yeast ARF (what is and isn't essential in those cells). As is, the authors miss an opportunity to highlight the importance of their findings as they relate to current knowledge of ARFology.

We agree to add a discussion of information on embryonic lethality and disease.

Reviewer #2 (Significance (Required)):

This is an important paper for the ARF field and people interested in ARF signaling will be glad to read about the findings and perhaps also use the developed KO cell lines - this is a significant advancement. The impact would be even higher if some of the experiments suggested above were incorporated into the manuscript.

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

This paper describes the application of CRISPR/Cas9 to systematically delete from HeLa cells all four Arf genes, either singly or in combination. The authors find it is possible to generate a number of double deletions (notably one lacking both Class I Arfs), and a triple deletion lacking all but Arf4. The authors characterise the structure of the Golgi in these mutants as well as retention of ER residents. The work is a comprehensive study of an exceptionally high technical standard. There is excellent validation of the deletions, and then the application of a wide range of methods including immunofluorescence, electron microscopy and mass-spectrometry, all with careful and extensive quantitation. The finding that cells can survive without Class I Arfs is interesting and unexpected, as is the fact that Arf4 alone is sufficient. This work will provide an excellent platform for future studies on Arf protein function in human cells. There are of course many questions that arise from these findings, but given the scope and quality of the work they would seem better left for future publications. There is one experiment that could be added, and some additions needed to the text for clarity and minor adjustments to the figures (all listed below), but if these are addressed, this would be a high quality paper of wide audience to a cell biological audience.

**Specific comments:**

1) Have the authors tested the levels and/or localisation of the KDEL receptor in the various lines? This is not essential, but if it were easily done, it would add to the work on ER resident secretion.

We plan to perform immunoblot analysis for KDELR to test for changes in levels in Arf deletion cells, and immunofluorescence microscopy to analyze changes in KDELR localization.

2) The work is entirely done in HeLa cells. The authors should note that the situation might be different in other cells types and cell lines. For instance, the DepMap CRISPR database suggests that quite a lot of cell lines are strongly affected by loss of Arf1.

We agree to add a discussion on known effects in other tissues.

3) Figure 2. Please show single channels as grey scale, and only merge as RGB. This is easier to see, especially for the colour blind. Likewise, Figure 3D would be clearer in greyscale rather than green, and 6B better in grey than in red.

We will make these changes.

4) Figure 5C. A brief comment is needed as to why it might be that BiP and calreticulin are not so efficiently secreted when Arf5 is knocked out in addition to Arf4.

This was a mistake in labeling that lane and will be corrected. It should read "Arf3+5ko" not "Arf4+5ko. Thank you for pointing this out.

5) Discussion:

a) The authors should relate these studies to work in other species. For instance, in yeast reduction of Arf levels causes the Golgi to enlarge (PubMed ID 9487133).

We can discuss this.

1. b) Some more discussion is needed of the fact that Arfs may not all act in the same part of the Golgi, which could explain some of the differences observed between the various deletions.

We can add this point in the discussion.

Reviewer #3 (Significance (Required)):

The Arf GTPases have been studied extensively for over 30 years as major regulators of Golgi function. They are essential for the recruitment to Golgi membranes of both COPI and clathrin/AP-1 coats, as well as various other proteins that regulate Golgi function. In addition, they have been reported to have roles in viral replication, and even other cellular processes such as lipid droplet formation and mitochondrial division. In humans there are four Arfs, Arf1 and Arf3 (Class I Arfs), and Arf4 and Arf5 (Class II Arfs). All are present on the Golgi, but their precise individual roles have remained unclear. Attempts have been made to deplete individual Arfs using RNAi, but incomplete knockdowns have made the results hard to interpret.

**Referee Cross-commenting**

There is probably no need for a prolonged debate about this, but I agree that the importance of Arf4 is striking, but it reflects the nature of this work that CRISPR has finally allowed these sorts of questions to be addressed unequivocally. COPI is also involved in recycling of Golgi resident enzymes, and it may be that Arf1 acts in this role.

If the authors check levels of COPI by blotting, and measure the intensity over the Golgi by quantitative IMF, that will reveal whether stability or membrane association if affected without fractionation which is probably less reliable.

If they want to do some extra experiments, then it would be quite easy to check the levels of some Golgi enzymes, or look at lectin binding as a proxy for glycosylation enzyme levels.

Overall, I agree with the positive comments of Reviewers 1 and 2, and it good that we all recognise the quality and importance of the work. However, I feel that one or two of their requests go beyond the scope of a single publication, or would add rather little for a lot of additional work. It is of course easy to propose experiments that someone else has to do!

**[On] Reviewer 1:**

Point 4. I agree that it would be useful to perform a blot to determine if the levels of coatomer are effected in the various KO lines. I am not sure if Reviewer 1 is also proposing fractionation to determine cytosol vs membrane ratio, but if so, then this would be less useful as peripheral membrane proteins tend to fall off membranes during fractionation and so such analysis is generally questionable. A blot, and clarification of the way the COPI/GM130 ratio is determined, would answer the key points in a relatively straightforward way.

Point 5. I agree that the defect in retrieval of ER residents in Arf4-KO is striking, but it a clear effect even if the reviewer does not understand it themselves! It does not seem so surprising to me, given that Arf4 is likely to act on the early Golgi were such retrieval occurs from. However, the experiment suggested by myself and Reviewer 32 of checking the levels and localisation of the KDEL-receptor would seem to me a good first step to addressing possible mechanism, and certainly sufficient for an initial publication.

Point 7. I am not sure that it has been unequivocally established that Arf1 is important in retrograde traffic. The reality is that many labs have taken Arf1 as being representative of all others and so concentrated biochemical and in vivo studies on this protein. This paper is really important as it highlights the need to investigate both Class I and Class II Arfs, and to bear in mind that their roles in vivo may well be more distinct than their in vitro properties would lead one to suspect. Perhaps, the simplest explanation for this is that the GEFs that activate them have a strong preference for one or the other.

Follow up Comment 1. I was not suggesting that the authors repeat all this in a cell line other than HeLa cells, as this is clearly impractical. HeLa cells are widely used, and so the findings are useful, and whilst it seems certain that some other cell lines would give different data (and indeed the DepMap data show this), then testing one other line would not change the conclusions much. All I wanted the authors to do is to clearly state in the text that what they see in HeLa cells may well be different in other cell lines. This does not detract from the fact that their HeLa cells will provide an excellent platform for focused studies on the role of individual Arfs.

Follow up Comment 2. I agree that Arf6 is not relevant to this paper (as discussed in detail below).

Follow up Comment 3. agree that a simple IMF experiment would suffice to check polarity and immuno-EM is technically very demanding and would add little in this context. The authors have already shown that the Golgi forms stacks in the KO cell lines, and I cannot see how this could occur without the stack being polarised - it has to form at one end and then mature to the other. In addition, after decades of working on the Golgi I have yet to see a credible report of a change to cells causing a loss of Golgi polarity, but maintaining a stacked structure. If the Golgi is not polarised it could not form a stack.

Follow up Comment 4. I agree that one month is perhaps too short to look at KDEL-R, COPI levels and checking polarity by IMF. As noted above, I am NOT suggesting that they repeat all this in a different cell line.

**[On] Reviewer 2.**

Point 1. I agree with Reviewer 1 that the authors are correct to ignore Arf6. It is a completely different GTPase with a distinct function in a different part of the cell. The family of Arf1-Arf5 arose in metazoans from a single Arf, but Arf6 had already split away from the Arf1-5 family in the last eukaryotic common ancestor, as Arf6 is present in plants and yeasts. There is overwhelming evidence that Arf1-Arf5 are partially redundant and this has hampered their study. Arf6 does not share these roles. The fact that it is acts on endosomes and has been reported to be on the Golgi (which is not widely agreed), is also true of many other GTPases. Indeed, other distant relatives of Arf1-5 are actually on the Golgi (Arl1, Arl5 etc), but these are also not relevant as like Arf6 they do not bind coat proteins and other major effectors of Arf1-5.

Point 2. As noted above, it is hard to see how polarity could be affected given that a Golgi stack is formed, but, at most, a simple application of IMF would seem sufficient to confirm this.

Point 3. Agreed.

Point 5. I agree with the reviewer that this is (much!) too much to ask for an initial publication. Various labs have already reported analysis of the effectors of Class II Arfs and they tend to overlap with Class I. Moreover, it is quite possible that the difference of role in vivo reflects differing interactions with regulators.

Point 6. Agreed.

Author Response:

Reviewer #1 (Public Review):

In this work, Panigrahi et. al. develop a powerful deep-learning-based cell segmentation platform (MiSiC) capable of accurately segmenting bacteria cells densely packed within both homogenous and heterogeneous cell populations. Notably, MiSiC can be easily implemented by a researcher without the need for high-computational power. The authors first demonstrate MiSiC's ability to accurately segment cells with a variety of shapes including rods, crescents and long filaments. They then demonstrate that MiSiC is able to segment and classify dividing and non-dividing Myxococcus cells present in a heterogenous population of E. coli and Myxococcus. Lastly, the authors outline a training workflow with which MiSiC can be trained to identify two different cell types present in a mixed population using Myxococcus and E. coli as examples.

While we believe that MiSiC is a very powerful and exciting tool that will have a large impact on the bacterial cell biological community, we feel explanations of how to use the algorithm should be more greatly emphasized. To help other scientists use MiSiC to its fullest potential, the range of applications should be clarified. Furthermore, any inherent biases in MiSiC should be discussed so that users can avoid them.

We thank the reviewer for the positive feedback and comments to help disseminate MiSiC to the broad bacterial cell biology community as it is meant to. As described above we have largely addressed this comment via the redaction of a comprehensive handbook. As detailed below, we now also provide precise measurements of the MiSiC segmentation accuracy compared to ground truth for the various imaging modalities and bacterial species segmentation.

Major Concerns:

1) It is unclear to us how a MiSiC user should choose/tune the value for the noise variance parameter. What exactly should be considered when choosing the noise variance parameter? Some possibilities include input image size, cell size (in pixels), cell density, and variance in cell size. Is there a recommended range for the parameter? These questions along with our second minor correction can be addressed with a paragraph in the Discussion section.

Setting the noise parameters is now detailed in the handbook (section 1.d). A set of thumb rules and recommendations are provided. In addition a paragraph explaining the importance of noise addition for images with sparse bacterial cell density has been added in the results section.

“Associated Figure S1. Background noise can lead to spurious cell detection by MiSiC. SI images retain the shape/curvature information of the intensities in a raw image through eigenvalues of the hessian of the image and an arctan function, creating the smooth areas corresponding to cell bodies and propagating noisy regions where there is no shape information. Thus, MiSiC segments the cells by discriminating between “smooth” and “rough” regions. In effect, when adjusting the size parameter, scaling smooths out the image noise, leading to background regions that have a smoother SI than in the raw image. Some of these areas could be falsely detected as bacterial cells. This effect is shown here: When an image with uniform and random intensity values is segmented with MiSiC with increasing smoothening (here using a gaussian blur filter), spurious cell detection becomes apparent. In addition, since the SI keeps the shape information and not the intensity values, background objects that are of relatively low contrast (ie dead cells or debris) may be detected as cells. All these artifacts can be mitigated by adding synthetic noise to the scaled images.”

2) Could the authors expand on using algorithms like watershed, conditional random fields, or snake segmentation to segment bacteria when there is not enough edge information to properly separate them? How accurate are these methods at segmenting the cells? Should other MiSiC parameters be tuned to increase the accuracy when implementing these methods?

We thank the reviewer for raising this point as it is important to make clear that post-processing algorithms can certainly improve the accuracy of MiSiC masks downstream. To show this specifically, we further processed MiSiC masks of Bacillus subtilis filamentous cells to resolve division septa using the watershed algorithm. This example is now provided as Figure S3. Importantly, there is no particular MiSiC adjustment that needs to be performed prior to running these processing steps, which can be done directly in Image-J or its bacterial cell analysis plug-in, MicrobeJ. It is worth noting that the post- processing strategy may depend on the scientific question under consideration. In the handbook, we also give an example of post-processing methods that may be used.

“Associated Figure S3. Refining cell separations with watershed. Watershed methods may be used to obtain a more accurate segmentation of septate filaments such as Bacillus subtilis. In this example applying this method to the MiSiC mask effectively resolves cell boundaries that are not captured in the prediction but are visible by eye (arrows).”

3) Can the MiSiC's ability to accurately segment phase and brightfield images be quantitatively compared against each other and against fluorescent images for overall accuracy? A figure similar to Fig. 2C, with the three image modalities instead of species would nicely complement Fig. 2A. If the segmentation accuracy varies significantly between image modalities, a researcher might want to consider the segmentation accuracy when planning their experiments. If the accuracy does not vary significantly, that would be equally useful to know.

This is a very important issue that was also raised by reviewer 3 and which we decided to address in full. For each imaging modality and distinct species, we measured the Jaccard Index as a function of the threshold set for the Intersection over Union (ioU). The resulting curves are now provided in two separate Figures 2 and 3 and a supplemental Figure S2; they provide a robust measure of the segmentation for each modality/tested species.

“Figure 2. MiSiC predictions under various imaging modalities. a) MiSiC masks and corresponding annotated masks of fluorescence, phase contrast and bright field images of a dense E. coli microcolony. b) Jaccard index as a function of IoU threshold for each modality determined by comparing the MiSiC masks to the ground truth (see Methods). The obtained Jaccard score curves are the average of analyses conducted over three biological replicates and n=763, 811, 799 total cells for Fluorescence, Phase Contrast and Bright Field, respectively (bands are the maximum range, the solid line is the median). The fluorescence images were pre-processed using a Gaussian of Laplacian filter to improve MiSiC prediction (see methods).”

“Associated Figure S2. MiSiC predictions under various imaging modalities. a) MiSiC masks and corresponding annotated masks of fluorescence, phase contrast and bright field images of a dense M. xanthus microcolony. b) Jaccard index as a function of IoU threshold for each modality determined by comparing the MiSiC masks to the ground truth (see Methods). The obtained curves are the average of analyses conducted over three biological replicates and n=193,206,211 total cells for Fluorescence, Phase Contrast and Bright Field, respectively. The fluorescence (bands are the maximum range, the solid line is the median) images were pre-processed using a Gaussian of Laplacian filter to improve MiSiC prediction (see methods). c) A human observer is slightly less performant than MiSiC. The same ground truth as used in Figure 2 (dashed lines) was compared to an independent observer’s annotation (solid lines) and Jaccard score curves were constructed as shown in Figure 2. BF: Bright Field, PC: Phase Contrast, Fluo: Fluorescence.”

“Figure 3. MiSiC predictions in various bacterial species and shapes. a) MiSiC masks and corresponding annotated masks of phase contrast images of another Pseudomonas aeruginosa (rod-shape), Caulobacter crescentus (crescent shape) and Bacillus subtilis (filamentous shape). b) Jaccard index as a function of IoU threshold for each species determined by comparing the MiSiC masks to the ground truth (see Methods). The obtained Jaccard score curves are the average of analyses conducted over three biological replicates and n=1149,101,216 total cells for P. aeruginosa, B. subtilis and C. crescentus, respectively (bands are the maximum range, solid line the median). Note that the B. subtilis filaments are well predicted but edge information is missing for optimal detection of the cell separations.”

4) The ability of MiSiC to segment dense clusters of cells is an exciting advancement for cell segmentation algorithms. However, is there a minimum cell density required for robust segmentation with MiSiC? The algorithm should be applied to a set of sparsely populated images in a supplemental figure. Is the algorithm less accurate for sparse images (perhaps reflected by an increase in false-positive cell identifications)? Any possible biases related to cell density should be noted.

In fact, MiSiC performs well both with densely or sparsely populated images. In the case of sparsely populated images it is however possible that non-cell objects can occasionally appear in the MiSiC mask. As mentioned above, inclusion of noise can help remove these objects in the sparsely populated images. This issue is now fully explained in a supplemental Figure S1. Of note, non-cell objects -if they were to remain after noise addition- can be eliminated using additional general morphometric filters or specific models fitting bacterial cells, as for example those included in Microbe-J and Oufti. These points are now clarified in the text.

“Associated Figure S1. Background noise can lead to spurious cell detection by MiSiC. SI images retain the shape/curvature information of the intensities in a raw image through eigenvalues of the hessian of the image and an arctan function, creating the smooth areas corresponding to cell bodies and propagating noisy regions where there is no shape information. Thus, MiSiC segments the cells by discriminating between “smooth” and “rough” regions. In effect, when adjusting the size parameter, scaling smooths out the image noise, leading to background regions that have a smoother SI than in the raw image. Some of these areas could be falsely detected as bacterial cells. This effect is shown here: When an image with uniform and random intensity values is segmented with MiSiC with increasing smoothening (here using a gaussian blur filter), spurious cell detection becomes apparent. In addition, since the SI keeps the shape information and not the intensity values, background objects that are of relatively low contrast (ie dead cells or debris) may be detected as cells. All these artifacts can be mitigated by adding synthetic noise to the scaled images.”

and:

“Along similar lines, non-cell objects can appear in the MiSiC masks and while some can be removed by the introduction of noise, an easy way to do it is to apply a post-processing filter, for example using morphometric parameters to remove objects that are not bacteria. This can be easily done using Fiji, MicrobeJ or Oufti."

5) It is exciting to see the ability of MiSiC to segment single cells of M. xanthus and E. coli species in densely packed colonies (Fig. 4b). Although three morphological parameters after segmentation were compared with ground truth, the comparison was conducted at the ensemble level (Fig. 4c). Could the authors use the Mx-GFP and Ec-mCherry fluorescence as a ground truth at the single cell level to verify the results of segmentation? For example, for any Ec cells identified by MiSiC in Fig. 4b, provide an index of whether its fluorescence is red or green. This single-cell level comparison is most important for the community.

We have now performed this comparison and determined Jaccard indexes for E. coli and Myxococcus detection using the individual fluorescence images as a reference (figure 5b). Since we were only able to make this comparison in relatively small fields we also kept the comparison of expected morphometric parameters in large images. Taken together, these data now demonstrate that semantic classification as performed does well separate Myxococcus cells from E. coli cells (see more details in our response to reviewer 3).

Reviewer #2 (Public Review):

Panigrahi and co-authors introduce a program that can segment a variety of images of rod-shaped bacteria (with somewhat different sizes and imaging modalities) without fine-tuning. Such a program will have a large impact on any project requiring segmentation of a large number of rod-shaped cells, including the large images demonstrated in this manuscript. To my knowledge, training a U-Net to classify an image from the image's shape index maps (SIM) is a new scheme, and the authors show that it performs fairly well despite a small training set including synthetic data that, based on Figure 1, does not closely resemble experimental data other than in shape. The authors discuss extending the method to objects with other shapes and provide an example of labelling two different species - these extensions are particularly promising.

The authors show that their network can reproduce results of manual segmentation with bright field, phase and fluorescence input. Performance on fluorescence data in Fig. 1 where intensities vary so much is particularly good and shows benefits of the SIM transformation. Automated mapping of FtsZ show that this method can be immediately useful, though the authors note this required post-processing to remove objects with abnormal shapes. The application in mixed samples in Fig. 4 shows good performance. However, no Python workflow or application is provided to reproduce it or train a network to classify mixtures in different experiments.

We thank the reviewer for the positive comment. As discussed in our answer to reviewer 1, the classification presented in Figure 4 (now Figure 5) is meant to provide an example of how MiSiC can be further used to train networks to classify species in interspecies communities by generating two datasets, one per species of interest, to further train a U-Net. Here, the secondary U-Net was developed to specifically discriminate Myxococcus from E. coli, which is a very specialized application. Hence it was not included in the MiSiC package. Nevertheless the code is accessible at https://github.com/pswapnesh/MyxoColi (which is mentioned in the Methods).

Performance was compared between SuperSegger with default parameters and MiSiC with tuned parameters for a single data set. Perhaps other SuperSegger parameters would perform better with the addition of noise, and it's unclear that adding Gaussian noise to a phase contrast image is the best way to benchmark performance. An interesting comparison would be between MiSiC and other methods applying neural networks to unprocessed data such as DeepCell and DeLTA, with identical training/test sets and an attempt to optimize free parameters.

In fact, we believe that it does make sense to test how MiSiC performs in the presence of noise and show that it is robust, making it suitable for use on complex multi-tile images. For this analysis we kept the comparison with Superseger, which provides a reference as it is done on a data set optimized for Superseger segmentation. Importantly, we keep the parameters constant throughout the analysis because it would not be feasible to tweek parameters tile-by-tile in a multi-tile image. This analysis shows that MiSiC is more adapted for this application.

INSTALLATION: I installed both the command line and GUI versions of MiSiC on a Windows PC in a conda environment following provided instructions. Installation was straightforward for both. MiSiCgui gave one error and required reinstallation of NumPy as described on GitHub. Both give an error regarding AVX2 instructions. MiSiCgui gives a runtime error and does not close properly. These are all fairly small issues. Performance on a stack of images was sufficiently fast for many applications and could be sped up with a GPU implementation.

We have updated the pip install script available in GitHub for MiSiCgui that remediates some of these issues : There is no more numpy error, it closes properly and there are only warning messages concerning future deprecations in the napari packages. We have tested in Windows 10, Linux Ubuntu 18, and Mac OS Catalina. For the moment it seems impossible to install in Mac OS BigSur maybe due to the python 3.7 requirement. We will work on this problem in the near future. We have removed the command line interface as we are developing future version with an easiest way to provide MiSiC as Napari or FIJI/ImageJ plugin

TESTING: I tested the programs using brightfield data focused at a different plane than data presumably used to train the MiSiC network, so cells are dark on a light background and I used the phase option which inverts the image. With default settings and a reasonable cell width parameter (10 pixels for E. coli cells with 100-nm pixel width; no added noise since this image requires no rescaling) MiSiCgui returned an 8-bit mask that can be thresholded to give segmentation acceptable for some applications. There are some straight-line artifacts that presumably arise from image tiling, and the quality of segmentation is lower than I can achieve with methods tuned to or trained on my data. Tweaking magnification and added noise settings improved the results slightly. The MiSiC command line program output an unusable image with many small, non-cell objects. Looking briefly at the code, it appears that preprocessing differs and it uses a fixed threshold.

We thank the reviewer for testing the programs. Tiling related artifacts may now be avoided by excluding a few pixels at the border in the new version of MiSiC code. This is now implemented in the MiSiC.segment function as segment(im,invert = False,exclude = 16). Without seeing the reviewers data it is difficult for us to see how the segmentation (which is said to be acceptable) could be further improved. The command line program has now been removed in favor of continuous development on the graphical interface.

Reviewer #3 (Public Review):

The authors aimed to develop a 2D image analysis workflow that performs bacterial cell segmentation in densely crowded colonies, for brightfield, fluorescence, and phase contrast images. The resulting workflow achieves this aim and is termed "MiSiC" by the authors.

I think this tool achieves high-quality single-cell segmentations in dense bacterial colonies for rod-shaped bacteria, based on inspection of the examples that are shown. However, without a quantification of the segmentation accuracy (e.g. Jaccard coefficient vs. intersection over union, false positive detection, false negative detection, etc), it is difficult to pass a final judgement on the quality of the segmentation that is achieved by MiSiC.

We thank the reviewer for this comment. To address it we divided the previous Figure 2 into two figures (and associated supplemental figures) separately showing how MiSiC performs (i), to segment two very distinct bacterial species E. coli and Myxococcus under various imaging modalities. (ii) to segment other bacterial species: rods (P. aeruginosa), filaments (B. subtilis) and crescent shapes (C. crescentus). The results now clearly show both the strength and limitations of the system.

A particular strength of the MiSiC workflow arises from the image preprocessing into the "Shape Index Map" images (before the neural network analysis). These shape index maps are similar for images that are obtained by phase contrast, brightfield, and fluorescence microscopy. Therefore, the neural network trained with shape index maps can apparently be used to analyze images acquired with at least the above three imaging modalities. It would be important for the authors to unambiguously state whether really only a single network is used for all three types of image input, and whether MiSiC would perform better if three separate networks would be trained.

A single network is using a shape-index-map rather than the original images as an input. As mentioned by the reviewer this is a major strength of the workflow given that it permits segmentation, independent of the imaging modality, which we now measure for each modality.

As the reviewer hints, three different models specific to each modality (CP, Fluorescence and BF) could also be used to train three networks, allowing the direct end-to-end segmentation of raw images. In theory, this could improve the segmentation (although this might lead to negligible benefits given the actual segmentation quality).

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Reviewer #1 (Evidence, reproducibility and clarity (Required)): **Summary:** In this study authors investigated the role of NAMPT, NAD+ and PARP1/parthanatos in skin inflammation using a zebrafish psoriasis model with an hypomorphic mutation of spint1a and human organotypic 3D skin models of psoriasis. Authors showed that genetic deletion and/or pharmacological inhibition of Nampt/PARP1/AIFM1/NADPH oxidases reduced oxidative stress, inflammation, keratinocyte DNA damage, hyperproliferation and cell death in zebrafish models of chronic skin inflammation. Authors also showed the expression of pathology-associated genes in human organotypic 3D skin models of psoriasis with pharmacological inhibition of Nampt/PARP1/AIFM1/NADPH oxidases. The key finding of this study is that PARP1 hyperactivation caused by ROS-induced DNA damage mediates skin inflammation through parthanatos. **Major comments:** This is a very comprehensive study to investigate the role of PARP1 in skin inflammation. The main conclusion was made based on the genetic inhibition and/or pharmacological inhibition of Nampt/PARP1/AIFM1/NADPH oxidases. Although the finding of this study that NAMPT-derived NAD+ fuels PARP1 to promote skin inflammation through parthanatos is interesting and important, there are lots of major concerns and questions, which have to be addressed to better support the main conclusion. In addition, the data and methods were not presented with sufficient detail.

1. This study is heavily relied on pharmacology inhibition. However, the specificity and selectivity of many inhibitors were not tested in this study.

At least 3 concentrations of each inhibitor were tested and the lowest one able to rescue the phenotype was then used for further testing (please, see Table S1). More importantly, the specificity of all compounds used were confirmed by genetic inhibition of their targets.

Fig. 1: it is quite confusing how NAD+ increases H2O2 levels? Is NAD+ cell permeable? It is not clear if NAD+ has been really up taken by cells in the larvae. If NAD+ fuels PARP1 to promote skin inflammation, why NAM treatment increased H2O2 levels but NMN precursor failed to increase skin oxidative stress? No reasonable explanation has been provided.

This is an interesting point. We have shown that exogenous NAD+ added in the water of larvae increased larval NAD+ (please, see Fig. 2K). It has been shown that neurons can take up NAD+ through CX43 (Fig. S7), so a similar mechanism may operate in larval skin. As regards, the effect of NAM and NMN, a recent study has demonstrated that NAM supplementation increased zebrafish larval NAD+; however, NA, NMN and NR failed to boost larval NAD+ level (PMID: 32197067). These results are consistent with our data.

Fig. 1E and 1G: it is not clear what is the green channel. Similarly, there is no clear description what is red or green in many other figures.

To help the interpretation of larval pictures, we have indicated in all figures what is analyzed in each fluorescent channel.

1. Fig. 1K and 1L: It is hard to understand why FK-866 reduced H2O2 release, but it increased neutrophils infiltration. How to interpret this conclusion?

Fig. 2C-D: Why low doses FK-866 reduced neutrophil infiltration whereas high dose FK-866 increased neutrophil infiltration?

Answer to 4&5: As it was explained in lines 145-156, FK-866 induces NF-kB activation in the muscle and neutrophil infiltration in this tissue when used at 100 uM. This result may be deleted if the reviewers think it is confusing, since a 10 uM dose was used in all subsequent experiments to study the impact of Nampt in skin inflammation. This dose has no effects in the muscle but robustly reduced skin H2O2 production and neutrophil skin infiltration.

Fig. 2I-J: it is not clear how NF-kB activity was measured. Is that based on green fluorescence shown in Fig. 2J? if so, the representative images were not consistent with the quantification data shown in I. Similarly, many other representative images were also not consistent with their quantification data throughout the manuscript. For example, Fig. 3C/D, 3E/F, 3G/H, 3L/M, Figure S2C/D, S2G/H, Fig. 4C/D, 4J/K.

The quantification of NFkB was measured in the skin, as it has already been reported previously (Candel et al., 2014). This is indicated in M&M section. The images show the whole larvae and NFkB is expressed at high levels in different tissues, such as neuromasts. To clarify this, we have included an additional figure to explain the ROI used for quantification of H2O2 and NfkB (Fig. S1G).

Figure S1C, Nampta/Namptb protein expression should be checked and shown after its KO using crispr/cas9 technique.

Unfortunately, we have used to different antibodies and both failed to crossreact with zebrafish Nampta/b. However, we have included the efficiency of CRISPR-Cas9 in Fig. S1F of the revised version. The efficiency is relatively low, probably indicating that is indispensable for zebrafish development, as occurs in mice (PMID 28333140).

Fig. 3I: protein expression of nox1, nox4 and nox 5 should be checked after genetic inhibition using CRISPR/Cas9 technique.

Unfortunately, we do not have antibodies able to recognize zebrafish Nox1, Nox4 and Nox5. However, we have provided the efficiency of the gRNA used for each gene (Fig. S3) and it is about 65%.

Fig. 4: If Olaparib treatment increased DNA damage, will it increase PARP1 activation and PAR formation?

As it has widely used in mammalian models, parthanatos is triggered by overactivation of PARP1 following DNA damage. Therefore, although inhibition of olaparib may further induces DNA damage, it blocks parthanatos. This is consistent with our results showing that olaparib reduces PARylation (Fig. S4H) and cell death (Figs. 4J, 4K).

Fig. 4M: it is not clear what staining has been done. No difference was observed among different groups.

As indicated in the figure legends, pγH2Ax+ (green) keratinocytes (red) are shown. We have indicated this in the figure and include arrows to show pγH2Ax+ cells. The quantitation of this experiment (Fig. 4L) show that FK-866 robustly reduced, while olaparib increases, keratinocyte DNA damage.

Authors used N-phenylmaleimide (NP) to block AIF nuclear translocation. How does this inhibitor work? what is its actual effect on AIF nuclear translocation? Experiments are required to show this inhibitor actually blocks AIF nuclear translocation.

NP has been shown to block AIFM1 nuclear translocation, since it inhibits cysteine proteases which are required for its cleavage which precedes nuclear translocation (PMID 8879205). Although we have shown that genetic inhibition of Aifm1 rescues skin inflammation, confirming the specificity of the inhibitor, we agree on this point. Therefore, we have performed additional experiments and showed nuclear Aifm1 in keratinocyte aggregates of Spint1-deficient larvae and that NP treatment blocked nuclear translocation (Fig. S6C). In addition, we have also shown increased nuclear translocation of AIFM1 in keratinocytes of lesional skin from psoriasis patients (Figs. 6C, 6D).

Figure S4: it is hard to understand why lane #2 with Olaparib has the highest PAR signal.

We are sorry for this mistake labeling the WB. The right legend is: 1 +/+, 2 -/- treated with DMSO, 3 -/- treated with FK-866 and 4 -/- treated with olaparib.

Does spint1a-/- zebrafish show parthanatos cell death? It is not clear how cell death was measured.

We have shown that skin keratinocytes from Spint1a-deficient fish show increased cell death, as assayed by TUNEL, that is fully reversed by olaparib (Figs. 4J, 4K). In addition, skin keratinocytes from the mutant fish also have increased PARylation that is reversed by either FK-866 or olaparib (Fig. S4G, S4H). Further, pharmacological and genetic inhibition of Aifm1 inhibition and forced expression of Parga also rescue skin inflammation. Finally, we have included new experiments showing Aifm1 nuclear translocation in both Spint1a-deficient larvae and psoriasis patient lesional skin. Therefore, all these results show that Spint1a-deficient fish show parthanatos cell death-induced inflammation.

NAD+ levels were regulated by 3 different pathways. Expression of many genes involved in these 3 pathways were altered in psoriasis. However, it is not clear if the other two pathways play a role in PARP1-mediated inflammation.

NAD+ salvage pathway has been shown to be the major pathway regulating NAD+ levels in most tissues. The inhibition of this pathway with FK-866 rescues all skin phenotypes observed in Spint1a-deficient larvae as well as in organotypic 3D skin models of psoriasis. These results were further validated using another inhibitor (GMX1778) and genetic inhibition. Therefore, our results support that the salvage pathway is the one involved in psoriasis and inhibition of this pathway would rescue inflammation. However, it will be worthy to investigate if other pathways play a role in psoriasis and specifically upon inhibition of the salvage pathway.

**Minor comments:**

1. Page 9: To test this hypothesis, we used N-phenylmaleimide (NP), a chemical inhibitor of Aifm1 translocation from the nucleus to the mitochondria (Susin et al., 1996). The statement is not correct.

We are sorry for this mistake. It has been amended to: “To test this hypothesis, we used N-phenylmaleimide (NP), a chemical inhibitor of Aifm1 translocation from the mitochondria to the nucleus (Susin et al., 1996).”

Page 12: To the best of our knowledge, this is the first study demonstrating the existence of parthanatos in vivo. This statement is not correct.

We have removed this statement.

Figure S3 and S6E: they should be presented in an easy understandable way for the general readers.

We have explained in the legends the graph output of TIDE analysis.

Figure legends should be presented in a clearer way.

We have tried our best writing the legends. All suggestions and request were made.

Reviewer #1 (Significance (Required)): Parthanatos is a new type of cell death distinct from apoptosis, necrosis, necroptosis and plays a pivotal role in ischemic stroke and neurodegenerative diseases (Wang Y et a., Science. 2016; Kam TI et al., Science 2018). The current study may provide new evidence of the importance of PARP1 and parthanatos in skin inflammation and potential targets for the treatment of skin inflammation. We thank the reviewer’s opinion on the significance of our study.

The reviewer has the expertise in oxidative stress, PARP1 and parthanatos research. Reviewer #2 (Evidence, reproducibility and clarity (Required)): **Summary:** The manuscript entitle "NAMPT-derived NAD+ fuels PARP1 to promote skin inflammation through parthanatos" is well written, divided and organized. This work demonstrated that models of psoriasis are characterized by ROS stress, inflammation and cell death. It was clear that NAMPT, a rate-limiting enzyme of NAD salvage pathway, and PARP1, a Poly-ADP-ribose polymerase, could be targeted to decrease ROS stress and inflammation that are contributing to cell death through parthanatos pathway. However, it was not clear that NAD+ are the responsible for fuel these processes in the psoriasis models analyzed. Nevertheless, the present work demonstrated that the cell death observed in the psoriasis model analyzed was correlated to an unidentified programmed cell death pathway, parthanatos that up to date has not been demonstrated.

We are pleased with the reviewer’s comments on our study.

**Major comments:** Most of the data showed confirmed that inhibition of NAMPT or PARP1 seems to be beneficial for the relief of some characteristics related to oxidative stress and inflammation in the skin. However, the author should show data about NAD+ levels only instead of the ratio NAD+/NADH to state that NAMPT-derived NAD+ is promoting oxidative stress (line 366-368) (fig2K).

The data shown in Fig 2K are NAD+ plus NADH. Considering that cytosolic and nuclear NAD+/NADH ratio typically ranges from 100 to 1000 (PMID: 21982715), these data mainly show intracellular NAD+ concentration in larvae.

Some data images are not convincing, or they don't really show an increase or decrease as the author showed in graph data. (Fig1D, 1E - 1F,1G).

The quantification of H2O2 and NFkB was measured in the skin, as it has already been reported previously (Candel et al., 2014). To clarify this, we have shown the ROI used for quantification of H2O2 and NfkB in Fig. S1G.

What is the relevance to analyze muscle and what is the relevance of the results obtained, since the effect of FK-866 in muscle increases the NFKB activity?

This is essentially a similar concern raised by reviewer 1. FK-866 induces NF-kB activation in the muscle and neutrophil infiltration in this tissue when used at 100 uM. This result may be deleted if the reviewers think it is confusing, since a 10 uM dose was used in all subsequent experiments to study the impact of Nampt in skin inflammation. This dose has no effects in the muscle but robustly reduced skin H2O2 production and neutrophil infiltration.

Figure S4H is not convincing with what the author wrote.

We are sorry for this mistake labeling the WB. The right legend is: 1 +/+, 2 -/- treated with DMSO, 3 -/- treated with FK-866 and 4 -/- treated with olaparib. Both FK-866 and olaparib rescue PARylation in the skin of Spint1a-deficient larvae.

The author should make the keratinocyte aggregation experiment with FK-866 treatment to better substantiate what they are proposing.

These results are shown in Figs. 2E and 2F.

**Minor comments:** Line 281: "NP, a chemical inhibitor of Aifm1 translocation from the nucleus to the mitochondria..." should be the opposite: NP, a chemical inhibitor of Aifm1 translocation from mitochondria to nucleus.

We are sorry for this mistake. It has been amended.

Line 299 "figure 6A" should be Figure 6B.

We have checked and it is correct.

How the author explains the relationship between all the results being related to NAMPT and supposedly to NAD+, but an important precursor to make NAD through salvage pathway (NMN) and a well NAD+ booster didn't show any effect?

This is an interesting point that was also raised by reviewer 1. A recent study has demonstrated that NAM supplementation increased zebrafish larval NAD+; however, NA, NMN and NR failed to boost larval NAD+ level (PMID: 32197067). This explains our results. We have discussed this point in the revised manuscript.

Line 178: should be NAMPT inhibitor stead of FK-866 inhibitor.

Thanks a lot. It has been amended.

Line 191-192: I suggest reformulating this sentence since the result showed was only the ratio NAD/NADH.

Please, see our response above. We are measuring NAD+ plus NADH. We have amended the text to clarify this fact.

Reviewer #2 (Significance (Required)): The present work greatly demonstrated the relevance of PARP1 and NAMPT in the field of inflammation and ROS in the skin that contribute to diseases like psoriasis. Although it is not a lethal disease, as the author mentioned, it affects the physical and mental health of the individual. Understanding the mechanism that underlie this condition would help to trigger new and more efficient treatments. It was clear that the result showed a promising strategy in targeting NAMPT and PARP1. Furthermore, inhibitor for them is already know and may be useful for future treatment of psoriasis disease. We thank this comments on the impact of our study.

Reviewer #3 (Evidence, reproducibility and clarity (Required)): This study shows NAMPT derived NAD facilitates PARP activation to promote skin inflammation via parthanatos. The authors used the zebrafish model and organoid models of psoriasis and observed that inhibition of NAMPT reduces inflammation in zebrafish and human skin organoid models. They also observed that NADPH oxidase-derived oxidative stress activates PARP, and PARP inhibition or over-expression of PARG or AIF mimics protection mediated by NAMPT inhibition. This is an interesting study, but there are several weaknesses to support the conclusions of this study. While pharmacological inhibition is a powerful tool, complementary methods (knock out of PARP-1) are critical for this paper's conclusions. PARP inhibitor used in this study may not specifically inhibit PARP1 but other PARPs too. Therefore, genetic knockout of PARP will make the make this conclusions/interpretation of this study strong.

We thank these comments on our manuscript. All pharmacological inhibitions used in this study were confirmed by genetic experiments, including Parp1. The genetic inhibition of Parp1 is shown in Figs. S4C-S4F.

Additional comments include: This study's primary focus is PARP activation and PAR-mediated parthanatos, but it is not shown how different inhibitors used in this study and supplementations of NAD alter PARP activation and PAR formation.

We have shown through the quantitation of PARylation that Spint1a-deficient skin shows increased PAR activity and that pharmacological inhibition of either Nampt or Parp was able to fully reverse it (Figs S4g & S4H). In addition, we have also shown a dramatically increased PAR activity in lesional skin biopsies from psoriasis patients (Fig. 6E).

NAMPT is not the only NAD biosynthesis pathway; how other NAD pathways respond when NAMPT is inhibited with FK-866.

NAD+ salvage pathway has been shown to be the major pathway regulating NAD+ levels in most tissues. The inhibition of this pathway with FK-866 rescues all skin phenotypes observed in Spint1a-deficient larvae as well as in organotypic 3D skin models of psoriasis. Therefore, our results support that the salvage pathway is the one involved in psoriasis and inhibition of this pathway would rescue inflammation. However, we agree that it will be worthy to investigate if other pathways play a role in psoriasis and specifically upon inhibition of the salvage pathway. However, this is out of the scope of this manuscript.

PARG is used in this study, but the protein levels of PARG are not shown, and it is not clear whether the PARG overexpression is sufficient to reduce PAR levels in the models used. AIF pharmacological and genetic manipulation of AIF is used, but it is not shown that AIF translocates to the nucleus in this model.

We agree on these points, so we have analyzed Aifm1 translocation in Spint1a-deficiet larvae and psoriasis patient lesional skin (please, see above our response to reviewer 1) and PARylation upon forced expression of Parga (Fig. 5M).

Does NAMPT inhibition reduce NAPD oxidase activity?

Our results indicate that Nampt inhibition reduce NAPDH oxidase activity, since a drastic reduction of H2O2 production was observed in the skin of Spint1a-deficient larvae treated with FK-866.

PAR plots provided in fig S4 need quantification, and the blots (Fig S4 G&H) should be run on the same gel to make sure the exposure levels are the same. It is not clear which group is represented in lane 4 of Fig S4 G.

We have provided the quantitation. The problem is that we mislabeled the legend of Fig. S4H. The right legend is: 1 +/+, 2 -/- treated with DMSO, 3 -/- treated with FK-866 and 4 -/- treated with olaparib. Therefore, either Nampt or Parp inhibition robustly reduces PARylation of Spint1a-deficient skin to the levels of their wild type counterparts.

Reviewer #3 (Significance (Required)): This study in interesting potentially showing the role of PARP-1 activation and Parthanatos in skin inflammation. It could be very significant if above identified weaknesses are addressed.

We are pleased with this reviewer’s assessment on the significance of our study.

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We thank the reviewers for the positive assessment of our work and for the constructive comments that helped us to improve the quality of our manuscript. We have carefully considered each point and have addressed most by modifying the manuscript text to increase clarity of our work. Based on a suggestion by Reviewer 2 we have also included the results of a new experiment.

In addition to addressing all comments of the reviewers, we have expanded the part of the study analysing the functionality of Caulobacter’s DnaA Nt in the heterologous host E. coli. Furthermore, we have replaced our original set of fluorescence data by a new data set that has been acquired using optimized measurement parameters (bottom read and 100 for the detector gain - see Material and Methods for details), which have improved the signal-to-noise ratio and the overall quality of the fluorescence profiles. Importantly, these new data do not change, but rather strengthen, our conclusions.

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

Felletti et al provide compelling new evidence that a CDS element in the dnaA mRNA is required for nutrient dependent translationol control. This provides a mechanisms by which dnaA translation is shut off during carbon starvation, and is supported by a rather rigorous analysis of the mRNA performed both in vitro and in vivo. Overall it was a pleasure to read and the data are generally very compelling. My specific comments are below:

**Major Comments:**

While the authors rule out differences in charging of different ala-tRNAs as controlling the nutrient dependent repression in translation, the authors assume that this must be due to the nascent sequence. However, could it also be possible that all ala-tRNA isoacceptors have lower charging after C-starvation?

We thank the reviewer for raising this important point. As Reviewer 1 pointed out, we cannot conclusively exclude that carbon starvation could lead to reduced charging levels of all isoacceptor Ala-tRNAs. However, based on the available literature, we consider it unlikely. In a first work by Elf et al 2003 (confirmed later by Dittmar et al 2005 and Subramaniam et al 2014) the authors argued that under amino acid-limiting conditions the charging levels of the different isoacceptor tRNAs depend directly on their codon usage during translation. Importantly, in our work we could show that Nt mediates the inhibition of translation independent of the synonymous codon choice, suggesting that aa-tRNA levels are not limiting in our experimental conditions. To address this comment of Reviewer 1, we discussed this matter in a greater detail in the revised version of the manuscript (line 374-379).

**Minor comments:**

It was observed many years ago that tmRNA is required for the proper timing of DNA replication initiation in Caulobacter (Cheng and Keiler J Bact 2009). Since the AAI motif is appearing to alter translation elongation, it might be interesting to discuss the AAI motif may be linked to ribosome arrest and rescue.

We appreciate this suggestion. Cheng and Keiler 2009 proposed an indirect involvement of the tmRNA in the transcriptional regulation of DnaA over Caulobacter’s cell cycle. In the revised version of the manuscript, we mention the tmRNA and ArfB protein as possible factors involved in ribosome rescue following Nt-induced ribosome stalling and we refer to Keiler et al 2000 and Feaga et al 2014.

Line 49 - add "initiation"

The word “initiation” was added to the text.

Line 61 - is "cleared" meant to be proteolyzed or simply meaning to have a lower protein level?

We apologize if we were not clear. We rephrased the text as follows: “[…] DnaA levels decrease at the onset of carbon starvation […]”.

Line 92-93 - is this 5' UTR based on a previously defined TSS determined in their previous study?

dnaA TSS has been first determined by primer extension (Zweiger and Shapiro 1994) and later by global 5’RACE (Schrader et al 2014 and Zhou et al 2015). In the new version of the manuscript, we include references to these previous studies (line 94).

Line 115-118 - this is interesting, might this conserved 5' UTR be added to rfam?

We thank the reviewer for this suggestion. We will submit our alignment to rfam after publication of the manuscript in a journal.

Line 126-127, 131,189 - Is the 3nt sequence the authors found here considered a Shine-Dalgarno site? I would imagine that this would be too small to consider this. Perhaps calling it SD-like sequence might be more appropriate.

We agree with this comment. In the new version of the manuscript, we refer to the identified 3-nucleotide sequence as a “SD-like sequence”.

Lines 136-140, 208-210 - Would the authors consider this upstream site with a potential CUG start codon a standby site? It appears to fit many of the criteria which could be used to define one.

According to our probing data, the mRNA region in proximity of the CUG start codon forms a very stable stem-loop structure. Based on our previous experience (especially the extensive work by the Wagner lab), typical ribosome standby sites only occur in largely unstructured regions. Furthermore, in Supplementary Fig. 4 we show that the deletion of stem P4 does not affect eGFP expression levels. For these reasons, we consider it unlikely that the putative CUG start codon is part of a ribosome standby site.

Lines 253-255 - this is a beautiful experiment, but very hard to understand from the text. Perhaps add a sentence or two to explain it in more detail.

We thank the reviewer for this comment. In the revised version of the manuscript, we provide a more detailed description of the dfsNt reporter mutant. We hope this will address the reviewer’s concerns.

Line 307 - add "synonomous"

The word “synonymous” was added in the revised version of the manuscript

When dnaA is depleted, it was observed that the chromsome can be erroneously segregated by the ParA/B/S system (mera et al PNAS). Does this occur in C-starvation when DnaA levels drop?

In a separate study we have also observed that in a fraction of DnaA depleted cells the origin of replication is erroneously translocated from the stalked to the swarmer cell pole. We have not studied this phenomenon under carbon starvation, as it lies outside the scope of this paper. However, if the ParA/B/S remains functional under carbon starvation, this might also happen in G1-arrested starved cells.

Reviewer #1 (Significance (Required)):

Appears to be quite significant to researchers studying regulation of bacterial cell cycle and translation. Since DnaA is conserved across bacteria, and this mechanism works in E. coli, it appears that the findings will likely be important in many bacterial systems.

Referee Cross-commenting

All the reviewer comments I read seem reasonable. Specifically, I found the point about E. coli 30S ribosomes is very important that the authors address. This could be done in writing, but should be better listed as a caveat to those experiments.

As suggested by the reviewers, we have partially rephrased some parts of the text describing the toeprint results. Moreover, we have inserted in the main text and in Fig. 1 legend explicit references to the use of purified E. coli 30S subunits and tRNA-fMet. We believe these changes will address the reviewers’ concerns.

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

**Summary:** The Jonas lab provide good evidence that they have found a new mechanism to regulate the amount of the DnaA protein by a starvation signal. The DnaA protein is the key chromosome replication initiator probably for most bacteria and as such DnaA is the target of many regulatory inputs. The authors created an accurate reporter system that allows them to dissect the 5' mRNA translated and untranslated sequences of dnaA and they have convincingly demonstrated that the N-terminal DnaA peptide sequence and not the RNA mediate the response to starvation by glucose exhaustion. This is potentially a model example for global translational responses in bacteria.

**Major comments:**

The main conclusion, i.e. that the DnaA leader peptide "Nt" mediates this response is convincing. However, there were 2 major problems that should be easily addressed. These do not subtract from the main conclusion.

Problem 1

E. coli 30S subunits were used in the "Toeprint" assay of Fig. 1. Obviously Caulobacter 30S Ribosome subunits should have been used, or a justification should be given. One remedy would be to make this supplementary information.

We thank Reviewer 2 for this comment. We agree that it would be better to use Caulobacter 30S ribosome subunits in our toeprint experiments. However, because toeprint assays with E. coli 30S ribosome were already established in our lab (i.e. the Wagner lab, where the assays were performed) and because works by other groups have shown that E. coli 30S subunits can be used to study the translation of mRNAs from other bacteria, we decided to use this experimental set up. Based on our results, we also had no reason to doubt the suitability of the E. coli 30S subunits. The toeprint showed that translation starts at the in silico predicted translation start site, which was further confirmed by our in vivo mutagenesis experiments. For these reasons, we are confident that the toeprint assays indicate the true translational start site. However, we acknowledge that we could have been more explicit about the use of the purified E. coli 30S subunits and tRNA-fMet in toeprinting assay. To increase clarity and transparency, in this revised version of the manuscript, some parts of the main text were rephrased and references to the use of E. coli 30S and tRNA-fMet were introduced (including Fig. 1 legend). We hope that these changes will address the reviewer’s concerns.

Problem 2

The results in Fig. 6B could be due to the Nt simply making the hybrid protein more unstable in E. coli. This is the main impression given by the drop in signal. In this case, the conclusion would be wrong, and Nt is not transferring a starvation translation block from C. crescentus to E. coli. Nt is just making the protein unstable. These results should be treated as preliminary pending protein stability measurements. However, this defect does not subtract from the other main points and without the Fig. 6 E. coli experiments they still make a complete and interesting story. One remedy would be to make this also supplementary information.

It is indeed striking that a drop of normalised fluorescence is observed for the 5’UTRdnaA-Nt construct in E. coli but not in Caulobacter. In order to address if this behavior can be explained by reduced protein stability, we have performed a translation shut-off assay using the 5’UTRdnaA-Nt E. coli reporter construct. The results of this experiment (shown in Supplementary Fig. 9A and described in line 327-329) show that the normalised fluorescence remains stable over 10 hours after chloramphenicol addition to the culture, ruling out that the presence of Nt significantly affects eGFP protein stability in E. coli. Importantly, this experiment also showed that in contrast to the chloramphenicol treated culture, in which the OD600 decreased after reaching stationary phase, the OD600 of the non-treated cultures slightly increased between 2 and 10 hours (Supplementary Fig. 9A). Because this increase was not observed in carbon starved Caulobacter cultures, we consider the different growth dynamics between E. coli and Caulobacter to be the most likely explanation for differences in eGFP accumulation at later time points during the experiment.

To further strengthen our E. coli data, we have analysed additional relevant Nt mutants that we identified as most critical mutants in our Caulobacter experiments presented in Fig. 5, namely dfsNt, mutD1, mutD2, ΔAAI and AAI>DDK. Determination of Δt and Δf values for the E. coli strains carrying these different Nt constructs showed similar results as for the corresponding constructs in Caulobacter. Collectively, these new data further support the notion that Nt operates in E. coli through a conserved inhibitory mechanism of translation. These data are now included in a reorganized new version of Fig. 6 (panels A, B) as well as in Supplementary Fig. 9.

**Minor comments:**

There are also 6 minor issues that are easily addressed, most by small changes to the text, and these should improve this otherwise fine manuscript.

Issue 1

Line 88 Fig. 1A shows DnaA degradation upon entering stationary phase from a low glucose media and not a simple starvation response to one component like glucose. Did the authors consider trying simple washout experiments, i.e. resuspend the cells in glucose-free media? This would have the advantage of suddenly exposing the cells to starvation and thereby studying the sudden response rather than the slower lingering response which would be due to many factors and not just glucose removal.

In a previous work from our lab (Leslie et al 2015), we have conclusively shown that the downregulation of DnaA synthesis depends primarily on the nutrient content of the growth medium.

Besides being in continuity with our previous work, we think that the starvation protocol that we used in the present study, and that was also used by the Sean Crosson lab (Boutte et al. 2012), might better reproduce what happens in the natural environment when nutrient levels gradually decrease until becoming limiting for bacterial growth.

Issue 2

Reference 16 should be cited are the first publication to show that glucose and other starvations induce DnaA degradation in Caulobacter.

We have added Reference 16 to the first sentence of the results section, in which we state that DnaA levels decrease when cells are shifted from a glucose-supplemented minimal medium to a glucose-limiting medium.

Issue 3

Fig. 1D shows that the TOEprint is not changed by adding the ribosome, very surprising considering its size and SD docking & alignment. 2 Minor bands then appear when the tRNA-Met is further added. These are presumably the "toeprints". A control with just the added tRNA-Met would make this result much more significant.

In the existing literature, there is a common consensus to consider real toeprints (i.e., indicative of the presence of an assembled translation pre-initiation complex) as only those bands that appear faintly in the presence of the 30S ribosome subunit but that become clearly enhanced upon addition of the initiator tRNA-fMet. Some examples can be found in Hoekzema et al 2019, Romilly 2014, Romilly 2020. In cases when the translation start site is buried in a structural element, the intensity of the toeprint signal is further increased when the mRNA is rendered unfolded, as seen in our data.

tRNA-30S-independent bands always show up in toeprint experiments, but their intensities differ with the sequence of the mRNA and sometimes the choice of RT used for primer extension. Addition of initiator tRNA-fMet alone is commonly not done in toeprint experiments (see references mentioned above). Finally, we want to point out again (see also our answer on “Problem 1”) that the toeprint data are very much consistent with our in silico predictions and our in vivo mutagenesis data. Therefore, we are confident that the observed toeprint upstream of the AUG corresponds to the true ribosome binding site.

Issue 4

Why does the cell OD drop, e.g. in Fig. 2, is it cell death from starvation?

We don’t think that the slight reduction of OD600 observed in our experiments is due to cell death. Based on our knowledge, carbon starved cells remain viable up to 24 hours after the starvation onset. Instead, we have observed a cell volume reduction, which may at least partially explain the observed OD600 decrease.

Issue 5

Line 327 Discussion "This study reveals a new mechanism, by which some bacteria can regulate the synthesis of the replication initiator DnaA in response to nutrient availability by modulating the rate of translation." Rate of translation or rate of translation abortions (as implied in Fig. 6)?

The rate of translation is the result of multiple contributions such as initiation, elongation, abortion and termination. Our data indicate that Nt is a regulator of DnaA translation elongation responding specifically to the nutritional state of the cell. Translation abortion could be one of the possible outcomes (but not the only one) of ribosome stalling. For these reasons, in the new version of the manuscript, we added the word “elongation” at the end of the sentence mentioned by Reviewer 2 (line 354).

Issue 6

It seems that that for most experiments with the eGFP the translation and protein decay components of the signal could have been easily uncoupled by running a parallel +chloramphenicol control. For example, this would simplify the interpretation of Fig. 6 where Nt eGFP stabilities are an issue and it is important to establish that comparable protein stability with and without the Nt peptide.

To address the reviewer’s comment, we have now included a chloramphenicol control experiment (stability assay) performed with E. coli carrying the 5’UTRdnaA-Nt reporter construct (Supplementary Fig. 9A). Please, see the response above for more details. For the experiments with the Caulobacter 5’UTRdnaA-Nt reporter we show in Supplementary Fig. 7 that the Nt peptide has no destabilising effect on eGFP.

Reviewer #2 (Significance (Required)):

Caulobacter crescentus is a model bacterium that has provided many insights into bacterial physiology that are now exploited to understand many organisms. These present results may provide one such example. It is known that the first amino acids of translated peptides can influence increase or impede exit from the ribosome, so this is a potential translation-level regulatory point that might be used by many organisms. This manuscript gives a concrete and important example of such usage suggesting that it many be widespread. Therefore, this work should find a wide audience and it should stimulate research in many other systems.

My lab also studies Caulobacter crescentus and we studied the same dnaA gene and protein including starvation responses. We at present do not have projects on dnaA but we do study other regulators and regulatory mechanisms of chromosome replication in Caulobacter crescentus.

Author Response:

Reviewer #1:

This meta analysis addresses a double-edged sword in evolutionary biology. Group living may be beneficial for many reasons, but has costs in terms of increased rates of parasitism. Furthermore, if groups are highly related, parasites that are genetically able to infect on member of the group may be able to infect all of them, putting the entire group at risk. In the her presented meta analysis, many original studies working on questions related to parasitism, relatedness and group living are brought together in one unifying framework. The authors find that indeed, group living can facilitate the spread of infectious diseases. However, they also find that the negative effects of disease can be overcompensated by the benefits of being social. The authors stress that experimental studies are necessary to disentangle these effects. The study is of high standard and well-conducted. The take home message is clear and of general interest.

The study highlights that experimental work is important to understand the relationship between parasitism, relatedness and living in groups. However, I missed an important aspect here. Experiments tend to stretch factors (sometimes to extremes), which may go square to the biology of the species. In some cases, this results in non-social organisms to be pressed in a group-environment. For example, the monoculture effect as we know it in agriculture is highly artificial. Clonal lines of crop are planted in high density, promising high yield, if pathogens stay out. These plants do not have a history of evolving mechanisms to deal with the effect of high relatedness. In contrast animals living in social groups, may never experience setting with non-relatives. Social insects evolved to deal with parasites by expressing specific adaptations, such a grooming, hygiene and social structure in the colony. Many social insects may never experience conditions of low relatedness. Thus, I expect it makes a difference if you experimentally force a non-social organism to be social, or a social organism to be asocial. I would be happy if this factor could be included in the reasoning, and maybe even analyzed quantitatively. For example, I would expect that non-social species made artificially to grow in groups of relatives, suffer much more from parasites than typical social animals with the same degree of relatedness.

This is an important point. One of the main motivations for conducting this study was to test if species that typically live with kin have evolved adaptations to minimise any increase in susceptibility to pathogens brought about by living in groups with relatives. We therefore collected data on whether species are: a) typically social or non-social, and b) average levels of relatedness between individuals in groups under natural conditions (see Methods section ‘Data on species characteristics’).

a) Testing differences between social and non-social species. All species included in our dataset had some part of their life-cycle where they were social (note we specifically excluded any studies on non-natural systems such as crops and domesticated species). This meant that only comparisons between species that are obligately social versus species that are social during specific life stages could made. This is problematic as assumptions need to be made about the strength of selection during different life cycle phases. For example, mortality caused by pathogens maybe particular high during the social juvenile phases of otherwise non-social species, resulting in selection for adaptions to reduce pathogen spread being similar to species that are obligately social. An additional problem was that experimental studies (a key factor highlighted by our analyses) of species that are non-social apart from specific life-cycle phases were rare (n=1, Rana latastei) precluding any meaningful comparisons.

We have now added the following sentences to the methods to clarify this point:

“We also collected data on whether species always lived in social groups (‘obligately social’) or whether species were only social during specific life stages (‘periodically social’). However, it was not possible to analyse this data as experimental manipulations of pathogens, a key factor influencing the relationship between relatedness and mortality and pathogen abundances, were only performed for one periodically social species (Rana latastei)” (Lines 425-430).

b) Testing differences between species that typically live with kin and non-kin. The third aim of the paper was to test if species that typically live with kin have evolved to deal with pathogens as the referee suggests. We found that species that live with kin, such as social insects, have similar rates of mortality and pathogen abundances to species that live with non-kin (Figure 3). However, species that typically live with kin had lower rates of mortality in groups with higher relatedness when pathogens were absent compared to species that typically live with non-kin. This suggests that pathogens represent an omnipresent threat to all species, but that adaptations have evolved to reap the benefits of living with relatives in social species.

In summary, as suggested by the referee we analysed whether “species made artificially to grow in groups of relatives, suffer much more from parasites than typical social animals with the same degree of relatedness” as much as was possible given the limitations of the published data. We have edited parts of the manuscript to emphasise that this was a key aim of the paper (Lines 66-74; 92-94; 136-153).

The term (and concept) "monoculture" is typically used to describe clonal populations, predominantly in agricultural settings. I understand that the authors like to expand this term (as have others done before) to include social animals. However, for most people this would be a change in terminology and may cause misunderstandings. I would prefer if you could stick with the mainstream terminology and avoid pressing this concept into a new costume.

We included the term “monoculture effect” to facilitate links to existing literature, both in the fields of agriculture and evolutionary biology (e.g. Ekroth at al 2019). While we think that making the reader aware of relevant work in other fields is valuable, we understand its prominence could give the impression that we included agricultural studies. Therefore, we have removed it from the abstract, but have chosen to keep one reference to the monoculture effect in the introduction.

Reviewer #2:

This study uses an unusually broad comparative data set to disentangle the positive (relatedness) and negative (pathogen pressure) effects of living in groups. The authors largely succeed in this task even though the data do not allow answers to all outstanding issues. Not unexpectedly, experimental manipulation studies appear to be most informative. The results are broadly consistent with expectations based on kin-selection theory and clarify the effects of a number of important covariables. The study is thoroughly executed and innovative in its approach. I expect this study to be interesting for a broad readership and this method of searching literature data to have considerable impact. Some suggestions strengthening this paper are below:

• I think it would be helpful for readers to have the Discussion start with a few lines on what your study achieved in language that is complementary to the abstract, perhaps followed by a brief explanation of which angles/ambiguities/challenges you will be taking up in the paragraphs to follow.

We have now edited the beginning of the discussion in accordance with this suggestion. It reads:

“Our analyses show that pathogens can increase rates of mortality in groups of relatives. The detrimental effects of pathogens were, however, counteracted by high relatedness reducing mortality when pathogens were rare, particularly in species that live in kin groups. Such contrasting effects of relatedness meant that experimental manipulations were crucial for detecting the costs and benefits of living with relatives when the presence of pathogens varied. Additionally, high relatedness resulted in more even abundances of pathogens across groups, but more variable rates of mortality, highlighting the importance of population genetic structure in explaining the epidemiology of diseases. We discuss these findings in relation to the environments favouring the evolution of different social systems, the mechanisms that have evolved to prevent disease spread in social groups, and the types of study system where more experimental data are required” (Lines 171-181).

• The rationale of this study is (often implicitly) that tendencies to live with relatives or not is a continuous variable. This surprised me because the senior author has written influential papers showing that family groups are different from non-family groups. In some contexts of this study it seems crucial to make that distinction. For example, a number of data points come from studies of social insects (bumblebees, honeybees, ants). Here, living with non-relatives is not an option but a given. It is well documented that these caste-differentiated colonies originated from ancestors that had exclusively full-sib colonies, so maximal relatedness was ancestral and became only diluted secondarily in some lineages. Would it be possible to check statistically whether the social insect data points always showed the same pattern as the other data points? That would test whether it matters that low relatedness is either derived or ancestral (as I think we implicitly assume to be the case in all other organisms).

The primary studies included in our analyses were conducted on a diverse set of species where relatedness was often reported and measured on a continuous scale (range 0 to 1). Our rationale and statistical treatment of the data (the effect size of Pearson’s correlation coefficient captures continuous variation in relatedness) reflect the measures reported in the primary studies. This does not mean, however, that we believe groups evolve from along a continuum of within-group relatedness.

As the referee points out there are two distinct routes to group formation that set the limits to relatedness within groups. In species, where offspring do not disperse from their natal patch (‘family’ groups) the opportunity for interacting with relatives is high, whereas in species where groups form after individuals disperse from natal patches (‘non-family’ groups) relatedness is typically low. Some variation in within-group relatedness subsequently arises within these two categories because of a number of modifying factors (breeder turnover, number of males and females founding groups, ‘budding’ dispersal and so on). However, the potential for kin selection to favour adaptations, including those that limit pathogen spread, remains fundamentally different between family and non-family groups. We tried to capture such differences by classifying species as typically living with kin and non-kin using life- history information (dispersal patterns, mating systems) and direct estimates of relatedness.

We used the terms kin and non-kin rather than family and non-family because across such a diverse set of study species, with variable types of information (e.g. some species only had molecular genetic estimates of relatedness others had only life-history information), it was not possible to ascertain exactly how groups form for each species. Nevertheless, our analyses are aimed at addressing if species that typically live with kin, such as the social insects, have more effective mechanisms for reducing the impact of pathogens amongst relatives than species that live with non-kin.

The referee makes an additional valuable point that for social insects ancestral levels of relatedness in groups are known to be high, with lower levels of relatedness being derived. Examining whether species with low versus high contemporary estimates of relatedness may therefore shed light on the importance of current versus past evolutionary responses to pathogens.

Unfortunately, the sample sizes are just too limited to conduct any meaningful analyses. Only one species of social insect in our dataset was classified as living with non-kin (r <0.25). We also examined finer scale predictors of relatedness applicable to social insects (queen mating frequency: monogamous (r = 0.5) versus polyandrous (r > 0.25 & <0.5)). Sample sizes for crucial comparisons were again too small for formal analysis (Number of monogamous species with experimental data: pathogens present = 3, Pathogens absent = 3. Number of polyandrous species with experimental data: pathogens present = 2, Pathogens absent = 1).

We have extended the discussion highlighting that more work on species with ancestral and derived levels of high and low levels of relatedness will aid our understanding of the evolutionary history of adaptations to minimise pathogen spread in groups (Lines 248-250). We have also checked and edited the manuscript to remove any implication that groups originate from a continuum of relatedness.

• I wondered whether you could (interpretationally, i.e. in the discussion) do more with comparative data on pathogen pressure in the wild. The 1987 Hamilton chapter that you cite has lots of interesting natural history observations, which are now often supported by better data. I think he speculates about how altruistic soldiers evolved in aphids and thrips and connects their sociality with living in their own food (galls), which should mean low parasite pressure. The same is true for the lower termites. Would your results allow you to conjecture that all independent lineages that evolved differentiated castes (only possible in families with full siblings; or clones as in aphids) likely had to do that in disease free habitats?

This is an interesting point and an area where further research would be very valuable. It fits in nicely with our current discussion of how the evolution of groups with high relatedness maybe more likely to occur in environments where pathogens are rare. This was rather vertebrate focused before and so we are grateful for the referee’s suggestion, which has broadened this point. The section now reads:

“Parallel arguments have been made for social insects. Species with sterile worker castes, that only evolved in groups with high levels of relatedness, are thought to have arisen in environments protected from pathogens (Hamilton 1987). For example, sterile soldier castes have evolved at least six independent times in clonal groups of aphids, and the majority of these cases form galls that provide protection against pathogens (Hamilton, 1987; Stern and Foster, 1996). Escape from pathogens may therefore be a general feature governing the evolutionary origin, as well as the current ecological niches, of species living in highly related groups” (Lines 190-197).

• I think some effort should be made to make Figures 2,3 and 4 easier to interpret. The ultra-brief acronyms along the y-axis take a while to digest and to realize the nestedness of the analyses. Could you give one piece of information on the left axis (spelled out like 'experimental data' and 'observational data' and the other piece on the right axis (spelled out as 'pathogens absent' and pathogens present'? It would also be helpful if the reader could fully understand the figures without first having to go through the entire method section, so I recommend you extend the legend to explain: 1. What Zr stands for. 2. What the directionality is (so the cryptic line just below Zr can become a proper sentence in the legend), and 3. The rationale of the multifactorial analyses with four or eight combinations (as you describe in the methods; I believe Figure 4 is an example of eight, but this remains rather hazy).

Many thanks for these suggestions. We have now revised the axis labels and figure legends to improve interpretability.

Reviewer #3 (Public Review): 

Brochet et al. find that four species of the Lactobacillus Firm-5 lineage, one of the core bacterial lineages of the honey bee microbiome, are able to coexist because they utilize different pollen-derived flavonoids and sugars. They demonstrated this both in vivo, in gnotobiotic bees, and in vitro with laboratory co-cultures. Simple yet robust experiments involving diet or growth media with just simple sugars resulted in loss of diversity, whereas diets and media supplemented with pollen allowed the persistence of all four Firm-5 species over multiple serial passages. The authors then proceeded to examine the genes that were differentially expressed in response to different nutrient growth conditions, as well as the presence of metabolites to infer utilization of pollen-derived nutrients. The results paint a convincing picture of niche partitioning via differentiation in both encoded metabolic capabilities and in the differential expression of commonly encoded genes among co-resident bacterial species. 

Overall, the paper is strong and the arguments and conclusions put forth are well supported by the data. I only have a few suggestions: 

1) The study focuses on one strain each of the 4 Firm-5 species; however, there is diversity within each species. This is only briefly mentioned in the paper at the very end, and I think the authors should address this a bit more directly. In particular, they have previously generated a large amount of genomic data from some of these other strains, so it is likely possible to infer or speculate, based on this data, whether they expect different strains within each species to utilize similar nutrients. Also, I'm wondering if the authors can comment on how their findings could extend to the related bumble bee gut microbiome. Such a discussion would help enhance the applicability and importance of this study. 

2) It is interesting that different species ended up dominating in the in vivo vs. in vitro simple sugar-based communities. What do the authors think may be behind this difference? 

3) Since the observed coexistence of these gut microbes is largely due to nutritional niche partitioning, it would be helpful if the authors can comment on the natural variation of key pollen derived metabolites, and if/how we could expect ecological variation in the bee microbiome due to plant pollen availability based on biogeography and seasonality. 

4) The supplementary information is nicely documented and accessible, but I think it would be even more useful if genome-wide data for the RNA-seq results, not just for select genes, are made available. Furthermore, I suggest including descriptive titles and labels within the supplementary Excel files, as there are many separate sheets and it is not always clear what each one shows.

His definition of a Memex is simply a mechanized (or what we would now call digitized) commonplace book, which has a long history in the literature of knowledge management.

I'll note here that he's somehow still stuck on the mechanical engineering idea of mechanized. Despite the fact that he was the advisor to Claude Shannon, father of the digital revolution, he is still thinking in terms of mechanical pipes, levers, and fluids. He literally had Shannon building a computer out of pipes and fluid while he was a student at MIT.

the idea of an "[[associative trail]]" here brings to mind both the ars memorativa and the method of loci as well as--even more specifically--the idea of songlines.

Bush's version is the same thing simply renamed.

<small><cite class='h-cite ht'>↬ <span class='p-author h-card'>Jeremy Dean</span> in Via: ‘What I Really Want Is Someone Rolling Around in the Text’ - The New York Times (<time class='dt-published'>06/09/2021 14:50:00</time>)</cite></small>

Strange that we also want to do more than the material is capable of, but we still want the sense of material interaction. Why?

I thought he was going to get to benefits for health/medicine/disabilities but alas...it is all reviewed as a benefit for masculine and able-bodied intellectualism

I'd disagree with this. Old and forgotten media have had great use; if they were not extended or needed that doesn't cancel out their former use (thinking of the work of Lisa Gitelman here)

What a weird metaphor! A square-rigged ship—I think—was a type of vessel that had been improved over several hundreds of years and was commonly used in the nineteenth century. Bush seems to be using it to represent something that was formerly considered a hallmark of civilization (a tool of conquest, nationalism, exploration), but was outdated in a 20thC technological environment

This astonishment at the "findings and conclusions of thousands of other workers" seems connected to our "information overload" not only in the amount of information available, but in how we relate to it. There is so much out there; is this useful in de-centering individual intellectual authority, or harmful in making all discourse relative?

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

The authors wish to thank all three Reviewers for their appreciative comments regarding our ECPT and for very useful suggestions. Response to all points raised are presented below, we hope that the responses and new experiments proposed in the following pages will fully address remaining concerns.

Reviewer’s comments to the BiorXiv paper by Chesnais et al, 2021

“High content Image Analysis to study phenotypic heterogeneity in endothelial cell monolayers”

https://www.biorxiv.org/content/10.1101/2020.11.17.362277v3

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

The authors highlight the importance of endothelial heterogeneity using endothelial cells from different tissues. They examined aortic and pulmonary endothelium as well as HUVECs. They cultured the cells in identical conditions and also stimulated them with a physiological concentration of vascular endothelial growth factor as well high concentrations as would be found in cancers. They developed a profiling tool that allowed analysis of individual endothelial cells within a monolayer and quantification of inter-endothelial junctions, Notch activation, proliferation and other features.

**Major comments**

1. It would be useful to apply this technology one step beyond two-dimensional culture, to use vessels opened up longitudinally so that one can see the monolayer of endothelial cells and assess whether it is relevant in primary material in situ. I think this would be a major utility of the whole approach.

R: We thank this reviewer for the suggestion. In vivo analysis is not in the objectives of the paper. However, we propose to perform “En face” staining of murine blood vessels following the protocol in the reference below. We will perform stainings for murine CDH5 (VE-Cadherin), NOTCH1 intracellular domain, HES1 and DNA which parallel that used in vitro on human EC. We will then apply our revised ECPT workflow and present data in a new Figure.

En Face Preparation of Mouse Blood Vessels. Ko KA, Fujiwara K, Krishnan S, Abe JI. J Vis Exp. 2017 May 19;(123):55460. doi: 10.3791/55460. PMID: 28570508

2. There are some very nice images here but disappointed not see a field that could show staining and markers for several of the target proteins and thus show the heterogeneity and randomness or organisation of the endothelial cells.

R: We thank the reviewer for the appreciative comment. We propose to include representative microphotographs to illustrate the heterogeneity of different EC monolayers in the revised version of the manuscript. Furthermore, to further illustrate these aspects we will also include spatial correlation maps of cells and features measured with ECPT as explained below.

3.

• The Notch signalling is an important aspect of this work, particularly evidence of lateral inhibition would have been of value. For example, one might expect cells adjacent to each other to have alternating high and low NICD. R: We thank the reviewers for the suggestion. To address this, we are currently developing a new module to perform spatial autocorrelation analysis based on cell maps built using ECPT. In particular we have developed a new module to export cell maps as spatial objects in R which can be then analysed using the adespatial R package and provide correlation metrics such as the Moran’s autocorrelation index (see reference below). The index works with continuous data, removing the need to establish arbitrary thresholds and thus provides formal metrics to demonstrate heterogeneity in EC monolayers. We have derived this index as an example of such analysis for synthetic data and for one ECPT cell map as shown below.

Figure 1: Moran’s spatial autocorrelation analysis using R and adespatial package. Moran’s index has values between –1 and 1. If adjacent cells had a consistent tendency to acquire alternate high and low NICD values, the corresponding bivariate Moran’s index would have an I+ value ~ 0 and an I- value approaching -1. In the example cell map both I+ and I- have relatively small absolute values and large p values which suggest a random cell distribution. The analysis was performed on synthetic data and ECPT derived data (HUVEC at baseline).

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Community ecology in the age of multivariate multiscale spatial analysis

S Dray et al, Ecological Monographs, 2012. doi:10.1890/11-1183.1

• NICD staining alone does score the extent of the signalling because of many factors that can influence the transport of the cleaved NICD. Really a marker of Notch signalling downstream e.g. HES or HEY family, DLL4 fis needed to give more information about this critical aspect. R): We thank the reviewer for the suggestion. We are currently performing HES1 staining (with no Pha staining) along with a new NICD mAb (see below). Preliminary qualitative data (Fig 2) show that HES1 staining also reveals single cell heterogeneity of NOTCH activation in the same monolayer. We will include ECPT analysis of HES1 and correlation with NICD and other features as suggested. We will reformat the current Fig 5 to include HES1 analysis and improved metrics of NOTCH pathway activation including spatial analysis (point 3 above).

Figure 2: HES1 immunostaining on HUVEC (Image enhanced for visualisations). Cell nuclei labelled as 1, 2 and 3 have raw mean grey values of HES1 signal equal to 2271, 11210 and 48261 (C2/C1 and C3/C2 >4 folds).

I really do not think that in Figure 5 it is justified to have a red line drawn through the cloud of points. The correlation coefficient is so low that this is meaningless. The failure to distinguish a P value from biological relevant is worrying. Much better comparison would have been between NICD staining and a downstream gene regulated by notch.

R: We appreciate the reviewer’s concerns and are presenting our analyses of NOTCH activation using new immunostainings (HES1) and robust metrics for NOTCH activation as discussed above. We will therefore remove the mentioned corelation plots from the reviewed version of the manuscript.

It is important to know that the antibodies used for staining have be validated by the investigators. They would need to show a single band on Western blots or be able to block staining on immunohistochemistry. We all know the manufacturers can be unreliable and use high concentrations of proteins for Western blots. These should be added as a supplementary figure.

R: While the paper was under revision the AB8925 (NICD, Abcam) has been retracted from the market. To address this major concern, we have decided to acquire a different antibody targeting the intracellular portion of NOTCH receptor and validated its specificity by western blot. Fig 3 below, show western blots demonstrating a clean band at ~98 Kd as expected for cleaved NOTCH1 intracellular domain (NICD).

We are currently repeating the whole experiment presented in the current version of the manuscript and the ECPT analysis using the new antibody and including HES1 one of the canonical NOTCH target genes as also suggested by this and other Reviewers. We will provide WB analysis of all antibodies used in the paper in a supplementary figure in the revised manuscript.

Figure 1, WB analysis (NOTCH1 intracellular domain, AB52627, Abcam). of HUVEC (lanes 2,3), HAoEC (lanes 4,5) and HPMEC (lanes 6,7)

Reviewer #1 (Significance (Required)):

This represents a valuable and thorough methodology likely to be highly useful to many groups and show new insights into endothelial biology.

Wide audience, cancer, cardiology, vascular disease-covid.

My expertise >100 papers on angiogenis in cancer, basic mechanism, therapy models, bioinformatics IHC, patients, clinical trial. H score 190 Google Scholar

R: We thank Reviewer One for their very appreciative comments and we hope that the proposed revisions will fully address remaining concerns.

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

The manuscript by Chesnais et al reports development of workflow for analysis of cultured endothelial cells , which they call Endothelial Cell Profiling tool (ECPT). Using ECPT they analyse several parameters in three different endothelial cell types (HuAEC, HUVEC and HPMEC), such as cell morphology, activation of cytoskeleton, VE-cadherin junctions, cell proliferation and Notch activation, under steady conditions and upon treatment with VEGF. The analysis allows to observe some predicted changes, such as increase in cell cycle and junctional activation in cells treated with VEGF-A, and such changes are highly heterogeneous. Overall, this is a potentially useful albeit not revolutionary tool for batch analysis of cultured endothelial cell phenotypes.

I have the following comments:

1. To make their case the authors should provide a comparison with other currently used approaches for EC phenotypic analysis in vitro - what is the advantage of using ECPT? The authors repeatedly use the term "single-cell level of analysis ", but this is in fact the case of any IF based analysis of cultured cells.

R: We thank the reviewer for the suggestions. Indeed, several tools for imaging based single cell phenotyping are available. However, ECPT represents an improvement under several aspects. First, it allows improved segmentation of difficult-to-segment and heterogeneous cells; second, ECPT allows multi-parametric analysis on large image datasets in a semi-automated and structured way facilitating downstream data analysis; third, ECPT is open source.

Furthermore, ECPT is a very flexible workflow including tools which facilitate and automate several tasks such as systematic images re-labelling and grouping. We will now draft a table including a complete list of features and improvements in comparison to other available tools and include it in revised manuscript in appendix1 and include analyses which are not implemented in any currently available software such as spatial autocorrelation.

I strongly recommend to stain HPMECs for PROX1, these cells are frequently 100% lymphatic endothelial cells. In this case the authors compare different lineages and not blood endothelial cells from different locations.

R: We thank the reviewer for the suggestion. We will address this with a new characterisation as supplementary figure in the revised manuscript. We are currently performing a qRT-PCR screening of several EC marker including arterious, venous and lymphatic markers (e.g., CXCR4, Tie2, CDH5, PROX1, LYVE1 as well as baseline NOTCH1 and Dll4 and downstream genes such as HES1 and HEY2.

Please provide evidence for specificity of NICD antibody.

R: We thank this reviewer for the suggestion. Please see response to Reviewer one point 5.

Figure 1: HPMEC picture appears out of focus

R: We thank this reviewer for noticing, we will now include a clearer picture in revised version of the manuscript.

Figure 3 A - it is not entirely clear what is the difference between activated and stressed phenotype, they look quite similar.

R: We will clarify the definitions of cell activation in revised version of the manuscript and present this analysis as supplementary material to demonstrate the flexibility of our ECPT rather than in main figures. We have removed Pha staining from the new experiments we are performing to allow HES1 staining and address NOTCH signalling in more details. The assessment of Pha and stress fibres in previous experiments will be moved to supplementary material. The classification is based on PhA staining using CPA classifier which was trained to distinguish among the two by the presence of stress fibres. The general rule to place cells in the stressed category during training of the CPA model was the observation of stress fibres crossing the nucleus while cells with peripheral bundles of actin were placed in the activated category.

Figure 5 - what is the difference in NICD localization between "high" and "On" conditions?

R:

Since it has been noted by this and other reviewers that this classification might be difficult to interpret and in fact, the established thresholds are somehow arbitrary, we will completely revise the way we present analysis of NOTCH activation data including downstream analysis and more formal metrics of spatial correlation and extent of activation eliminating the need to impose thresholds (also see response to Reviewer one point 3).

Since the authors make a correlation between Notch activity and junctional stabilization, it would be important to confirm this by other means, such as analysis of Notch target genes.

R: We thank this reviewer for the comment which resonate with this and other Reviewers’ comments. We will include HES1 analysis in the revised manuscript, please see Response to point 6 and reviewer’s one point 3 above.

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**Technical and minor**

1. Methods mentions HDMECs (human dermal microvascular endothelial cells) but the authors discuss HPMEC throughout the text 2. Please add scale bars on all microscopy pictures. 3. Please provide the information on what isoform of VEGF-A was used for stimulation and the rationale for selecting the concentration.

R: We thank this reviewer for flagging these imprecisions and we will fix them in revised version of the manuscript.

Reviewer #2 (Significance (Required)):

The authors provide a workflow for the phenotypic analysis of cultured cells. Such tool is potentially useful, although the examples the authors show do not reveal striking examples of why such analysis is better in comparison to existing approaches. My guess is that the analysis may be faster and less tedious, once the training sets are generated, but this is not specified. My speciality is endothelial cells biology.

R: We thank this reviewer for their very useful and appreciative comments. As mentioned above we will expand appendix 1 to fully explain potential and utility of our ECPT and review the main text to clearly highlight main advantages.** We hope that our plan for revision will fully address remaining concerns.

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

**SUMMARY:**

The manuscript by Chesnais et al. presents a novel endothelial cell (EC) profiling tool (ECPT) which provides spatial and phenotypic information from individual ECs, and was tested with a variety of specialized EC subtypes (arterial, venous, microvascular). They present a high throughput immunostaining and imaging-based platform using culture of human ECs on 96-well plates and capture of fixed, stained samples on a Perkin Elmer Operetta CLS system. The authors report the use of this ECPT tool to investigate EC phenotypes from human umbilical vein ECs (HUVEC), human aortic ECs (HAoEC) and human pulmonary microvascular (HPMEC) in relation to 50 ng/mL VEGF stimulation for 48 hours, and the general parameters of proliferation, Notch activation and stress fiber rearrangement (F-actin), and present this as a prospective platform to examine differences in EC phenotypes and responses at a more individualized level.

**MAJOR COMMENTS:**

1. Fundamentally, the advantage of single cell technologies is the ability to segregate populations to make novel observations. One area that would be of interest to explore in this manuscript using this ECPT platform would be reporting the results from single cell analysis that is then subsequently pooled within a sub-population, rather than sub-stratifying populations to reflect the multiple phenotypes that may be present within a single "confluent" well. With analysis of EC heterogeneity, it would be of interest to differentiate heterogeneity within EC subtypes at the culture/treatment conditions presented, and heterogeneity between EC subtypes.

R: We thank this reviewer for the suggestions, we believe that the new approach to evaluate heterogeneity through spatial autocorrelation can provide a much better and clearer picture of this aspect (see responses to Reviewers One point 3 and Two points 6 and 7. Furthermore, we are currently restructuring the ECPT data structure to a more intuitive layout (list of lists rather than a single huge data frame) without affecting downstream data presentation. We will also update our Shiny App to enable the user to perform analyses on data subsets of interest without any R coding, we will present examples and walkthrough of this approach in appendix.

2.

The term "stable IEJ" is used and refers to 48h after seeding 40,000 cells on a 96-well plate, but it is unclear how the authors defined or demonstrated a "stable" junction. In previous reports, longer-term culturing of EC monolayers well beyond the point of confluence has been shown to result in junctional complex rearrangement (Andriopoulou P et al. Arterioscler Thromb Vasc Biol. 1999; reviewed in Bazzoni G & Dejana E. Physiol Rev. 2004). To this point, the fact that the different EC subtypes investigated had different percentages of "quiescent cells" suggests that the monolayers were not completely quiescent. The statement that the IEJ classification is "an immediate index of EC activation in contrast to quiescence" should be further supported by references or data. The definition of quiescent EC as simply non-proliferating, non-migrating is somewhat reductionist, and oversimplifies EC states. The authors state that HAoEC and HUVEC "...appeared more active...", but it is unclear what "active" means, and whether this may simply reflect that these cells had not yet reached confluence or quiescence in the 48h total culture time. As well, it is unclear how "migratory phenotypes" could occur in confluent monolayers. It would be helpful to see the data for these observations. If leaving ECs longer in culture, are the authors able to achieve a higher percentage of quiescent cells?

R: We thank this reviewer for the very insightful comments and for suggesting the references. Indeed, we considered these aspects carefully. Regarding cell culture density and confluency, we previously tested seeding densities of 30000-60000 cell/well of 96 well plates (0.32 cm2, ~95000-190000cells/cm2) and we selected 40000 as the maximum seeding density allowing adhesion of >99% of cells. For HUVEC, a seeding density of 40000 cells/well (125000 cells/cm2) produced a high-density culture immediately after seeding (close to what reported for long-confluent cultures in Andriopoulou P et al, ATVB 1999, 140000 cells/cm2). We allowed further 48h culture aiming to achieve junctional “stabilisation” and “maximal” cell density. For consistency, we also seeded 40000 HAoEC and HPMEC per well in all our experiments, however both cell types are significantly larger than HUVEC (Fig 4). For all cells cultures we used EGM2 medium which has few differences with that reported in Andriopoulou P et al, namely, absence of antibiotics and antimycotics and use of defined cocktail of recombinant growth factors instead of Endothelial Cells Growth Supplement. In the past we compared HUVEC cultured in EGM2 and supplemented M199 medium and in our experience EGM2 promotes higher proliferation rates in sub-confluent cultures but similar morphology upon confluency. Is notable that several other factors (including flow, matrix, perivascular cells) are absent in our culture conditions and therefore the homeostatic balance found in vivo might not be fully achievable under our experimental conditions. However, we argue that the described culture conditions should be sufficient to reach a bona fide relatively quiescent EC phenotype in culture.

Save these considerations, we agree with this reviewer that providing examples of longer-term cultures would help substantiating our findings and further validate the ECPT approach. We will perform a supplementary experiment to evaluate this aspect by comparing 48h cultures with longer culture times (72h and 96h). Furthermore, we will expand the methods section with the details discussed above and in relation to the suggested references.

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Regarding the definition of “stable IEJ” and “active EC”, we used this terminology referring exclusively to our measures of IEJ stability (STB index) and Pha based cell classification where we used the terms of “quiescent”, “active” or “stressed”. Therefore, all statements mentioning more or less “stable IEJ” or “active” EC are relative to the specific context of our experiment (not in absolute terms).

Overall, we appreciate that the terminology we employed is a source confusion and might suggest inappropriate over-interpretation of our results. We will correct the text in the manuscript to avoid this confusion and to clarify that our observations are valid within the context of our in vitro conditions. In particular, we will present the data regarding junctions as proportions of different junction per cell, and we will rename cell “activation” categories based on PhA immunostaining using more neutral terms (e.g., No Fibres, Peripheral Bundles, Stress fibres). Finally, we will also attempt to generalise our observations to more physiologic context by performing immunostaining on “en face” preparation of murine blood vessels (cfr response to R1 point 1).

Fig 4: Cell area density distribution for HUVEC, HAoEC and HPMEC in baseline conditions.

Could the authors comment on the baseline NICD immunoreactivity in the nuclei in HAoEC and HPMEC compared to HUVEC? Is this a reflection of active NOTCH signaling? Or rather, is it possible contact-inhibition (and downregulation of NOTCH) may not have occurred? Demonstration of EC quiescence would help to ensure similar cell cycle states. The definition of "Notch-positive" and "Notch-negative" cells is a bit misleading, as NICD levels and localization are a better indication of canonical Notch activation, and not the presence or absence of Notch protein(s). Further, NICD activation is also dependent on the levels of Notch ligands, which was not addressed. Are the authors able to confirm "OFF", "Low", "High", and "ON" classifications based on NICD intensity and localization with downstream Notch gene activation at a single-cell level? Or correlation between NICD status and the phase of cell cycle or proliferation status?

R: We thank this reviewer for the comment. Overall, NICD either nuclear or cytoplasmic can give a measure of how much a cell is relaying canonical notch signalling in a small timescale (minutes, which is also the timescale affected by lateral inhibition, Sjoqvist M and Andersson ER, Dev Biol, 2019). By evaluating single cells in the context of their population in multiple fields of view and samples we can get an indication of how frequently a particular cell type tends to actively transduce canonical NOTCH (under confluent conditions). As this and other reviewers have pointed out NOTCH signal transduction mediated by NICD can be affected by several factors limiting the potential to infer actual activation of the pathway (i.e., downstream gene transcription. As suggested by this and other reviewers we are including measures of downstream gene activation, in particular we have included HES1 staining in our workflow, and we will include these data in a new analysis (also see response to R1 point 3). We will also provide new metrics of spatial autocorrelation to evaluate the tendency to lateral inhibition (R1 point 3) and correlation between parameters using continuous mesures and therefore we will remove the previous classification based on thresholds. Finally, we are performing a qRT-PCR screening to assess baseline levels of DLL4, NOTCH1 and JAG1 which we will present as supplementary material.

Do as I say, Not(ch) as I do: Lateral control of cell fate

Sjoqvist M and Andersson ER, Dev Biol, 2019

PMID: 28969930

The existing workflow/platform is adapted for images obtained from the Operetta CLS system (Perkin Elmer) and Harmony software (proprietary), which may not be available for broader users in the EC field. It would be helpful to include ImageJ macros for the bulk automatic import of TIFF, renaming and upscaling of resolution/bit quality to match the formats that are compatible with the software.

R: We thank this reviewer for the comment. We have now included an ImageJ macro (available in the GitHub repository) which in principle can import and elaborate images from any source. We didn’t include a specific option in our current user interface because the relabelling operates by parsing original filenames into fields which are then renamed according to user input and each HT platform adopt different regular expression to encode filename. Any user with a basic literacy in ImageJ macro scripting can achieve relabelling and elaboration of their own file given that their filenames use regular expressions which can be parsed. Also, it is relatively easy (again by modifying the macro) to include user defined pre-processing steps including image scaling. An example of parsing method for Operetta CLS filenames is provided in appendix 1.

Could the authors comment on the manpower (hours from start to finish for experiments, staining, imaging, analysis, etc.) and cost of the ECPT pipeline relative to emerging single cell technologies such as single cell-RNA sequencing.

Further, one major advantage of imaging technologies is the ability to assess live cell dynamics, which are particularly relevant in response to stimuli and agonists. Have the authors utilized the ECPT platform for these approaches, in particular, to assess the differential EC subtype dynamics in proliferative conditions?

R: In terms of manpower the workflow is not very demanding. Our current dataset is based on images extracted form 4 independent experiments (18 wells each). The process is sequential, therefore a single user trained in cell biology, automated microscopy and in the use of the different ECPT components (ImageJ, CP, CPA and R) could perform the experiment alone. The timing of each experiment will depend on circumstantial factors, however once the ECPT is trained for specific user’s requirements (which can require some trials and errors depending on user’s experience) the whole process from cell fixation and staining, through image acquisition (~2 h acquisition for each experiment on an Operetta CLS system), to dataset build-up can take less than one week. For example, elaborating the current image database (~6000 images for four fluorescence channels) which data are presented throughout, had the following raw elaboration times on a Mac Book Pro 2017 equipped with an intel i7 processor and 16 Gb of RAM:

- Image pre-processing and relabelling ~1h

- Generation of probability maps for VEC and NICD ~3h

- CP pipeline run (Cell segmentation, objects measurements and classification) ~16h

- Data import (R studio) ~20m

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We will measure these parameters more precisely in the new experimental run and present timings for each step in a new table in appendix 1.

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After main dataset is created R studio can perform most statistical analyses and data plotting almost instantly.

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We fully appreciate the value of employing ECPT in live imaging setups and we believe it is one of the most promising future applications. We didn’t address live microscopy experiments in the context of ECPT development and validation presented in the current manuscript therefore we cannot present example data or proof of concept. However, we can confidently comment that time lapse experiment would not endow further layers of complexity in terms of image analysis workflow. Therefore, given appropriate set of live markers (e.g., transgenic fluorescently tagged CDH5 for EC segmentation and junctions analysis) we believe that the current implementation of ECPT is already fully equipped to facilitate elaboration and analysis of imaging data derived from time lapse experiments.

The authors should discuss the ability to amend or revise of the ECPT platform to incorporate analysis of additional markers that may be obtained through imaging, and discuss greater implications and utility to specifically tailor the workflow for other researchers in vascular biology, or to other monolayer culture systems. Further, they should better highlight the novel observations obtained with the ECPT compared to traditional methodology.

R: We thank this reviewer for the comments. We will provide evidence of ECPT flexibility within this manuscript by including, during the time of this review process, a new analysis for downstream NOCTH signalling (HES1). We will move analysis of cell “activation” (i.e., stress fibres analysis) to supplementary information and include a more through discussion of how automated single cell classification could improve content, speed, reliability and robustness of quantification tasks which are currently exposed to long and tedious processing times and conscious/unconscious observer biases.

**MINOR COMMENTS:**

We thank this reviewer for the very thorough revision of the manuscript. It is truly invaluable to us to improve it. Below responses to specific technical points, we will fix all stylistic, formatting and typographical issues in revised version of the manuscript.

1. There are minor typographical, capitalization and grammatical errors throughout.

R1: Thanks, we will fix these in updated version of the manuscript.

Why was fibronectin used to coat plates, and what was rationale for using this ECM substrate versus gelatin (most commonly used in EC cultures) or type I collagen?

R2: We used fibronectin for immunostaining experiments similar to what reported in our previous work (Veschini et al, 2007, 2011, Wiseman et al, 2019) and also in Andriopoulou P et al,1999. In general, in our experience FN gives better cell adhesion in comparison to gelatin when culturing EC on glass or other substrates different from cell culture plastic. FN is the cell culture substrate recommended by Promocell therefore, we also used FN for cell expansion to avoid any phenotypic change which might have been caused by switch in cell culture substrate.

3. Based on the various box plots present throughout the figures, it appears that some parameters have a large range of values. Is it possible or helpful to set minimum and maximum exclusionary criteria? Further, in the way that these data are presented, it is difficult to appreciate the effects of a treatment such as 48h of VEGF, as the magnitude of STB Index difference, for example, appears small, and it is difficult to understand whether these significant differences are biologically relevant, as assessed.

R3: We agree that in absence of exclusion criteria it is difficult to infer biologic meaning out of subtle differences (e.g., the tiny difference in STB index between HAoEC in presence or absence of VEGF). In the current version of the manuscript, we attempted to be agnostic in regards whether some observed small but significant mean differences could endow biologic meaning and discussed larger variation as biologically meaningful, for example the differences in STB index among cell types. We argue that tiny differences in the distribution of some selected parameter across experimental conditions could reflect underlying mechanisms masked by biologic noise, therefore catching a glimpse of these variations via ECPT could inspire novel experiments to specifically address their full biologic significance.

To the interest of better understanding of the current manuscript we will re elaborate our data to provide more immediate metrics and highlight outstanding features.

Use of arrows and further description in Figure 1 would help the reader understand what specific features are different in the various EC subtypes. As well, the representative micrographs for HPMEC appear blurry compared to other panels (Fig. 1).

In Figure 2, the panels in A, B and C do not correspond horizontally, and it may be cleared to demonstrate "Segmentation & features extraction" overlays from the same representative micrographs shown in panel A. Labeling of the individual panels and software used for panel B would help the readership understand what is being quantified and how. The second panel in "C" appears blurry.

In Figure 3, labelling the color code for quiescent, activated and stressed categories on graphs and in legend would be helpful to easily identify populations.

R4-6: Thanks, we will fix these in updated version of the manuscript.

For Figure 4, line separators or more obvious grouping to distinguish discontinuous, linear and stabilized junction types in panel A. What proportion of the different EC subtypes contains discontinuous, linear and stabilized junctions at confluence/quiescence? Is there a correlation between discontinuous junctions and proliferating cells?

R7: We will perform new analyses to address correlation between proliferation and junctions and proliferation vs HES1. We will restructure data presentation on junctions to display different proportion of junctions per cell or per cell type rather than a unified value (STB index).

It would be useful to distinguish the effects of published mediators on junctional integrity in intact EC monolayers (i.e. histamine; VEGF) from those shown in this automated quantitation. It appears that 50 ng/mL of VEGF treatment for 48h only slightly increases STB index based on panel C.

R7c__: __We agree that increase of STB index in HAoEC and HPMEC upon VEGF treatment might not be highly biologically meaningful, save consideration in point 3 above. However, difference in HUVEC (+- VEGF) is visually appreciable in images (i.e., VEGF treated HUVEC seem to have more linear junctions) therefore we believe that the ~16 units difference in STB index is biologically meaningful. As discussed in point 7 above, we will restructure data presentation to better clarify these aspects.

Figure 5 panel B should provide legend in graphs/figures or figure legends to highlight the color-coding matching the OFF, Low, High and ON groups. Further, it is unclear the difference between "High" and "ON" groups. The authors state that "thresholds were selected empirically", however, it is unclear whether this was derived through utilization of known Notch activators or inhibitors, and how this relates to the threshold of Notch activity necessary to enhance proliferation or maintain quiescence. In Supplementary Figure 4 (which I believe is mislabelled as Supplementary Figure 5), shows only a weak positive correlation between nuclear NICD intensity and mean STB index. It would be of interest to see the plot from Supplementary Figure 5 for each of the EC subtypes, in the presence and absence of VEGF. As well, for Figure 5, on C and D panels, it would improve clarity to revise "Low" and "High" descriptors with "Low NICD activity" and "High NICD activity".

R8: As discussed above we will revise our analyses to remove NOTCH categories and instead show spatial autocorrelation analyses which work on continuous data.

In Supplementary Table 1, "Widt/length" should be "Width/length"

R9: Thanks, we will fix this in updated version of the manuscript.

For Supplementary Figure 3, it would be of use to show DNA distribution intensities from proliferating, non-confluent EC subtypes to demonstrate the validity of this methodology to identify cells in G0/G1, S and G2/M phases, as highlighted in panel A. Could the authors comment on the discrepancy between the percentage of cells identified as quiescent by ECPT and the percentage of cells in G0/G1? The comment that "VEGF induced a small detectable increase in proliferation rate in all EC" is curious, as a dose of 50 ng/mL of VEGF should be a relatively strong stimulator of proliferation/migration in ECs.

R10: We will perform ECPT analysis on sub-confluent or sparse cells to further validate our analysis. Qualitative data on preliminary images seems to confirm that the proliferation rate in sparse cells is very high (>70%). To perform the evaluation we followed and improved a previously published method (Roukos et al, Nat Prot, 2015)

Regarding the relation between cell in G0/G1 and assessment of “quiescent” phenotype (which nomenclature will be revised as discussed above), it is important to highlight that we reported data on stress fibres analysis (i.e., classification into “quiescent”, “activated” and “stressed” cells) only on the cells in G0/G1 (i.e., we excluded proliferating cells from this analysis as we assumed that all proliferating cells would be “not quiescent” and bias our estimation).

For Supplementary Figure 5, "Nuclear NOTCH intensity" on the Y-axis should read "Nuclear NICD intensity", as it does not appear that Notch was stained. It would also be of benefit to overlay the ranges for "OFF, Low, High and "ON" to appreciate ranges of activation. Is there any correlation between NICD nuclear intensity and proliferative index?

R11: We will present correlation between NICD or HES1 and proliferation in revised version of the manuscript.

Definitions should be provided for many terms. i.e. vascular endothelial-cadherin (VE-CAD; CDH5); HUVEC (human umbilical vein endothelial cell); HAoEC (human aortic endothelial cell); HDMEC (human dermal microvascular endothelial cell); NICD (NOTCH intracellular domain); VEGF (vascular endothelial growth factor); etc. at first appearance.

R12: We will add this information in revised version of the manuscript.

For EC subtypes purchased from commercial vendor, it would be of interest to understand how many unique donors these cells/data were derived from, and whether there are any differences in basic donor information such as age, sex, etc. Further, Promocell catalogs proliferative rate for each of their lot numbers, and it would be of interest how this compares to the values determined using the ECPT software analysis package.

R13: We will add this information in revised version of the manuscript.

1 In the "Cell culture" section of the methods, HDMEC from Promocell are listed, however, the manuscript and figures show data from HPMEC. Both EC subtypes are available from Promocell, however, HDMEC are from dermal origin.

1 Vascular endothelial-cadherin should be abbreviated "VE-CAD" or "CDH5" and not "VEC", as this is not a standard or gene notation, and will likely be confused with the more common abbreviations for venous or vascular EC. It seems as though "CDH5" is used most commonly throughout manuscript, so this should be used throughout.

1 The authors refer to "activated NOTCH" when describing antibodies in the methods, however, it would be clearer to the reader to simply refer to the antibody target (NICD), and mention that this reflects canonical NOTCH downstream activation.

The sentence in the "Immunostaining" methods "CDH5 is a lineage marker..." should be moved to results/discussion as these details are out of place in methods.

How were the 3 areas captured per wells designated? Were these locations the automated, and the same for all wells?

"Appendix - Figure" notation should be revised to "Appendix Figure" for consistency and to avoid confusion.

R14-19: Thanks, we will fix these in updated version of the manuscript.

How were artifacts and mis-segmented cell objects excluded?

R20: We will add this information in the revised appendix. As general rules, cells containing NaNs values in any of the parameters, cells fragments or merged cells (evaluated using area measurements) and cells with no detectable junctions were all excluded (total cell excluded from analysis were ~ 2.5 % of the initial dataset).

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In "Statistical analysis" "Tuckey's" should be "Tukey's". "HSD" should be defined "honestly significant difference" or simply removed, as Tukey's is most common name.

In "Statistical analysis", "significative" should be "significant" or "statistically significant".

Scale bars should be added to micrographs.

R21-23: Thanks, we will fix these in updated version of the manuscript.

Could the authors comment on the necessity of µclear plates, which substantially increases the cost per plate/experiment.

R24: m**clear plates were used to allow image acquisition with a 40x water immersion objective in the Operetta CLS (impossible with standard 96 well plates). Cell grown on coverslips and mounted on microscopy slides could be used as well with significant increase in acquisition time (Wiseman et al, 2019).

• *

Were other seeding densities and times investigated?

R25: We will evaluate sparse cells in revised version of the manuscript as discussed above.

More description on potentially novel observations between these three primary EC subtypes would be informative for the readership to appreciate

The references do not appear in chronological order. Further, consistency of reference formatting should be reviewed, and appropriate journal name abbreviations should be used.

R26-27: Thanks, we will fix these in updated version of the manuscript.

Reviewer #3 (Significance (Required)):

• This manuscript presents a conceptual and technical advance, introducing a high throughput imaging platform to assess endothelial phenotypes
• Within the field of angiogenesis, several tools exist, either proprietary, or leveraging ImageJ software to assist in assessment of cells. The ECPT provides a more complex analysis platform to integrate analysis of multiple endpoints
• This work would be of interest to vascular biology laboratories to adopt a more comprehensive view of heterogeneous endothelial phenotypes in vitro
• As a vascular biology researcher, I have had extensive experience with in vitro culture of various endothelial cell subtypes from human and mouse. My field of expertise gives me the perspective of the nuances of the direct handling and phenotyping of ECs, and have worked specifically worked with HUVEC, HAoEC and HPMEC, and assessed the impact of key factors relevant in angiogenesis such as VEGF, Notch and other mediators.

R: We thank the reviewer for the very appreciative comments, and we hope that with the revised version of the manuscript we will be able to fully address remaining concerns.

I've been seeing some of this in the digital gardening space online. People are actively hosting their annotations, thoughts, and ideas, almost as personal wikis.

Some are using RSS and other feeds as well as Webmention notifications so that these notebooks can communicate with each other in a realization of Vanmevar Bush's dream.

Networked academic samizdat anyone?

Reviewer #2 (Public Review): 

Orije et al. employed diffusion weighted imaging to longitudinally monitor the plasticity of the song control system during multiple photoperiods in male and female starlings. The authors found that both sexes experience similar seasonal neuroplasticity in multisensory systems and cerebellum during the photosensitive phase. The authors' findings are convincing and rely on a set of well-designed longitudinal investigations encompassing previously validated imaging methods. The authors' identification of a putative sensitive window during which sensory and motor systems can be seasonally re-shaped in both sexes is an interesting finding that advances our understanding of the neural basis of seasonal structural neuroplasticity in songbirds. 

Overall, this is a strong paper whose major strengths are: 

1) The longitudinal and non-invasive measure of plasticity employed 

2) The use of two complementary MR assays of white matter microplasticity 

3) The careful experimental design 

4) The sound and balanced interpretation of the imaging findings 

I do not have any major criticism but just a few minor suggestions: 

# Pp 6-7. While the comparative description of canonical DTI with respect to fixel-based analysis is well written and of interest to readers with formal training in MR imaging, I found this entire section (and especially the paragraphs in page 7) too technical and out of context in a manuscript that is otherwise fundamentally about neuroplasticity in song birds. The accessibility of this manuscript to non-MR experts could be improved by moving this paragraph into the methods section, or by including it as supplemental material. 

# Similarly, many sections, especially results, are in my opinion too detailed and analytical. While the employed description has the benefit of being systematic and rigorous, the ensuing narrative tends to be very technical and not easily interpretable by non experts. I think the manuscript may be substantially shortened (by at least 20% e.g. by removing overly technical or analytical descriptions of all results and regions affected) without losing its appeal and impact, but instead gaining in strength and focus especially if the new result narrative were aimed to more directly address the interesting set of questions the authors define in the introductory sections. 

# The possible effect of brain size has been elegantly controlled by using a medial split approach. Have the authors considered using tensor-based morphometry (i.e. using the 3D RARE scans they acquired) to account for where in the brain the small differences in brain size occur? That could be more informative and sensitive than a whole-brain volume quantification. 

# I think Figures Fig. 3 and Fig. 4 may benefit from a ROI-based quantification of parameters of interests across groups (similar to what has been done for Fig. 7 and its related Fig. 8). This could help readers assess the biological relevance of the parameter mapped. For instance, in Fig. 3, most FA differences are taking place in low FA (i.e. gray matter dense?) regions. 

# In Abstract: "We longitudinally monitored the song and neuroplasticity in male.." Perhaps something should be specified after the "the song"? Did the authors mean "the neuroplasticity of song system"?

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Note: we included 6 Figures in our response, yet the ReviewCommons system does not appear to support including images as part of the response. These Figures are in the original "Initial Response" file available to ReviewCommons. We requested that Review Commons post our "Initial Response" file that contains these figures so that this information is available.

Reviewer #1

*In the paper by Gowthaman et al., the authors aim at better understanding the molecular mechanisms controlling divergent non-coding transcription (DNC). They describe a high-throughput yeast genetic screen using two strains in which two loci consisting of a coding and a divergent non-coding transcription unit (CGC1-SUT098 or ORC2-SUT014) were replaced by a bidirectional fluorescent reporter construct encoding mCherry in the coding direction and YFP in the non-coding direction. The two reporter strains were crossed with the yeast deletion library and mutants leading to increased or decreased YFP signal were selected as potential DNC repressors or activators. The two screens identified a number of common potential repressors and activators. Components of the Hda1C histone deacetylase complex were identified as DNC repressors in both screens. This phenomenon was confirmed genome-wide by performing NET-Seq in WT as well as hda1D and hda3D strains. This experiment allowed to identify 1517 DNC transcripts repressed by Hda1. Further analyses indicate that Hda1C represses DNC genome-wide independently of expression levels and that loss of Hda1 does not substantially affect coding transcription.

Live-cell imaging of transcription was then used to show that loss of Hda1 increases DNC transcription frequency rather than duration providing novel information on the link between DNC transcription initiation kinetics and chromatin regulation. Finally, using Chip-seq, the authors show that the level of acetylation over the divergent non-coding units is increased in the absence of Hda1 and some experiments suggest that H3K56 acetylation also contributes to DNC regulation, further strengthening the importance of elevated histone acetylation in efficient DNC.

Importantly, several components of the SWI/SNF chromatin remodeling complex were identified as activators confirming earlier observations (Marquardt et al., 2014). SAGA subunits were also among potential DNC activators, however these effects could not be confirmed through validation experiments. The authors conclude that DNC may be independent of specific activators and mainly due to transcriptional noise resulting from the adjacent NDR.

Overall this paper is very well structured, clearly written and the experiments are well controlled. The genetic screen identifies novel factors involved in the regulation of DNC. The study clearly demonstrates that the level of acetylation is a key regulator of divergent non-coding transcription and that histone deacetylation by Hda1 reduces the frequency of DNC initiation events. While this conclusion is strongly supported by the Net-Seq and Chip-seq metagene analyses, the fluorescence mCherry and YFP values or qRP-PCR analyses of specific genes do not always behave as expected when looking at absolute values rather than mCherry/YFP or GCG1/SUT098 ratios, which is sometimes disturbing when reading the paper. Therefore, the following points should be clarified.*

We are grateful for the kind appreciation of our manuscript and clarify the remaining questions in the revised manuscript.

**Major points**

#1.1: Figures 2 and S2A: Figures 2C and D show the mCherry/YFP fluorescence and GCG1/SUT098 RT-qPCR gene expression ratios respectively, which are consistent with a repressive effect of Hda1C on DNC transcription and a potential DNC activating effect of SAGA components. However, the absolute mCherry and YFP or GCG1 and SUT098 expression values presented in Figures S2A and S2B show the opposite: loss of Hda1C subunits rather leads to a decrease in mCherry with not much effect on YFP; moreover loss of Hda3 results in decreased SUT098, which is inconsistent with the whole model. The same comment is valid for the SAGA mutants. It would be good to provide some explanation for these a priori contradictory observations, especially for the Hda1c mutants, which are the major focus of the study. The Net-Seq analyses are certainly more reliable since less subject to protein or RNA stability effects, which may underlie some of the inconsistencies between protein and RNA absolute levels.

Thank you for this comment. We offer enhanced clarity in the revised manuscript.

In general, transcription in each direction shows a weak yet highly statistically relevant positive correlation (Spearman rho = 0.26, p-value = 4.94e-24). We are enclosing a plot based on NET-seq data that supports the correlation in each direction of a NDR as part of our response below (RFig.1). To unpick relative effects the ratio captures these effects well, in our experience better than the individual fluorescence measurements or RT-qPCR. Of course, we are ultimately interested in transcription and fluorescence measurements or RT-qPCR of steady-state RNA are only an approximation. Resources and time constraints limit how many mutations we can examine by techniques such as NET-seq, which are arguably most informative. The positive correlation between transcription in each direction has the effect that relative differences can manifest themselves through detectable effects of the other fluorophore. As this reviewer mentions, we can be most confident of results that we could further validate by NET-seq or live-cell imaging.

(INSERT Rfig1)

RFig1: Scatterplot of NET-seq data for DNC/host gene pairs. Each point corresponds to a bidirectional gene promoter overlapping with a nucleosome-depleted region (NDR). The values represent NET-seq FPKM values in protein-coding (x-axis) vs non-coding (y-axis) directions. These data support a statistically significant correlation (Spearman test: rho = 0.2554876, p-value = 4.939658e-24).

#1.2: Figure 3: this figure examines the effect of Hda1 and Hda3 on the 1517 DNC transcripts. Does loss of this HDAC also increase the expression of all the other 2219 non-coding transcripts identified by Net-Seq, which would make Hda1C a more general repressor of non-coding transcription?

We have performed the analysis for all other non-coding transcripts in Hda1C mutant NET-seq data and added it as part of this response RFig2. Quantification of CUTs, SUTs and other lncRNAs that are not resulting from DNC in Hda1C mutants results in a slight increase in the nascent transcription that is not statistically significant. These data do not offer strong support for the idea that Hda1C represents a more general repressor. We added this plot as novel supplementary figure S3D and adjusted the text of the revised manuscript (line 214).

(INSERT Rfig2)

RFig2: Metagene plot of NET-seq data for non-coding RNA that are not classified as DNC. Metagene plot shows genomic windows [TSS - 100 bp, TSS + 500 bp] relative to the annotated starts of ncRNA transcripts.

#1.3: Moreover, does loss of Hda1 or Hda3 reveal DNC transcripts that were not detected in wild-type? This may increase even more the number of genes with divergent transcription.

We are grateful for the opportunity to clarify this point. We noticed that the yeast genome shows evidence for much more non-coding transcription than annotated. In this paper, we used TranscriptomeReconstructoR for a data-driven annotation of yeast non-coding transcripts, with an emphasis on the boundaries. See also:__ ( DOI: 10.1186/s12859-021-04208-2 ). The set of non-coding transcripts was for example informed by the previously published NET-seq data on wild-type samples (Churchman et al., 2009; Marquardt et al., 2014; Harlen et al., 2016; Fischl et al., 2017). We have clarified relevant Methods sections to make this point more accessible (line 733). The combination of these NET-seq datasets gives a very good sequencing coverage. The Hda1C mutant NET-seq data does not have a better coverage than this combined reference set, so it would be very hard to find new transcripts without prior evidence in our exhaustive set of combined NET-seq data. However, our Supplementary table S3 contains the fold-change values for all DNC transcripts in mutant compared to wild type. Loci with a high fold-change could arguably be regarded as hda-specific. __

#1.4: Figures S3A, B, C: are the 3 groups of DNCs derepressed to the same extent by loss of Hda1 or Hda3? This is difficult to judge given the differences in y-axis scales. Figures S3D, E: the authors show the Net-Seq snapshots for the GCG1 and ORC2 loci. It would be good to add the quantifications as presented in Figure 3 for YPL172C and YDRr216C.

Thank you for the suggestion. We replaced S3A-C with plots that show the same range of the y-axis in the supplementary figure. Hda1C represses DNC in all three cohorts stratified by DNC expression strength. We also added a quantification boxplot for NET-seq signal in the GCG1 and ORC2 loci in revised S3F-I.

#1.5: Figures S4A, B, C and D are not well explained. What does the y axis frequency correspond to? Is it the % of cells showing a signal? Is the intensity of SUT098 higher because the transcription initiation frequency is higher and therefore the transcription site signal is more intense?

We improved the annotation for the supplementary figure S4. We clarified in the legend that the y-axis frequency represents the percentage of frames recorded for transcription initiation spots (TS). The bars represent transcription intensity in all the frames recorded, with active transcription ‘ON’ and without TS ‘OFF’. The intensity increases with higher initiation rates and thus the intensity of SUT098 transcription initiation is high.

#1.6: Figures S4 A-I should be more specifically cited in the text.

We have cited the figures in the text in the revised version.

#1.7: Figure 5A: it is really unexpected and unclear why the mCherry/YFP in the WTH3/hda1D and WTH3/hda1D/H3K56mut is increasing compared to WTH3, since DNC is supposed to increase. Similar comment for Figure S5C. This should be clarified in the text.

Thank you for pointing this out. We missed to address this in the text. The isogenic control “H3 wild type” carries only one copy of the two genes coding for H3, which has a general effect on transcription. We added data showing this as part of our response (RFig3.), and explained this part more clearly in the revised text (line 263). Essentially, the genetic background of the yeast synthetic histone mutant collection sensitizes for a decreased ratio of mCherry/YFP (RFig3.). This result is also included in table S2, where deletions of the histone genes HHT2 (H3) and HHF2 (H4) are listed as shared repressors in both screens. Hda1C mutations show the increased ratio in the sensitized “H3 wild type” background, but not in backgrounds we tested that contain a wild-type dosage of histone genes.

These data remain valid to support the genetic interaction of hda1D along with the substitution mutants of H3K56.

(INSERT Rfig3)

RFig3. Fluorescence signal values of H3WT and BY4741 strains with GCG1pr FPR. The H3WT affects general transcription of coding transcript and decreases the ratio of mCherry/YFP fluorescence.

#1.8: More generally, as already mentioned above, the fluorescence data are expressed either as mCherry/YFP ratio or as absolute values. It would be good to systematically show the ratios and the absolute values of mCherry and YFP signal; the same for coding and DNC RT-qPCR as well as Net-Seq values when available.

We ensured that the absolute data values for flow cytometry and qPCR have been represented in the supplementary figures S2 and S5. The FPKM values for NET-seq data for individual transcript units are provided in the supplementary table S3.

#1.9: Figures S5A and B are not referred to in the text. It should be mentioned and explained how normalization to H3 affects the levels of acetylated H3 over the NDR.

We now refer to the figures in the main text and explained the rationale for normalization.

#1.10:* p. 12 "Our data thus suggest to extend the transcriptional noise hypothesis with activities limiting DNC transcription to account for genome-wide variation in non-coding transcription".

If DNC is the result of "transcriptional noise", it is surprising that in the case of CGC1-SUT098, the transcription frequency is higher in the non-coding versus the coding direction. Is the SUT098 behaving like the coding unit in this case? The authors should comment on that. *

This is very interesting point. One interpretation of the “transcriptional noise” hypothesis is indeed that non-coding transcription is at low level. We selected loci with high DNC expression, so these loci are somewhat contradictory to this idea a priori. Nevertheless, identifying a biological function of non-coding RNAs is challenging, and it remains to be tested if SUT098 represents particularly “loud noise” or if the high transcription indicates that it carries a yet unknown cellular function. In theory, this screen is suitable to identify factors that may be required to induce DNC, perhaps even specifically. To identify such factors a locus with high DNC is needed to facilitate detection, since our previous screen using the PPT1/SUT129 system had lower SUT expression and failed to identify such mutants systematically. This is important, since a mutation lowering DNC needs to start from a sufficiently high fluorescence signal to distinguish it from background fluorescence. Since the results presented did not clearly uncover such factors, we favor the hypothesis of DNC arising due to the promoter architecture at NDRs, see also positive correlation plot in RFig1. The many repressive pathways are also acting on highly expressed DNCs, which is certainly an interesting information provided by this manuscript.

**Minor comments**

#1.11: p. 4 should one talk about Hda1C-linked histone acetylation facilitates... (should be deacetylation...??)

Done.

#1.12: The authors should explain why they chose two coding/non-coding pairs that are cac2D insensitive and whether other criteria, such as level of DNC transcription, were also considered, since GCG1-SUT098 represents one of the most highly expressed divergent non-coding transcripts.

The GCG1 and ORC2 loci were chosen based on i) high DNC levels, ii) a low fold-change of NET-seq data in the cac2∆ and iii) a DNC region free from other transcriptional units. However, this was based on the state-of-the-art annotation in 2015 when we started this project. Also, when we categorized genes as affected by cac2∆, we used a fold-change expression cut-off that suggested that about a third of DNCs are repressed by CAF-I. It appears that we still underestimated the effect of CAF-I, since our data show that the target regions of our new screens are also affected by CAF-I. DNC expression at these loci is high, which would result in a low fold-change in mutants that further increased DNC here.

#1.13: It is hard to understand why both the H3K56A and H3K56Q mutations lead to increased DNC, a result already presented in the Marquardt et al. 2014 paper. It would be helpful to provide a more extensive explanation or hypothesis.

The H3K56 substitution mutant Q is expected to mimic the acetylation state and A is devoid of post-translational modifications. We observe an increase of signal ratio in the mutants because the H3K56ac is both responsible for incorporation and eviction of -1 nucleosomes (Marquardt et al., 2014). Mutations affecting H3K56 can thus result in less -1 nucleosome density and more DNC through reducing incorporation or enhancing eviction. We have improved the revised text to highlight this. We have clarified this in the text (line 271).

#1.14: What defines the level of DNC repression? How does the level of repression correlate with the level of coding transcription?

We have added RFig.1 to address the question about correlation. There is a statistically significant positive correlation between transcription in each direction by NET-seq data in wild type samples genome-wide. However, the correlation is weak (rho = 0.26), which is consistent with locus-specific adjustments of transcriptional strength in each direction. For DNC, several chromatin-based pathways contribute to repression. The resulting level of DNC transcription thus reflects the combined action of several pathways. Here, we characterize Hda1C as a novel player with a genome-wide effect on this phenomenon. Elucidating the mechanistic interplay at specific target DNC loci will be an exciting future research question.

Reviewer #1 (Significance (Required)):

This is a very interesting and innovative study using cutting edge genetic approaches, genome-wide sequencing as well as single cell imaging to extend our understanding of non-coding transcription regulation and its potential impact on gene expression. It is a nice continuation and complement of an earlier study from the same author (Marquardt et al., 2014) and will certainly be of interest to a large chromatin biology audience.

We are grateful for the appreciation of our research on this topic.

Reviewer #2

Promotors are frequently transcribed in both directions. The divergent, \upstream' transcript is frequently unstable. Transcription initiation is regulated through the acetylation of promoter-proximal nucleosomes, where HDAC-dependent deacetylation of histones typically represses transcription initiation.*

*The current manuscript addresses the question whether initiation of coding and divergent, non-coding (DNC) transcription is regulated by the same factors. Previously Marquardt and others showed that H3K56ac-mediated histone exchange has a differential effect on coding and DNC transcription.

Using a clever reporter system, the authors screened for positive and negative regulators that preferentially affect DNC transcription. They discover the Hda1 deacetylase complex as a DNC-biased repressor and diverse HATs as DNC-biased activators. The role of activators could not be validated, presumably due to high variability of the system.*

Focusing on Hda1c the authors present data suggesting a larger effect of Hda1c on 'upstream' nucleosomes associated with DNC transcription than in coding transcription. Genome-wide NET-seq mapping was consistent with this differential regulation. Life cell imaging of one specific case argues that Hda1-mediated repression reduced the time between initiation events. The authors employ state of the art methods and in general the data are of very good quality. The effect size is very small, which raises the broader question whether the results, while statistically significant is biological relevant. I have a few comments that the authors may use to revise their manuscript.

Thank you for the appreciation of our very good data quality. We hope our revision plan will help to clarify some confusion about the scope and effect size.

#2.1) The differentially regulated coding and DNC transcription are defined by a directionality score. The screen was performed with two reporter loci that are strongly biased for DNC transcription (the idea to detect activators did not work out). Considering that coding and DNC transcription may not be totally independent because of the proximity of target nucleosomes, and sense and antisense transcription may compete for regulators, the question arises how levels of coding transcription affect DNC transcription in wildtype and mutants. The authors stratified their results according to levels of DNC transcription, but discussion and data analysis of the effect of coding transcription on the directionality score may be relevant.

We added the plot in RFig.1 above to address the question of correlation between transcription in each direction. NET-seq data supports a weak but highly statistically significant positive correlation between transcription in each direction genome-wide (rho = 0.26, p-value = 4.94e-24). We agree that it is relevant to discuss the effect of coding transcription on the directionality scores and revised the discussion accordingly (line 315). We have used both the coding and DNC signal values to create the comprehensive quadrant scatter plot in Fig. 1D-E. Analysis of mutants along the diagonal illustrates that many mutations affect coding transcription as well as DNC. The directionality score measures deviations from the axis of positive correlation, which requires us to use the information of both fluorophores.

#2.2) The study is strong where the findings can be generalized. The single-molecule live-cell imaging analysis, while done properly, has only limiting impact, because the corresponding coding transcript could not be detected. This si more an anecdotal finding.

There seems to be a misunderstanding, the live-cell imaging measurements of transcription for SUT098 are stand-alone data. SUT098 by itself is a transcription unit, so we measure DNC of this unit independently from GCG1 that has much lower expression. The measurements are specific to SUT098 transcription and the quantification provides new information about the mechanisms involved in the regulation of DNC. We clarified the text in this regard (line 233).

#2.3) The effect size is small (20%, on average) and the variability is high. The fact that the HATs that emerged as very robust activators of DNC transcription could not be validated and that the Hda2 subunit of the HDAC complex was not found statistically significant show the limitations of the study. To their credit, the authors discuss these limitations appropriately.

We have worked on the Methods in the revised manuscript to clarify this confusion (line 712). For the screen, the median signal values represent data from up to 50,000 individual cells. These experiments are remarkably accurate and highly reproducible, especially for molecular biology where n=3 is common. We have uploaded these data to the FlowCore public repository. We encourage any colleague to exploit the opportunity to analyze these data independently to experience the high data quality. With high number of observations, 20% average is a large effect and reflects a rather big shift of the population. As is standard for genetic screens, resource constraints are prohibitive to pursue all hits. In addition, it is expected that only some hits will be affecting transcription of DNC since the fluorescence reporter can be affected by many other cellular events. We focused on the effects on DNC in this manuscript.

There seems to be some misunderstanding, Hda2 is a statistically significant hit in the ORC2/SUT14pr screen; this information is in Fig. 1E. The Hda1C subunits are labeled in purple.

#2.4) Figure S3C suggests that the Hda effect is largest at genes that are poorly expressed, and smaller at more average expression levels. Are we looking at a phenomenon that mainly applies to repressed genes?

Thank you very much for this suggestion. We replaced S3A-C with revised panels where the data is shown with the same y-axis scale, please see also #1.4. We believe the revised presentation also helps to clarify that the mutations increase DNC for all cohorts stratified by DNC expression.

**Minor issues**

#2.5) The NET-seq study involves two replicates. How well did they correlate?

The WT and mutant NET-seq replicates have good correlation (Spearman’s correlation coefficient was above 0.6 for WT and above 0.8 for the mutants).

(INSERT Rfig4)

RFig4. Correlation scatter plot of individual NET-seq replicates of WT, hda1D and hda3D. Spearman correlation coefficients of WT, hda1D and hda3D are 0.677, 0.8 and 0.825, respectively.

#2.6) For the live-cell imaging replicates were not mentioned. Were replicate studies performed?

We have updated the text to make this important point more accessible (line 230). For live-cell imaging studies, transcription is recorded as movies of cells over time. We took multiple movies, and pooled the data from all the cells to improve statistical power. Data from each movie represent individual repeats. We monitored 130 cells on average for the WT and mutant strains over time.

#2.7) Fig 4E is not mentioned in the text (mislabeled as 4D)

Done.

#2.8) Fig S5 is not mentioned in the main text.

__Done.

__Reviewer #2 (Significance (Required)):

In summary, this is a high-quality study that presents the results of a genome-wide screen that will be of interest to colleagues in the narrower field. Due to the small effects the results may appeal less to a general readership.

We are grateful for appreciating our manuscript as a high-quality study. We hope our revisions help to clarify confusion concerning effect size.

Reviewer #3

In this manuscript, Gowthaman et al describe the results and follow up of their screen aimed at identifying regulators of divergent noncoding (DNC) transcription in S. cerevisiae. From this screen, they identify Hda1C as a repressor of DNC transcription, and perform follow experiments to support and detail this finding. In addition to RTqPCR to confirm the reporter and endogenous changes, the authors perform NET-seq to look at global DNC alteration upon Hda1C subunit deletion and identify a number of non-coding transcripts with altered expression levels. In addition, the authors perform live cell imaging to demonstrate that there is a modest restriction of initiation frequency when one of the subunits of Hda1C is deleted. Finally, the authors explore changes to pan-H3 acetylation and the genetic overlap between Hda1C and H3K56ac demonstrating independent genetic pathways, but overall increases in H3 acetylation over DNCs when Hda1C is deleted. Overall, the screen and results are of interest, but the authors overstate some of the conclusions (perhaps most importantly within the title!). I have the following suggestions to improve the manuscript:

Thank you for recognizing the interest in our results. We have revised the manuscript to state the conclusions more cautiously.

**Major comments**

#3.1. The title of the manuscript is based on the single molecule live cell imagining experiments presented in Figure 4. While there is a statistically significant decrease in initiation frequency from deletion of one Hda1C subunit, there is no statistical decrease in deletion of the other two. Furthermore, these experiments were performed at one locus. As a result, I find the title to be an overstatement of the findings of the paper and suggest the authors refocus on the more robust findings of the manuscript.

Live-cell imaging requires extensive engineering of the target loci. Perhaps this was lost in the Methods, but it is a 5-step process to integrate the stem-loops. We tried to engineer other loci, but this is far from trivial and this technique does not work for all loci tested. The hairpins are also unstable, and need to be carefully checked prior to experimentation, which challenges scaling this approach up to a higher-throughput. It appears that we undersold this point, but the fact that we now provide a locus and strains for the community that makes such studies possible for DNC represent a tremendous achievement. Since hda1D also decreases time between initiations, we generalized the finding to Hda1C.

However, we recognized that the reviewer makes a helpful suggestion to choose a more careful title since there is no statistically significant reduction of initiation frequency in some mutants. We have revised the title to “__Hda1C limits divergent non-coding transcription and restricts transcription initiation frequency__” in the revised manuscript to address this point.

#3.2. Relatedly, in Figure 4, the authors present the findings from the single molecule live cell imaging experiments. Within this experiment, the authors include a cac2 deletion (CAF-1 subunit) strain, and observe a modest effect, similar to hda1 deletion. This is surprising as the authors mentioned this location (GCG1/SUT098) was selected as CAF-1 was NOT shown to regulate the DNC previously (Marquardt et al 2014; as mentioned at the beginning of the Results section). The similar decrease in initiation frequency between cac2 deletion and hda1 deletion further concerns me regarding the use of these data as the headlining finding.

We believe there is a misunderstanding. We clarify that selection of the GCG1 locus was based on a cut-off value for cac2D effect, as is also shown in Fig S1C. The fold-change is small, but since DNC transcription of the chosen loci is high in wild type, an increase in a mutant would not necessarily give a high fold-change. Hence, we need to be cautious to conclude that CAF-I does not regulate DNC at this locus. The fold-change analysis suggested it, but it remained possible. CAF-I appears to affect even more loci than initially identified with the chosen cut-off. We see the same trend as in Hda1C mutants as in cac2, which offers support to the exciting idea that modulation of the initiation frequency may be a shared mechanism by chromatin-based regulators acting on DNC.

#3.3. It is unclear to me why the change in mRNA expression is included within the screen. Why not solely look at the expression change of the DNC? Importantly, the authors note in the discussion that perhaps the reason the SAGA complex was identified was due to regulating mRNA expression and not DNC expression and therefore was identified in the screen. Could the authors not just present the fold change in DNC expression using their YFP reporter, and not the YFP vs mCherry?

The regulation of initiation frequency in each direction is super-imposed on a general positive correlation __(rho = 0.26, p-value = 4.94e-24) between the coding and non-coding directions__, please see also RFig.1. For the purpose of this study about selective effects on the direction of transcription, it is vital to incorporate both sides of the reporter. Otherwise, we would select for factors that activate or repress the transcription from the target promoter NDR. This point is accessible in Fig.1D-E, where mutations that affect YFP usually also have an effect on mCherry. The aim of this study was to identify mutants that affect the relative expression, and therefore a focus on one fluorophore would not improve the analysis. We clarified this important point more accessibly in the revised manuscript (line 315).

Please also note that all the raw data are available, so colleagues are in the position to perform their independent analyses. We believe that it is very valuable for the community to have access to these data since they may be useful for other purposes and could be analyzed in many different ways. In fact, we have tried several methods and approaches over the years and present what we believe is most appropriate in this manuscript. For example, Hda1C comes out as a convincing hit with a range of different approaches to analyze the data, which is also a reason we feel confident about the characterization of Hda1C.

#3.4. This is absolutely beyond the scope of the paper, but limiting the screen to only nonessential proteins likely misses important regulators. In the future, perhaps the authors could pursue a SATAY screen to look for essential proteins as well? Again, the findings of this paper are appropriate, and the screen is a great undertaking, but I want to suggest this to the authors for potential future projects.

Thank you for this excellent suggestion. We agree that capturing the role of essential factors would be very informative, and the saturated transposition approach would be promising. However, as the reviewer points out, performing these analyses is beyond the scope of the current manuscript.

#3.5. The authors perform NETseq experiments in deletion strains and identify ~1500 DNC transcripts with altered expression. Later the authors look into the mechanism and demonstrate an increased H3ac in hda1 deletion strains. The authors could enhance the representation of these datasets by correlating the change in H3ac with the change in DNC transcription - do they correlate?

Thank you for bringing up this excellent point. We present the correlation data of change in H3ac and DNC transcription in the hda1D mutant (RFig5.). The ChIP-seq and NET-seq values of hda1D were divided by respective WT values in order to quantify the relative increase of H3 acetylation or nascent transcription in hda1D). The data showed a weak (Spearman rho= 0.23) but significant (pval=3.0e-20) positive correlation between the ratio values. The hda1D-dependent increase in H3 acetylation correlates with hda1D-dependent increase of RNAPII occupancy in DNC transcripts. We enhanced our representation of these data by including this plot as S5D in the revised manuscript as suggested.

(INSERT Rfig5)

RFig5__: Scatterplot of hda1D/WT NET-seq (y-axis) and ChIP-seq (x-axis) ratios. Each point corresponds to a bidirectional gene promoter overlapping with an NDR. The x-axis shows ChIP-seq ratios, and the y-axis shows the NET-seq ratios. These data support Spearman correlation test: rho = 0.234 and a statistically significant p-value = 3.0e-20.__

#3.6. In Figure 5, the authors argue that Hda1C works non-redundantly with K56ac, using point mutants to mutate K56 to A or Q. Did the screen identify anything else in the K56ac pathway? Rtt109 or Asf1, for example? Because Hda1C deacetylates H3, including but not limited to K56, it is a bit surprising the K56 point mutations result in a larger increase in SUT098-YFP levels. The authors discuss within the text that Hda1C has multiple targets; but coming back to my previous point that CAF-I was not supposed to impact this location, I am having a hard time understanding these results.

This is an excellent point. We improved the manuscript by highlighting other factors with links to H3K56ac in our scatter plots, for example Rtt109 in Fig 2A. Nevertheless, the reviewer may wish to satisfy his/her curiosity by exploring table S2 in more detail. Table S2 lists the top candidates from both screens.

We hope our answer to point #3.2 helped to clarify the aspect of this comment related to CAF-I.

**Minor comments**

#3.7. The authors follow up the screen using RTqPCR for GCG1/SUT098 in newly made deletion strains. I was surprised the authors choose this locus rather than the ORC2/SUT014 locus, as the screen showed a strong increase for this reporter. While I appreciate generating the deletion strains within the reporter is beyond necessary, assessing the endogenous locus within the deletion strains by RTqPCR seems reasonable.

We chose GCG1 locus since the fold change in directionality by genetic screen was high for the activator mutants. We will perform this experiment and add the missing validation experiment for the ORC2 locus in the revised manuscript.

#3.8. The authors tend to show their genomic data as metaplots; it would be nice to see heatmaps where more can be gleaned from the display of all the loci. This applies to the NET-seq data (Figure 3) and the ChIP-seq data (Figure 5).

We appreciate the suggestion and generated the requested heatmaps using the NET-seq tracks of WT and hda mutants (RFig6.). The heatmap represents the same genomic intervals as on the corresponding metagene plot (Figure 3A). We find that the differences between WT and hda samples are more clearly accessible at first glance on the metagene plot rather than on the heatmap. We believe that this could be because the heatmaps do not represent what transcripts have in common and rather underlines the differences. In contrast, the metagene plots reveal the common trends by taking the average of signal. We thus prefer showing metagene plots in the manuscript, as they allow for overlay of multiple tracks on the same plot, thus enhancing visual comparison for the readers.

(INSERT Rfig6)

RFig6. Heatmap representing NET-seq data in WT, hda1D and hda3D. Genomic intervals covering [TSS - 100 bp, TSS + 500 bp] of DNC transcripts (n=1517) are shown. The color indicates the log2-transformed NET-seq values.

#3.9. In Figure 5B, the authors present H3ac ChIP-seq data, presented as a ratio of H3ac/total H3. While this is a perfectly acceptable way to present the data, I was surprised to see a decrease in total H3 levels when examining the supplemental data. Has this decrease in H3 occupancy upon hda1 deletion been shown previously? This finding should be discussed within the manuscript.

We appreciate that the reviewer noticed this. We do not think this has been explicitly stated before, as the focus thus far had been on the effects towards the mRNA. However, the effect is not statistically significant between the WT and hda1D as observed in S5B. We thus prefer to remain cautious about this conclusion.

#3.10. In Supplemental Figure S3, the authors break down the NET-seq data by DNC FPKM, which is very nice. Very minor point that the font here is quite small.

Thanks, we improved the font size. Note that we also revised the y-axis scale in response to comment #1.4.

Reviewer #3 (Significance (Required)):

\*Significance:** *

The regulation of divergent non-coding RNAs is an understudied field. In this paper, the authors perform a screen for all non-essential yeast proteins in regulating the expression of these ncRNAs. The screen results and follow up defining the role of Hda1C in broadly repressing the expression of these ncRNAs is of interest to the field.

We are grateful to the reviewer for highlighting the interest of our work to the field.

\*Context:** *

This work follows from Marquardt's previous 2014 study that identify Caf1 as regulating DNCs in S. cerevisiae.

\*Audience:** *

Broadly, the chromatin and transcription field. Anyone interested in how chromatin regulates transcription, regulation of ncRNAs, and functions of histone modifying enzymes.

\*Expertise:** *

I am a member of the chromatin and transcription field, largely performing genomic experiments. We do not perform microscopy, although sufficiently understand the experiments and results presented here.

This term in specific as been seen and even critiqued in my writing. I think that this is due to the fact that may students and writers attempt to use more educational or specific words that we may not always understand how to use. By attempting to use them in new ways, we risk confusing the audience or writing an argument that has a meaning different from what we intended.

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

Point-by-point response:

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

**SUMMARY**

This MS tackles a largely unknown topic of vessel formation: how vessels anastomose and lumenise. The authors demonstrate that a matrix protein svep1 produced by neural tube during zebrafish embryogenesis plays a key role with blood flow to orchestrate anastomose formation. Actually in absence of this protein concomitantly with blood flow reduction results in significant decrease of lumenised DLAV segments.

In absence of svep1 they observed an expansion of apelin positive endothelial cells connected with a defect in tip/stalk cell specification. Interestingly the phenotype is amplified by blocking the kinase activity of VEGFR2

**MAJOR COMMENTS**

The most solid evidence on the role of blood flow in cooperating with svep1 relies on the use of tricaine, which reduces heart contractility. Interestingly the authors report some data by using embryo lacking cardiac troponin T2. In my opinion I suggest the author to better analyze the phenotype obtained by the deletion of svep1 together a dose-dependent reduction of tnnt2. This approach is more elegant and physiologic than the use of a chemical compound. Furthermore this approach will allow to better analyze the relations ship between blood flow and the expression of svep1 in neural tube. It should be relevant to establish a sort of flow threshold required to dampen lumenisation. *

Response: We appreciate the comment and have previously attempted to titrate the tnnt2 morpholino as published to have a graded reduction in blood flow. In our hands, this has not proved to be a robust approach, but we are willing to give it another try. In addition, we propose use alternative compounds to tricaine for blood flow reduction without affecting neural physiology. Alternatively we will use a-bungarotoxin mRNA injection to selectively affect neural activity to immobilize the embryos without effects on heart rate and blood flow (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4526548/)

To further improve the findings here reported I suggest to analyze the expression of klf2, which is a well known mechano-sensor of blood flow in several animal species including zebrafish.

Response: We will perform klf2 expression analysis

It's likely that apelin is relevant in the observed phenotype. Which is the phenotype of a double mutant lacking both apl and svep1? Is there a direct influence of blood flow on apl expression?

Response: We will investigate the double loss of function. However, double mutants would take some time, and a combination of morpholino and mutant would likely be the first and best option to answer this question in a reasonable time frame. The effect of flow on apl expression can be tested.

Is there any suggestion that this mechanism is oprative in mammalian?

Response: This is an interesting question and certainly relevant for follow up studies. At present, we can only speculate on a possible connection with flow, given that Svep1 mutations have recently been associated with artherosclerosis. However, whether the anastomosis defect we identify is conserved remains to be seen.

*Reviewer #1 (Significance (Required)):

The data here reported might represent a step forward in the field because a new mechanism is suggested.

The interest is sufficiently broad.

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

**Summary:**

The authors demonstrated that loss of svep1 in zebrafish contributed to defective anastomosis of intersegmental vessels, in addition, such Svep1 acted synergistically with blood flow to modulate vascular network formation in the zebrafish trunk.

**Major comments:**

The expression of svep1 is localized in neurons of neural tube, dorsal epithelial cells (as indicated by transgenic zebrafish) and ventral somite boundary (as indicated by in situ) but is excluded from endothelial cells nor the vasculature. It remains puzzling and the authors have not addressed this very reason of how a gene that is expressed in non-vascular tissue play a crucial role in vessel anastomosis, ie DLAV, ISV lumenization, during angiogenesis. As the entire story of this svep1 is related to its function in angiogenic sprout and lumen formation of vascular tissues, it will be helpful for reader to be able to put the pieces together of how such gene may be functionally involved in such angiogenic process. Previous publication of this gene involved in lymphoangiogenesis, as in this manuscript the authors could provide more evidence of how such gene and its localized expression contribute to different tissue in the vascular system, ie DLAV, instead of the neural tube, dorsal epidermis or ventral somite boundary.*

Response: We appreciate the wish to understand exactly how non-endothelial expression of Svep1 causes an endothelial phenotype selectively under reduced flow conditions. The very nature of this new phenotype requires analysis in vivo, and can not easily be transferred to an ex vivo assay. Therefore, selective loss of function in different cell populations is not easily available. More importantly, the interpretation of such efforts, when mosaic, are marred with issues. At this point, we feel that full molecular characterization of how Svep1 affects endothelial cells during anastomosis will require entirely new approaches and lies beyond what can be achieved in this manuscript.

We will however attempt to clarify the findings and the potential mechanisms in the discussion.

Another puzzling point is that tricaine is the center of the subject in this study. As the authors claim that tricaine-dependent blood flow reduction synergistically augmented the effect of svep1 deficiency. However, tricaine is known acting on neural voltage-gated sodium channels, whether svep1 function was affected by tricaine in the neural tissues and possibly its expression, the authors could provide more explanation and argument in the discussion.

Response: As mentioned in our response to reviewer 1, we will perform additional experiments to try to clarify whether an effect of tricaine on neuronal sodium channels contributes to the phenotype.

It is unclear on p12 "These results suggest that while svep1 loss-of-function produces a cardiac defect that enhances the effect of tricaine on reducing blood flow, svep1 has an additive effect in modulating blood vessels anastomosis" that svep1 deficiency enhances the effect of tricaine leading to reduced blood flow, however, it is not accurate to state that svep1 loss-of-function produces a cardiac defect. It is not sure if the effect of svep1 was actually neural rather than cardiovascular tissue, for example, tricaine acts on neural voltage-gated sodium channel that slowing down heart beat. Whether the authors can explore the possibility that svep1 function in neural rather than cardiovascular tissues, may be discuss why the authors think svep1 enhances the blood flow defect (tnnt2a knockdown or tricaine) on angiogenesis such as DLAV phenotype.

Response: We will attempt to dissect potential contributions by neural effects from cardiac and flow related effects as stated above. Tnnt2 MO and alternative drugs to reduce heart function selectively will be used. We will also clarify the discussion.

On p13, the authors stated that svep1 expression was inhibited by reduced blood flow, however, is it really the effect of reduced blood flow or caused by the chemical tricaine? If tnnt2a knockdown showed a similar phenotype, then it may be more convincing.

Response: see above

\*Minor comments:**

The work on "svep1 loss-of-function and knockdown are rescued by flt1 knockdown" was beautifully done and it is very clear and convincing.

The last two sections, "Vegfa/Vegfr signalling is necessary for ISV lumenisation maintenance and DLAV formation" and "Vegfa/Vegfr signalling inhibition exacerbates svep1 loss-of-function DLAV phenotype in reduced flow conditions" are more related to the flt1 knockdown phenotype. These 3 different sections are actually related in the sense that the rescue phenotype should be explained in the vegf signaling pathway. They are better off to discuss more cohesively about this vegf pathway that will help readers to appreciate more their work in svep1. *

Answer: We agree and will do so.

*Reviewer #2 (Significance (Required)):

This manuscript of svep1 in zebrafish provides new insight in angiogenesis, particularly in development of vessel anastomosis in zebrafish embryo, is very significant in the field and readers who are interested in angiogenesis and zebrafish development, including myself.

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

This manuscript reports that the secreted extra-cellular matrix protein Svep1 plays a role in vascular anastomosis during developmental angiogenesis in zebrafish. Further, the study demonstrates that flow and Svep1 modulate the vascular network in a synergistic fashion. This is a high quality manuscript presenting novel data which compellingly support the conclusions that are made. I have no suggestions for further experimentation but list minor points below.

1. The final paragraph of Discussion is underdeveloped in that it claims regulation of phenotypic robustness in angiogenesis and its failure promises crucial insights into the mechanisms causing breakdown of vascular homeostasis in human disease. However, this issue is not pursued in any substantial way in Discussion. For example, are there known mutations in humans which lead to anastomosis defects and, if so, do any of them relate to the molecules or signaling pathways which are the subject of this manuscript? *

Response: We agree with the wish to see more substantial discussion of the issue of phenotypic robustness and potential links to human disease. The question of anastomosis itself is something that has not been addressed in humans, as it is a rather detailed phenotype observable where predictive patterning occurs and can be dynamically studied. As such, there is a lack of literature and knowledge on signalling pathways that drive anastomosis in humans, and also not many that have been identified in experimental systems or animal models. Flt1 and Vegf signalling, junctional molecules and a few other pathways have been shown to be involved, but nothing is known so far about Svep1 and anastomosis in other system. We will attempt to complement the discussion to make this more clear.

• There are typographical errors in the text so a further proof-read is required. *

Response: thank you, these will be corrected

*Reviewer #3 (Significance (Required)):

This manuscript provides an incremental conceptual advance in our understanding of the molecular mechanisms responsible for vascular anastomosis during developmental angiogenesis. The manuscript will be of interest to developmental biologists and vascular biologists.

My field of expertise pertains to angiogenesis and lymphangiogenesis in the setting of cancer and other diseases. *I am not a developmental biologist.