Author Response

Reviewer #3 (Public Review):

In this manuscript, Kim et al. use a deep generative model (a Variational Auto Encoder previously applied to adult data) to characterize neonatal-fetal functional brain development. The authors suggest that this approach is suitable given the rapid non-linear development taking place in the human brain across this period. Using two large neonatal and one fetal datasets, they describe that the resultant latent variables can lead to improved characterization of prenatal-neonatal development patterns, stable age prediction and that the decoder can reveal resting state networks. The study uses already accessible public datasets and the methods have been also made available.

The manuscript is clearly written, the figures excellent and the application in this group novel. The methods are generally appropriate although there are some methodological concerns which I think would be important to address. Although the authors demonstrate that the methods are broadly generalisable across study populations - however, I am unsure about the general interest of the work beyond application of their previously described VAE approach to a new population and what new insight this offers to understanding how the human brain develops. This is a particular consideration given that the major results are age prediction (which is easily done with various imaging measures including something as simple as whole brain volume) and recapitulation of known patterns of functional activity in neonates. As such, the work will be of interest to researchers working in fMRI analysis methods and deep learning, but perhaps less so to a wider neuroscience/clinical readership.

Specific comments:

1) (M1) If I understand correctly, the method takes the functional data after volume registration into template space and then projects this data onto the surface. Given the complexities of changing morphology of the development brain. would it not be preferable to have the data in surface space for standard space alignment (rather than this being done later?). This would certainly help with one of the concerns expressed by the authors of "smoothing" in the youngest fetuses leading to a negative relationship between age and performance.

While projecting onto the cortical surface has its advantages, as suggested here18, several studies have also shown that with careful registration, such as in the current study, volumetric registration can yield comparable performance19. Regardless, we did attempt to directly generate cortical surfaces for our fetuses. We refer the reviewer to our response to the RE-M2 [page 9].

Regarding the “smoothing” effect in the youngest fetuses, we want to clarify that the smoothing effect in the scans of young fetuses is not unique to the choice of registration method. In other words, the same smoothing effect must be seen with cortical registration as well. Regarding this perspective, we kindly refer the reviewer to our response to RE-M1 [page 7]. Regarding the specific change made in the revised manuscript, we kindly refer to our response to R1-m5 [p21] or [page 9 line 191-213] in the main manuscript.

2) (M2) A key limitation which I feel is important to consider if the method is aiming to be used for fetuses is the effects of the analysis being limited only to the cortical surface - and therefore the role of subcortical tissue (such as developmental layers in the immature white matter and key structures like the thalami) cannot be included. This is important, as in the fetal (and preterm neonatal) brain, the cortex is still developing and so not only might there be not the same kind of organisation to the activity, but also there is likely an evolving relationship with activity in the transient developmental layers (like the subplate) and inputs from the thalamus.

The reviewer raises an important point. We agree with the reviewer that the subcortical region plays a critical role in fetal and newborn neurodevelopment. Unfortunately, our current VAE model cannot utilize such information without a major change in the model structure. We added this as a limitation of our study and discussed why our VAE model, in its current form, did not include subcortical areas. Please see our detailed response to RE-M1 [page 4] or [page 25 line 558-570] in the main manuscript.

3) (M3) As the authors correctly describe, brain development and specifically functional relationships are likely evolving across the study time window. Beyond predicting age and a different way of estimating resting state networks using the decoding step, it is not clear to me what new insight the work is adding to the existing literature - or how the method has been specifically adapted for working with this kind of data. Whilst I agree that these developmental processes are indeed likely non-linear, to put the work in context, I think the manuscript would benefit from explaining how (or if) the method has been adapted and explicitly mentioning what additional neuroscientific/biological gains there are from this method.

We appreciate the reviewer’s critical insights. In the revised paper, we included additional results that, we hope, can address the reviewer’s concerns. We believe that the strength of the VAE model is that, relative to linear models, it can be more generalizable across different datasets and ages (adult vs. full-term babies vs. preterm babies vs. fetuses). In the original manuscript, this was supported by the superior age prediction performance of the VAE over linear models when applied to different datasets covering the fetal to neonatal periods. Age prediction could also be done using other imaging modalities, as the reviewer pointed out. However, we do not think this undermines the potential impact of having the ability to accurately estimate age based on functional connectivity patterns. Brain function-structure relationships may not exactly be one-to-one20. It is entirely possible that for one disease, brain functional connectivity alterations precede structural changes such that delayed growth trajectories will first manifest in the functional space. There are also certain aspects of brain function that cannot be mapped directly to its structural characteristics (i.e., structural connectivity patterns). For example, brain changes its functional connectivity patterns dynamically over different brain states (resting vs. task-engaging)21, mental disorders (depression22, anxiety23, Schizophrenia24), cognitive traits25, 26, and individual uniqueness25, etc. Therefore, we believe that estimating the functional age of fetuses and neonates given their functional connectivity profiles may provide a biomarker for tracking neurodevelopment trajectories, allowing clinicians to identify deviations early and intervene in a timely manner if necessary. For these reasons, we believe that superior age prediction performance of the VAE model compared to linear models is scientifically significant.

The value of the VAE lies in its ability to capture FC features that are otherwise not modeled by linear strategies. For example, here, we showed that only the VAE model can extract latent variables representing brain networks that are similar across different datasets. In contrast, linear models, showed higher network pattern similarity between full-term and preterm infants within the dHCP dataset. This suggests that the VAE model can be a very useful tool for capturing common brain networks in datasets acquired using different recording parameters and preprocessing steps. Moreover, the VAE representations predicted age with higher accuracy compared to linear representations. Together, these findings show that the methodology is effective in extracting functionally relevant features of the brain. Please see RE-M1 [page 3] and R1-m13 regarding the specific changes made in the revised manuscript.

4) (M4) The unavoidable smoothing effect of VAE is very noticeable in the figures - does this suggest that the method will be relatively insensitive to the fine granularity which is important to understand brain development and the establishment of networks (such as the evolving boundaries between functional regions with age) - reducing inference to only the large primary sensory and associative networks? This will also be important to consider for the individual "reconstruction degree" - (which it would likely then overstate - and would need careful intersubject comparison also) if it was to be used as a biomarker or predictor of cognition as suggested by the authors.

Regarding the first concern, yes. Greater smoothing will tend to yield less granular network patterns; this is true for all representational models (not only VAE, but also models like ICA or PCA). This effect becomes ever more pronounced when representations consist of fewer components (e.g., IC50); the smoothing effect becomes stronger, leading to coarser brain patterns (see Fig. 3 in the revised manuscript). In this regard, higher number of components is desired, but on the flipside, IC maps with higher components are generally less interpretable. In short, there will always be trade-offs between interpretability and spatial resolution. Also, higher components tend to cause over-fitting issue, as shown in our age prediction performance across different datasets (worse performance in the IC300 vs. IC50). In this sense, what matters for the representations is how informative each latent variable (or component) is. In the revised Fig. 2, we showed that latent variables from the VAE model were more informative in representing rsfMRI than linear representations. It is also noteworthy that the smoothing effect of the VAE is comparable to IC300 (similar effect to manual smoothing at the level of FWHM=5mm; revised Fig. 3). Given above results, we believe the VAE model may be more suitable for investigating finer scale of brain networks, than linear models. The above perspective was updated in the revised manuscript as [page 23 line 506-511]:

"Another interesting observation was that the smoothing effect of the VAE is comparable to IC300 (similar effect to manual smoothing at the level of FWHM=5mm; Fig. 3). Given the above, we believe the VAE model may be more suitable for investigating finer scale of brain networks, than linear models. Perhaps, the VAE model with a greater number of latent variables (e.g., 512 or 1024 instead of 256 in the current VAE) can be utilized to find brain networks at finer scale."

On top of the points raised above, network mapping with linear models is limited when it comes to mapping the spatial evolution of brain networks over aging due to their linear nature. This limitation can be observed in the ICA study with dHCP dataset (Fig. 4 in 7). On the other hand, thanks to its nonlinearity nature, the VAE model may have a potential to observe the spatial gradient of brain network over aging, while this expectation needs confirmation. To that end, we revised our discussion to reflect our perspective. We refer the full change made in the revised manuscript to our response to R1-m13.

Author Response

We thank the reviewers for their positive feedback and thoughtful suggestions that will improve our manuscript. Here we summarise our plan for immediate action. We will resubmit our manuscript once additional experiments have been performed to clarify all the major and minor concerns of the reviewers and the manuscript has been revised. At that point, we will respond to all reviewer’s points and highlight the changes made in the text.

Reviewer #1 (Public Review):

The authors have tried to correlate changes in the cellular environment by means of altering temperature, the expression of key cellular factors involved in the viral replication cycle, and small molecules known to affect key viral protein-protein interactions with some physical properties of the liquid condensates of viral origin. The ideas and experiments are extremely interesting as they provide a framework to study viral replication and assembly from a thermodynamic point of view in live cells.

The major strengths of this article are the extremely thoughtful and detailed experimental approach; although this data collection and analysis are most likely extremely time-consuming, the techniques used here are so simple that the main goal and idea of the article become elegant. A second major strength is that in other to understand some of the physicochemical properties of the viral liquid inclusion, they used stimuli that have been very well studied, and thus one can really focus on a relatively easy interpretation of most of the data presented here.

There are three major weaknesses in this article. The way it is written, especially at the beginning, is extremely confusing. First, I would suggest authors should check and review extensively for improvements to the use of English. In particular, the abstract and introduction are extremely hard to understand. Second, in the abstract and introduction, the authors use terms such as "hardening", "perturbing the type/strength of interactions", "stabilization", and "material properties", for just citing some terms. It is clear that the authors do know exactly what they are referring to, but the definitions come so late in the text that it all becomes confusing. The second major weakness is that there is a lack of deep discussion of the physical meaning of some of the measured parameters like "C dense vs inclusion", and "nuclear density and supersaturation". There is a need to explain further the physical consequences of all the graphs. Most of them are discussed in a very superficial manner. The third major weakness is a lack of analysis of phase separations. Some of their data suggest phase transition and/or phase separation, thus, a more in-deep analysis is required. For example, could they calculate the change of entropy and enthalpy of some of these processes? Could they find some boundaries for these transitions between the "hard" (whatever that means) and the liquid?

The authors have achieved almost all their goals, with the caveat of the third weakness I mentioned before. Their work presented in this article is of significant interest and can become extremely important if a more detailed analysis of the thermodynamics parameters is assessed and a better description of the physical phenomenon is provided.

We thank reviewer 1 for the comments and, in particular, for being so positive regarding the strengths of our manuscript and for raising concerns that will surely improve the manuscript. At this point, we propose the following actions to address the concerns of Reviewer 1:

1) We will extensively revise the use of English, particularly, in the abstract and introduction, defining key terms as they come along in the text to make the argument clearer.

2) We acknowledge the importance of discussing our data in more detail and we propose the following. We will discuss the graphs and what they mean as exemplified in the paragraph below.

Regarding Figure 3 - As the concentration of vRNPs increases, we observe an increase in supersaturation until 12hpi. This means that contrary to what is observed in a binary mixture, in which the Cdilute is constant (Klosin et al., 2020), the Cdilute in our system increases with concentration. It has been reported that Cdilute increases in a multi-component system with bulk concentration (Riback et al., 2020). Our findings have important implications for how we think about the condensates formed during influenza infection. As the 8 different genomic vRNPs have a similar overall structure, they could, in theory, behave as a binary system between units of vRNPs and Rab11a. However, a change in Cdilute with concentration shows that our system behaves as a multi-component system. This means that the differences in length, RNA sequence and valency that each vRNP have are key for the integrity of condensates.

3) The reviewer calls our attention to the lack of analysis of phase separations. We think that phase separation (or percolation coupled to phase separation) governs the formation of influenza A virus condensates. However, we think we ought to exert caution at this point as the condensates we are working with are very complex and that the physics of our system in cells may not be sufficient to claim phase separation without an in vitro reconstitution system. In fact, IAV inclusions contain cellular membranes, different vRNPs and Rab11a. So far, we can only speculate that the liquid character of IAV inclusions may arise from a network of interacting vRNPs that bridge several cognate vRNP-Rab11 units on flexible membranes, similarly to what happens in phase separated vesicles in neurological synapses. However, the speculative model for our system, although being supported by correlative light and electron microscopy, currently lacks formal experimental validation.

For this reason, we thought of developing the current work as an alternative to explore the importance of the liquid material properties of IAV inclusions. By finding an efficient method to alter the material properties of IAV inclusions, we provide proof of principle that it is possible to impose controlled phase transitions that reduce the dynamics of vRNPs in cells and negatively impact progeny virion production. Despite having discussed these issues in the limitations of the study, we will make our point clearer.

We are currently establishing an in vitro reconstitution system to formally demonstrate, in an independent publication, that IAV inclusions are formed by phase separation. For this future work, we teamed up with Pablo Sartori, a theorical physicist to derive in- depth analysis of the thermodynamics of the viral liquid condensates. Collectively, we think that cells have too many variables to derive meaningful physics parameters (such as entropy and enthalpy) as well as models and need to be complemented by in vitro systems. For example, increasing the concentration inside a cell is not a simple endeavour as it relies on cellular pathways to deliver material to a specific place. At the same time, the 8 vRNPs, as mentioned above, have different size, valency and RNA sequence and can behave very differently in the formation of condensates and maintenance of their material properties. Ideally, they should be analysed individually or in selected combinations. For the future, we will combine data from in vitro reconstitution systems and cells to address this very important point raised by the reviewer.

From the paper on the section Limitations of the study: “Understanding condensate biology in living cells is physiologically relevant but complex because the systems are heterotypic and away from equilibria. This is especially challenging for influenza A liquid inclusions that are formed by 8 different vRNP complexes, which although sharing the same structure, vary in length, valency, and RNA sequence. In addition, liquid inclusions result from an incompletely understood interactome where vRNPs engage in multiple and distinct intersegment interactions bridging cognate vRNP-Rab11 units on flexible membranes (Chou et al., 2013; Gavazzi et al., 2013; Haralampiev et al., 2020; Le Sage et al., 2020; Shafiuddin & Boon, 2019; Sugita, Sagara, Noda, & Kawaoka, 2013). At present, we lack an in vitro reconstitution system to understand the underlying mechanism governing demixing of vRNP-Rab11a-host membranes from the cytosol. This in vitro system would be useful to explore how the different segments independently modulate the material properties of inclusions, explore if condensates are sites of IAV genome assembly, determine thermodynamic values, thresholds accurately, perform rheological measurements for viscosity and elasticity and validate our findings”.

Reviewer #2 (Public Review):

During Influenza virus infection, newly synthesized viral ribonucleoproteins (vRNPs) form cytosolic condensates, postulated as viral genome assembly sites and having liquid properties. vRNP accumulation in liquid viral inclusions requires its association with the cellular protein Rab11a directly via the viral polymerase subunit PB2. Etibor et al. investigate and compare the contributions of entropy, concentration, and valency/strength/type of interactions, on the properties of the vRNP condensates. For this, they subjected infected cells to the following perturbations: temperature variation (4, 37, and 42{degree sign}C), the concentration of viral inclusion drivers (vRNPs and Rab11a), and the number or strength of interactions between vRNPs using nucleozin a well-characterized vRNP sticker. Lowering the temperature (i.e. decreasing the entropic contribution) leads to a mild growth of condensates that does not significantly impact their stability. Altering the concentration of drivers of IAV inclusions impact their size but not their material properties. The most spectacular effect on condensates was observed using nucleozin. The drug dramatically stabilizes vRNP inclusions acting as a condensate hardener. Using a mouse model of influenza infection, the authors provide evidence that the activity of nucleozin is retained in vivo. Finally, using a mass spectrometry approach, they show that the drug affects vRNP solubility in a Rab11a-dependent manner without altering the host proteome profile.

The data are compelling and support the idea that drugs that affect the material properties of viral condensates could constitute a new family of antiviral molecules as already described for the respiratory syncytial virus (Risso Ballester et al. Nature. 2021).

Nevertheless, there are some limitations in the study. Several of them are mentioned in a dedicated paragraph at the end of a discussion. This includes the heterogeneity of the system (vRNP of different sizes, interactions between viral and cellular partners far from being understood), which is far from equilibrium, and the absence of minimal in vitro systems that would be useful to further characterize the thermodynamic and the material properties of the condensates.

We thank reviewer 2 for highlighting specific details that need improving and raising such interesting questions to validate our findings. We will address all the minor comments of Reviewer 2. To address the comments of Reviewer 2, we propose the actions described in blue below each point raised that is written in italics.

1) The concentrations are mostly evaluated using antibodies. This may be correct for Cdilute. However, measurement of Cdense should be viewed with caution as the antibodies may have some difficulty accessing the inner of the condensates (as already shown in other systems), and this access may depend on some condensate properties (which may evolve along the infection). This might induce artifactual trends in some graphs (as seen in panel 2c), which could, in turn, affect the calculation of some thermodynamic parameters.

The concern of using antibodies to calculate Cdense is valid. We will address this concern by validating our results using a fluorescent tagged virus that has mNeon Green fused to the viral polymerase PA (PA-mNeonGreen PR8 virus). Like NP, PA is a component of vRNPs and labels viral inclusions, colocalising with Rab11 when vRNPs are in the cytosol without the need of using antibodies.

This virus would be the best to evaluate inclusion thermodynamics, where it not an attenuated virus (Figure 1A below) with a delayed infection as demonstrated by the reduced levels of viral proteins (Figure 1B below). Consistently, it shows differences in the accumulation of vRNPs in the cytosol and viral inclusions form later in infection. After their emergence, inclusions behave as in the wild-type virus (PR8-WT), fusing and dividing (Figure 1C below) and displaying liquid properties. The differences in concentration may shift or alter thermodynamic parameters such as time of nucleation, nucleation density, inclusion maturation rate, Cdense, Cdilute. This is the reason why we performed the thermodynamics profiling using antibodies upon PR8-WT infection. For validating our results, and taking into account a possible delayed kinetics, and differenced that may occur because of reduced vRNP accumulation in the cytosol, this virus will be useful and therefore we will repeat the thermodynamics using it.

As a side note, vRNPs are composed of viral RNA coated with several molecules of NP and each vRNP also contains 1 copy of the trimeric RNA dependent RNA polymerase formed by PA, PB1 and PB2. It is well documented that in the cytosol the vast majority of PA (and other components of the polymerase) is in the form of vRNPs (Avilov, Moisy, Munier, et al., 2012; Avilov, Moisy, Naffakh, & Cusack, 2012; Bhagwat et al., 2020; Lakdawala et al., 2014), and thus we can use this virus to label vRNPs on condensates to corroborate our studies using antibodies.

Figure 1 – The PA- mNeonGreen virus is attenuated in comparison to the WT virus. A. Cells (A549) were infected or mock-infected with PR8 WT or PA- mNeonGreen (PA-mNG) viruses, at a multiplicity of infection (MOI) of 3, for the indicated times. Viral production was determined by plaque assay and plotted as plaque forming units (PFU) per milliliter (mL) ± standard error of the mean (SEM). Data are a pool from 2 independent experiments. B. The levels of viral PA, NP and M2 proteins and actin in cell lysates at the indicated time points were determined by western blotting. C. Cells (A549) were transfected with a plasmid encoding mCherry-NP and co-infected with PA-mNeonGreen virus for 16h, at an MOI of 10. Cells were imaged under time-lapse conditions starting at 16 hpi. White boxes highlight vRNPs/viral inclusions in the cytoplasm in the individual frames. The dashed white and yellow lines mark the cell nucleus and the cell periphery, respectively. The yellow arrows indicate the fission/fusion events and movement of vRNPs/ viral inclusions. Bar = 10 µm. Bar in insets = 2 µm.

2) Although the authors have demonstrated that vRNP condensates exhibit several key characteristics of liquid condensates (they fuse and divide, they dissolve upon hypotonic shock or upon incubation with 1,6-hexanediol, FRAP experiments are consistent with a liquid nature), their aspect ratio (with a median above 1.4) is much higher than the aspect ratio observed for other cellular or viral liquid compartments. This is intriguing and might be discussed.

IAV inclusions have been shown to interact with microtubules and the endoplasmic reticulum, that confers movement, and also undergo fusion and fission events. We propose that these interactions and movement impose strength and deform inclusions making them less spherical. To validate this assumption, we compared the aspect ratio of viral inclusions in the absence and presence of nocodazole (that abrogates microtubule-based movement). The data in figure 2 shows that in the presence of nocodazole, the aspect ratio decreases from 1.42±0.36 to 1.26 ±0.17, supporting our assumption.

Figure 2 – Treatment with nocodazole reduces the aspect ratio of influenza A virus inclusions. Cells (A549) were infected PR8 WT and treated with nocodazole (10 µg/mL) for 2h time after which the movement of influenza A virus inclusions was captured by live cell imaging. Viral inclusions were segmented, and the aspect ratio measured by imageJ, analysed and plotted in R.

3) Similarly, the fusion event presented at the bottom of figure 3I is dubious. It might as well be an aggregation of condensates without fusion.

We will change this, thank you for the suggestion.

4) The authors could have more systematically performed FRAP/FLAPh experiments on cells expressing fluorescent versions of both NP and Rab11a to investigate the influence of condensate size, time after infection, or global concentrations of Rab11a in the cell (using the total fluorescence of overexpressed GFP-Rab11a as a proxy) on condensate properties.

We will try our best to be able to comply with this suggestion as we think it is important.

Reviewer #3 (Public Review):

This study aims to define the factors that regulate the material properties of the viral inclusion bodies of influenza A virus (IAV). In a cellular model, it shows that the material properties were not affected by lowering the temperature nor by altering the concentration of the factors that drive their formation. Impressively, the study shows that IAV inclusions may be hardened by targeting vRNP interactions via the known pharmacological modulator (also an IAV antiviral), nucleozin, both in vitro and in vivo. The study employs current state-of-the-art methodology in both influenza virology and condensate biology, and the conclusions are well-supported by data and proper data analysis. This study is an important starting point for understanding how to pharmacologically modulate the material properties of IAV viral inclusion bodies.

We thank this reviewer for all the positive comments. We will address the minor issues brought to our attention entirely, including changing the tittle of the manuscript and we will investigate the formation and material properties of IAV inclusions in the presence and absence of nucleozin for the nucleozin escape mutant NP-Y289H.

References

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Avilov, S. V., Moisy, D., Naffakh, N., & Cusack, S. (2012). Influenza A virus progeny vRNP trafficking in live infected cells studied with the virus-encoded fluorescently tagged PB2 protein. Vaccine, 30(51), 7411-7417. doi:10.1016/j.vaccine.2012.09.077

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Chou, Y. Y., Heaton, N. S., Gao, Q., Palese, P., Singer, R. H., & Lionnet, T. (2013). Colocalization of different influenza viral RNA segments in the cytoplasm before viral budding as shown by single-molecule sensitivity FISH analysis. PLoS Pathog, 9(5), e1003358. doi:10.1371/journal.ppat.1003358

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Klosin, A., Oltsch, F., Harmon, T., Honigmann, A., Julicher, F., Hyman, A. A., & Zechner, C. (2020). Phase separation provides a mechanism to reduce noise in cells. Science, 367(6476), 464-468. doi:10.1126/science.aav6691

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Author Response

Reviewer #1 (Public Review):

The manuscript by Shaikh and Sunagar addresses the question of the origin of spider venom proteins. It has been known for many years that an important component of spider venoms is a diverse group of small proteins known as disulfide-rich peptides (DRPs). However, it has not been clear whether this group of proteins has a common origin or evolved convergently in different lineages. The authors collected sequences of the genes encoding these proteins from publicly available genomes of spiders from a range of families. They aligned the sequences using the structural cysteines as guides and carried out a phylogenetic analysis of the different sequences, ultimately classifying the different proteins into over 50 super-families. One thing that is not clear from the text or from the references cited (I am not an expert on spider venom) is how many of these superfamilies were known before and how many are novel. There is also no clear indication of what criteria were used to define a subset of sequences as a superfamily. Nonetheless, the authors show that all these superfamilies have a single common ancestor, predating the divergence of araneomorphs and mygalomorphs and that the DRPs underwent independent diversification in each of these two lineages.

We have identified 78 novel superfamilies in this study and 33 were previously identified (Pineda et al. 2020 PNAS). We had previously described information in lines 90, 101 and 106 regarding the description of novel superfamilies from previous studies and the ones described in this study.

Line 90 “Recently, using a similar approach, 33 novel spider toxin superfamilies have been identified from the venom of the Australian funnel-web spider, Hadronyche infensa (9).”

Line 101 “This approach enabled the identification of 33 novel toxin superfamilies along the breadth of Mygalomorphae (Figures S1 and S2).”

Line 106 “Moreover, analyses of Araneomorphae toxin sequences using the strategy above resulted in the identification of 45 novel toxin superfamilies from Araneomorphae, all of which but one (SF109) belonged to the DRP class of toxins (Figures S3 and S4).”

Spider toxin superfamilies have been named after gods/deities of death, destruction and the underworld based on nomenclature introduced by Pineda et al. (2014 BMC genomics). We have now included this explanation in the manuscript under the methods and results sections. We have also provided additional details pertaining to this nomenclature in Table S1.

The authors also looked at selective forces acting on the sequences using dN/dS analyses. They reach the conclusion that there are different modes of selection acting on different sequences based on their role - defensive or predatory venoms - building on previous work by the lead author on venom sequence evolution in diverse animals.

All in all, this is an admirable piece of molecular evolution work, providing new data on the evolution of spider venom proteins. There are some confusions in terminology that need to be cleared up, and somewhat more context needs to be given for non-specialists as detailed in the points below:

We thank the reviewer for their constructive and critical suggestions, as well as the kind words of encouragement. Their suggestions have helped us in significantly improving the quality of our work.

Suggestion 1) Common names of the main spider infraorders should be given.

We thank the reviewer for their helpful input. We have now introduced spider infraorders with well-known spiders and their common names under the introduction section. Furthermore, we have also included a schematic representation of the spider phylogeny, and highlighted lineages under investigation as Figure 1.

Suggestion 2) Opisthothelae is not the common ancestor of Mygalomorphae and Araneamorphae, but the clade that encompasses those two clades. This incorrect statement appears in several places. Further on, it is stated that Opisthothelae is the common ancestor of all extant spiders. This is wrong both from a terminological point of view (a clade cannot be ancestral to another clade) and from a factual point of view, since there are extant spiders not included in Opisthothelae.

We thank the reviewer for pointing out this oversight. We have now corrected it to suborder Opisthothelae as the clade encompassing Mygalomorphae and Araneomorphae spiders.

Suggestion 3) Several proteins and proteins families are mentioned without being introduced, e.g. knottin. Please provide short descriptions.

We have now provided a short introduction to terms such as Knottin.

Reviewer #2 (Public Review):

This interesting study looks into the evolution of putative spider venom toxins, specifically disulfide-rich peptides (DRPs). The authors use published sequence data to gain new insights into the evolution of DRPs, which are the major component of most spider venoms. Through a series of sequence comparisons and phylogenetic analyses they identify a substantial number of new spider toxin superfamilies with distinct cysteine scaffolds, and they trace these back to a primitive scaffold that must have been present in the last common ancestor of mygalomorph and araneomorph spiders. Looking at the taxonomic distribution of these putative venom DRPs, they conclude that mygalomorph and araneomorph DRPs have evolved in different ways, with the former being recruited into venom at the level of genera, and the latter at the level of families. In addition, they perform selection analyses on the DRP superfamilies to uncover the surprising result that mygalomorph and araneomorph DRPs have evolved under different selective regimes, with the evolution of the former being characterised by positive selection, and the latter by purifying (negative) selection.

However, I don't think that in the current state of the manuscript these conclusions are robustly supported for several reasons. First, it seems that not all previously published data were included in the phylogenetic analyses that were used to identify new superfamilies of DRPs.

We have, indeed, analysed all spider toxin sequences available to date. We have relied on the signal and propeptide regions for identifying novel superfamilies, which is an accepted convention: Pineda et al. (2014 BMC Genomics); Pineda et al. (2020 PNAS).

Although many additional superfamilies can be identified, we have only retained those sequences for which there were at least 5 representatives for the identification of toxin superfamilies, and 15 representatives for selection analyses to ensure robustness. This filtering step ensured that the generated alignments, phylogenetic trees, and evolutionary assessments were robust and devoid of noise that stems from single-representative groups. Adding in those sequences would have enabled us to identify many more superfamilies, solely based on the signal and propeptide examination, but it wouldn’t have been possible to support them with other lines of evidence that were provided for all other superfamilies in this study, jeopardising the overall quality of the manuscript. Nonetheless, there is strong evidence that the left-out sequences are also related to the ones analysed in this study (Figure S10). In future, when more transcriptomes are sequenced, it would be possible to designate these newer toxin superfamilies with much stronger support.

Second, much of the data were obtained from whole-body transcriptome data, which leaves a degree of uncertainty that these data indeed derive from the venom glands that produce the toxins.

We respectfully disagree with the reviewer that ‘much of the data’ are from the whole-body transcriptomes. Nearly all sequences in our study are sourced from Pineda et al. (2014 BMC Genomics and 2020 PNAS), Sunagar et al (2013 Toxins), Cole and Brewer (2020 bioRxiv) and transcriptome sequence assembly data from established online repositories NCBI (NR and TSA) and ENA. All the above-mentioned studies (KS is a part of many of these) under their methods section clearly state that the transcriptomes were generated using mRNA isolated from venom gland tissue (BioProject accessions: PRJEB14734; PRJEB6062; PRJNA189679, PRJNA587301 and PRJNA189679, where source tissue type is designated as venom gland).

We would like to direct the reviewer’s attention to the following excerpts from reference papers from which data for this study has been sourced:

1. Pineda S et al. (2020 PNAS): “Three days later, they were anesthetized, and their venom glands were dissected and placed in TRIzol reagent (Life Technologies). Total RNA from pooled venom glands was extracted following the standard TRIzol protocol.”
2. Sunagar et al (2013 Toxins): “Paired venom glands were dissected out and pooled from nine mature females on the fourth day after venom depletion by electrostimulation. Total RNA was extracted using the standard TRIzol Plus method ...”
3. Cole and Brewer (2020 bioRxiv): “... the venom glands of each ctenid were dissected out, whole RNA was isolated from the venom glands …”

We would also like to point out that hexatoxins are widely studied and are some of the most well-understood spider venom toxins. Many representatives have been functionally characterised and shown to be potent in affecting prey and predatory species [Sunagar et al (2013 Toxins); Pineda et al. (2014 BMC Genomics and 2020 PNAS); Volker, et al. (2020 PNAS) - KS is a part of most of these studies as well]. However, the current technologies do not permit the high-throughput screening of the enormous diversity of toxins in spiders, which is why not every toxin sequence identified from the venom gland is functionally characterised. Nonetheless, venom researchers will not contest the role of these highly expressed venom gland proteins in envenoming, especially given that they share significant sequence identities with toxins that are functionally well-characterised.

The only exception to the above is non-ctenid araneomorph toxin superfamily sequences, which are retrieved from whole-body transcriptomes (Cole and Brewer; 2020 bioRxiv). The authors of the paper indicated these as putative toxins. As explained above, homologs of these peptides are well-characterised to be venom toxins. Additionally, in our phylogenetic trees (Figures 3, 4, S6 and S9), they are nested within the toxin clades, reaffirming their identity.

Third, the taxonomic representation of mygalomorph and araneomorph diversity in this study is so sparse that it becomes impossible to distinguish whether toxin recruitments have happened at the level of genera, families, or even higher-level taxa.

We respectfully disagree with this suggestion. The taxonomic breadth investigated in this study isn’t sparse. Analysed sequences belong to groups across the breadth of the spider phylogeny. To address this criticism, we are now including a schematic representation of spider phylogeny, where lineages under investigation are highlighted (Figure 1A). Given this broader taxonomic breadth, all of our interpretations are parsimoniously extendable to their common ancestors. For instance, we establish the common origin of all DRPs in the members of these widespread spider families. Therefore, not including sequences from other sister groups will not invalidate this hypothesis, and the most parsimonious explanation will be that the missing members too are likely to have DRPs in their venom (which is also a common understanding of the spider venom research). Whether DRPs dominate the venoms of these missing groups will only come to light upon investigation, but their presence in the venom is highly likely. Moreover, please do note that we have analysed nearly all sequences available in the literature to date.

As for the recruitment of the toxin superfamily at the taxon level, we would like to point out the phylogenies in Figures 2 and 3 that clearly show the differential recruitment events. We would also like to point out lines 120 and 136 state that this may not only be a result of recruitment and could arise from differential rates of diversification (also evident in other analyses presented in Figures 5 and Tables S2 and S3).

Line 120 “Interestingly, the plesiotypic DRP scaffold seems to have undergone lineage-specific diversification in Mygalomorphae, where the selective diversification of the scaffold has led to the origination of novel toxin superfamilies corresponding to each genus (Figure 2).”

Line 136 “However, we also documented a large number of DRP toxins (n=32) that were found to have diversified in a family-specific manner, wherein, a toxin scaffold seems to be recruited at the level of the spider family, rather than the genus. As a result, and in contrast to mygalomorph DRPs, araneomorph toxin superfamilies were found to be scattered across spider lineages (Figure 3; Figure S6; node support: ML: >90/100; BI: >0.95).”

Adding any number of missing lineages will neither change the fact that araneomorphs ‘appear’ to have recruited these superfamilies at the genera level, nor the family-level recruitment of toxin superfamilies in a large number of examined mygalomorphs.

We have now introduced a new figure (Figure 7) that highlights the different scenarios that explain the observed differences in the evolution of mygalomorph and araneomorph spider toxins. We have also included additional text in the manuscript to explain this better.

Fourth, only a selection of DRP superfamilies was used for natural selection analyses, without the authors explaining how this selection was made. Yet, they attempted to draw general conclusions about toxin evolution in mygalomorphs and araneomorphs, even though most of the striking differences they found were restricted to just two mygalomorph genera, and one family of araneomorphs.

From our experience and previous reports [Sunagar and Moran (2015, PLoS genetics); Sunagar, et al. (2012, MBE); Yang, Z. (2007, MBE)], the unavailability of enough sequences from datasets results in inaccurate estimation of omega values. For instance, if there are only a couple of sequences in a superfamily, both of which are slightly different from one another, then even these minor differences in them would be exaggerated. Hence, we have resorted to performing selection analysis on datasets for which there are at least 15 sequences. No doubt that this conservative approach reduces the number of datasets analysed, but it also ensures that our findings are well-supported. We have now clarified this in our manuscript under the methods section.

However, we did previously include sequences from all toxin superfamilies described to date in our alignment figure (Fig S10) and analysed their signal and propeptide regions. They were only excluded from selection analyses. It can be seen that they too are DRPs, but they belong to distinct superfamilies from the ones being described here.

If these concerns are addressed this study can shed important new light on venom toxin evolution in one of the most diverse venomous taxa on Earth.

We thank the reviewer for their constructive inputs and suggestions which have enabled us to make this manuscript more accessible to a wider audience.

Reviewer #3 (Public Review):

This work aims to elucidate the evolutionary origins of disulfide-rich spider toxin superfamilies and to determine the modes of natural selection and associated ecological pressures acting upon them. The authors provide a compelling line of evidence for a single evolutionary origin and differing factors (e.g., prey capture strategies and methods of anti-predator defense) that have shaped the evolution of these toxins. Additionally, the two major spider infraorders are claimed to have experienced differing selective pressures regarding these toxins.

The results presented here are novel and generally well-presented. The evidence for a single origin of DRP toxins in spiders is exciting and changes the paradigm of spider venom evolution.

The data are well analyzed, but the methods lack enough detail to reproduce the results. More information regarding the parameters passed to each software package, version numbers of all software employed, and models of molecular evolution employed in phylogenetic analyses are among the necessary missing information.

We thank the reviewer for their kind words and constructive and critical suggestions. Their suggestions have contributed towards improving the quality of our work. Upon their suggestion, we have now expanded the methods section to include more details.

The differences in the evolutionary pressures between mygalomorphs and RTA-clade spider DRP toxins are clear, but expanding RTA results to all araneomorphs may be overreaching. Additional araneomorph sequence data is available, despite the claims within this manuscript (e.g., see Jiang et al.. 2013 Toxins; He et al.. 2013 PLoS ONE; and Zobel-Thropp et al.. 2017 PEERJ). These papers include cDNA sequences of spider venom glands and contain representatives of inhibitory cysteine knot toxins, which are DRP toxins. These data would greatly enhance the strengths of the results presented herein.

In response to the expansion of RTA results to araneomorphs, we would like to point out that RTA comprises about 50% of the diversity recorded in Araneomorphae. The araneomorph data analysed in our study covers a range of araneomorph family divergence time Agelenidae (<70 MYA), Pisauridae (<50 MYA) and Theridiidae (~200 MYA, Magalhaes 2020, Biological Reviews 95.1). We report a strong signature of purifying selection influencing the evolution of araneomorph toxin SFs, despite the long evolutionary time separating them (50 - 200 MYA). We firmly believe that further addition of toxin sequence data from other groups will not deviate from the general trend of molecular evolution observed in both these lineages across such large period of time; barring certain certain exceptions (such as SF13 a defensive toxin identified from Hadronyche experiencing purifying selection; Volker, et al. 2020 PNAS).

We had initially excluded non-ctenid datasets from our analyses on account of poor sequence annotation and lack of representative sequence data. However, we have now incorporated Dolomedes mizhoanus (DRP) (Jiang et al. 2013 Toxins) and Latrodectus tredecimguttatus (non-DRP) (He et al. 2013 PLoS ONE) toxin dataset into our analyses, following reviewer’s suggestion. This has led to identification of 5 novel superfamilies, providing additional support to our spider venom evolution hypothesis.

Author Response

Reviewer #1 (Public Review):

Lin et al. characterise cellular pathologies in PLA2G6 mutant patient-derived neuronal cells (neuronal progenitor cells, NPCs, and IPSc-derived dopaminergic neurones) and a novel compound heterozygous PLA2G6 mutant mouse model. They build on their previous findings in an INAD fly model (lacking PLA2G6) to show that lysosomal and mitochondrial defects are evolutionary conserved in PLA2G6 deficiency. The authors proceed to use their INAD fly model and to screen a number of compounds that are predicted to modulate endo-lysosomal function using a bang sensitivity assay. They then show that the drugs that can rescue this fly behavioural phenotype also reduce LAMP2 expression in patientderived NPCs on Western blot analysis. Lastly, the manuscript reports the creation of new genetic constructs that express human PLA2G6 and study expression levels in a human kidney cell line as well as in patent-derived NPCs. In the latter neuronal model, they show that expression of human PLA2G6 can rescue mitochondrial fragmentation associated with PLA2G6 loss-of-function. Lin et al then show that ICV (intracerebroventricular) and IV (intravenous) injection of a human PLA2G6-containing construct is able to partially rescue the rotarod phenotype in PLA2G6 transheterozygous PLA2G6 mutant mice between ~110 and 150 days. There is also an associated improvement in lifespan and body weight.

The strengths of this work are that the authors use a number of different model organism systems, including patient-derived neuronal cells, Drosophila models (INAD flies) and mouse models to study PLA2G6-associated neurodegeneration (PLAN) at the cellular level. They also screen drug compounds that are predicted to target endo-lysosomal trafficking and sphingolipid metabolic pathways to ameliorate PLAN, thus identifying potential new therapeutic strategies. The work in mice, showing that gene therapy with human PLA2G6 can rescue a behavioural phenotype and lifespan is the first proof-ofconcept of such an advancement. This work will hopefully lead to further studies for optimisation toward clinical advancement.

We thank the reviewer and editor for the positive comments about our manuscript.

The major weaknesses are that the pathogenic mechanisms shown in the patient-derived neuronal cells and mice do not extend as far as those previously shown in the fly model published by the authors. Of note, ceramide levels and retromer function are not studied, both key pathologies described in the previous fly models. In addition, the drug screening is limited by its testing in one fly behavioural assay and LAMP2 Western blot analysis on patient derived NPCs.

The results, in general, support the conclusions of the authors and represent well-performed work. However, the significance of elevated glucosylceramide levels is not clear in the present study. Although this was previously found to be elevated in INAD flies, it was ceramide levels that were thought to be the main toxic insult, with drugs aimed at reducing ceramide levels being shown to rescue INAD flies.

We addressed these concerns. Please refer to our response to each of the specific point listed below.

This work will no doubt be of significant interest to the field, confirming several previous findings in the Drosophila model of PLA2G6 (iPLA2-VIA) knockout. It also extends upon the fly work by identifying compounds that can be further studied for potential drug-re-purposing for the treatment of PLA2G6associated disease. The gene therapy studies are also very interesting and a first proof-of-principle in PLAN using ICV and IV delivery in a mouse model.

We thank the reviewers and editor as addressing all these concerns really improved the manuscript.

Reviewer #2 (Public Review):

This article aims to extend human disease-related studies of PLA2G6 from fly models to iPS-neurons, mouse models, to look for drugs that suppress phenotypes and test them, and to attempt AAV whole body rescue. Generally, each of these questions/aims/experiments is excellent, but as presented, it's a bit of an underdeveloped hodgepodge of results, with each experiment somewhat underdeveloped or analyzed for the respective phenotype, in my opinion. I think the general thrust of the experiments is excellent. But the data are relatively cursory in many instances. Further development and characterization of the phenotypes would require quite a bit of work but vastly improve the paper.

We thank the reviewer for the positive comments about our manuscript. We have addressed most of the concerns.

Author Response

Reviewer #1 (Public Review):

Like other sensory organs, the inner ear has a rich population of pericytes, essential for sensory hair cell heath and normal hearing. In this study, using an inducible and conditional pericyte depletion mouse (PdgfrbCreERT2/iDTR) model, the authors demonstrate that the pericytes play critical roles in maintaining vascular volume and integrity of spiral ganglion neurons (SGNs) in the cochlea. Moreover, using the coculture models, they show vigorous vascular and neuronal growth in neonatal SGN explants in the presence of exogenous pericytes. Mechanistically, this study demonstrates that these roles are achieved mainly through the interactions between pericyte-released exosomes containing VEGF-A and VEGFR2-expressing the vessels and SGNs.

Overall, the data are analyzed thoroughly, and the conclusions are novel and convincing. It is mechanistically solid. The study is somewhat translationally limited. Nevertheless, understanding the roles of organ-specific pericytes is paramount, making this study timely and significant.

We thank Reviewer #1 for the positive comment. We agree the pericyte depletion model is not a translational disease model. However, pericyte pathologies, including the decline in pericyte number, pericyte migration, and pericyte trans-differentiation, are frequently seen in aging and noise-induced hearing loss animal models. Moreover, hearing dysfunction due to pericyte pathology has been demonstrated in recent studies (Hou et al., 2020; Hou et al., 2018; Neng et al., 2015).

Reviewer #2 (Public Review):

The present study from Xiaorui Shi's lab investigated the effect of pericyte depletion on spiral ganglion neurons and auditory function. Results in vitro culture system proposed that pericyte-derived exosomes contain VEGF, and promote not just vascular stability but neuronal survival through Flk1. This study is an extension of their previous study showing pericyte depletion causes auditory dysfunction, which is ameliorated by VEGF gene therapy (Zhang et al., JCI insight 2021). Overall, the data are clear and sophisticated and promote our understanding of the biological roles of pericytes in neuronal function. Several points should be thoroughly discussed or supported by definitive experiments like analysis of neuron-specific Flk1 KO mice.

We thank Reviewer #2 for the encouraging positive comments on our study. We especially appreciated the reviewer’s view that there would be value in using neuron-specific Flk1 KO mice to consolidate the results. However, since our in vitro adult SGN neuron cell culture model cearly demonstrates the direct role of exosome-VEGF-A signaling on adult SGN health, as shown in Figs. 5D & E and Figs. 9C & E, we are confident our conclusion is valid. A recent study used neuron-specific Flk1 conditional KO mice to demonstrate neuronal atrophy and dysfunction in memory impairment (Deyama et al., 2020). We do presume disruption of neuronal VEGF/FLK1 signaling in a specific neuronal Flk-1 deletion animal model would cause similar spiral ganglion death and subsequent hearing loss. To test this possibility, we are seeking a Cre-SGN driver animal model from the auditory community and Flk1 floxed mice from the larger research community. Of course, obtaining these models and setting up for a future study will require some time. Nevertheless, reviewer #2’s suggestion is excellent, we have added discussion of the suggestion to the Discussion section.

Reviewer #3 (Public Review):

Zhang et al focus on investigating the role of pericytes in the vasculature of the inner ear. They propose that pericyte-derived VEGF is required for vessels and SGN survival. Functionally, they show that pericyte ablation leads to hearing loss.

This work is interesting to the scientific community. It describes a very specific organ vasculature and its potential crosstalk with the neuronal compartment in the peripheral nervous system.

Major strengths and weaknesses:

• The study is well explained, written, and discussed;

• The design of the experiments is adequate;

• The study is performed in vivo, in vitro, and with functional readouts;

• Results are convincing.

We thank the reviewer for the positive comments on our study. We especially appreciate the reviewer’s suggestions for improving the soundness and quality of the study. We address Review#3’s specific concerns below.

The main conclusion of the study is that pericyte-derived VEGF acts on inner ear vessels and SGNs to maintain their functionality and survival. While all presented data supports this model, there could be other potential interpretations that should be tested and validated with further evidence:

The in vitro experiments are performed with SGN explants. Using this system the authors see that pericyte-derived conditioned medium or exosomes lead to increase vessel branching and SGN neurite outgrowth. As explants contain vessels and neurons, there is the possibility that VEGF is primarily acting on endothelial cells, which then in turn signal to neurons (independent of VEGF, even when neurons express VEGFR2). This should be tested. Perhaps by targeting VEGFR2 specifically in neurons, or by culturing isolated SGN neurons and testing the effect of pericyte-derived exosomes.

This is a great point. To confirm the effect of exosome VEGF-A on SGN neurite outgrowth, we treated isolated adult SGNs with exosomes. As shown in Figs.9C & E, we found much greater SGN dendrite and branch growth in the treated than in the untreated groups.

• Pericyte ablation via DTA might result in the activation of the immune system, which could also influence vessel and neuronal survival. It should be checked whether there is immune activation upon pericyte ablation.

Excellent point. We checked on macrophage activation at two weeks after pericyte depletion. We didn’t see any obvious signs of macrophage activation, but we did notice a decrease in macrophage number. We presume the reduction in macrophage number results from insufficiency blood flow and nutrient availability.

Author Response

Reviewer #2 (Public Review):

The authors seek to determine how various species combine their effects on the growth of a species of interest when part of the same community.

To this end, the authors carry out an impressive experiment containing what I believe must be one of the largest pairwise + third-order co-culture experiments done to date, using a high-throughput co-culture system they had co-developed in previous work. The unprecedented nature of this data is a major strength of the paper. The authors also discover that species combine their effect through "dominance", i.e. the strongest effect masks the others. This is important as it calls into question the common assumption of additivity that is implicit in the choice of using Lotka-Volterra models.

A stronger claim (i.e. in the abstract) is that joint effect of multiple species on the growth of another can be derived from the effect of individual species. Unless I am misunderstanding something, this statement may have to be qualified a little, as the authors show that a model based on pairwise dominance (i.e. the strongest pairwise) does a somewhat better job (lower RMSD, though granted, not by much, 0.57 vs 0.63) than a model based on single species dominance. This is, the effect of the strongest pair predicts better the effect of a trio than the effect of the larger species.

This issue makes one wonder whether, had the authors included higher-order combinations of species (i.e. five-member consortia or higher), the strongest-effect trio would have predicted better than the strongest-effect pair, which in turn is better predictor than the strongest-effect species. This is important, as it would help one determine to what extent the strongest-effect model would work in more diverse communities, such as those one typically finds in nature. Indeed, the authors find that the predictive ability of the strongest effect species is much stronger for pairs than it is for trios (RMSD of 0.28 vs 0.63). Does the predictive ability of the single species model decline faster and faster as diversity grows beyond 4-member consortia?

Thank you for raising this important point. It is true that in our study we see that single species predict pairs better than trios, and that pairs predict trios better than single species. As we did not perform experiments on more diverse communities (n>4), we are not sure if or how these rules will scale up. We explicitly address these caveats in our revised discussion.

Reviewer #3 (Public Review):

A problem in synthetic ecology is that one can't brute-force complex community design because combinatorics make it basically impossible to screen all possible communities from a bank of possible species. Therefore, we need a way to predict phenomena in complex communities from phenomena in simple communities. This paper aims to improve this predictive ability by comparing a few different simple models applied to a large dataset obtained with the use of the author's "kchip" microfluidics device. The main question they ask is whether the effect of two species on a focal species is predicted from the mean, the sum, or the max of the effect of each single "affecting" species on the focal species. They find that the max effect is often the best predictor, in the sense of minimizing the difference between predicted effect and measured effect. They also measure single-species trait data for their library of strains, including resource niche and antibiotic resistance, and then find that Pearson correlations between distance calculations generated from these metrics and the effect of added species are weak and unpredictive. This work is largely well-done, timely and likely to be of high interest to the field, as predicting ecosystem traits from species traits is a major research aim.

My main criticism is that the main take-home from the paper (fig 3B)-that the strongest effect is the best predictor-is oversold. While it is true that, averaged over their six focal species, the "strongest effect" was the best overall predictor, when one looks at the species-specific data (S9), we see that it is not the best predictor for 1/3 of their focal species, and this fraction grows to 1/2 if one considers a difference in nRMSE of 0.01 to be negligible.

As suggested, we have softened our language regarding the take-home message. This matter is addressed in detail above in response to 'Essential Revisions'. Briefly, we see that the strongest model works best when both single species have qualitatively similar effects, but is slightly less accurate when effects are mixed. We also see overall less accurate predictions for positive effects. In light of these findings, we propose that focal species for which the strongest model is not the most accurate is due to the interaction types, and not specific to the focal species.

We made substantial changes to the manuscript, including the first paragraph of the discussion which more accurately describes these findings and emphasizes the relevant caveats:

"By measuring thousands of simplified microbial communities, we quantified the effects of single species, pairs, and trios on multiple focal species. The most accurate model, overall and specifically when both single species effects were negative, was the strongest effect model. This is in stark contrast to models often used in antibiotic compound combinations, despite most effects being negative, where additivity is often the default model (Bollenbach 2015). The additive model performed well for mixed effects (i.e. one negative and one positive), but only slightly better than the strongest model, and poorly when both species had effects of the same sign. When both single species’ effects were positive, the strongest model was also the best, though the difference was less pronounced and all models performed worse for these interactions. This may be due to the small effect size seen with positive effects, as when we limited negative and mixed effects to a similar range of effects strength, their accuracy dropped to similar values (Figure 3–Figure supplement 5). We posit that the difference in accuracy across species is affected mainly by the effect type dominating different focal species' interactions, rather than by inherent species traits (Figure 3–Figure supplement 6)." (Lines 288-304)

The same criticism applies to the result from figure 2-that pairs of affecting species have more negative effects than single species. Considered across all focal species this is true (though minor in effect size, Fig 2A). But there is only a significant effect within two individual species. Again, this points to the effects being focal-species-specific, and perhaps not as generalizable as is currently being claimed.

Upon more rigorous analysis, and with regard to changes in the dataset after filtering, we see that the more accurate statement is that effects become stronger, not necessarily more negative (in line with the accuracy of the strongest model). The overall trend is towards more negative interactions, due to the majority of interactions being negative, but as stated this is not true for each individual focal. As such the following sentence in the manuscript has been changed:

"The median effect on each focal was more negative by 0.28 on average, though the difference was not significant in all cases; additionally, focals with mostly positive single species interactions showed a small increase in median effect (Fig. 2D)" (Lines 151-154)

As well as the title of this section: "Joint effects of species pairs tend to be stronger than those of individual affecting species" (Lines 127-128)

Another thing that points to a focal-species-specific response is Fig 2D, which shows the distributions of responses of each focal species to pairs. Two of these distributions are unimodal, one appears bimodal, and three appear tri-modal. This suggests to me that the focal species respond in categorically different ways to species addition.

We believe this distribution of pair effects is related to the distribution of single species effects, and not to the way in which different focal species respond to the addition of second species. Though this may be difficult to see from the swarm plots shown in the paper, below is a split violin plot that emphasizes this point.

Fig R1: Distribution of single species and pair effects. Distribution of the effect of single and pairs of affecting species for each focal species individually. Dashed lines represent the median, while dotted lines the interquartile range.

These differences occur even though the focal bacteria are all from the same family. This suggests to me that the generalizability may be even less when a more phylogenetically dispersed set of focal species are used.

We have added the following sentence to the discussion explicitly emphasizing the phylogenetic limitations of our study:

"Lastly, it is important to note that our focal species are all from the same order (Enterobacterales), which may also limit the purview of our findings." (Lines 364-366)

Considering these points together, I argue that the conclusion should be shifted from "strongest effect is the best" to "in 3 of our focal species, strongest effect was the best, but this was not universal, and with only 6 focal species, we can't know if it will always be the best across a set of focal species".

As mentioned above, we have softened our language regarding the take-home message in response to these evaluations.

My second main criticism is that it is hard to understand exactly how the trait data were used to predict effects. It seems like it was just pearson correlation coefficients between interspecies niche distances (or antibiotic distances) and the effect. I'm not very surprised these correlations were unpredictive, because the underlying measurements don't seem to be relevant to the environment tested. What if, rather than using niche data across 20 nutrients, only the growth data on glucose (the carbon source in the experiments) was used? I understand that in a field experiment, for example, one might not know what resources are available, and so measuring niche across 20 resources may be the best thing to do. Here though it seems imperative to test using the most relevant data.

It is true that much of the profiling data is not directly related to the experimental conditions (different carbon sources and antibiotics), but in addition to these we do use measurements from experiments carried out in the same environment as the interactions assays (i.e. growth rate and carrying capacity when growing on glucose), which also showed poor correlation with the effects on focals. Additionally, we believe that these profiles contain relevant information regarding metabolic similarity between species (similar to metabolic models often constructed computationally). To improve clarity, we added the following sentence to the figure legend of Figure 3–Figure supplement 1:

"The growth rate, and maximum OD shown in panel A were measured only in M9 glucose, similar to conditions used in the interaction assays." (Lines 591-592)

Additionally and relatedly, it would be valuable to show the scatterplots leading to the conclusion that trait data were uninformative. Pearson's r only works on an assumption of linearity. But there could be strong relationships between the trait data and effect that are monotonic but not linear, or even that are non-monotonic yet still strong (e.g. U-shaped). For the first case, I recommend switching to Spearman's rho over Pearson's r, because it only assumes monotonicity, not linearity. If there are observable relationships that are not monotonic, a different test should be used.

Per your suggestion, we have changed the measurement of correlation in this analysis from Pearson's r, to Spearman's rho. As we observed similar, and still mostly weak correlations, we did not investigate these relationships further. See Figure 3–Figure supplement 1.

Additionally, we generated heat maps including scatterplots mapping the data leading to these correlations. We found no notable dependency in these plots, and visually they were quite crowded and difficult to interpret. As this is not the central point of our study, we ultimately decided against adding this information to the plots.

In general, I think the analyses using the trait data were too simplistic to conclude that the trait data are not predictive.

We agree that more sophisticated analyses may help connect between species traits and their effects on focal species. In fact, other members of our research group have recently used machine learning to accomplish similar predictions (https://doi.org/10.1101/2022.08.02.502471). As such we have changed the wording in to reflect that this correlation is difficult to find using simple analyses:

"These results indicate that it may be challenging to connect the effects of single and pairs of species on a focal strain to a specific trait of the involved strains, using simple analysis." (Lines 157-159)

Author Response

Reviewer #1 (Public Review):

The authors examined the impact of pre-gravid obesity in human mothers on the monocytes of newborns by collecting umbilical cord blood. Additionally, the authors also used a non-human primate (NHP) model of diet-induced obesity to isolate fetal macrophage and assess the impact of maternal obesity on fetal macrophage function. The comprehensive analysis of the human umbilical cord blood monocytes by studying cytokine release, bulk RNA-seq and bulk ATAC-seq, single cell RNA-seq and single cell ATAC-seq, responses to pathogen stimulation as well as metabolic studies such as glucose uptake are major strength of the work. They present convincing evidence that the monocytes of offspring with obese mothers have epigenetic and transcriptomic profiles consistent with impaired immune responses, both during baseline conditions and upon stimulation.

We thank the reviewer for these positive remarks

However, it is not clear from the data how the epigenetic data and the transcriptomic data are related to each other. The implication that the epigenetic changes drive the downstream transcriptional differences is not clearly demonstrated. Furthermore, it is not clear which of the observed attenuations of monocyte transcriptional responses overlap with chromatin accessibility differences. Such an overlap would make a stronger case for the mechanistic link.

We thank the reviewer for this suggestion. We have included an integration section - with overlap of baseline ATAC-Seq (data from this study) with gene expression responses (from a previous study; https://doi.org/10.4049/jimmunol.1700434) following LPS stimulation in lean and obese groups - Figure 4E. Additionally, we report overlap of LPS induced chromatin changes with gene expression changes following LPS, E.coli and RSV stimulation in Figure 5I. Collectively, these changes provide the reader with a better link between chromatin accessibility and gene expression differences and their discordance with maternal obesity.

The increased phagocytosis of E.coli in umbilical cord monocytes of newborns with obese mothers appear counter-intuitive because it implies greater host defense capacity.

E.coli uptake assay is a standard way of measuring cellular phagocytosis by flow cytometry. We would like to clarify that despite impaired ex vivo cytokine responses and poor migration, UCB monocytes demonstrate higher ability to phagocytize pathogens. This is counterintuitive but not surprising, given that enhanced phagocytosis is a hallmark of regulatory monocytes/macrophages.

One of the most remarkable aspects of the manuscript is the analysis of the fetal macrophages in a non-human primate (NHP) model of diet induced obesity because of the challenge of studying fetal macrophages in humans. The cytokine assays nicely show that the fetal macrophages in the obesity model show impaired cytokine production, consistent with what was seen in the umbilical cord blood monocytes of human newborns. This is especially important because circulating monocytes or monocyte progenitors seed the fetal tissues and give rise to fetal macrophages, thus elegantly linking the human work on circulating umbilical cord blood monocytes to the tissue macrophages in the NHP model. However, the NHP studies do not show any additional macrophage characterization beyond the cytokine assays. Flow cytometry analysis of the macrophage phenotype and functional assays would strengthen the conclusions regarding macrophage dysregulation.

We have now included phenotyping data for ileal and splenic macrophages in Figure 6C-6E, which were collected during cell sorting. We unfortunately are not able to carry out additional functional assays since we don’t have any additional cells from these animals.

Reviewer #2 (Public Review):

This paper will be of interest to scientists studying the molecular effects of maternal obesity on offspring health. The paper represents an extension to earlier findings that have linked epigenomic alterations of monocyte population to aberrant immune responses in offsprings of obese mothers. Bulk and single cell technologies have been implemented to characterize monocytic responses to bacterial and viral pathogens at the transcriptional and epigenetic level. A macaque model of western-style diet induced obesity is also described to provide in vivo evidence in support of monocyte/immune cell reprogramming by western diet/obesity. However, enthusiasm for the paper is significantly dampened by a lack of clarity in data presentation and robustness of the analysis

We thank the reviewer for this comprehensive summary and thoughtful assessment

Reviewer #3 (Public Review):

The manuscript by Sureshchandra et al is a very extensive analysis of monocyte function and their molecular landscape in cord bloods from lean and obese mothers. They aimed to analyze the effects of pre-pregnancy BMI on the functioning of the innate immune system in newborns in a very extensive way. The combination of functional and molecular analyses strengthens their observations and shows many different sides of monocyte activation. I think this approach needs to be praised and should be an inspiration to many others who study monocyte function. This allows for a broad view on the matter and also shows where potential targeting will be necessary in the future. Overall, the manuscript and particularly the methods section is very well written and extensive, making it easy to study how robust the data are.

We thank the reviewer for their comprehensive and positive assessment of our work

Author Response

Reviewer #1 (Public Review):

This study provides further detailed analysis of recently published Fly Atlas datasets supplemented with newly generated single cell RNA-seq data obtained from 6,000 testis cells. Using these data, the authors define 43 germline cell clusters and 22 somatic cell clusters. This work confirms and extends previous observations regarding changing gene expression programs through the course of germ cell and somatic cell differentiation.

This study makes several interesting observations that will be of interest to the field. For example, the authors find that spermatocytes exhibit sex chromosome specific changes in gene expression. In addition, comparisons between the single nucleus and single cell data reveal differences in active transcription versus global mRNA levels. For example, previous results showed that (1) several mRNAs remain high in spermatids long after they are actively transcribed in spermatocytes and (2) defined a set of post-meiotic transcripts. The analysis presented here shows that these patterns of mRNA expression are shared by hundreds of genes in the developing germline. Moreover, variable patterns between the sn- and sc-RNAseq datasets reveals considerable complexity in the post-transcriptional regulation of gene expression.

Overall, this paper represents a significant contribution to the field. These findings will be of broad interest to developmental biologists and will establish an important foundation for future studies. However, several points should be addressed.

In figure 1, I am struck by the widespread expression of vasa outside of the germ cell lineage. Do the authors have a technical or biological explanation for this observation? This point should be addressed in the paper with new experiments or further explanation in the text.

Thank you for pointing this out. We found that our single cell dataset shows a similar (low) level of vasa expression outside the germline, suggesting that this is not due to single nucleus versus single cell RNA-seq (cluster 1, red in the lefthand umap).

Analyzing the single nucleus RNA-seq in more detail revealed that, compared to the germline, both the fraction of cells in a cluster expressing vasa and the level at which they express it are very low. This analysis is included in a new Figure 1 – figure supplement 1. It is likely that much of this is due to a technical artifact, such as ambient RNA. Finally, we note in the resubmission that vasa is in fact expressed in embryonic somatic cells, and thus some of the vasa expression we observe may be real (Renault. Biol Open 2012; https://doi.org/10.1242/bio.20121909).

Plots in the original submission drew undue attention to the few somatic cells that exhibited vasa signal, due to the fact that expressing cell points were forced to the front of the plot. Given our new analysis reporting the low levels and fraction of cells exhibiting vasa expression (Figure 1 – figure supplement 1), we have modified the panels of Figure 1, changing point size to more faithfully reflect the small proportion of somatic cells with some vasa expression.

The proposed bifurcation of the cyst cells into head and tail populations is interesting and worth further exploration/validation. While the presented in situ hybridization for Nep4, geko, and shg hint at differences between these populations, double fluorescent in situs or the use of additional markers would help make this point clearer. Higher magnification images would also help in this regard.

We thank the reviewer for their suggestions on clarifying the differences between HCC and TCC populations. As suggested, we have repeated the FISH experiments of Nep4 and geko with higher resolution, and included the additional marker Coracle that demarcates the junction between HCC and TCC (Figure 6O,Q,S,T). These panels replaced previous Nep4 and geko FISH images (see previous Figure 6Q,U,U’). FISH for Nep4 validated the split, and the enrichment of geko strongly suggests that this arm represents one cell type (HCCs). We have not yet identified a gene reciprocally enriched to the other arm. Therefore, in the revised submission, we call the assignment of TCC identity, and to a lesser extent, HCC identity ‘tentative’, but point out that genes predicted to be enriched to one or the other arm represent fertile candidates for the field to test.

Reviewer #2 (Public Review):

In this manuscript the authors explain in greater detail a recent testis snRNAseq dataset that many of these authors published earlier this year as part of the Fly Cell Atlas (FCA) Li et al. Science 2022. As part of the current effort additional collaborators were recruited and about 6,000 whole cell scRNAseq cells were added to the previous 42,000 nuclei dataset. The authors now describe 65 snRNseq clusters, each representing potential cell types or cell states, including 43 germline clusters and 22 somatic clusters. The authors state that this analysis confirms and extends previously knowledge of the testis in several important areas.

“However, in areas where testis biology is well studied, such as the development of germ cells from GSC to the onset of spermatocyte differentiation, the resolution seems less than current knowledge by considerable margins. No clusters correspond to GSCs, or specific mitotic spermatogonia, and even the major stages of meiotic prophase are not resolved. Instead, the transitions between one state and the next are broad and almost continuous, which could be an intrinsic characteristic of the testis compared to other tissues, of snRNAseq compared to scRNAseq, or of the particular experimental and software analysis choices that were used in this study.”

Note that the referee raises the same issue later in their review also. To respond succinctly, we placed the relevant sentence from a later portion of this referee’s comment here

“Support for the view that the problems are mostly technical, rather than a reflection of testis biology, comes from studies of scRNAseq in the mouse, where it has been possible to resolve a stem cell cluster, and germ cell pathways that follow known germ cell differentiation trajectories with much more discrete steps than were reported here (for example, Cao et al. 2021 cited by the authors).”

Respectfully, we have a different interpretation of other work as cited by this referee. Our data, as well as that from others, supports the notion that transitions are generally broad and continuous and are indeed a feature of testis biology. As we report here, data from both single cell and single nucleus RNAseq exhibit transitions from one cluster to the next. Thus, this feature cannot be due to the choice of method (single cell versus single nucleus).

In fact, prior scRNA-seq results on systems containing a continuously renewing cell population, such as is the case in the testis, do indeed exhibit a contiguous trajectory rather than discrete, well-separated cell states in gene expression space (that is, in a UMAP presentation). For example, this is the case from single-cell or single-nucleus sequencing from spermatogenesis in mouse (Cao et al 2021), human (Sohni et al 2019), and zebrafish (Qian et al 2022).

Along differentiation trajectories in these tissues, successive clusters are defined by their aggregate, transcript repertoire. Indeed, differentially-expressed genes can be identified for clusters, with expression enriched in a given cluster. However, expression is rarely restricted to a cluster. For instance, Cao et al. subcluster spermatogonia into four subgroups, termed SPG1-4. They state clearly that these SPG1-4 “follow a continuous differentiation trajectory,” as can be inferred by marker expression across cells in this lineage. Similar to our findings, while the spermatogonia can fall into discrete clusters, gene expression patterns are contiguous. For example, the “undifferentiated” marker used in Cao et al, Crabp1, clearly shows expression in SPG1-3, annotated as spermatogonial stem cells, undifferentiated spermatogonia, and early differentiated spermatogonia, respectively. Likewise, markers for the “SPG3” state spermatogonia have detectable expression in SPG2 and SPG4, and likewise for markers of the “SPG4” state (with expression found also in SPG3). <br /> Analogous study of human spermatogenesis arrives at a similar conclusion. In that work, although clusters are named as “spermatogonial stem cell (SSC)”, the authors are careful to specifically point out that, “…while we refer to the SSC-1 and SSC-2 cell clusters as ‘‘SSCs,’’ scRNA-seq is not a functional assay and thus we do not know the percentage of cells in these clusters with SSC activity. These subsets almost certainly contain other A-SPG cells [A type spermatogonia], including SPG progenitors that have committed to differentiate.” (Sohi et al 2019)

Thus, the work in several disparate systems, all involving renewing lineages, finds that discrete clusters, such as a “stem cell cluster” are not identified. In the Drosophila testis, germline differentiation flows in a continuous-like manner similar to spermatogenesis in several other organisms studied by scRNA-seq, and our finding is not a function of the methodology, but rather a facet of the biology of the organ.

Operating in parallel with continuous differentiation, we did find evidence of, and extensively discussed in concert with Figure 4, huge and dramatic shifts in transcriptional state in spermatocytes compared to spermatogonia, in early spermatids compared to spermatocytes, and in late spermatid elongation. Lastly, as we describe further below, new data in this resubmission identify four distinct genes with stage-selective expression as predicted by our analysis (new Figure 2 - figure supplement 1), illustrating the utility of our study for the field to find new markers and new genes to test for function.

A goal of the study was to identify new rare cell types, and the hub, a small apical somatic cell region, was mentioned as a target region, since it regulates both stem cell populations, GSCs and CySCs, is capable of regeneration, and other fascinating properties. However the analysis of the hub cluster revealed more problems of specificity. 41 or 120 cells in the cluster were discordant with the remaining 79 which did express markers consistent with previous studies. Why these cells co-clustered was not explained and one can only presume that similar problems may be found in other clusters.

Our writing seems not to have been clear enough on this point and we thank the reviewer. We have revised the section. In addition, we have added new data (Figure 7 - figure supplement 2). We had already stated that only 79 of these 120 nuclei were near to each other in 2D UMAP space, while other members of original cluster 90 were dispersed. Thus the 79 hub nuclei in fact clustered together on the UMAP. Other nuclei that mapped at dispersed positions were initially ‘called’ as part of this cluster in the original Fly Cell Atlas (FCA) paper (Li et al., 2022), making it obvious that a correction to that assignment was necessary, which we carried out. To our eye, no other called cluster was represented by such dispersed groupings. For the hub, we definitively established the 79 nuclei to represent hub cells by marker gene analysis, including the identification of a new maker, tup, that was included in the 79 annotated hub nuclei but excluded from the 41 other nuclei (Figure 7). In this resubmission, to independently verify the relationship of the 79 nuclei to each other, we subjected the 120 nuclei from the original cluster 90 defined by the FCA study to hierarchical clustering using only genes that are highly expressed and variable in these nuclei (Figure 7 - figure supplement 2). This computationally distinct approach strongly supported our identification of the 79 definitive hub nuclei.

Indeed, many other indications of specificity issues were described, including contamination of fat body with spermatocytes, the expression of germline genes such as Vasa in many somatic cell clusters like muscle, hemocytes, and male gonad epithelium, and the promiscuous expression of many genes, including 25% of somatic-specific transcription factors, in mid to late spermatocytes. The expression of only one such genes, Hml, was documented in tissue, and the authors for reasons not explained did not attempt to decisively address whether this phenomenon is biologically meaningful.

We discussed the question of vasa expression in somatic clusters in some detail above, in response to referee #1, and included new analysis in the resubmission.

With respect to the observation of ‘somatic gene’ expression in spermatocytes, we are also intrigued. We do not believe this is due to “contamination,” but rather a spermatocyte expression program that includes expression of somatic genes. First, these somatic markers were not observed in other germline clusters, which would be expected if this was due to general transcript contamination. Second, we observed expression of somatic markers in spermatocytes independently in the single-cell and single-nucleus data, making it unlikely to be an artifact of preparation of isolated nuclei. Finally, in the resubmission, in addition to Hml, we validated ‘somatic’ marker expression in spermatocytes by FISH of a somatic, tail cyst cell marker, Vsx1. Vsx1 is predicted to be expressed at low levels in spermatocytes in our dataset and is clearly visible in germline cells by FISH (Figure 3 – figure supplement 2G,H). We also refer the referee to Figure 6K, where the mRNA for the somatic cyst cell marker eya was observed by FISH at low levels in spermatocytes.

A truly interesting question mentioned by the authors is why the testis consistently ranks near the top of all tissues in the complexity of its gene expression. In the Li et al. (2022) paper it was suggested that this is due an inherently greater biological complexity of spermiogenesis than other tissues. It seems difficult to independently and rationally determine "biological complexity," but if a conserved characteristic of testis was to promiscuously express a wide range of (random?) genes, something not out of the question, this would be highly relevant and important.

We agree that the massive transcriptional program found in spermatocytes is, indeed, truly interesting. There are many speculations as to why spermatocytes are so highly transcriptional, including the possibility of “transcriptional scanning” (e.g., Xia et al. 2020) regulating the evolution of new genes. Testing such models is beyond the scope of this paper. However, one must also keep in mind that spermatogenesis involves one of the most dramatic cellular transformations in biology, where cellular components spanning from nuclei to chromatin to Golgi, cell cycle, extensive membrane addition, changes in cell shape, and building of a complex swimming organelle all must occur and be temporally coordinated. Small wonder that many genes must be expressed to accomplish these tasks.

Unfortunately, the most likely problems are simply technical. Drosophila cells are small and difficult to separate as intact cells. The use of nuclei was meant to overcome this inherent problem, but the effectiveness of this new approach is not yet well-documented. Support for the view that the problems are mostly technical, rather than a reflection of testis biology, comes from studies of scRNAseq in the mouse, where it has been possible to resolve a stem cell cluster, and germ cell pathways that follow known germ cell differentiation trajectories with much more discrete steps than were reported here (for example, Cao et al. 2021 cited by the authors).

We respectfully disagree with the referee about this collection of statements. First, the use of snRNASeq has been extensively characterized and compared to scRNA-seq in brain tissue by McLaughlin et al., 2021 (cited in the original submission) and was shown to be effective (McLaughlin, et al. eLife 2021;10:e63856. DOI: https://doi.org/10.7554/eLife.63856). snRNA-seq has a distinct advantage when dealing with long, thin cells, such as neurons or cyst cells (as featured in this work), where cytoplasm can easily be sheared off during cell isolation. Second, in a previous portion of our response to this referee, we discussed how our interpretation of Cao et al., 2021 differs from that expressed by this referee. Lastly, as requested in ‘Essential revision’ 2, we adjusted clustering methods and selected four genes, two predicted to be markers for early stage germline cells, and two for mid-spermatocyte stage development. FISH analysis demonstrates that expression for each of these maps to the appropriate stages (new Figure 2 - figure supplement 1). This confirms that the datasets we present in this manuscript can be mined to identify unique, diagnostic markers for various stages.

The conclusions that were made by the authors seem to either be facts that are already well known, such as the problem that transcriptional changes in spermatocytes will be obscured by the large stored mRNA pool, or promises of future utility. For example, "mining the snRNA-seq data for changes in gene expression as one cluster advances to the next should identify new sub-stage-specific markers." If worthwhile new markers could be identified from these data, surely this could have been accomplished and presented in a supplemental Table. As it currently stands, the manuscript presents the dataset including a fair description of its current limitations, but very little else of novel biological interest is to be found.

“In sum, this project represents an extremely worthwhile undertaking that will eventually pay off. However, some currently unappreciated technical issues, in cell/nuclear isolation, and certainly in the bioinformatic programs and procedures used that mis-clustered many different cells, has created the current difficulties.

Most scRNAseq software is written to meet the needs of mammalian researchers working with cultured cells, cellular giants compared to Drosophila and of generally similar size. Such software may not be ideal for much smaller cells, but which also include the much wider variation in cell size, properties and biological mechanisms that exist in the world of tissues.”

We appreciate the referee’s acknowledgement that this ‘undertaking will eventually pay off’. It was not our intention to address ‘function’ for this study, but rather to make the system accessible to the broadest community possible. We are uncertain if there is any remaining reservation held by this referee. A brief summary of what we covered in the manuscript may help allay any residual concern. Obviously, study of the Drosophila testis and spermatogenesis benefits from the knowledge of a large number of established cell-type and stage-selective markers. Thus, we extensively used the community’s accepted markers to assign identity to clusters in both the sn- and sc-RNA-seq UMAPs. We believe that effort well establishes the validity and reliability of the dataset . Furthermore, we identified upwards of a dozen new markers out of the cluster analysis, and verified their expression by FISH or reporter line in various figures throughout (tup, amph, piwi, geko, Nep4, CG3902, Akr1B, loqs, Vsx1, Drep2, Pxt, CG43317, Vha16-5, l(2)41Ab). To our mind, these contributions, coupled with annotation of the datasets, suggest strongly that they will serve the community well. This is especially true as we provide users with objects that they can feed into commonly used software algorithms such as Seurat and Monocle to explore the datasets to their purposes. Rather than simply relying on default settings within some of the applications, we also adjusted parameters for various clusterings as called for; some of which were in response to astute comments from referees, and included in the resubmission. Of course, it is possible that rare issues may arise in the datasets as these are further studied, but that is the case with all scRNA-seq data, and is not specific to work on this model organism.

Reviewer #3 (Public Review):

In this study, the authors use recently published single nucleus RNA sequencing data and a newly generated single cell RNA sequencing dataset to determine the transcriptional profiles of the different cell types in the Drosophila ovary. Their analysis of the data and experimental validation of key findings provide new insight into testis biology and create a resource for the community. The manuscript is clearly written, the data provide strong support for the conclusions, and the analysis is rigorous. Indeed, this manuscript serves as a case study demonstrating best practices in the analysis of this type of genomics data and the many types of predictions that can be made from a deep dive into the data. Researchers who are studying the testis will find many starting points for new projects suggested by this work, and the insightful comparison of methods, such as between slingshot and Monocle3 and single cell vs single nucleus sequencing will be of interest beyond the study of the Drosophila testis.

We greatly appreciate the reviewer’s comments.

Reviewer #4 (Public Review):

This is an extraordinary study that will serve as key resource for all researchers in the field of Drosophila testis development. The lineages that derive from the germline stem cells and somatic stem cells are described in a detail that has not been previously achieved. The RNAseq approaches have permitted the description of cell states that have not been inferred from morphological analyses, although it is the combination of RNAseq and morphological studies that makes this study exceptional. The field will now have a good understanding of interactions between specific cell states in the somatic lineage with specific states in the germ cell lineage. This resource will permit future studies on precise mechanisms of communication between these lineages during the differentiation process, and will serve as a model for studies of co-differentiation in other stem cell systems. The combination of snRNAseq and scRNAseq has conclusively shown differences in transcriptional activation and RNA storage at specific stages of germ cell differentiation and is a unique study that will inform other studies of cell differentiation.

Could the authors please describe whether genes on the Y chromosome are expressed outside of the male germline. For example, what is represented by the spots of expression within the seminal vesicle observed in Figure 3D?

Prior work demonstrated that proteins encoded by Y-linked genes are not expressed outside of the germline (Zhang et al. Genetics 2020. https://doi.org/10.1534/genetics.120.303324). In our snRNAseq dataset, we find that genes on the Y chromosome are not highly expressed outside of the male germline (on the order of ~100-fold lower in other tissues). In fact, we observe Y chromosome transcripts at this level in many nuclei across tissues collected for the Fly Cell Atlas project, including the ovary. Since we have not followed up on the Fly Cell Atlas observations directly using FISH to examine Y chromosome transcript expression outside the germline, we cannot rule out the possibility that such low level expression is real. However, the detection across several tissues argues that this is likely technical artifact. With regard to ‘spots of expression within the seminal vesicle’ (Figure 3D), a spot is colored red if the average expression level of genes on the Y chromosome is greater in that cell than in an average cell on our plot. These red spots are likely due to ambient RNA being carried over.

I would appreciate some discussion of the "somatic factors" that are observed to be upregulated in spermatocytes (e.g. Mhc, Hml, grh, Syt1). Is there any indication of functional significance of any of these factors in spermatocytes?

This is an excellent question. Although we validated expression for several (Hml, Vsx1 and eya), we did not test for their function here and this issue remains to be studied. This is now directly stated in the main text.

In the discussion of cyst cell lineage differentiation following cluster 74 the authors state that neither the HCC or TCC lineages were enriched for eya (Figure 6V). It seems in this panel that cluster 57 shows some enrichment for eya - is this regarded as too low expression to be considered enriched?

We thank the reviewer for their insightful comment and we agree with their conclusions. We have modified the text to reflect the low, but present, expression of eya in the HCC and TCC lineages. The text now reads as follows at line (insert line # here): “Enrichment of eya was dramatically reduced in the clusters along either late cyst cell branch compared to those of earlier lineage nuclei (Figure 6J,U).”

Author Response

Reviewer #2 (Public Review):

This is an interesting study investigating the effects of sensory conflict on rhythmic behaviour and gene expression in the sea anemone Nematostella vectensis. Sensory conflict can arise when two environmental inputs (Zeitgeber) that usually act cooperatively to synchronize circadian clocks and behaviour, are presented out of phase. The clock system then needs to somehow cope with this challenge, for example by prioritising one cue and ignoring the other. While the daily light dark cycle is usually considered the more reliable and potent Zeitgeber, under some conditions, daily temperature cycles appear to be more prominent, and a certain offset between light and temperature cycles can even lead to a breakdown of the circadian clock and normal daily behavioural rhythms. Understanding the weighting and integration of different environmental cues is important for proper synchronization to daily environmental cycles, because organisms need to distinguish between 'environmental noise' (e.g., cloudy weather and/or sudden, within day/night temperature changes) and regular daily changes of light and temperature. In this study, a systematic analysis of different offsets between light and temperature cycles on behavioural activity was conducted. The results indicated that several degrees of chronic offset results in the disruption of rhythmic behaviour. In the 2nd part of the study the authors determine the effect of sensory conflict (12 hr offset that leads to robust disruption of rhythmic behaviour) on overall gene expression rhythms. They observe substantial differences between aligned and offset conditions and conclude a major role for temperature cycles in setting transcriptional phase. While the study is thoroughly conducted and represents and impressive amount of experimental and analytical work, there are several issues, which I think question the main conclusions. The main issue being that temperature cycles by themselves do not seem to fulfil the criteria for being considered a true Zeitgeber for the circadian clock of Nematostella.

Major points:

Line 53: 'However, many of these studies did not compare more than two possible phase relationships.....'. Harper et al. (2016) did perform a comprehensive comparison of different phase relationships between light and temperature Zeitgebers (1 hr steps between 2 and 10 hr offsets), similar to the one conducted here. I think this previous study is highly relevant for the current manuscript and -- although cited -- should be discussed in more detail. For example, Harper et al. show that during smaller offsets temperature is the dominant Zeitgeber, and during larger sensory conflict light becomes the dominant Zeitgeber for behavioural synchronization. Only during a small offset window (5-7 hr) behavioural synchronization becomes highly aberrant, presumably because of a near breakdown of the molecular clock, caused by sensory conflict. Do the authors see something similar in Nematostella? Figure 3 suggests otherwise, at least under entrainment conditions, where behaviour becomes desynchronized only at 10 and 12 hr offset conditions. But in free-run conditions behaviour appears largely AR already at 6 hr offset, but not so much at 4 and 8 hr offsets (Table 2). So there seems to be at least some similarity to the situation in Drosophila during sensory conflict, which I think is worth mentioning and discussing.

We have added a more detailed discussion of our results in the context of Harper et al. 2016 (L468-476).

Line 111: The authors state that 14-26C temperature cycle is 'well within the daily temperature range experienced by the source population'. Too me this is surprising, as I was not expecting that water temperature changes that much on a daily basis. Is this because Nematostella live near the water surface, and/or do they show vertical daily migration? Also, I do not understand what is meant by '...range of in situ diel variation (of temperature)'. I think a few explanatory words would be helpful here for the reader not familiar with this organism.

In fact, one of our motivations for studying temperature is that Nematostella naturally experience extreme temperature variation. The data we cite (Tarrant et al. 2019) are from in-situ water measurements. Nematostella live in extremely shallow water (in salt marshes), and the local population in Massachusetts experience wide swings in temperature due to the temperate latitude.

We have added this information to the Introduction (L88-90), and we also added a discussion of Nematostella’s ecology in the Discussion section (L591-654).

Lines 114-117: I was surprised that clock genes can basically not be synchronized by temperature cycles alone. Only cry2 cycled during temperature cycles but not in free-run, so the cry2 cycling during temperature cycles could just be masking (response to temperature). Later the authors show robust molecular cycling during combined LD and temperature cycles (both aligned and out of phase), indicating that LD cycles are required to synchronize the molecular clock. Moreover, a previous study has demonstrated that LD cycles alone (i.e., at constant temperature) are able to induce rhythmic molecular clock gene expression (Oren et al. 2015). Similarly, the free running behaviour after temperature cycles does not look rhythmic to me. In Figure 2A, 14-26C there is at best one peak visible on the first day of DD, and even that shows a ~6 phase delay compared to the entrained condition. After the larger amplitude temperature cycle (8:32C) behaviour looks completely AR and peak activity phases in free-run appear desynchronized as well (Fig. 2B). Overall, I think the authors present data demonstrating that temperature cycles alone are not sufficient to synchronize the circadian clock of Nematostella. One way to proof if the clock can be entrained is to perform T-cycle experiments, so changing the thermoperiod away from 24 hr (e.g., 10 h warm : 10 h cold). If in a series of different T-cycles the peak activity always matches the transition from warm to cold (as in 12:12 T-cycles shown in Fig. 1A) this would speak against entrainment and vice versa.

Thank you for these thoughtful comments and constructive suggestions. We have conducted an additional experiment, which provides further evidence that temperature cycles can, in fact, synchronize the circadian clock. To do this, we measured the behavior of animals entrained in cycles with a short (12h) period, half the length of a circadian period. This takes advantage of a phenomenon called “frequency demultiplication”, in which organisms in 12h environmental cycles display both 12h and 24h components--essentially, the clock perceives every other cycle as a “day” (Bruce, 1960; Merrow et al., 1999). The important thing is that the 24h behavioral component can only occur if the signal is entraining a circadian clock—otherwise, we would only observe a directly-driven 12h behavior pattern.

We first show that this phenomenon occurs with 6:6 LD cycles—which we expected, because we know light is a zeitgeber. We then show that animals entrained to a temperature cycle with a 12h period also display 24h behavioral rhythms—and in fact the 24h component is stronger than the 12h component. We believe this is strong evidence that temperature is a bona fide zeitgeber in this system. This experiment is now explained in the Results (L127-154) and in Figure 2–Figure supplement 1.

In terms of our original data, the reviewer is correct that the statistically-detectable free-running rhythms were weak and not visually obvious). Our confidence in thermal entrainment came from the fact that some individual animals had 24h rhythmicity in free-run, even if the signal was weak in the mean time series—this suggested that temperature must be at least capable of synchronizing internal clocks. It is also important to note that even light-entrained rhythms are “noisy” in cnidarians, which is why we were not surprised that the signal was weak. We have added a discussion of this observation in L601-612.

Lines 210-226: As mentioned above, I think it is not clear that temperature alone can synchronize the Nematostella clock and it is therefore problematic to call it a Zeitgeber. Nevertheless, Figure 3A, B, D show that certain offsets of the temperature cycle relative to the LD cycle do influence rhythmicity and phase in constant conditions. This is most likely due to a direct effect of temperature cycles on the endogenous circadian clock, which only becomes visible (measureable) when the animals are also exposed to certain offset LD cycles. My interpretation of the combined results would be that temperature cycles play only are very minor role in synchronizing the Nematostella clock (after all, LD and temperature cycles are not offset in nature), perhaps mainly supporting entrainment by the prominent LD cycles.

With our new data (see previous point), we believe we can safely say that temperature is a zeitgeber. We are not totally clear on what is meant by “a direct effect of temperature cycles on the endogenous circadian clock.” We argue that, because we see changes in free-running behavior during certain offsets, the timing of temperature cycles must affect the internal clock in a way that persists during constant conditions—it can’t just be a direct (clock-independent) effect of temperature.

Gene expression part: The authors performed an extensive temporal transcriptomic analysis and comparison of gene expression between animals kept in aligned LD and temperature cycles and those maintained in a 12 hr offset. While this was a tremendous amount of experimental work that was followed by sophisticated mathematical analysis, I think that the conclusions that can be drawn from the data are rather limited. First of all, it is known from other organisms that temperature cycles alone have drastic effects on overall gene expression and importantly in a clock independent manner (e.g., Boothroyd et al. 2007). Temperature therefore seems to have a substantially larger effect on gene expression levels compared to light (Boothroyd et al. 2007). In the current study, except for a few clock gene candidates (Figure 2C), the effects of temperature cycles alone on overall gene expression have not been determined. Instead the authors analysed gene expression during aligned and 12 h offset conditions making it difficult to judge which of the observed differences are due to clock independent and clock dependent temperature effects on gene expression. This is further complicated by the lack of expression data in constant conditions. I think the authors need to address these limitations of their study and tone down their interpretations of 'temperature being the most important driver of rhythmic gene expression' (e.g., line 401). At least they need to acknowledge that they cannot distinguish between clock independent, driven gene expression and potential influences of temperature on clock-dependent gene expression rhythms. Moreover, in their comparison between their own data and LD data obtained at constant temperature (taken from Oren et al. 2015), they show that temperature has only a very limited effect (if any) on core clock gene expression, further questioning the role of temperature cycles in synchronising the Nematostella clock. Nevertheless, I noted in Table 3 that there is a 1.5 to 3 hr delay when comparing the phase of eight potential key clock genes between the current study (temperature and LD cycles aligned) and LD constant temperature (determined by Oren et al.). To me, this is the strongest argument that temperature cycles at least affect the phase of clock gene expression, but the authors do not comment on this phase difference.

We agree with these points about the limitations of our study, and have revised the manuscript to phrase our conclusions more carefully. We still think it is reasonable to observe that temperature was a stronger drive of gene expression than light in our study, but this may not be true in other contexts.

In terms of the comparison with Oren et al. 2015, we didn’t want to over-interpret these results because there are other differences between the studies (L1181-1185), including the use of a different source population. In addition, we would prefer denser sampling (2h time points rather than 4h) and larger sample sizes to make claims about phase differences.

Network analysis: This last section of the results was very difficult to read and follow (at least for me). For example, do the colours in Figure 6A correspond to those in Figure 6B, C? A legend for each colour, i.e., which GO terms are included in each colour would perhaps be helpful. As mentioned above, I also do not think we can learn a lot from this analysis, since we do not know the effects of temperature cycles alone and we have no free-run data to judge potential influence on clock controlled gene expression. Under aligned conditions genes are expressed at a certain phase during the daily cycle (either morning to midday, or evening to midnight), which interestingly, is very similar to temperature cycle-only driven genes in Drosophila (Boothroyd et al. 2007). Inverting the temperature cycle has drastic effects on the peak phases of gene expression, but not so much on overall rhythmicity. But since no free-run data are available, we do not know to what extend these (expected) phase changes reflect temperature-driven responses, or are a result of alterations in the endogenous circadian clock.

We have revised and streamlined this section and Fig. 6, including removing panel 6C. The colors do correspond across panels in the figure. For space, GO terms of select modules are included in Fig. 6, and GO results for all modules are included in the Supplemental Data and discussed in the Results.

It is true that we can’t distinguish temperature-driven versus clock effects here, and it does seem like many modules simply follow the temperature cycle (which we say in this section). The most interesting finding from this section is probably that the co-expression structure (correlations between rhythmic genes) are substantially weakened during SC, and we do discuss certain modules of genes that lose or gain rhythmicity. We have revised this section to focus on the main points and have cut several of the less pertinent results.

Reviewer #3 (Public Review):

This article reflects a significant effort by the authors and the results are interesting.

For the third set of experiments, are temperature and light really out of synch? While peak in temperature no longer occurs along with lights on, we do still have two 24 hour cycles where changes in the environmental cues still occur simultaneously (lights on with peak in temperature, lights off with min in temperature). I wonder what would happen if light remained at a 24 hour cycle and temperature became either sporadic (randomly changing cycles) or was placed on a longer cycle altogether (temperature taking 20 hours to increase from min to max, and then another 20 hours to go from max to min).

Thank you for your interesting suggestions for future experiments. This point is addressed in our revisions responding to Reviewer #1, who requested a discussion of the phrase “sensory conflict.” We agree that the binary “in-sync vs. out-of-sync” may be too simplistic. Our original conception of sensory conflict was a situation in which light and temperature provide different phase information, as informed by experiments with only light (prior literature) or only temperature (this work).

In our revised manuscript, we discuss the idea that “sensory conflict” is not always a useful framework because there are many possible relationships between light and temperature. Although our 12h offset is certainly less “natural” than our aligned time series, it may be useful to think of them simply as 2 different possible light and temperature regimes in which the two signals interact, rather than abstract ideals of “aligned” or “misaligned.”

An area that could significantly benefit a broader readership would be to improve overall clarity of figures and rethink if all the results are necessary to convert the key findings of the paper. As written, the results sections is somewhat confusing.

We have revised Figs. 1 and 6 for clarity, and we have also shortened the network analysis portion of the Results.

Author Response

Reviewer #1 (Public Review):

Here the authors sought to understand how BPGM/2,3-BPG levels are involved in adaptive responses to hypoxia and whether they are involved in fetal growth restriction. In the current state, I find the data to be confusing and lacking in mechanistic data to justify that increased BPGM is an adaptive response to hypoxia. While the authors find increased staining for the enzyme BPGM in SpA-TGCs after hypoxia, they did not assess 2,3-BPG in cord blood. This would show that increased enzymatic levels have a downstream impact. MRI experiments assessing placental and fetal haemoglobin-oxygenation, showed no differences. Human FGR samples, however, showed reduced 2,3-BPG in cord blood. Further evidence is required to show hypoxia increases BPGM as a compensatory mechanism to permit adequate 2,3-BPG and placental-fetal oxygenation levels as the authors claim.

Additional experiments that demonstrate that BPGM is advantageous in the context of hypoxia would strengthen the authors arguments, and would provide a novel mechanism for adaptive responses to hypoxia in the placenta which is highly interesting.

Obtaining cord-blood from mouse embryos and analyzing its 2,3 BPG content is technically not feasible thus we concentrated on the human data only. However note that the dominant physiological effect would be on maternal blood in the placenta, where local elevation of 23BPG can aid in oxygen release.

Reviewer #2 (Public Review):

Summary:

This manuscript will be of interest for investigators in the field of development and the biology of pregnancy. The major strengths of the data are the detailed description of a hypoxia-induced mouse model of fetal growth restriction, where phenotypes, tissue histology, MRI images and metabolic analysis combine to characterize the experimental system. The data seem descriptive and preliminary, and the comparison to human pregnancy is neither supportive nor rigorous.

Strengths

• The mouse pregnancy has been used by the authors and by others as a model for placental insufficiency. The manuscript provides incremental data to characterize hypoxia- induced fetal growth restriction

• The 15.2T MR imaging technology is high quality and informative, even if the results did not reveal marked changes.

• The detailed characterization of BPGM expression in the apical mouse placental surfaces is valuable.

• The provided model may be useful for future studies by the authors.

Weaknesses

• The metabolic analysis was restricted to one enzyme and metabolite. Placental analysis of 2,3-BPG and BPGM were already published (ref 29-30). At best, if the 2,3 BPG is related to the phenotype, it night be interpreted as a part of the injury in human cases, and adaptive response in the mouse models (as the authors suggested lines 286-288 and 332-336.). However, these assumptions are not tested.

In the paper of Pritlove et al. (ref. 29) the authors demonstrated the expression of BPGM in normal human cohort. However, they did not test BPGM expression or 2,3 BPG levels in FGR placentae. In the paper of Gu et al. (ref. 30) the authors analyze murine placental BPGM expression secondary to igf2 deletion. Our study is the first to demonstrate the impact of maternal hypoxia on placental BPGM levels in murine gestational hypoxia models .

• The human cases are not very informative. The causes of FGR were not known, but clearly (Table 1) not analogous to that of the mouse model. Systemic hypoxia in humans might have been more informative. In its absence, the value of cross-species comparison is low. -

• While the provided experiments are of good quality, the approach is very descriptive and not advancing mechanistic understanding of FGR-related placental insufficiency.

The human placenta were specifically selected to exclude known causes of FGR such as heavy smoking or iron deficiency. We will work to expand the diversity of cases to test the potential role of BPGM in those cases as well.

Author Response

Reviewer #1 (Public Review)

This manuscript describes a new method to perform online movement correction and extraction of calcium signals from a miniscope. The efficiency of the algorithm is tested by quantifying the accuracy of animal location decoding from hippocampal place cells. The online decoding happens with virtually no delay which is promising for closed-loop methods. It seems to be superior to online decoding without motion correction, which was the state of the art.

The strength of this technique is therefore that it achieves real-time processing.

The weakness of the study is the lack of comparison of the decoding accuracy with what can be obtained with electrophysiological state of the art, which prevents really estimating how precise the technique is.

In revision, we present data showing that when our system is used to decode contour-based calcium traces from N≈50 neurons, the decoder achieves a mean distance error of ~30 cm which is worse than the mean error of ~20 cm achieved using maximum likelihood decoding of single unit spike trains from electrophysiological recordings (Fig. 7E). However, when decoding of N=900 contour-free calcium traces from the same image frames in the same rats, the mean decoding error goes down to ~15 cm, which is better than the mean for electrophysiological recordings. From this we conclude that real-time decoding of position from calcium traces achieves accuracies similar to those achievable with electrophysiology.

Although less critical, there is no demonstration of a closed-loop application.

It is true that we have not yet demonstrated a real-time closed loop application, but by demonstrating short latency generation of TTL outputs triggered by the decoder, we demonstrate the capability for closed-loop applications.

Real-time position decoding is technically nice, but the position can be obtained from tracking the animal so it is practically useless.

We offer two points in reply to this comment. First, decoding position from neural activity could offer useful (though not yet demonstrated) capabilities that would not be achievable with simple position tracking; for example, the position decoder could be trained on CA1 signals obtained during waking and then used to read out position trajectories generating during REM sleep.

Second, and more importantly, position decoding was selected as a benchmark for performance testing mainly because it allows highly precise comparisons between decoder predictions and ground truth, which is important for establishing that the fidelity of calcium signals imaged in real time is adequate for accurate decoding of behavior at short latencies.

It is also clear that decoding position on a linear track is easier than on a 2D arena, therefore it is difficult to estimate how much the efficiency of the method can be challenged in harder settings.

It is true that decoding in a 2D arena would be a greater challenge than a 1D linear track, but in pursuit of our goal to rapidly disseminate a system with capabilities for short latency decoding of behavior from calcium signals, optimizing system performance for one specific application (e.g,, position decoding) is not our main priority. A higher priority is to offer versatility for a wide range of experimental applications. To better demonstrate such versatility, the revised manuscript includes a new section in the Results that demonstrates categorical classification of behaviors during an instrumental touchscreen task.

Reviewer #2 (Public Review):

In this paper, the authors developed a new device for online decoding of position based on calcium imaging in freely moving rodents. This device could be used in the brain-computer interface to investigate neurofeedback-based therapies for neurological disorders. The technical part is properly done and gives convincing results that can be truly helpful for the scientific community using the miniscope. Nevertheless, as a methodological article, there should be more details regarding the accuracy of the decoding and of the different steps to follow if someone wants to use their methodology. Moreover, a true online real-time experiment should be performed to validate the device.

Please find below my comments:

• From what I read the authors did not perform a true real-time experiment. I think this step iscrucial to ensure the quality of their device.

It is unclear from this comment where to draw the bar for a “true real-time experiment.” Some previous publications of real-time approaches (such as refs #6,#11,#26) have proposed causal algorithms without performance tests in hardware at all, whereas others (such as ref #14) have performance tested their system in hardware by carrying full experiments using closed-loop feedback (albeit with much smaller numbers of calcium trace predictors than we demonstrate here) without comparing different algorithmic approaches. Here we use an intermediate strategy of feeding raw offline video from a virtual sensor through the hardware processing pipeline (verifying that calcium trace outputs were identical for the real and virtual sensors). We adopted this intermediate approach to achieve the dual objectives of testing a true hardware implementation on real-time performance measures (e.g., microsecond processing latencies) while also benchmarking different algorithms (such as CB versus CF trace extraction as in Fig. 3, or raw calcium traces versus deconvolved spikes as in panel A of the Supplement to Fig. 3) against one another on the same datasets.

• There should be a validation against a classical offline Bayesian decoding.

We have presented an accuracy comparison for decoding linear track position from calcium traces with DeCalciOn versus decoding from single-unit spikes with electrophysiological recording data (Fig. 7E); decoding from single-unit spikes utilized a classical Bayesian maximum likelihood approach (see Methods), so Fig. 7E not only offers a comparison between calcium imaging versus electrophysiology, but between online linear classifier versus classical offline Bayesian approaches as well. In addition, we compared the performance of the linear classifier to a naïve Bayes decoder in panel B of the Supplement to Fig 3, showing that performance is better for the linear classifier than naïve Bayes.

• "To mimic these steps using the virtual sensor in our performance tests, one session of imagedata was collected and stored from each of the 13 rats, yielding ~7 min (8K-9K frames) of sensor and position tracking data per rat. The linear classifier was then trained on data from the first half of each session and tested on data from the second half." This sentence is not clear enough. The authors should clearly describe the exact time needed for each experimental step. What is the time needed for instance for the experimental step 2, during which the linear classifier is trained to decode behavior from the initial dataset? This is crucial information if someone wants to use this device.

In response to this comment, the Results section of the revised manuscript includes an extensive subsection (‘Steps of a real-time imaging session’) that describes each experimental step in detail (pages 4-6), including the time required for each step. In addition, this information is now more thoroughly summarized in the diagram of Fig. 1B.

How the accuracy varies with the duration (or the quality) of the initial dataset? It is important that the authors provide an investigation of this to validate their device.

This issue is now discussed in the Results near the bottom of page 5. In addition, Fig. 3G now plots how position decoding improves as a function of the size of the training dataset.

• For instance, what is the decrease in decoding accuracy 1) with fewer place cells?

The scatterplots in the right panels of Fig. 3D show that decoding accuracy improves as a function of the number of neurons imaged in given rat.

What is the approximative number of place cells to obtain reliable decoding?

This question is addressed by showing how decoding accuracy improves with the number of imaged neurons (Fig. 3D scatterplots). We also address this issue on our performance comparison of CB versus CF and CF+ traces since differing numbers of calcium trace predictors appear to be an important factor in accounting for the observed performance differences, as discussed in the main text (page 16, last paragraph).

2) With the duration of the initial recording session. Here it seems to be of the order of 3-4 min.What if the recording session is shorter? Is there some constraint about this recording session (in terms of speed, stops, etc...) to obtain good decoding?

The revised Fig. 3G plots how position decoding improves as a function of the size of the training dataset.

3) Is there a link between the decoding accuracy and the number of place cells nearby?

We did not select calcium traces that met a spatial criterion (i.e, “place cells”) to be include in the decoding analysis, Instead, all detected CA1 calcium traces provided input to the decoder, regardless of their spatial tuning properties (Fig. 3D and panels D,E of the Supplement to Fig. 3 show that many cells were indeed spatially tuned). Also note that when contour-free (CF) trace extraction methods were used, each calcium trace could detect fluorescence from multiple neurons. Under this methodology it is not straightforward to analyze how decoding accuracy at a given position varies with the “number of place cells nearby” and we are not convinced that presenting such an analysis would advance our main goal of demonstrating DeCalciOn’s capabilities to researchers.

• The authors specified the time delay of 2.5ms for their device. Yet, it is pointless regarding thepurpose of the decoding. The important information is the precise position of the animal when the device is used to trigger a stimulation at a given location. Again, a true online experiment should be done to validate that a TTL can be triggered by the device at a precise location (with a quantification of the error made).

We agree that this is an important issue, and it has been thoroughly addressed in the revised manuscript.

• There is no information on the accuracy of the decoding with respect to the location in thelinear track. It is likely that the extremities of the linear track will be better identified. Figure 4C does not provide a clear description of the error made. The choice of D=2 (which seems to represent the spatial bin) is not justified. Two spatial bins seem to represent +/-40 cm which is quite large.

Polar plots in Fig. 3F of the revised manuscript show mean accuracy in each position bin for decoders trained on offline, CB, CF,. and CB+ calcium traces.

• The movement artefacts are not equally observed in the maze. The way they are correctedmight be captured by the linear decoder. These artefacts might have a strong influence on the decoding. Please provide a quantification of the correction made during steps 1 and 2 in relation to the position of the animal on the linear track. The authors should provide a correlation between the presence of these corrections with the decoding accuracy.

Regardless of whether analysis is done offline or online, any calcium imaging and decoding experiment is vulnerable to two potential problems arising from motion artifact:

PROBLEM #1. Image motion can generate noise in calcium signals that disrupts the accuracy of decoding.

PROBLEM #2. Image motion that is correlated with behavior can convey uncontrolled information that allows the decoder to learn predictions from image motion rather than calcium signals. Very few published in-vivo calcium imaging experiments provide adequate controls for these two possible sources of artifact (again, such controls are just as necessary for offline as for online experiments). In response to the referee comments, we have provided controls for these confounds in our performance tests of DeCalciOn’s online decoding capabilities.

Fig. 4B of the revised paper shows that without online motion correction, several rats in the linear track experiment show a significant correlation between position error and motion artifact (indicated by positive values on the y-axis); hence, motion artifact impairs decoding of position on the linear track in these rats (problem #1 above). This correlation between motion artifact and decoding error is reduced or eliminated by online motion correction (as indicated by values near zero on the x-axis), demonstrating that online motion correction helps to prevent motion artifact from impairing the accuracy of decoding.

Fig. 6 of the revised paper shows that during an operant touchscreen experiment, motion artifact occurs preferentially during specific behaviors such as visiting the food magazine (reward retrieval, Fig. 6A) or touching the screen to make a response (correct choice, Fig. 6B). When motion correction is not used (top graphs in Figs. 6C-F), the average motion artifact is higher during frames when the decoder accurately predicts behavior than during frames when the decoder fails to predict behavior; hence, motion artifact appears to improve the accuracy of predicting these behaviors (problem #2 above). When motion correction is used, the average motion artifact no longer differs for correctly versus incorrectly decoded frames (except in one case, bottom right graph of Fig. 6E), indicating that motion correction helps to prevent the decoder from learning to predict behavior from motion artifact.

• Besides the methodological part, I have some physiological questions. It is quite common inlinear tracks to have bi-directional and unidirectional place cells. Is it the case here? How many? It is difficult to see this in figure C. Is there an error due to the online decoding of the position in the two directions of the linear track?

Again, since we did not select calcium traces that met a spatial criterion (i.e, “place cells”) to be include in the decoding analysis, and since CF traces could detect fluorescence from multiple neurons, we are not convinced that presenting a detailed analysis of this issue would advance our primary goal of demonstrating DeCalciOn’s capabilities to reseachers.

Reviewer #3 (Public Review):

DeCalciOn is an innovative contribution to the toolbox of real-time processing of calcium imaging data. It provides calcium traces from hippocampal CA1 neurons with a roughly two-millisecond latency and uses them to decode the position of rats running along a linear track - setting the stage for closed-loop experiments requiring fast interpretation of neural activity. The manuscript would be strengthened by a more systematic, empirical comparison to other, currently available alternative approaches. In addition, the decoding analysis does not fully account for the possibility of artifactual motion in the imaging video being informative of position.

We suggest strengthening this manuscript by addressing the following four points:

1) In the discussion of other platforms, the authors state that "Any system that lacks motionstabilization would also be vulnerable to artifactually decoding behavior from brain motion (which can be correlated with behavior) rather than neural activity." It follows that the same problem might also occur with incomplete motion correction. While the motion-corrected video shown in Supplementary Video 1 has reduced motion compared to the raw video, motion is still visible, including outside of the marked jitter. It remains possible that the linear decoders for the position in the linear track are utilizing brain motion-induced, as opposed to calcium fluorescence-induced, signal changes. A critical first step to assess this issue is to ask whether the motion in the video is related to the rat's behavior. One could test whether the 2D motion displacement traces can be used to predict rat position using linear classifiers.

Briefly, we show that motion correction helps to prevent the decoder from learning to predict behavior from motion artifact.

2) The manuscript would benefit from repeating the experiment in a more complex environment,such as a 2D arena. This would increase the generalizability of the findings. In addition, increasing the complexity of the environment would reduce the possibility that particular types of brain motion are closely linked with positions in the environment.

We have diversified our performance testing by presenting results for decoding calcium activity from a different brain region (OFC rather than CA1) during a different kind of behavior (an instrumental touchscreen task rather than a linear track).

3) The authors present an interesting comparison between "contour-free" and traditionalcontour-based source extraction. A more comprehensive discussion on the history or novelty of "contour-free" calcium imaging processing would contextualize this result.

The revised Discussion section contains a new subsection titled “Source identification” to contextualize this issue.

4) In the discussion, the authors compare DeCalciOn to two previous online calcium imagingalgorithms. The technical innovations of this work would be better highlighted by directly testing all three of these algorithms, ideally on similar datasets.

Briefly, one of the two cited systems is designed for compatibility with benchtop 2P microscopes and does not interface with miniscopes; public resources are not available for the other cited online algorithm.

Author Response

Reviewer #3 (Public Review):

This is an interesting study to examine how alveolar bone responds to oral infection using unbiased scRNA-seq. The manuscript is well-written and the results are convincing.

1) The authors should revise the abstract. The study did nothing with the understanding of healing. The whole conditions were performed under infection and inflammation which actually induce bone loss, but not healing.

Thank you for raising this point. We have revised the manuscript accordingly.

2) Since periapical inflammation causes progressive bone loss, how MSC with increasing osteogenic potentials contributes to bone loss? The authors should discuss it.

We would like to thank the reviewer for this important comment. Although AP is an inflammatory disease with periapical bone loss, the progression of AP is usually self-limiting in which a new equilibrium has been established between root canal pathogens and anti-infective defense mechanisms (Wang, Zhang, Xiong, & Peng, 2011). Animal experiments revealed that the bone lesion size reached to stable 21 days after establishing AP, which was resulted from a balance of bone remodeling (Márton & Kiss, 2014; Wang et al., 2011). Previous studies have shown that human apical granulation tissues contain osteogenic cells (Maeda, Wada, Nakamuta, & Akamine, 2004). A population of MSCs were isolated from human periapical cysts, which tended to be directed to differentiate toward the osteogenesis lineage (Marrelli, Paduano, & Tatullo, 2013, 2015; Tatullo et al., 2015). Activated by inflammatory bone destruction, these MSCs with increased osteogenic potentials may rescue the bone resorption process, which reach the equilibrium between bone formation and resorption then drive the progression of AP into stable states (Márton & Kiss, 2014). Since the pathologic stimuli exists constantly, the protective actions can alleviate the bone loss to some extent. In clinical practice, root canal therapy (RCT) aims to disinfect and remove the pathogenic factors, which makes the protective activities overweigh the destructive ones (L. M. Lin, Ricucci, Lin, & Rosenberg, 2009). The bone lesions of AP patients receiving RCT usually fully recovered with resolution of radiolucency after the inflammation is controlled in apical area (Soares, Santos, Silveira, & Nunes, 2006). The healing of AP lesion is highly correlated with the osteogenic potential of inflamed MSCs (L. M. Lin et al., 2009).

We added the related contents in the discussion section.

3) Did the authors detect osteoclasts by scRNA-seq? If not, are there any precursors of osteoclasts identified in inflammatory alveolar bones? 1) I suggest that the authors provide a more detailed analysis of inflammation since this is a unique model to study oral bone inflammation.

Thank you for this valuable point. Bone destruction is a major pathological factor in chronic inflammatory diseases such as AP. Various cytokines including TNF-α, IL-1α, IL-6 were released by immunocytes to recruit the osteoclast precursors and induce the maturation of osteoclasts. We detected osteoclast markers including Ctsk, Acp5, Mmp9 and Nfatc1 by scRNA-seq. Moreover, Csfr1, Cx3cr1, Itgam, and Tnfrs11a were used to identify osteoclast precursors. The expression pattern of these osteoclast-related markers in all clusters were presented in Figure 3A. Markers of osteoclast and osteoclast precursors were highly expressed in the clusters of monocyte and macrophage. The expression levels of these markers were analyzed in all clusters (Figure 3B). The GO analysis showed that inflammation related immune reactions and bone resorption activity were significantly enriched in macrophage cluster (Figure 3C). Moreover, pseudotime analysis was performed for the clusters of macrophage and monocyte. Two independent branch points were determined and five monocyte/macrophage subclusters scattered at different branches in the developmental tree (Figure 3D, G). The results showed that the monocyte cluster differentiated into the macrophage cluster (Figure 3E). During this trajectory, the gene expression pattern across pseudotime showed that osteoclastic genes, such as Ctsk, Acp5, Mmp9, Atp6v0d2, and Dcstamp were progressively elevated (Figure 3F). Of note, we have observed a branch which was highly positive for Ctsk and Acp5 (Figure 3H), indicating the mature osteoclasts were differentiated from monocyte/macrophage lineage and contributed to inflammatory bone resorption during AP. We have also analyzed the expression of osteoclast related genes using the bulk RNA-seq library built on mandibular samples extracted from mice with AP. Markers of osteoclast and osteoclast precursors were significantly upregulated, confirming the osteoclasts activity in the inflammatory-related bone lesion (Figure 3I). Please see page 9 and figure 3.

4) It is known that macrophages can be classified into M1 and M2. Based on scRNA-seq, did the authors observe these two types?

We appreciate this point raised by the reviewer. We used CD86, CD80, IL1β, and TNF as markers of M1-like macrophages. CD163, CD206, MSR1 and IL-10 were used as markers to detect M2 subset in the macrophage cluster. The analysis of macrophage cluster showed the M1-like macrophage accounted for the vast majority in AP lesions. The expression pattern of M2 markers were also presented in macrophage cluster (Figure 3-figure supplement 1A, B).

Author Response

Reviewer #1 (Public Review):

This study intended to identify the metabolic at-risk profile within PLWH on ART, by integrating and analyzing the multiomics data from multi-omics including untargeted plasma metabolomic, lipidomic, and fecal 16s microbiome. The overall strength of the study is the long-term treatment (~15 years) of the study subjects with well-recovered CD4 cell count and viral suppression. The integration and analysis of multi-omics data using similarity network fusion and factor analysis, etc. to group or differentiate HIV patients are informative and useful. The weakness of the study is the lack of presentation of comparability between patients and healthy controls and the use of multiple regression analysis for controlling potential confounders.

We are thankful to the reviewer for the critical reading of our manuscript. The primary aim of our study was to identify the molecular data-driven phenotypic patient stratification in a cohort of PLWHART with prolonged suppressive therapy to identify the at-risk metabolic profile following long-term successful therapy. We and others have reported in several studies (e.g., Ref#9 and 10) that there were distinct systemic patterns in multi-omics data. However, as suggested, we have now provided Table 1-source data 1. We have kept HC in the analysis to define which group is presenting an HC-like profile among HIV, but we are not using them to perform statistics and draw conclusions.

Reviewer #2 (Public Review):

This study systematically integrates multi-omics (plasma lipidomic and metabolomic, and fecal 16s microbiome) data to identify the metabolic at-risk profiles within people living with HIV on antiretroviral therapy (PLWHART). As a result, three groups of PLWHART (SNF-1 to 3) were identified, which showed distinct phenotypes. Such insights cannot be obtained by a single type of omics data or clinical data, and have implications in personalized medicine and lifestyle intervention. Connecting the findings in this study with specific medical/clinical insights is the next challenge.

We are thankful to the reviewer for the suggestion. System biology's application in identifying a disease state's biological mechanism in HIV-infected individuals is a relatively new field. We agree with the reviewer that connecting the findings in this study with specific medical/clinical insights is the next challenge. However, the first proof-of-concept study on 108 patients showed that multi-omics studies could generate a correlation network of communities of related analytes associated with physiology and disease. More importantly, the behavioral coaching informed by personal data helped participants to improve clinical biomarkers [PMID: 28714965]. The applications of multi-omics data are more and more valuable in non-communicable diseases [PMID: 35528975, PMID: 36503356 etc.]. As suggested by the reviewer, we have now elaborated on the medical/clinical value in identifying metabolic at-risk profiles, in particular the potential to improve individual risk stratification and to personalize lifestyle interventions. Still, as our study is an association study, data should be regarded as exploratory, and not sufficient to suggest any changes in clinical practice.

We have concluded the manuscript as follows:

“However, alterations in the metabolomics profile and higher CD4 T-cell count at the time of sample collection indicate a complex systemic interplay between host immunity and metabolic health. It can lead to an aggravated higher inflammation profile leading to a cardiometabolic risk profile among the MSM that might affect healthy aging in this population. Integrative analytical approaches that reflect the overall systemic health profile of PLWH may improve patient stratification and individual therapeutic and preventive strategies. Given the complex interplay between the clinical and molecular metabolic profile, the application of the multi-omics data for much larger cohorts of PLWH might facilitate a better identification of network perturbations and molecular network connections to detect early disease transition toward metabolic complications at an earlier stage. Developing a more personalized model or targeting the interaction networks rather than individual clinical or omics features may provide novel treatment strategies in countering dysregulated metabolic traits, aiming to achieve healthier aging.”

Author Response

eLife assessment:

This study addresses whether the composition of the microbiota influences the intestinal colonization of encapsulated vs unencapsulated Bacteroides thetaiotaomicron, a resident micro-organism of the colon. This is an important question because factors determining the colonization of gut bacteria remain a critical barrier in translating microbiome research into new bacterial cell-based therapies. To answer the question, the authors develop an innovative method to quantify B. theta population bottlenecks during intestinal colonization in the setting of different microbiota. Their main finding that the colonization defect of an acapsular mutant is dependent on the composition of the microbiota is valuable and this observation suggests that interactions between gut bacteria explains why the mutant has a colonization defect. The evidence supporting this claim is currently insufficient. Additionally, some of the analyses and claims are compromised because the authors do not fully explain their data and the number of animals is sometimes very small.

Thank you for this frank evaluation. Based on the Reviewers’ comments, the points raised have been addressed by improving the writing (apologies for insufficient clarity), and by the addition of data that to a large extent already existed or could be rapidly generated. In particularly the following data has been added:

1. Increase to n>=7 for all fecal time-course experiments

2. Microbiota composition analysis for all mouse lines used

3. Data elucidating mechanisms of SPF microbiome/ host immune mechanisms restriction of acapsular B. theta

4. Short- versus long-term recolonization of germ-free mice with a complete SPF microbiota and assessment of the effect on B. theta colonization probability.

5. Challenge of B. theta monocolonized mice with avirulent Salmonella to disentangle effects of the host inflammatory response from other potential explanations of the observations.

6. Details of all inocula used

7. Resequencing of all barcoded strains

Additionally, we have improved the clarity of the text, particularly the methods section describing mathematical modeling in the main text. Major changes in the text and particularly those replying to reviewers comment have been highlighted here and in the manuscript.

Reviewer #1 (Public Review):

The study addresses an important question - how the composition of the microbiota influences the intestinal colonization of encapsulated vs unencapsulated B. theta, an important commensal organism. To answer the question, the authors develop a refurbished WITS with extended mathematical modeling to quantify B. theta population bottlenecks during intestinal colonization in the setting of different microbiota. Interestingly, they show that the colonization defect of an acapsular mutant is dependent on the composition of the microbiota, suggesting (but not proving) that interactions between gut bacteria, rather than with host immune mechanisms, explains why the mutant has a colonization defect. However, it is fairly difficult to evaluate some of the claims because experimental details are not easy to find and the number of animals is very small. Furthermore, some of the analyses and claims are compromised because the authors do not fully explain their data; for example, leaving out the zero values in Fig. 3 and not integrating the effect of bottlenecks into the resulting model, undermines the claim that the acapsular mutant has a longer in vivo lag phase.

We thank the reviewer for taking time to give this details critique of our work, and apologies that the experimental details were insufficiently explained. This criticism is well taken. Exact inoculum details for experiment are now present in each figure (or as a supplement when multiple inocula are included). Exact microbiome composition analysis for OligoMM12, LCM and SPF microbiota is now included in Figure 2 – Figure supplement 1.

Of course, the models could be expanded to include more factors, but I think this comment is rather based on the data being insufficiently clearly explained by us. There are no “zero values missing” from Fig. 3 – this is visible in the submitted raw data table (excel file Source Data 1), but the points are fully overlapped in the graph shown and therefore not easily discernable from one another. Time-points where no CFU were recovered were plotted at a detection limit of CFU (50 CFU/g) and are included in the curve-fitting. However, on re-examination we noticed that the curve fit was carried out on the raw-data and not the log-normalized data which resulted in over-weighting of the higher values. Re-fitting this data does not change the conclusions but provides a better fit. These experiments have now been repeated such that we now have >=7 animals in each group. This new data is presented in Fig. 3C and D and Fig. 3 Supplement 2.

Limitations:

1) The experiments do not allow clear separation of effects derived from the microbiota composition and those that occur secondary to host development without a microbiota or with a different microbiota. Furthermore, the measured bottlenecks are very similar in LCM and Oligo mice, even though these microbiotas differ in complexity. Oligo-MM12 was originally developed and described to confer resistance to Salmonella colonization, suggesting that it should tighten the bottleneck. Overall, an add-back experiment demonstrating that conventionalizing germ-free mice imparts a similar bottleneck to SPF would strengthen the conclusions.

These are excellent suggestions and have been followed. Additional data is now presented in Figure 2 – figure supplement 8 showing short, versus long-term recolonization of germ-free mice with an SPF microbiota and recovering very similar values of beta, to our standard SPF mouse colony. These data demonstrate a larger total niche size for B. theta at 2 days post-colonization which normalizes by 2 weeks post-colonization. Independent of this, the colonization probability, is already equivalent to that observed in our SPF colony at day 2 post-colonization. Therefore, the mechanisms causing early clonal loss are very rapidly established on colonization of a germ-free mouse with an SPF microbiota. We have additionally demonstrated that SPF mice do not have detectable intestinal antibody titers specific for acapsular B. theta. (Figure 2 – figure supplement 7), such that this is unlikely to be part of the reason why acapsular B. theta struggles to colonize at all in the context of an SPF microbiota. Experiments were also carried to detect bacteriophage capable of inducing lysis of B. theta and acapsular B. theta from SPF mouse cecal content (Figure 2 – figure supplement 7). No lytic phage plaques were observed. However, plaque assays are not sensitive for detection of weakly lytic phage, or phage that may require expression of surface structures that are not induced in vitro. We can therefore conclude that the restrictive activity of the SPF microbiota is a) reconstituted very fast in germ-free mice, b) is very likely not related to the activity of intestinal IgA and c) cannot be attributed to a high abundance of strongly lytic bacteriophage. The simplest explanation is that a large fraction of the restriction is due to metabolic competition with a complex microbiota, but we cannot formally exclude other factors such as antimicrobial peptides or changes in intestinal physiology.

2) It is often difficult to evaluate results because important parameters are not always given. Dose is a critical variable in bottleneck experiments, but it is not clear if total dose changes in Figure 2 or just the WITS dose? Total dose as well as n0 should be depicted in all figures.

We apologized for the lack of clarity in the figures. Have added panels depicting the exact inoculum for each figure legend (or a supplementary figure where many inocula were used). Additionally, the methods section describing how barcoded CFU were calculated has been rewritten and is hopefully now clearer.

3) This is in part a methods paper but the method is not described clearly in the results, with important bits only found in a very difficult supplement. Is there a difference between colonization probability (beta) and inoculum size at which tags start to disappear? Can there be some culture-based validation of "colonization probability" as explained in the mathematics? Can the authors contrast the advantages/disadvantages of this system with other methods (e.g. sequencing-based approaches)? It seems like the numerator in the colonization probability equation has a very limited range (from 0.18-1.8), potentially limiting the sensitivity of this approach.

We apologized for the lack of clarity in the methods. This criticism is well taken, and we have re-written large sections of the methods in the main text to include all relevant detail currently buried in the extensive supplement.

On the question of the colonization probability and the inoculum size, we kept the inoculum size at 107 CFU/ mouse in all experiments (except those in Fig.4, where this is explicitly stated); only changing the fraction of spiked barcoded strains. We verified the accuracy of our barcode recovery rate by serial dilution over 5 logs (new figure added: Figure 1 – figure supplement 1). “The CFU of barcoded strains in the inoculum at which tags start to disappear” is by definition closely related to the colonization probability, as this value (n0) appears in the calculation. Note that this is not the total inoculum size – this is (unless otherwise stated in Fig. 4) kept constant at 107 CFU by diluting the barcoded B. theta with untagged B. theta. Again, this is now better explained in all figure legends and the main text.

We have added an experiment using peak-to-trough ratios in metagenomic sequencing to estimate the B. theta growth rate. This could be usefully employed for wildtype B. theta at a relatively early timepoint post-colonization where growth was rapid. However, this is a metagenomics-based technique that requires the examined strain to be present at an abundance of over 0.1-1% for accurate quantification such that we could not analyze the acapsular B. theta strain in cecum content at the same timepoint. These data have been added (Figure 3 – figure supplement 3). Note that the information gleaned from these techniques is different. PTR reveals relative growth rates at a specific time (if your strain is abundant enough), whereas neutral tagging reveals average population values over quite large time-windows. We believe that both approaches are valuable. A few sentences comparing the approaches have been added to the discussion.

The actual numerator is the fraction of lost tags, which is obtained from the total number of tags used across the experiment (number of mice times the number of tags lost) over the total number of tags (number of mice times the number of tags used). Very low tag recovery (less than one per mouse) starts to stray into very noisy data, while close to zero loss is also associated with a low-information-to-noise ratio. Therefore, the size of this numerator is necessarily constrained by us setting up the experiments to have close to optimal information recovery from the WITS abundance. Robustness of these analyses is provided by the high “n” of between 10 and 17 mice per group.

4) Figure 3 and the associated model is confusing and does not support the idea that a longer lag-phase contributes to the fitness defect of acapsular B.theta in competitive colonization. Figure 3B clearly indicates that in competition acapsular B. theta experiences a restrictive bottleneck, i.e., in competition, less of the initial B. theta population is contributed by the acapsular inoculum. There is no need to appeal to lag-phase defects to explain the role of the capsule in vivo. The model in Figure 3D should depict the acapsular population with less cells after the bottleneck. In fact, the data in Figure 3E-F can be explained by the tighter bottleneck experienced by the acapsular mutant resulting in a smaller acapsular founding population. This idea can be seen in the data: the acapsular mutant shedding actually dips in the first 12-hours. This cannot be discerned in Figure 3E because mice with zero shedding were excluded from the analysis, leaving the data (and conclusion) of this experiment to be extrapolated from a single mouse.

We of course completely agree that this would be a correct conclusion if only the competitive colonization data is taken into account. However, we are also trying to understand the mechanisms at play generating this bottleneck and have investigated a range of hypotheses to explain the results, taking into account all of our data.

Hypothesis 1) Competition is due to increased killing prior to reaching the cecum and commencing growth: Note that the probability of colonization for single B. theta clones is very similar for OligoMM12 mouse single-colonization by the wildtype and acapsular strains. For this hypothesis to be the reason for outcompetition of the acapsular strain, it would be necessary that the presence of wildtype would increase the killing of acapsular B. theta in the stomach or small intestine. The bacteria are at low density at this stage and stomach acid/small intestinal secretions should be similar in all animals. Therefore, this explanation seems highly unlikely

Hypothesis 2) Competition between wildtype and acapsular B. theta occurs at the point of niche competition before commencing growth in the cecum (similar to the proposal of the reviewer). It is possible that the wildtype strain has a competitive advantage in colonizing physical niches (for example proximity to bacteria producing colicins). On the basis of the data, we cannot exclude this hypothesis completely and it is challenging to measure directly. However, from our in vivo growth-curve data we observe a similar delay in CFU arrival in the feces for acapsular B. theta on single colonization as in competition, suggesting that the presence of wildtype (i.e., initial niche competition) is not the cause of this delay. Rather it is an intrinsic property of the acapsular strain in vivo,

Hypothesis 3) Competition between wildtype and acapsular B. theta is mainly attributable to differences in growth kinetics in the gut lumen. To investigate growth kinetics, we carried our time-courses of fecal collection from OligoMM12 mice single-colonized with wildtype or acapsular B. theta, i.e., in a situation where we observe identical colonization probabilities for the two strains. These date, shown now in Figure 3 C and D and Figure 3 – figure supplement 2, show that also without competition, the CFU of acapsular B. theta appear later and with a lower net growth rate than the wildtype. As these single-colonizations do not show a measurable difference between the colonization probability for the two strains, it is not likely that the delayed appearance of acapsular B. theta in feces is due to increased killing (this would be clearly visible in the barcode loss for the single-colonizations). Rather the simplest explanation for this observation is a bona fide lag phase before growth commences in the cecum. Interestingly, using only the lower net growth rate (assumed to be a similar growth rate but increased clearance rate) produces a good fit for our data on both competitive index and colonization probability in competition (Figure 3, figure supplement 5). This is slightly improved by adding in the observed lag-phase (Figure 3). It is very difficult to experimentally manipulate the lag phase in order to directly test how much of an effect this has on our hypothesis and the contribution is therefore carefully described in the new text.

Please note that all data was plotted and used in fitting in Fig 3E, but “zero-shedding” is plotted at a detection limit and overlayed, making it look like only one point was present when in fact several were used. This was clear in the submitted raw data tables. To sure-up these observations we have repeated all time-courses and now have n>=7 mice per group.

5) The conclusions from Figure 4 rely on assumptions not well-supported by the data. In the high fat diet experiment, a lower dose of WITS is required to conclude that the diet has no effect. Furthermore, the authors conclude that Salmonella restricts the B. theta population by causing inflammation, but do not demonstrate inflammation at their timepoint or disprove that the Salmonella population could cause the same effect in the absence of inflammation (through non-inflammatory direct or indirect interactions).

We of course agree that we would expect to see some loss of B. theta in HFD. However, for these experiments the inoculum was ~109 CFUs/100μL dose of untagged strain spiked with approximately 30 CFU of each tagged strain. Decreasing the number of each WITS below 30 CFU leads to very high variation in the starting inocula from mouse-to-mouse which massively complicates the analysis. To clarify this point, we have added in a detection-limit calculation showing that the neutral tagging technique is not very sensitive to population contractions of less than 10-fold, which is likely in line with what would be expected for a high-fat diet feeding in monocolonized mice for a short time-span.

This is a very good observation regarding our Salmonella infection data. We have now added the fecal lipocalin 2 values, as well as a group infected with a ssaV/invG double mutant of S. Typhimurium that does not cause clinical grade inflammation (“avirulent”). This shows 1) that the attenuated S. Typhimurium is causing intestinal inflammation in B. theta colonized mice and 2) that a major fraction of the population bottleneck can be attributed to inflammation. Interestingly, we do observe a slight bottleneck in the group infected with avirulent Salmonella which could be attributable either to direct toxicity/competition of Salmonella with B. theta or to mildly increased intestinal inflammation caused by this strain. As we cannot distinguish these effects, this is carefully discussed in the manuscript.

6) Several of the experiments rely on very few mice/groups.

We have increased the n to over 5 per group in all experiments (most critically those shown in Fig 3, Supplement 5). See figure legends for specific number of mice per experiment.

Reviewer #2 (Public Review):

The goal of this study was to understand population bottlenecks during colonization in the context of different microbial communities. Capsular polysaccharide mutants, diet, and enteric infection were also used paired to short-term monitoring of overall colonization and the levels of specific strains. The major strength of this study is the innovative approach and the significance of the overall research area.

The first major limitation is the lack of clear and novel insight into the biology of B. theta or other gut bacterial species. The title is provocative, but the experiments as is do not definitively show that the microbiota controls the relative fitness of acapsular and wild-type strains or provide any mechanistic insights into why that would be the case. The data on diet and infection seem preliminary. Furthermore, many of the experiments conflict with prior literature (i.e., lack of fitness difference between acapsular and wild-type strain and lack of impact of diet) but satisfying explanations are not provided for the lack of reproducibility.

In line with suggestions from Reviewer 1, the paper has undergone quite extensive re-writing to better explain the data presented and its consequences. Additionally, we now explicitly comment on apparent discrepancies between our reported data and the literature – for example the colonization defect of acapsular B. theta is only published for competitive colonizations, where we also observe a fitness defect so there is no actual conflict. Additionally, we have calculated detection limits for the effect of high-fat diet and demonstrate that a 10-fold reduction in the effective population size would not be robustly detected with the neutral tagging technique such that we are probably just underpowered to detect small effects, and we believe it is important to point out the numerical limits of the technique we present here. Additionally for the Figure 4 experiments, we have added data on colonization/competition with an avirulent Salmonella challenge giving some mechanistic data on the role of inflammation in the B. theta bottleneck.

Another major limitation is the lack of data on the various background gut microbiotas used. eLife is a journal for a broad readership. As such, describing what microbes are in LCM, OligoMM, or SPF groups is important. The authors seem to assume that the gut microbiota will reflect prior studies without measuring it themselves.

All gnotobiotic lines are bred as gnotobiotic colonies in our isolator facility. This is now better explained in the methods section. Additionally, 16S sequencing of all microbiotas used in the paper has been added as Figure 2 – figure supplement 1.

I also did not follow the logic of concluding that any differences between SPF and the two other groups are due to microbial diversity, which is presumably just one of many differences. For example, the authors acknowledge that host immunity may be distinct. It is essential to profile the gut microbiota by 16S rRNA amplicon sequencing in all these experiments and to design experiments that more explicitly test the diversity hypotheses vs. alternatives like differences in the membership of each community or other host phenotypes.

This is an important point. We have carried out a number of experiments to potentially address some issues here.

1) We carried out B. theta colonization experiments in germ-free mice that had been colonized by gavage of SPF feces either 1 day prior to colonization of 2 weeks prior to colonization. While the shorter pre-colonization allowed B. theta to colonize to a higher population density in the cecum, the colonization probability was already reduced to levels observed in our SPF colony in the short pre-colonization. Therefore, the factors limiting B. theta establishment in the cecum are already established 1-2 days post-colonization with an SPF microbiota (Figure 2 - figure supplement 8). 2) We checked for the presence of secretory IgA capable of binding to the surface of live B. theta, compared to a positive control of a mouse orally vaccinated against B. theta. (Fig. 2, Supplement 7) and could find no evidence of specific IgA targeting B. theta in the intestinal lavages of our SPF mouse colony. 3) We isolated bacteriophage from the intestine of SPF mice and used this to infect lawns of B. theta wildtype and acapsular in vitro. We could not detect and plaque-forming phage coming from the intestine of SPF mice (Figure 2 – figure supplement 7).

We can therefore exclude strongly lytic phage and host IgA as dominant driving mechanisms restricting B. theta colonization. It remains possible that rapidly upregulated host factors such as antimicrobial peptide secretion could play a role, but metabolic competition from the microbiota is also a very strong candidate hypothesis. The text regarding these experiments has been slightly rewritten to point out that colonization probability inversely correlates with microbiota complexity, and the mechanisms involved may involve both direct microbe-microbe interactions as well as host factors.

Given the prior work on the importance of capsule for phage, I was surprised that no efforts are taken to monitor phage levels in these experiments. Could B. theta phage be present in SPF mice, explaining the results? Alternatively, is the mucus layer distinct? Both could be readily monitored using established molecular/imaging methods.

See above: no plaque-forming phage could be recovered from the SPF mouse cecum content. The main replicative site that we have studied here, in mice, is the cecum which does not have true mucus layers in the same way as the distal colon and is upstream of the colon so is unlikely to be affected by colon geography. Rather mucus is well mixed with the cecum content and may behave as a dispersed nutrient source. There is for sure a higher availability of mucus in the gnotobiotic mice due to less competition for mucus degradation by other strains. However, this would be challenging to directly link to the B. theta colonization phenotype as Muc2-deficient mice develop intestinal inflammation.

The conclusion that the acapsular strain loses out due to a difference of lag phase seems highly speculative. More work would be needed to ensure that there is no difference in the initial bottleneck; for example, by monitoring the level of this strain in the proximal gut immediately after oral gavage.

This is an excellent suggestion and has been carried out. At 8h post-colonization with a high inoculum (allowing easy detection) there were identical low levels of B. theta in the upper and lower small intestine, but more B. theta wildtype than B. theta acapsular in the cecum and colon, consistent with commencement of growth for B. theta wildtype but not the acapsular strain at this timepoint. We have additionally repeated the single-colonization time-courses using our standard inoculum and can clearly see the delayed detection of acapsular B. theta in feces even in the single-colonization state when no increased bottleneck is observed. This can only be reasonably explained by a bona fide lag-phase extension for acapsular B. theta in vivo. These data also reveal and decreased net growth rate of acapsular B. theta. Interestingly, our model can be quite well-fitted to the data obtained both for competitive index and for colonization probability using only the difference in net growth rate. Adding the (clearly observed) extended lag-phase generates a model that is still consistent with our observations.

Another major limitation of this paper is the reliance on short timepoints (2-3 days post colonization). Data for B. theta levels over 2 weeks or longer is essential to put these values in context. For example, I was surprised that B. theta could invade the gut microbiota of SPF mice at all and wonder if the early time points reflect transient colonization.

It should be noted that “SPF” defines microbiota only on missing pathogens and not on absolute composition. Therefore, the rather efficient B. theta colonization in our SPF colony is likely due to a permissive composition and this is likely to be not at all reproducible between different SPF colonies (a major confounder in reproducibility of mouse experiments between institutions. In contrast the gnotobiotic colonies are highly reproducible). We do consistently see colonization of our SPF colony by wildtype B. theta out to at least 10 days post-inoculation (latest time-point tested) at similar loads to the ones observed in this work, indicating that this is not just transient “flow-through” colonization. Data included below:

For this paper we were very specifically quantifying the early stages of colonization, also because the longer we run the experiments for, the more confounding features of our “neutrality” assumptions appear (e.g., host immunity selecting for evolved/phase-varied clones, within-host evolution of individual clones etc.). For this reason, we have used timepoints of a maximum of 2-3 days.

Finally, the number of mice/group is very low, especially given the novelty of these types of studies and uncertainty about reproducibility. Key experiments should be replicated at least once, ideally with more than n=3/group.

For all barcode quantification experiments we have between 10 and 17 mice per group. Experiments for the in vivo time-courses of colonization have been expanded to an “n” of at least 7 per group.

Author Response

Reviewer #2 (Public Review):

This is a highly interesting paper that provides important insights into the understanding of how HC-derived osteoblasts contribute to trabecular bone formation. Using single-cell transcriptomics, the authors found that HC descendent cells activate MMP14 and the PTH pathway as they transition to osteoblasts in neonatal and adult mice. They further demonstrate that HC lineage-specific Mmp14 null mutants (Mmp14ΔHC) produce more bone. By performing a panel of elegant in vitro studies, the authors show that MMP14 cleaves the extracellular domain of PTH1R, dampening PTH signaling. The authors provide more in vivo evidence showing that HC-derived osteogenic cells respond to PTH which is enhanced in Mmp14ΔHC. Generally, this is a very well-performed study that may contribute important novel aspects to the field.

I have the following issues for the authors to address:

1) The novel mechanism identified in this study (i.e. MMP14-induced PTH1R cleavage) is intriguing. It is unclear how specific this pathway is in the transition of HCs to osteoblasts. Are other MMPs besides MMP14 involved in the PTH1R cleavage? Is PTH1R the only substrate of MMP14?

Thank you for your interest in our findings. ADAMs are known to cleave various transmembrane proteins such as RANKL. As described in supplementary fFgure 4A we tested A Disintegrin And Metalloproteinase (ADAMs) for their potential ability to cleave PTH1R. We did not find that ADAM10, 15, 17 could cleave PTH1R. The lack of the cleaved PTH1R peptide in extracts isolated from osteoblasts isolated from MMP 14 null bones (New Fig. 3E) suggest that there is not another major MMP that cleaves PTH1R. In regard to other substrates that are cleaved by MMP14 – we do review these in the manuscript and the possibility that the phenotype is contributed by deficiency in other substrates.

2) Would it be possible for the authors to detect the truncated PTH1R fragment(s) from the conditioned medium prepared from either 293T or osteoblast culture?

We tried to detect whether there could be PTH1R cleaved fragment in cultured medium by western blot of PCA precipitates of cultured medium. We could not detect any free peptide using anti-Flag or anti-HA antibody. It has been reported the ligand binding domain are linked by disulphide bond in vivo, therefore cleavage of PTH1R at the unstructured loop domain does not necessarily imply a release of cleaved fragment.

3) The finding that HC-descendants persist and contribute to the anabolic response to PTH in aged mice is interesting. Have the authors examined the changes in MMP14 expression in bone with age and in response to PTH treatment?

Thank you for your question, we added additional data showing induction of MMP14 expression upon PTH treatment in Figure 7—figure supplement 1. It has also been published that PTH stimulation increased MMP14 expression in osteocytes (1).

Author Response

Reviewer #2 (Public Review):

Susswein et al. analyze a fine-scale, novel data stream of human mobility, openly available from Safegraph, based on the usage of mobile apps with GPS and sampled from over 45 million smartphone devices. They define a metric $\sigma_{it}$, properly normalized, that quantifies the propensity for visits to indoor locations relative to outdoor locations in a given county $i$ at week $t$. For each pair of counties $i$ and $j$, they compute the Pearson correlation coefficient $\rho_{ij}$ between the corresponding $\sigma$ metrics. This generates a correlation matrix that can be interpreted as the adjacency matrix of a network. They then perform community detection on this network/matrix, effectively clustering together time series that are correlated. This identifies three main clusters of counties, characterized geographically as either in the north of the country, in the south of the country, and possibly in tourism active areas. They then show, via a simple model, how including over-simplified models of seasonality may affect infectious disease models.

This work is very interesting for the infectious disease modeling community, as it addresses a complex problem introducing a new data stream.

This work builds on several strengths, among which:

It is the first analysis of the Safegraph dataset to capture seasonality in indoor behavior.

It provides a simple metric to quantify indoor activity, that thanks to the dataset can be computed with a high level of spatial detail.

It aims at characterizing clusters of counties with a similar pattern of indoor activity.

It aims at quantifying the impact of neglecting finer-scale patterns of seasonality, for example considering seasonality to be homogeneous at the US level.

We thank the reviewer for the positive review of our work.

At the same time, it presents several weaknesses that should be addressed to improve the methodology, its results, and the implication:

There is no quantitative comparison of the newly introduced metric for indoor activity with other proxies of seasonality (e.g. temperature or relative humidity). The (dis)similarity with other proxies may help in assessing the importance of this metric, showing why it can not be exchanged with other data sources (like temperature data) that are widely available and are not affected by sampling issues (more on that later).

We have now added supplementary figures (Figure S3) to illustrate how indoor activity seasonality compares with temperature and humidity. We have also added text to the Results and the Discussion to discuss this point.

A major flow of the analysis is to perform community detection on a network defined by the correlation between time series with an algorithm that is based on modularity optimization. As explained in Macmahon et al.[1], all modularity optimization methods rely on null assumptions that in the case of correlation between time series are violated. Therefore, there is a very strong potential bias in their results that is not accounted for. Possible solutions could be to proceed via the methodology presented in [1] or via a different type of algorithm (e.g. Infomap [2]). In both cases, as the network is thresholded (considering only a correlation larger than 0.9), a more quantitative assessment of the impact of the threshold value should be included.

References

[1] Mel MacMahon and Diego Garlaschelli Phys. Rev. X 5, 021006 (2015).

[2] Martin Rosvall and Carl T. Bergstrom PNAS 105, 1118 (2008).

We thank the reviewer for making this excellent point. We have now added Supplementary Figures S13 and S14. In Figure S13, we demonstrate the robustness of our clustering results with different correlation thresholds. (We have also corrected a typo in our original Methods section which mistakenly stated our correlation threshold as 0.9 rather than the 90th percentile which is what we used.) In Figure S14, we show the clustering results using a different clustering algorithm. In an effort to test a non-network-based clustering approach, we use a hierarchical clustering approach and find a consistent partition of the US to our main results.

It is not clear what is the added value of the data on indoor activity, as no fitting to real data is performed. Although this may be considered beyond the scope of this paper, I think it would be crucial to quantify how much a data-informed model would better describe real epidemic data (for example in the case of COVID-19). For now, only the impact of neglecting heterogeneity in indoor activity is shown, comparing a model with region-average parameters vs a model with county-level average parameters. Given that the dataset comes with potential bias in sampling (more on this later) it would be good to assess its goodness in predicting real epidemic spread. When showing results from different models, no visible errors are shown on the plot. How have the errors been estimated?

We appreciate this point by the reviewer, and agree that future work will have to consider how indoor activity seasonality affects our ability to capture observed transmission trends. However, such work would additionally need careful characterization of other seasonal factors hypothesized to drive transmission (including environmental and other behavioral factors), and is beyond the scope of our work. Instead, in Figure 4 we aim to (a) provide the infectious disease modeling community with empirically-inferred parameters for a simple sinusoidal model which is commonly used in infectious disease models to capture transmission seasonality; and (b) demonstrate the implications of ignoring geographic heterogeneity in transmission seasonality in theoretical models of disease dynamics, which are commonly used for scenario analysis and model-based intervention design. As we demonstrate, transmission seasonality described by such sinusoidal models, even when they are empirically characterized as in our case, can lead to meaningfully different epidemic dynamics when transmission seasonality varies from the assumptions.

Additionally, there is no uncertainty included in Figure 4B because transmission seasonality is either based on empirical data point per time step, or on the fitted sinusoidal model (where the estimated parameters have negligible standard errors).

The dataset is presented as representative of the US population. However, this has not been assessed over time. As adherence to social distancing is influenced by several socio-economic determinants the lack of representativity in certain strata of the population at a given time may introduce an important bias in the dataset. Although this is an inherent limitation of the dataset, it should be discussed in the paper more thoroughly.

We agree with the reviewer that this is a limitation. However, we do not have any way of assessing demographic representation in the dataset over time. We have instead included an additional sentence into the Discussion section acknowledging this point.

In conclusion, I think that the methodology should be revised to account for the fact that the analysis is performed on a correlation matrix. Capturing seasonal patterns of indoor activity can help in tackling the crucial problem of seasonality in human behavior. This could help in identifying effective strategies of disease containment able to curb disease spread at a lower societal cost than fully-fledged lockdowns.

We thank the reviewer again for their helpful suggestions.

Author Response

Reviewer #1 (Public Review):

The authors characterized the expression of DDR2 in the developing craniofacial skeleton. The authors showed that Ddr2-deficient mice exhibited defects in craniofacial bones including impaired calvarial growth and frontal suture formation, cranial base hypoplasia due to aberrant chondrogenesis, and delayed ossification at growth plate synchondroses. The histological studies are well done. However, the studies as shown in this manuscript do not provide cellular and molecular mechanisms beyond what is already known, particularly beyond what the authors have already published in a similar study in Bone Research (Mohamed et al., 2022 Feb 9;10(1):11). With the same Cre lines and analytic approaches, the authors already showed in the Bone Research paper that Ddr2 in the Gli1+ cells is required for chondrocyte proliferation and polarity in growth plate development and osteoblast differentiation. Cartilage development and bone formation occur in both long bones and craniofacial skeleton, the authors showed similar functions of Ddr2 in similar skeletal tissues, although the location is different. One new point in this manuscript might be: the authors indicated that loss of Ddr2 led to ectopic chondrocyte hypertrophic (Fig. 7I). But what the data actually showed was delayed chondrocyte hypertrophy and abnormal location of the delayed hypertrophic chondrocytes, which could be well caused by abnormal chondrocyte polarity. This interesting defect was superficially described with no mechanistic investigation at cellular or molecular level.

New data is now provided showing that Ddr2 deficiency is associated with abnormal collagen organization and orientation as measured by second harmonic generation (SHG) (Fig 3-figure supplement 1). Specifically, collagen orientation as reflected by SHG anisotropy measurements was disrupted in Ddr2-deficient synchondroses. This result complements data showing that the distribution of type II collagen as measured by immunofluorescence changes with Ddr2 deficiency such that no collagen is seen in the interterritorial matrix between chondrocyte bundles (Fig 3a). This loss of collagen organization provides a potential mechanism to explain the disruption of chondrocyte polarity and altered localization of hypertrophic cells in synchondroses. In further support of this concept, other recently published studies described in the Discussion have shown that Ddr2 deficiency is associated with disruption of collagen fibril orientation in other experimental systems such as in CAF cells surrounding breast tumors as well as at sites of heterotopic ossification and that these abnormalities are associated with defective integrin signaling. Additional studies beyond the scope of the present communication will be required to determine if these matrix changes can explain the observed phenotypes. However, we believe this proposed mechanism is the most likely explanation for DDR2 effects based on current data.

Reviewer #2 (Public Review):

DDR2 is a collagen-binding receptor that is required for proper skull development. Ddr2 loss-of-function in humans is associated with the developmental disease spondylo-meta-epiphyseal dysplasia (SMED). Here, the authors aim to elucidate the role of DDR2 in skull development. In this work, the role of DDR2 in skull and face development is studied in mice, which exhibit SMED-like symptoms in the absence of Ddr2. Histological studies showed that Ddr2 knockout disrupts organization and proper differentiation within progenitor-rich regions of the skull from which bone growth occurs. Histology and lineage tracing studies revealed that DDR-expressing cells in/around these zones 1) generally also express the proliferation regulator Gli1, and 2) eventually contribute to osteogenic and chondrogenic lineages. Cell-type specific knockout studies were used to show that DDR2 has a development-specific role: knockout of Ddr2 in Gli+ cells re-capitulated the developmental abnormalities observed in global Ddr2 knockout mice; knockout in chondrocytes partially recapitulated developmental abnormalities, and osteoblast-specific knockout mice were indistinguishable from their wild-type littermates. This work also catalogues the locations of Ddr2 positive cells and their lineages at various stages of development. Additionally, the anatomical effects of loss of DDR2 function on skull and face development are thoroughly described in global and cell-type specific knockouts.

This work is a vital and stimulating contribution to the scientific literature. The authors' claims and conclusions are well supported by the evidence they present.

The scientific approach is sound and the conclusions important. However, a limitation of the work's discussion is a lack of attention paid to the specific biophysical mechanism that DDR2 is playing during development. The discussion of the positioning of the golgi is nice, but a lack of golgi polarity is likely a downstream effect of processes occurring within the cell adhesion and mechanotransduction machinery. Perhaps, like integrins, DDR2 is a mechanosensor that the cell needs to properly sense local collagen orientation, polarize, and secrete properly-organized COL2. It would be beneficial to put up some guideposts that will facilitate engagement from the molecular biophysics/mechanobiology community.

Thank you for this suggestion. In response, we added new studies showing that DDR2 is necessary for ECM organization (please see reviewer 1 comments and additions to the Discussion section). In addition, the Discussion has been revised to include speculation on the relationship between DDR2-dependent ECM organization, mechanical properties of the matrix and cell differentiation. Because very little is known about DDR2 from a mechanistic perspective, much of what we propose is currently conjecture, but hopefully can guide future study.

Reviewer #3 (Public Review):

From this work, the authors investigated a number of parameters in order to profoundly understand and demonstrate the vital role of ongoing interaction between components of extracellular matrix and particular stem cells to induce normal Craniofacial development. Thus, there was a focus on the genetic manipulation (knockout) impact of molecules behind the above-mentioned interaction, and on determining how such modification would be reflected on skull bone morphogenesis.

Strengths and Weaknesses

• Using different animals' backgrounds in the same experiment might impact work outcomes.

• Better to have (ethical approval) at the beginning of the material and methods in separate paragraphs.

• It is great that the authors precisely explain all the measurements.

• Supplementary file to have details of used antibodies might be required.

• All methods have been written in academic and clear ways.

• It is nice that there is a conclusion sentence by end of the results paragraph, which made it easy for readers to fully remember and understand.

• It is possible to see a reduction in proliferative chondrocyte, with no change in apoptosis rate?

Reductions in proliferation are certainly seen in many systems. Proliferation and apoptosis are not necessarily coupled.

• Results are supposed to be compatible.

• Very nice and representative images from the immunofluorescence protocol.

• Using different techniques to confirm observations is clearly manifested in methods and results.

It is clear that the author has used different methods and techniques in order to meet his work's objectives. Importantly, there was more than one procedure to confirm observations that are related to one or more than one aim.

Although determining to what extent the outcomes of this work could be applied to community need might require a subspecialist physician's opinion, it seems that observations of the present study are likely to require a series of further investigations in order to take it to the level of human users. Notably, identification of molecules and pathways behind skull development abnormalities would open a door to early diagnosis reasons for such deformities, thus mitigating future abnormalities either by developing new prevention methods or discovering unique medications.

Thank you for these comments. Additional commentary has been added to the Discussion to provide a more mechanistic interpretation of our results, however speculative they may be at this time. Ln 555-605

Author Response

Reviewer #1 (Public Review):

King et al. provide an interesting reanalysis of existing fMRI data with a novel functional connectivity modeling approach. Three connectivity models accounting for the relationship between cortical and cerebellar regions are compared, each representing a hypothesis. Evidence is presented that - contrary to a prominent theoretical account in the literature - cortical connectivity converges on cerebellar regions, such that the cerebellum likely integrates information from the cortex (rather than forming parallel loops with the cortex). If true, this would have large implications for understanding the likely computational role of the cerebellum in influencing cortical functions. Further, this paper provides a unique and potentially groundbreaking set of methods for testing alternate connectivity hypotheses in the human brain. However, it appears that insufficient details were provided to properly evaluate these methods and their implications, as described below.

Strengths:

• Use of a large task battery performed by every participant, increasing confidence in the generality ofthe results across a variety of cognitive functions.

• Multiple regression was used to reduce the chance of confounding (false connections driven by a thirdregion) in the functional connectivity estimates.

• A focus on the function and connectivity of the cerebellum is important, given that it is clearly essentialfor a wide variety of cognitive processes but is studied much less often than the cortex.

• The focus on clear connectivity-based hypotheses and clear descriptions of what would be expectedin the results if different hypotheses were true.

• Generalization of models to a completely held-out dataset further increases confidence in thegeneralizability of the models.

Concerns:

1) The main conclusion of the paper (including in the title) involves a directional inference, and yet it is notoriously difficult to make directional inferences with fMRI. The term "input" into the cerebellum is repeatedly used to describe the prediction of cerebellar activity based on cortical activity, and yet the cerebellum is known to form loops with the cortex. With the slow temporal resolution of fMRI it is typically unclear what is the "input" versus the "output" in the kinds of predictions used in the present study. Critically, this may mean that a cerebellar region could receive input from a single cortical region (i.e., the alternate hypothesis supposedly ruled out by the present study), then output to multiple cortical regions, likely resulting (using the fMRI-based approach used here) in a faulty inference that convergent signals from cortex drove the results. On pg. 4 it is stated: "We chose this direction of prediction, as the cerebellar BOLD signal overwhelmingly reflects mossy-fiber input, with minimal contribution from cerebellar output neurons, the Purkinje cells (Mathiesen et al., 2000; Thomsen et al., 2004)." First, it would be good to know how certain this is in 2022, given the older references and ongoing progress in understanding the relationship between neuronal activity and the BOLD signal (e.g., Drew 2019). Second, given that it's likely that activity in the mossy-fiber inputs has an impact on Purkinje cell outputs, and that some cortical activity supposedly reflects cerebellar output, it is possible that FC could also reflect the opposite direction (cerebellumcortex). It would seem important to consider these possibilities in the interpretation of the results.

We agree that making directional inferences with fMRI BOLD signals is difficult. We also note that because of the low temporal resolution of fMRI BOLD signals, we have not tried to extract directional information based on temporal lags. Rather, we emphasize that the relationship between neural activity and BOLD differs between the neocortex and cerebellum. In the cerebellum, mossy fiber activity releases glutamate which activates granule cells and the release of Nitric oxide (NO). NO is mostly released by granule cells and stellate cells. The release of NO increases the diameter of capillaries which in turn causes changes in blood flow and blood volume, two major contributors to BOLD signal changes (Alahmadi et al. 2016; Alahmadi et al. 2015; Drew 2019; Mapelli et al. 2017; Gagliano et al. 2022). Importantly, there is a negligible contribution of NO from the Purkinje cells. Taken together, these data make a strong case that the BOLD signal in the cerebellar cortex reflects activity at the input stage. We acknowledge that the references cited in our initial submission were somewhat dated. We have now provided additional references (which are in agreement with the findings from the earlier papers).. Based on this evidence, we chose to predict cerebellar activity from cortical activity.

References: Alahmadi, A. A., Samson, R. S., Gasston, D., Pardini, M., Friston, K. J., D’Angelo, E., ... & Wheeler-Kingshott, C. A. (2016). Complex motor task associated with non-linear BOLD responses in cerebro-cortical areas and cerebellum. Brain Structure and Function, 221(5), 2443-2458.

Alahmadi, A. A., Pardini, M., Samson, R. S., D'Angelo, E., Friston, K. J., Toosy, A. T., & Gandini Wheeler‐Kingshott, C. A. (2015). Differential involvement of cortical and cerebellar areas using dominant and nondominant hands: an FMRI study. Human brain mapping, 36(12), 5079-5100.

Mapelli, L., Gagliano, G., Soda, T., Laforenza, U., Moccia, F., & D'Angelo, E. U. (2017). Granular layer neurons control cerebellar neurovascular coupling through an NMDA receptor/NO-dependent system. Journal of Neuroscience, 37(5), 1340-1351.

Gagliano, G., Monteverdi, A., Casali, S., Laforenza, U., Gandini Wheeler-Kingshott, C. A., D’Angelo, E., & Mapelli, L. (2022). Non-Linear Frequency Dependence of Neurovascular Coupling in the Cerebellar Cortex Implies Vasodilation–Vasoconstriction Competition. Cells, 11(6), 1047.

Drew, P. J. (2019). Vascular and neural basis of the BOLD signal. Current Opinion in Neurobiology, 58, 61–69.

2) It would be helpful to have more details included in the "Connectivity Models" sub-section of the Methods section. The GLM-based connectivity approach is highly non-standard, such that more details on the logic behind it and any validation of the approach would be helpful. More specifically, it would be helpful to have clarity on how this form of functional connectivity relates to more standard forms, such as Pearson correlation and perhaps less standard multiple regression (or partial correlation) approaches. If I understand this approach correctly, each cortical parcel's time series is modulated (up or down) using that parcel's task-evoked beta weights, then "normalized" by the standard deviation of that parcel's time series, with the resulting time series then used in a multiple regression model to explain variance in a given cerebellar voxel's time series. It would be helpful if each of these steps were better explained and justified. For example, it is unclear what modulation of the cortical parcel time series by task-related beta weights does to the functional connectivity estimates, and thus how they should be interpreted.

All of the models are multiple regression models. The independent variables (X) are the fitted (task-evoked) time series of the cortical parcels and the dependent variables (Y) are the fitted time series of each cerebellar voxel. Coefficients from multiple regression are identical to partial correlation coefficients if the cortical and cerebellar time series are z-standardized (SD=1). Here we only standardized the cortical time series. This only retains the weighting of the different cerebellar voxels (a cerebellar voxel that has a strong task-related signal should contribute more to the overall evaluation than a voxel where the task-related signal is weak); beyond this, the conclusions will be the same as that obtained with a partial correlation analysis.

Because the number of predictors (#cortical parcels) approaches or outstrips the number of available observations (#task-related regressors), the ordinary-least-squares (OLS) solution to the multiple regression problem is not unique. We thus compared 3 common ways of regularizing a multiple regression problem: a) Picking only the most important regressor (a form of feature selection or optimal subspace selection), Ridge regression (L2 regularization) or Lasso regression (L1 regularization). Each method biases the solution in a particular way: The winner-take-all solution is obviously very sparse, the Lasso solution somewhat less sparse, and the Ridge solution quite dispersed. Here we exploited these differences in inductive bias, reasoning that the method with the bias that best matches the structure of the data-generating process will lead to better prediction performance on independent data.

The results clearly favored a distributed input to each cerebellar voxel from the cortical parcels. We have rewritten the method section on connectivity models to better communicate the main idea.

3) It appears that task-related functional connectivity is used in the present study, and yet the potential for task-evoked activations to distort such connectivity estimates does not appear to be accounted for (Norman-Haignere et al. 2012; Cole et al. 2019). For example, voxel A may respond to just the left hemifield of visual space while voxel B may respond to just the right hemifield of visual space, yet their correlation will be inflated due to task-evoked activity for any centrally presented visual stimuli. There are multiple methods for accounting for the confounding effect of task-evoked activations, none of which appear to be applied here. For example, the following publications include some options for reducing this confounding bias: (Cole et al. 2019; Norman-Haignere et al. 2012; Ito et al. 2020; Rissman, Gazzaley, and D'Esposito 2004; Al-Aidroos, Said, and Turk-Browne 2012). If this concern does not apply in the current context it would be important to explain/show why.

The papers cited by the reviewer focus on the problem of how to remove task-evoked activity to estimate the correlation of spontaneous (task-independent) fluctuations. Here we are doing the opposite. We removed almost all spontaneous fluctuations and noise by averaging across trials and runs in order to fit the task-evoked activity. Additionally, we used a crossed approach as a way to control for the influence of task-independent fluctuations on the regression models: Within each task set, cerebellar activity from one half of the runs was predicted from cortical activity from the other half of the runs. Returning to the papers cited by the reviewer, these are designed to look at connectivity not related to task-evoked activity. We briefly summarize each below:

● Cole et al. (2019): Demonstrates that the removal of mean task-evoked activations while preserving task-evoked response shape is an important preprocessing step for validating task-based FC.

● Ito et al. (2020): Addressed the issue of shared variability between brain regions during task-evoked activity by estimating time series variance. They removed task-evoked activity from the time series in order to get a direct measure of neural-to-neural correlations (e.g., “background connectivity”) rather than task-to-neural associations.

● Al-Aidroos et al. (2012): Confronted with a similar problem of interpreting intrinsic correlations related to a goal (e.g., attending to scenes) from correlations related to synchronized stimulus-evoked responses. To mitigate this confound, they removed stimulus-evoked responses from the data resulting in “background connectivity” which was then used to assess inter-region coupling.

● Rissman et al. (2004): Introduced a new approach to characterize inter-region correlations during event-related activity by allowing inter-regional interactions to be assessed independent of activity at individual stages of a task.

● Norman-Haignere et al. (2012): To assess inter-region interactions (between fusiform gyrus and parahippocampal cortex), the authors removed the mean stimulus-evoked response and examined the correlations that occurred in the background of stimulus-locked changes (e.g., background connectivity).

4) It is stated (pg. 21): "To reduce the influence of these noise correlations, we used a "crossed" approach to train the models: The cerebellar time series for the first session was predicted by the cortical time series from the second session, and vice-versa (see Figure 1). This procedure effectively negates the influence of noise processes, given that noise processes are uncorrelated across sessions." However, this does not appear to be strictly true, given that the task design (parts of which repeat across sessions) could interact with sources of noise. For example, task instruction cues (regardless of the specific task) likely increase arousal, which likely increases breathing and heart rates known to impact global fMRI BOLD signals. The current approach likely reduces the impact of noise relative to other approaches, but such strong certainty that noise processes are uncorrelated across sessions appears to be unwarranted.

We completely agree. What we meant to say is that the procedure “negates the influence of any noise process that is uncorrelated with the tasks.” If we can predict the cerebellar activity patterns in session 2 by the cortical activity patterns measured in session 1, we can conclude that this prediction must be based on task-related signal changes given that the sequence of tasks is randomized. However, we do not know whether these task-related signals are caused directly by neural processes or indirectly by physiological processes (for example increased heart-rate in some conditions). The procedure only removes the influence of noise processes that are unrelated to the tasks. In our experience, these noise correlations can be quite strong and methods to remove them can introduce biases. For task-related noise processes we relied on high-pass filtering, a standard approach in task-based GLM approaches (see Methods).

5) It appears possible that the sparse cerebellar model does worse simply because there are fewer predictors than the alternate models. It would be helpful to verify that the methods used, such as cross-validation, rule out (or at least reduce the chance) that this result is a trivial consequence of just having a different number of predictors across the tested models. It appears that the "model recovery" simulations may rule this out, but it is unclear how these simulations were conducted. Additional details in the Methods section would be important for evaluating this portion of the study.

Our methods ensure full correction for model complexity (see response to major comment #2). Note that the sparse methods select regressors from all available cortical parcels; as such, “model complexity” is not well summarized by the number of non-zero regressors. We have now clarified these issues in the Methods section and have also revised the paper to better describe our model recovery simulations designed to address the issue of possible biases caused by different degrees of collinearity between cortical regressors.

Reviewer #2 (Public Review):

The human cerebellum likely has a significant but understudied contribution to cognition and behavior beyond the motor domain. Clarifying its functional relationship with the cerebral cortex is a critical detail necessary for understanding cerebellar functions. This paper addresses this challenge by testing three simple but intuitive models: winner-take-all, one-to-one model versus two converging input models. Results showed that the convergence model outperformed the one-to-one mapping model, indicating that cerebellar regions received multiple converging inputs from the different cortical regions. Overall the paper is well-written, and the results are clean and interesting. The methodological rigor of using cross-validation and generalization is also a strength of this paper.

1) The authors concluded that some cerebellar regions receive converging inputs from multiple cortical regions because the Ridge and Lasso models outperformed the WTA model. The WTA model has a fixed diagonal pattern, in contrast, Ridge/Lasso models included more weights in the connectivity matrix. Considering what's being estimated in this matrix, then perhaps the findings are not surprising because even after penalizing and regularization, the ridge regression models are still more complex than the WTA model (more elements are allowed to vary). In other words, Lasso/Ridge models allow more variables from the X side to explain variances in Y, similar to how throwing in more regressors can always improve the R square. I am unsure if cross-validation mitigates this issue. It would be more straightforward for the authors to compare model performance in a way that controls for the number of variables in the Ridge/Lasso models.

We now recognize that we could have done a better job in explaining our approach on this issue in the original submission. The models (including connectivity weights and regularization parameter) are trained solely on data from Task set A. They are tested on 2 independent datasets: 1) Data from the same participants performing novel tasks; 2) Data from new participants performing novel tasks. This allows us to compare models of different structure and complexity.

2) The authors did an excellent job reviewing the anatomical relationship between the cerebral cortex and the cerebellum. There are several issues that the authors should address in the introduction or discussion. First, if the anatomical relationship between the cerebellum and the cortex is closed-loop as suggested in the intro, then how convergence can arise from multiple cortical inputs given there is no physical cross-talk? Second, there are multiple synapses connecting a cerebellar region and the cortex, and therefore could integration occur at other sites but not the cerebellum? For example, the caudate, the thalamus, or even the cortex (integrating inputs before sending to the cerebellum)?

We agree that the correlation structure of BOLD signals in the neocortex and cerebellum is shaped by the closed-loop (bi-directional) interactions between the two structures. As such, some of the observed convergence could be caused by divergence of cerebellar output. We have added a new section to the discussion on the directionality of the model (Page 18).

That said, there are strong reasons to believe that our results are mainly determined by how the neocortex sends signals to the cerebellum, and not vice versa. An increasing body of physiological studies (and this includes newer papers, see response to reviewer #1, comment #1 for details) show that cerebellar blood flow is determined by signal transmission from mossy fibers to granule cells and parallel fibers, followed by Nitric oxide signaling from molecular layer interneurons. Importantly, it is clear that Purkinje cells, the only output cell of the cerebellar cortex, are not reflected in the BOLD signal from the cerebellar cortex. (We also note that increases in the firing rate of inhibitory Purkinje cells means less activation of the neocortex). Thus, while we acknowledge that cerebellar-cortical connectivity likely plays a role in the correlations we observed, we cannot use fMRI observations from the cerebellar cortex and neocortex to draw conclusions about cerebellar-cortical connectivity. To do so we would need to measure activity in the deep cerebellar nuclei (and likely thalamus).

The situation is different when considering the other direction (cortico-cerebellar connections). Here we have the advantage that the cerebellar BOLD signal is mostly determined by the mossy fiber input which, at least for the human cerebellum, comes overwhelmingly from cortical sources. On the neocortical side, the story is admittedly less clear: The cortical BOLD signal is likely determined by a mixture of incoming signals from the thalamus (which mixes inputs from the basal ganglia and cerebellum), subcortex, other cortical areas, and local cortical inputs (e.g., across layers). While the cortical BOLD signal (in contrast to the cerebellum) also reflects the firing rate of output cells, not all output cells will send collaterals to the pontine nuclei. These caveats are now clearly expressed in the discussion section2.

On balance, there is an asymmetry: Cerebellar BOLD signal is dominated by neocortical input without contribution from the output (Purkinje) cells. Neocortical BOLD signal reflects a mixture of many inputs (with the cerebellar input making a small contribution) and cortical output firing. This asymmetry means that the observed correlation structure between cortical and cerebellar BOLD activity (the determinant of the estimated connectivity weights) will be determined more directly by cortico-cerebellar connections than by cerebellar-cortical connections. Given this, we have left the title and abstract largely the same, but have tempered the strength of the claim by discussing the influence of connectivity in the opposite direction.

3) The dispersion metric quantifying the spread level in cortical inputs is interesting. Could the authors expand this finding and show anatomically what the physical spread is like in cortical space? The metric is novel but hard to interpret. A figure demonstrating the physical spread in the cortex should help readers interpret this result.

Figure 3 (previously Figure 4) was included to provide examples of differences in the spatial spread of cortical inputs. For example, regions 1 and 2 are explained by a more restricted and spatially contiguous set of cortical inputs (e.g., primary motor cortices) whereas regions 7 & 8 are explained by a set of spatially disparate regions (e.g., angular gyrus, superior and middle frontal cortices, and superior temporal gyrus). Prompted by this comment, we have opted to reverse the order of Figures 3 and 4 to give the reader a chance to visualize differences in physical spread of cortical regions before we walk through the quantitative analysis.

4) At the end of the discussion section, the authors discussed how results are more likely driven by cortical inputs to the cerebellum but not the other way around. This interpretation is likely overstated given the hemodynamic blurring and low temporal resolution of BOLD. Without a faster imaging sequence and accurate models that account for differences in hemodynamic properties, the more parsimonious interpretation is results are driven by bidirectional cortico-cerebellar interactions. The results are still very interesting without this added nuisance.

Our analyses do not rely on the exact time course or delays between neocortical and cerebellar activation, but only on the activity profiles across a wide range of tasks. In terms of bidirectionality, please see our response above. We have added a dedicated section in the revised Discussion on this issue.

Author Response

Reviewer #1 (Public Review):

The authors sought to define the molecular mechanism of activation of the thrombopoietin receptor (TpoR), a very important cytokine receptor that regulates megakaryocyte differentiation and platelet production. They conducted a thorough series of experiments combining mutagenesis experiments with sophistical biological assays and that also includes solid-state NMR structural measurements. This work builds on a body of previous studies of TpoR from this group and from others. They focused both on (1) the role and impact of W515 located in the juxtamembrane cytosolic domain and (2) the impact of introducing either Asn at sites in the transmembrane domain to induce various dimerization modes, or insertion of pairs of Ala residues to induce helical rotation to the TM domain. There is a lot of nice data in this paper, which is fairly intricate - a tough read, but that's because it's a complicated system. The writing is excellent.

This paper presents a model for receptor activation in which the inactive receptor is the monomeric form of the receptor in which the juxtamembrane domain, including W515, maintains a helical structure. Activation of the receptor triggers dimerization of the transmembrane domain and loss of helicity of the juxtamembrane segment, which facilitates optimal interactions of the kinase domains with their JACK2 domain phosphorylation substrates.

There is a lot to like in this careful work and the resulting manuscript. There is one major shortcoming in this manuscript, which concerns W515. It is known that mutation of W515 to any of 17 of the canonical amino acids, including Phe, is sufficient to trigger homodimerization and receptor activation. The authors present some evidence that the phenomenon behind this is that mutation of W515 to almost any other residues disrupts the helical secondary structure of the critical juxtamembrane segment, which promotes dimerization and receptor activation. What I find puzzling is why a Trp at site 515 promotes helix formation, but nearly all other amino acids at this site disrupt helix formation. This strongly suggests the side chain of W515 must be interacting with another domain of the protein in the inactive state, in a manner that is responsible for how Trp stabilizes the juxtamembrane helix, which is a central feature that helps define that state. I think that for this paper, this dangling missing piece of their mechanistic model should be resolved.

We agree with the reviewers that the mechanism by which Trp515 stabilizes the TM helix is central to the mechanism of activation. More broadly, our studies over the past decade have sought to address the importance of the entire RWQFP insert in the TM domain. Our working model for this sequence has been that cation-π interactions are central to the role of the Trp and the accompanying amino acids.

Arginine and tryptophan both are over-represented at the cytoplasmic TM-JM boundaries of membrane proteins. Arginine is positively charged and part of the “positive-inside” rule for membrane protein insertion. Arginine and lysine define the cytoplasmic ends of TM helices and prefer to be accessible to the water-exposed membrane surface. In contrast, tryptophan residues prefer hydrophobic head-group or membrane interior locations. A revealing aspect of the RWQFP motif is that the arginine and tryptophan are located at the membrane to cytosolic border. As a result, in order to accommodate arginine in a more water-inaccessible membrane environment, it interacts with the surface of the tryptophan indole ring. Partitioning of the RWQF sequence in a more water-inaccessible environment also drives the formation of helical secondary structure as an unpaired backbone C=O...NH in a hydrophobic environment is estimated to cost 3-6 kcal/mol of energy.

We have taken two approaches in respond to this essential criticism of the reviewers: one structural and one computational. Additional NMR data (structural approach) has been included in the supporting information (see response to point 2 below). Computational approaches provide a second way to address whether a cation– interaction between Trp515 and the positively charged Arg514 is responsible for stabilizing the C-terminal TM helix. We have included a new supporting figure using Alpha-Fold 2.0 that probes the structural changes upon mutation of Trp515. In the wild-type receptor, Arg514 is predicted to form a cation– interaction with Trp515. In the W515K mutant, the helical secondary structure in the RKQFP sequence is disrupted and Arg514 forms a new cation– interaction with Trp529. Similar changes occur in other Trp515 mutants (e.g. W515A) highlighting the ability of Alpha-Fold to predict such interactions and the consequences of mutation. Overall, 15 out of 19 W515X mutants are predicted to be unfolded. Experimentally, 17 out of 19 mutations lead to activation. Importantly, W515C and W515P are the only two amino acid substitutions that do not cause constitutive activity experimentally (Defour, Chachoua, Pecquet, & Constantinescu, 2016). Computationally, these two sites do not predict helix unraveling. In short, the overall the predictions of Alpha-Fold agree with the unique nature of tryptophan at position 515.

In addition, we have expanded the arguments supporting the potential role of cation–π interactions by adding a new section entitled “Unfolding of the RWQF -helical motif is a common mechanism of receptor activation”.

These modifications are now in the revised manuscript starting with line 213:

Our working model for the mechanism of activation in the wild-type or mutant receptors is that the RWQF motif is stabilized in the inactive state as an -helix as a result of a cation- interaction between R514 and W515. This interaction allows the RWQF sequence to partition into the more hydrophobic head-group region of the bilayer. Both Arg and Trp are over-represented at the cytoplasmic ends of TM helices (von Heijne, 1992), but whereas Arg prefers a water-accessible environment, Trp prefers to be buried in a more hydrophobic environment (Yau, Wimley, Gawrisch, & White, 1998). Since Arg and Trp are located at the border between membrane and cytosolic domains and Arg precedes Trp in the sequence, partitioning into the membrane head-group region results in a favorable interaction of the positive charge associated with the guanidinium group of the R514 side chain with the partial negative charge associated with the aromatic surface of the W515 side chain. Partitioning of the RWQF sequence into the more water-inaccessible environment drives the formation of helical secondary structure as an unpaired backbone C=O...NH in a hydrophobic environment is estimated to cost 6 kcal/mol of energy (Engelman, Steitz, & Goldman, 1986). In this model, activation of the receptor results in or is caused by disruption of the R514-W515 cation-π interaction. In the W515 mutants, R514 is no longer stabilized in a membrane environment and the helix containing the RWQFP sequence unravels to allow the positively charged side chain to reach outside of the membrane. In the case of the Asn mutants and in the wild-type receptor with bound Tpo, dimerization of hTpoR (or rotation of the TM helices in mTpoR dimer), places W515 in the center of the helix-helix interface. The data suggest that a steric clash of the W515 side chains results in unraveling of the cytoplasmic end of the TM helix.<br /> Computational and additional NMR data are provided in the supplementary figures to support the model of helix unraveling suggested by the solid-state NMR studies. Computationally, we used AlphaFold 2.0 (Jumper et al., 2021) calculations of hTpoR TM-JM peptides to predict the influence of all possible mutations at position 515 on the TM-JM helix structure. Remarkably, -helix unraveling was predicted for 15 out of 20 possible amino acids at 515 (supplement 2 to Figure 3). Importantly, two of the mutations that are not predicted to cause helix unraveling are W515C and W515P. Experimentally, these two amino acid substitutions are the only ones that do not induce constitutive activity among all possible amin oacid substitutions at W515 (Defour et al., 2016). Introducing a Trp at the preceding position 514 instead of R/K in W515K/R mutants reverses helix unfolding in AlphaFold simulations (supplement 3 to Figure 3). This result agrees with our previous data that the WRQFP mutant is inactive and is essentially monomeric (J. P. Defour et al., 2013). Structurally, we have undertaken solution-NMR studies of the wild-type hTpoR TM-JM peptide and its W515K mutant. Relaxation measurements of the backbone 15N resonances show that W515K mutation leads to association of the TM helices, and that it induces upfield chemical shift changes in the RWQF sequence consistent with helix unraveling (supplement 1 to Figure 3).

Reviewer #2 (Public Review):

The thrombopoietin receptor (TpoR) regulates stem cell proliferation, platelet production, and megakaryocyte differentiation. Past cell biology and biophysical studies have established that ligand-induced dimerization constitutes the mechanism of activation of TpoR. Specifically, ligands bind to the extracellular domain of TpoR and generate an allosteric response that is transmitted to the transmembrane domain, activating downstream signaling. However, up to now the molecular details of how the allosteric signals are transmitted to the intramembrane domains have been elusive. In this manuscript, Constantinescu and co-workers combined NMR, in vitro, and in vivo assays to investigate the activation and oncogenicity of TpoR. The authors concluded that the unwinding of the juxtamembrane domain is the main structural event that determines TpoR activation and regulates oncogenicity. The solid-state NMR studies were carried out in lipid membranes with polypeptides spanning the juxtamembrane and transmembrane residues. The authors show a series of spectra of 13CO resonances that encompass the juxtamembrane domain that is diagnostic of a structural transition from a helical conformation to a partially disordered state. The unwinding of the helical juxtamembrane domain was confirmed by site-specific mutations in this region. The chemical shift changes clearly indicate the transition from order to disorder (and vice versa) for selected sites. These conclusions are compounded by INEPT-type experiments that detect the most dynamic region of polypeptides. To rationalize the molecular mechanism for activation, the authors also used Ala-Ala insertions at strategic positions along the transmembrane domain. These experiments showed that the specific orientation of the transmembrane residues is central for TpoR activation, and a slight rotation of the helix is critical for activation of the receptor. Transcriptional activity assays confirm the importance of the proper orientation of the transmembrane domain for receptor activation.

Overall, I believe the data are solid, and both biophysical and cell biology studies support the conclusions of the authors. These new findings represent a significant advancement in understanding cytokine receptor activation.

We thank the reviewer for these comments.

Reviewer #3 (Public Review):

The authors sought to propose a mechanism by which cancer-causing mutations in the thrombopoietin receptor (TpoR) activate the receptor. To do so, they used a systematic approach of introducing non-native and naturally occurring mutations into the receptor and use a combination of in-vivo and cell-based assays and solid-state NMR spectroscopy. They propose that the proximity of the asparagine mutations to the cytosolic boundary influences the secondary structure of the receptor and suggests that this structural change induces receptor activation.

The strengths of this work are the importance of the system being studied and tackling a problem that is not yet fully resolved. The authors acquired a large and convincing set of biological data, including in vivo experiments that support the gain-of-function/activating role of the mutations studied. The solid-state NMR data are of high quality as well. In particular, the INEPT data in figure 6a display very clear differences within one region of the wild-type compared to the mutants.

One significant weakness is the validity of the conclusions given the limited atomistic measurements presented. Namely, the authors make rather specific conclusions about protein folding based on a single set of 13C alanine carbonyl chemical shifts in the wild-type and mutant TM peptides. Essentially, the authors observe chemical shift perturbations at this carbonyl carbon when mutations are introduced into a protein and use this information to make conclusions about secondary structure. I am not convinced that the authors have presented sufficient evidence to justify the conclusion that the helix unwinds and that this is responsible for the mechanism of activation. While the other cell-based experiments in mutations are interesting, deciphering such a specific folding mechanism with limited atomistic data is not justified.

We added both computational data and solution NMR to support our conclusion.

Author Response

Reviewer #1 (Public Review):

Proton pumps are necessary to set up gradients necessary for myriad biological processes. The malaria-causing parasite Plasmodium falciparum, uses two main pathways to achieve this, the vacuolar ATPase (V-type ATPase) and a more ancient vacuolar pyrophosphatase (PfPV1). The proton motive force set up across the parasite plasma membrane holds particular significance since it is necessary for transport of nutrients and waste products into and out of the cell. Motivated by the observation that the V-type ATPase is no expressed until several hours after the parasite has entered host cells, the present study examines the function of PfVP1. The authors demonstrate PfVP1 depletion blocks the early development of Plasmodium-specifically the transition from the ring to the trophozoite stage-and this is associated with changes to cellular pH and pyrophosphate levels, consistent with predicted functions. Complementation of the conditional knockdown suggests that pyrophosphatase activity alone is not sufficient to overcome the loss of PfVP1. Overall, data supporting a critical role for PfVP1 in parasite energetics is compelling. However, the lack of several key controls somewhat weakens the conclusions of the paper when it comes to complementation of the mutants and description of which activities are needed for parasite survival. Because the proximal activities of the enzyme ATP generation and the proton motive force are incompletely examined, some of the major conclusions from the study remain speculative.

We thank the reviewer for these constructive comments. We are grateful to the reviewer for his/her recognition of the significance of our study. The major discovery of this manuscript is to uncover PfVP1’s essential role in the early-stage development of the 48h asexual lifecycle in P. falciparum. Our data suggest PPi is an energy source when ATP level is likely low in the ring stage malaria parasite and its transition to the trophozoite stage. We have performed additional experiments and tried the best to address each comment from the reviewer.

Reviewer #2 (Public Review):

In this work, the authors characterize a proton pump from the parasite Plasmodium falciparum that uses pyrophosphate as an energy source (PfVP1).

They looked at the expression and localization of the pump in different stages of the parasite and determined that it localizes to the plasma membrane and it is highly expressed in the ring stage. They studied the biochemical function by expressing the gene in Saccharomyces followed by isolation of vesicles and measurements of proton transport and PPi hydrolysis. They also characterized the biological role of PfVP1 in the parasites by creating conditional mutants that express PfVP1 when cultured in the presence of anhydrotetracycline (ATC). Upon removal of ATC the expression of PfVP1 is downregulated, which impacted growth and transition to the trophozoite stage. Mutant parasites struggled to progress through the ring state and failed to become trophozoites in the second intraerythrocytic cycle. They complemented the mutants with the yeast inorganic pyrophosphatase gene and the Arabidopsis vacuolar pyrophosphatase.

We thank the reviewer for positive and constructive comments. We have seriously worked on every comment raised by the reviewer. We have tried the best to perform additional experiments.

Reviewer #3 (Public Review):

Solebo and coworkers investigated the energy requirements of blood-stage malaria parasites (the stage of infection that causes symptoms). Traditionally, parasites were thought to be somewhat quiescent during the first half of their life cycle in red blood cells and become metabolically active as they prepare for replication. Consequently, antimalarial drugs are more active against parasites during the second half of their life cycle. In this report, the authors show that the metabolic by-product pyrophosphate is an essential energy source for the development of early-stage malaria parasites and that it is consumed by a vacuolar pyrophosphatase (PfVP1). Knock down studies showed that PfVP1 is required for the development of early-stage parasites and localization studies established that it is located in the parasite plasma membrane. Characterization of PfVP1 heterologously expressed in yeast confirmed that it is a pyrophosphate hydrolyzing proton pump. Consequently, loss of PfVP1 in early-stage parasites results in reduced pyrophosphate consumption and a reduction in pH (accumulation of protons). The authors further show that a similar vacuolar pyrophosphatase from Arabidopsis thaliana can complement the loss of the parasite ortholog, but a general pyrophosphatase enzyme cannot. Consistent with this result, mutations designed to inactivate either the pyrophosphatase activity or the proton-pumping activity demonstrated that both activities are essential for the development and survival of early-stage parasites.

The conclusions of this paper are firmly supported by data, often from more than one type of experimental approach. The conclusions provide fundamental information about the stage of parasite development that has been hard to target with antimalarial drugs. The most energy-consuming process in a cell is the maintenance of membrane potential and in malaria parasites, it is known that proton pumps (rather than sodium pumps) are responsible for this process. Although PfVP1 was previously reported to be located internally in an organelle of the parasite, the data presented in this report clearly define its location on the plasma membrane and its essential role in maintaining the membrane potential. PfVP1 inhibitors could preferentially target early stage malaria parasites and the current results support efforts to find these inhibitors. Perhaps the most exciting aspect of this work is the potential to act synergistically and enhance the effect of current antimalarial drugs on early stage parasites. In this vein, the authors tested four antimalarial compounds in conjunction with knockdown of PfVP1 to determine whether there was enhanced activity. These experiments were not conducted in a systematic way and this is perhaps the only weakness of the paper.

We thank the reviewer for positive, constructive, and encouraging comments. We really appreciate that. We are also very excited about our discovery that a non-ATP driven proton pump plays essential roles in the early-stage development of the asexual lifecycle. Our data suggest PPi is an energy source in the malaria parasite P. falciparum.

Author Response

Reviewer #1 (Public Review):

The manuscript by Xu et. al. does a very thorough characterization and molecular dissection of the role of SSH2 in spermatogenesis. Loss of SSh2 in germ cells results in germ cell arrest In step2-3 spermatids and eventually leads to germ cell loss by apoptosis. Molecular characterization of the mutant mice shows that the loss of SSH2 prevents the fusion of proacrosomal vesicles leading to the formation of a fragmented acrosome. The fragmentation of the acrosome is due to the impaired actin bundling and dephosphorylation of COFILIN. In short, this is a comprehensive body of work.

We thank the referee for these insightful comments.

Reviewer #2 (Public Review):

The acrosome is a unique sperm-specific subcellular organelle required for the fertilization process, and it is also an organelle undergoing extensive morphological and structural transformation during sperm development. The mechanism underlying the extensive acrosome morphogenesis and biogenesis remains incompletely understood. Xu et al in their manuscript entitled "The Slingshot phosphatase 2 is required for acrosome biogenesis during spermatogenesis in mice" reported that the Slingshot Phosphatase 2 is essential for acrosome biogenesis and male fertility through their characterization of spermatogenic and acrosomal defects in Ssh2 knockout mice they generated. Specifically, the authors provided molecular, genetic, and subcellular evidence supporting that Ssh2 mutation impaired the phosphorylation of an acting-binding protein, COFILIN during spermiogenesis and accordingly actin cytoskeleton remodeling, crucial for proacrosomal vesicle trafficking and acrosome biogenesis. The manuscript by Xu et. al. does a very thorough characterization and molecular dissection of the role of SSH2 in spermatogenesis. Loss of SSh2 in germ cells results in germ cell arrest In step2-3 spermatids and eventually leads to germ cell loss by apoptosis. Molecular characterization of the mutant mice shows that the loss of SSH2 prevents the fusion of proacrosomal vesicles leading to the formation of a fragmented acrosome. The fragmentation of the acrosome is due to the impaired actin bundling and dephosphorylation of COFILIN. In short, this is a comprehensive body of work.

We appreciate and thank Referee #2 for the positive feedback and insightful comments.

Strengths:

Nicely written manuscript, addresses an important mechanistic question of the roles of cytoskeleton remodeling in acrosome biogenesis and provided genetic, subcellular, and molecular evidence to build up their support for their hypothesis that Ssh2 regulates actin cytoskeleton remodeling, a process essential for proacrosomal vesicle trafficking and acrosome biogenesis, through dephosphorylation actin-binding protein during spermiogenesis.

We again thank to the Referee #2 for appreciating and encouraging us regarding our current research work.

Weaknesses:

For body weight, and testis weight of the mutants, the authors concluded that there is no significant difference between the mutant and wildtype (Fig 1E -1G), but they appear to use mice between 6-8 wk old, both the testis and body weight of males at 6-8 wks is still growing, with the number of mice analyzed being six, you could easily miss the significant difference of the testis size and or body weight with such a varied age and a small sample size.

We thank the referee for their prompting of this important discussion point, which we now cover in our revised manuscript. In our originally submitted manuscript, we only presented the data for body weight, testis weight, and T/B ratio for mice between the age of 6–8 weeks, however, we have added the additional data of mice with age more than 8 weeks in the revised manuscript in a new Figure 1E-1G with the sample size of 12 for each genotype. We have also updated the relevant content in the figure caption. The revised figure caption for Figure 1 panels E–G reads as follows: “(E-G) Body weights (26.3609 ± 0.4914 for WT; 25.1741 ± 0.5189 for Ssh2 KO), weights of the testes (0.0862 ± 0.0036 for WT; 0.0788 ± 0.0023 for Ssh2 KO), and the testis-to-body weight ratio (0.3281 ± 0.0153 for WT; 0.3154 ± 0.0135 for Ssh2 KO) of adult WT and Ssh2 KO males (n = 12). Data are presented as the mean ± SEM; p > 0.05 calculated by Student’s t-test. Bars indicate the range of the data.”

Other points:

Comments: 1) Could the uniform cytoplasmic distribution of diminutive actin filaments in the wild type and disrupted actin filament remodeling be examined at the EM level on the round spermatids?

We apologize for the confusion. Previously, we conducted a transmission electron microscopy (TEM) analysis on the testes samples to discover the distribution and ultrastructural organization of F-actin in WT and Ssh2 KO round spermatids. Unfortunately, even at high magnification (30,000x, right panel of Figure R1-Response Figure 1) by TEM of testicular section no diminutive actin filament was observed in the cytoplasm of round spermatids except for the acroplaxome-an actin-rich specialized structure anchors the acrosome-in WT spermatids as well as some thick bundle-like structures located at the acrosomal region of Ssh2 KO spermatids (Fig. R1). According to their unique characteristic of appearance, we interpreted these electron-dense bundles as the aberrantly aggregated actin filaments whose lengths are in accordance with the lengths of COFILIN-saturated F-actin fragments (Bamburg et al., 2021), suggesting the disrupted actin filament remodeling during acrosome biogenesis resulted from Ssh2 KO. However, due to the technological limitations of TEM and the complexity of intracellular environment of round spermatids, we only recognized few aggregated actin bundles with the loss of filamentous appearance in Ssh2 KO spermatids and no typical diminutive actin filament was detected which had been imaged under high-resolution cryo-TEM (Haviv et al., 2008) or live-cell total internal reflection fluorescence microscopy (Johnson et al., 2015) on the purified actin bundles and cultured cells. Given the lack of effective approaches to culture murine round spermatids in vitro, confocal microscopy of flourescence-labelled F-actin (e.g., IF staining by FITC-phalloidin) is a more accessible method for visualizing the disruption of actin remodeling than EM in murine spermatids as the actin-related findings that several other studies demonstrated (Djuzenova et al., 2015; Meenderink et al., 2019).

Comments: 2) Any other defects are seen besides acrosome in the mutant testis given the important roles of actin cytoskeleton network and high expression of Ssh2 in spermatocytes, were chromatoid bodies or mitochondria affected in any way? Any other defects in the mice overall including female fertility and other organs, given the previously reported roles in the nervous system. It could be helpful information for others interested in Ssh 2 protein and actin cytoskeleton's roles in general.

The referee has here raised an interesting point. Firstly, besides the acrosome-related defects in Ssh2 KO spermatids, we identified increased germ cell apoptosis and aberrant activation of apoptotic Bcl-2/Caspase-3 pathway in the testes of Ssh2 KO mice which were speculated to be triggered by the disordered COFILIN-mediated F-actin remodeling and have attracted our attention to further elucidate the underlying mechanisms in the future. Secondly, given the high expression of SSH2 in spermatocytes demonstrated by IF staining shown in figure 4B and 4C,we thus performed the surface chromosome spreading on spermatocytes to observe whether the morphology of chromatid bodies and the meiotic progression was affected by Ssh2 KO and no obvious defects were observed as shown in supplementary Figure S3 in originally submitted manuscript. Thirdly, no obvious morphological abnormality in chromatin or mitochondrial structure was detected in Ssh2 KO germ cells such as spermatocytes and round spermatids under TEM which prevents us to pursue it further. Fourthly, we have observed the potential effect(s) of Ssh2 KO on female fertility using Ssh2 KO female mice and did not find any obvious infertility defect in Ssh2 KO females compared to their WT littermates as demonstrated by the data of the body weight, ovary weight, ovary-to-body weight ratio, size of ovaries and fertility test as well as the images of ovarian HE staining (Fig. R1). Moreover, given that during our investigation period, Ssh2 KO males and females did not manifest any defective physical development, aberrant physiological status or mental disorder notwithstanding the roles of SSH2 in neurite extension had been reported (Endo, Ohashi, & Mizuno, 2007), we did not conduct the experiments to observe the effect(s) of SSH2 in other organs except for the female fertility.

Fig. R1 No reproductive defects were found in Ssh2 KO females. (A-C) Body weights, weights of the ovaries, and the ovary-to-body weight ratio of adult WT and Ssh2 KO females aged 8-10 weeks (n = 5); p > 0.05 calculated by Student’s t-test. Bars indicate the range of data. (D) The size of ovaries from Ssh2 KO were indistinguishable from ovaries of WT mice age 8 weeks, n = 4. (E) Histology of the ovaries from WT and Ssh2 KO mice. Sections were stained with hematoxylin and eosin. Scale bars: 200 μm. Images are representative of ovaries extracted from 8-week-old adult female mice per genotype. (F) Number of pups per litter from WT and Ssh2 KO male mice (8 weeks old) after crossing with WT adult male mice (n =3); p > 0.05 calculated by Student’s t-test. Bars indicate the range of the data.

Comments: 3) Providing detailed information on the number of animals used and cells analyzed in the legend is nice, but it might be even better for the readers to include sample size and the number of cells examined in the figure/graph if possible.

We appreciate the suggestions from the reviewer. We have integrated some information of sample size in the figures where appropriate. Firstly, we integrated sample size in the figure 1C, 1E, 1F, 1G and 1I. Secondly, we included sample size and the number of seminiferous tubule/epididymal duct we evaluated for TUNEL (+) cell counting in figure 2C and figure 2D. Thirdly, we included sample size and the number of spermatids for co-localization in figure 6B and figure 6D.

Comments: 4) Nice discussion and comparison with GOPC and GM130, how about comparison and discussion with other acrosome defective mutants like PICK1, and ATG to provide some insights into acrosome biogenesis and proacrosomal vesicle trafficking?

We greatly appreciate the referee for positive appraisal of our work with constructive suggestions, unfortunately, we are unable to address these defective mutants with certainty due to the lack of proper sample accessibility (only 3 of 16-month-old Ssh2 KO mice are accessible now). We compared the cytological staining of GM130 and GOPC in WT and Ssh2 KO spermatids using tubule squash sections as the description in the originally submitted manuscript which are prepared from fresh testes originated from 8-week-old mice and we now have several aged Ssh2 KO mice which prevent us to achieve the staining of PICK1 and ATG. PICK1 was previously reported to facilitate vesicle trafficking from the Golgi apparatus to the acrosome which co-localizes with GOPC in the proacrosomal granules (Xiao et al., 2009) and the phenotypes of Pick1 KO mice share a lot of similar characteristics with that of Ssh2 KO mice such as the fragmentation of the acrosome and increased germ cell apoptosis. Both autophagy-related ATG5 (Huang et al., 2021) and ATG7 (Wang et al., 2014) were reported to participate in the process of acrosome biogenesis and ATG7 is required for proacrosomal vesicle transportation/fusion by conjugating LC3 to the membrane of proacrosomal vesicles. Although the spermatids evaluated in these KO mice models could still be developed into spermatozoa with defective acrosome that is different from the situation in Ssh2 KO mice, it would be meaningful to discover the affects by Ssh2 KO on the localization of these regulators of acrosome biogenesis in spermatids and their potential interactions with SSH2. Indeed, in future work, we plan to pursue these issues and the content related to PICK1 has been added to the discussion in the revised manuscript as follows: “Moreover, it is intriguing to note that the phenotypes of Ssh2 KO mice share a lot of similarities with that of Pick1 KO model (Xiao et al., 2009) such as acrosome fragmentation and enhanced germ cell apoptosis, suggesting the possibility that SSH2 and PICK1 work together in a same trafficking machinery functioning in acrosome biogenesis which needs to be clarified further.”

Comments: 5) Given the literature on Cofilin's requirement for male fertility and the increased p-Cofilin in Ssh2 mutant testis by Western and IF, the authors have a strong case for their hypothesis. But given the general role of phosphatase, it might be prudent to discuss alternative possibilities.

We thank the reviewer for these valuable suggestions. Given that p-COFILIN is the only known substrate of SSH2 based on previous reports, we focused principally on this cascade to conduct our investigation. As a phosphatase, SSH2 is very likely to interact with many other proteins functioning in various cellular processes other than the actin-binding proteins which remain elusive. As directed, we now have added some content related to the regarding above concern in the discussion section of the revised manuscript as follows: “Given the diverse physiological roles reported for Slingshot family proteins, the possibility of the alternative mechanism underlying involvement of SSH2 in cellular events beyond the COFILIN-mediated actin remodeling should be noted. According to some publicly accessible databases as the indicators of potential protein–protein interactions such as BioGRID (Oughtred et al., 2019) and IntAct (Del Toro et al., 2022), SSH2 might interact with a set of actin-based molecular motors covering MYH9, MYO19 and MYO18A, which have been implicated in the maintenance of Golgi morphology and Golgi anterograde vesicular trafficking via the PI4P/GOLPH3/MYO18A/F-actin pathway (Rahajeng et al., 2019).”

Author Response

Reviewer #1 (Public Review):

Voltage-clamp fluorometry combines electrophysiology, reporting on channel opening, with a fluorescence signal reporting on local conformational changes. Classically, fluorescence changes are reported by an organic fluoropohore tethered to the receptor thanks to the cysteine chemistry. However, this classical approach does not allow fluorescent labeling of solvent-inaccessible regions or cytoplasmic regions. Incorporation of the fluorescent unnatural amino acid ANAP directly in the sequence of the protein allows counteracting these limitations. However, expression of ANAP-containing receptors is usually weak, leading to very small ANAP-related fluorescence changes (ΔFs).

In this paper, the authors developed an improved method for expression of full-length, ANAP-mutated proteins in Xenopus oocytes. In particular, they managed to increase the ratio of full-length over truncated proteins for C-terminal ANAP incorporation sites. Since C-terminally truncated P2X receptors are usually functional, it is important to maximize the full-length over truncated protein ratio to have a good correspondence between the observed current and fluorescence. Using their improved strategy, they screened for ANAP incorporation sites and ATP-mediated ANAP ΔFs along the whole structure of the P2X7 receptor: extracellular ligand binding domain (head domain), M2 transmembrane segment (gate), as well as a large extracellular domain specific for the P2X7 subtype, the "ballast" domain. The functional role of this domain and its motions following ATP application are indeed unknown. Monitoring ANAP fluorescence changes in this region following ATP binding provides a unique way to study those questions. By analyzing ATP-induced ΔFs from different parts of the receptors, the authors conclude that the ATP-binding domain mainly follows gating, while intracellular "ballast" motions are largely decoupled from ATP-binding

Strengths of the paper:

This paper provides an improved method for efficient unnatural amino acid incorporation in Xenopus oocytes. Thanks to this technique, they managed to enhance membrane expression of ANAP-mutated P2X7 receptors and observed strong fluorescent changes upon ATP application. The paper furthermore describes an impressive screen of ANAP-incorporation sites along the whole protein sequence, which allows them to monitor conformational changes of solvent-inaccessible regions (transmembrane domains) and cytoplasmic regions that were not accessible to cysteine-reactive fluorophores. This screen was performed in a very thorough manner, each ANAP mutant being characterized biochemically for membrane expression, as well as in term of fluorescence changes. The limitations of the approach -small ΔF upon ATP application on wt receptors, problem of baseline fluorescence variations in presence of calcium- are well explained. Overall, this study should thus not only serve as a guide to anyone willing to perform VCF on P2X7 receptors but it should be useful to the whole community of researchers using unnatural amino acids. Thanks to orthogonal labeling with TMRM and ANAP, the authors managed to simultaneously monitor the motions of the extracellular and intracellular domains of P2X7. Finally, they propose methods to simultaneously monitor intracellular domain motion and downstream signaling.

Weaknesses:

Although the fluorescence screen is impressive and well conducted, the biological conclusions remain superficial at this stage. The paper furthermore lacks quantitative analysis. Finally, the title only reflects a minor part of the paper and is therefore not representative of the paper content.

Quantitative analyses (DRCs and current rise times) were now added for the key mutations. In addition, we performed a variety of experiments to address the challenging question of mechanistic insight (mutants that track facilitation) and effects of intracellular factors (mutation of calmodulin binding site, FRET experiments with calmodulin). These data confirmed that deletion of a cysteine-rich intracellular region eliminates current facilitation (Roger et al., 2010) and that some of our mutants indeed track facilitation. However, mutation of the CaM binding site and FRET experiments did not support an effect of calmodulin or were inconclusive. As pointed out above, we think that VCF has limited capacity to identify novel biologically relevant consequences of receptor activation but is more suited to determine the sites and dynamics of already defined interactions.

The title was changed to: "Improved ANAP incorporation and VCF analysis reveals details of P2X7 current facilitation and a limited conformational interplay between ATP binding and the intracellular ballast domain"

Reviewer #2 (Public Review):

The authors aimed to elucidate the structural rearrangements and activation mechanisms of P2X7 upon ATP application by voltage clamp fluorometry (VCF) using fluorescent unnatural amino acid (fUAA) and other fluorophores. They improved the fUAA methodology and detected ATP binding evoked changes in the ATP binding region and other regions. They also observed facilitation of fluorescence (F) changes by repeated application of ATP associated with gating. The F change in the cytoplasmic ballast region was minor, and with their experimental data, they discussed this region is involved in activation by other cytoplasmic factors, such as Ca2+.

The strengths of the study are as follows.

(1) fUAA methodology was improved to enable experiments by one time injection to oocytes (Figs. 1 and Suppl).

(2) They performed intensive mutagenesis study of as many as 61 mutants (Figs. 3, 4, 5).

(3) A careful evaluation of the successful Anap incorporation and formation of full length proteins was performed by western blot analysis (Fig. 2).

(4) By three wave lengths F recording, they obtained better information, i.e. they classified the interpretation of F changes to, quenching, dequenching, increase in polarity and decrease in polarity (Fig. 3E).

(5) They detected F changes upon ATP application in various regions of P2X7, but not many in the ballast region, showing that the ballast region is not well involved in the ATP evoked gating.

(6) They analyzed the kinetics of F and current and their changes upon repeated ATP application to approach the known facilitation mechanisms. The data are very interesting. They concluded that it is intrinsic to the P2X7 molecule and that it is associated not with the ATP binding but with the gating process (Figs. 3F, 4D, 6A).

(7) They performed interesting analysis to clarify the mechanisms of activation by cytoplasmic factors, especially Ca2+ entered via P2X7 (Fig. 6).

The weaknesses of the study are as follows.

(1) As both structures of P2X in the open and closed states are already solved, and the ATP binding evoked structural rearrangements from the ATP binding site to the gate are already known in detail. The structural rearrangements detected in the extracellular region (Fig. 3) and TM region (Fig. 4) upon ATP application are just as expected. The impact and scientific merits of this part are rather limited.

We generally agree that the cryo-EM structures clarified basic principles of receptor function. However, considering the specific features of the P2X7 receptor and its likely regulation/modulation by membrane components and environment and the fact that the actual states of the receptor structures (e.g. facilitated or not?) is not known, we think that VCF analysis of its dynamics in a more native cellular environment is still required to confirm the predicted motions and also has the potential to identify details of "P2X7 fine tuning".

(2) The facilitation mechanism is of high interest. The authors showed it is intrinsic to P2X2 and associated with the gating rather than ATP binding. However, this reviewer cannot have better understanding about the actual mechanism. (a) What is the mechanistic trigger of facilitation? Possibilities are discussed, but it appears there is no clear answer with experimental evidences yet. (b) How is the memory of the 1st ATP application stored in the molecule, i.e. how does the P2X7 structure just before the 1st application differ from that just before the 2nd application of ATP?

These are indeed fundamental questions but based on the available information we do not see a rational approach to address this issue any further. Additional extensive "screening" for ideal fluorophore positions would probably be required and is beyond our possibilities in the present study.

(3) The structural rearrangement of the CaM-M13 region (Fig. 6B, C) attached at the C-terminus by Ca2+ influx through P2X7 upon ATP application is natural due course and not very surprising. Also, it is not accepted as an evidence proving that Ca2+ is the mediator of facilitation.

We apologize, this is a misunderstanding. We only provided protocols for parallel recordings of ANAP with other fluorophores for further analysis of downstream signaling pathways but we did not show or propose any functional consequences of the Ca2+ influx (see also point 7 above).

(4) As to the ballast region, data showed its limited involvement in the ATP-induced structural rearrangements. The function of the ballast region is not clear yet. A possible involvement in GDP binding and/ or metabolism is discussed, but there is no clear experimental evidence.

We are aware of these limitations. In the absence of a clear fluorescence change around the GTP/GDP-binding site or information about its role, it is difficult to investigate its molecular function by VCF. The fact, that (un-)binding of the guanosine nucleotide does not seem to be related to channel opening (McCarthy et al., 2019) further limits our options to study its function and currently it is not even known whether GDP/GTP has just a structural role. However, we identified A564* as a potential reporter for yet undefined processes that might affect GTP/GDP binding and/or metabolism.

Reviewer #3 (Public Review):

This research contributes to optimizing the amber stop-codon suppression protocol for voltage-clamp fluorometry (VCF) experiments using Xenopus oocyte heterologous expression system. By in vitro RNA synthesizing the tRNA and tRNA synthetases, combined with the dominant-negative release factor initially developed by Jason Chin's lab, L-Anap can be site-specifically labeled to proteins by a single microinjection of a mixture of molecular components into the cytoplasm of oocytes. Although it avoids nuclear microinjection to oocytes, it adds more RNA synthesis steps. This strategy of using eRF dominant negative variant (eRF1-E55D), was previously applied to the Anap incorporation system using mammalian cell lines and model proteins (Gordon et al, eLife, 2018). In this previous 2018 paper, with eRF1-E55D, the percentage of full-length protein expression increased substantially. Using oocytes in this paper, this percentage apparently did not increase significantly as shown in Fig. 1D, different from the previous paper. Nevertheless, the overall expression level increased successfully by this method, which could facilitate macroscopic fluorescence measurements, especially considering that L-Anap is relatively dim as a fluorophore.

Anap fluorescence change was measured mostly using its environmental sensitivity, which has limited information in interpreting structural changes. The structural mechanisms proposed could be potentially strengthened and the conclusions could be further validated by combining FRET or other distance ruler experiments with the VCF method. The engineered CaM-M13 FRET experiments mostly report the calcium entry, not measuring the rearrangements of P2X7 directly.

We tried FRET analyses with ANAP-labeled P2X7 and mNeonGreen-labeled CaM but unfortunately, results were inconclusive.

In addition, results of ATP dose-response relationship for channel activation correlated with ATP dose-dependent Anap fluorescence change, especially for sites showing a large percentage of ATP-induced change in fluorescence, would provide more insights regarding the allosteric mechanism of the channel.

We agree, but unfortunately, bleaching of ANAP and the variation of background fluorescence in individual oocytes prevented such analyses .

Author Response

Reviewer #2 (Public Review):

Weaknesses:

1) The relevance of the LPS-induced calvarial osteolysis model is not clear. Calvaria is mostly composed of cortical bone-like structures lacking marrow space, though small marrow space exists near the suture. Osteolysis appears to occur in areas apart from where marrow is located. The authors did not show in the manuscript which cells Adipoq-Cre marks in the calvaria.

We have shown in a recent publication that MALPs exist in the calvarial bone marrow (2). As shown in Fig. R1A, Td+ cells are layer of cortical bone (Fig. R1B, blue arrows). In WT mice, after LPS injection, the normal bone structure, including suture and cortical bone, were mostly eroded, and filled with inflammatory cells (green arrows). Thus, osteolysis does occur at the area where bone marrow is originally located. On the contrary, calvarial bone structure was preserved in the CKO mice, demonstrating that Csf1 deficiency in MALPs suppresses LPS-induced osteolysis. We included the H&E staining data in the revised manuscript:

"H&E staining showed that calvarial bone marrow is surrounded by a thin layer of cortical bone (Fig. 5C). After the LPS injection, normal calvarial structure, including suture and cortical bone, were mostly eroded and filled with inflammatory cells in WT mice, but unaltered in CKO mice."

Figure R1. Calvarial bone marrow structure. (A) Representative coronal section of 1.5-month-old Adipoq/Td mouse calvaria. Bone surfaces are outlined by dashed lines. Boxed areas in the low magnification image (top) are enlarged to show periosteum (bottom left), suture (bottom middle), and bone marrow (BM, bottom right) regions. Red: Td; Blue: DAPI. Adopted from our previous publication (2). (B) H&E staining of coronal sections of WT and Csf1 CKOAdipoq mice after LPS injection. Blue arrows point to bone marrow space close to suture (indicated by *). Green arrows point to the osteolytic lesion where cortical bone was eroded, and the space were filled with inflammatory cells.

2) Although the contrast between the two Csf1 conditional deletion models (Adipoq-Cre and Prx1-Cre) is very interesting, the relationship between these two cell populations are not well described. The authors did not clarify if MALPs are also targeted by Prx1-Cre, or these two cell types are from different cell lineages. "Other mesenchymal lineage cells" in the subtitle is not extremely helpful to place this finding in context.

We thank the Reviewer for this comment. The original article constructing Prx1-Cre mouse line demonstrates that Prx1-Cre targets all mesenchymal cells in the limb bud at early as 10.5 dpc (10). This early expression pattern ensures that all bone marrow mesenchymal lineage cells, including MALPs, are targeted by Prx1-Cre. In addition, based on our scRNA-seq data (1), Adipoq is mainly expressed in MALPs, while Prrx1 (Prx1) is highly expressed not only in MALPs but also in EMPs, IMPs, LMPs, LCPs, and OBs (Fig. R2). Thus, the fact that Prx1-Cre driven CKO mice have much more severer bone phenotypes than AdipoqCre driven CKO mice indicates that mesenchymal lineage cells other than MALPs also contribute Csf1 to regulate bone resorption. To avoid confusion, we changed the title and the first sentence in the Result session about Prx1 mice to the following:

"Csf1 from mesenchymal lineage cells other than MALPs regulate bone structure.

To explore whether Csf1 from MALPs plays a dominant role in regulating bone structure, we generated Prx1-Cre Csf1flox/flox (Csf1 CKOPrx1) mice to knockout Csf1 in all mesenchymal lineage cells in bone (10), including MALPs."

Figure R2. Dotplot of Prrx1 and Adipoq expression in bone marrow mesenchymal lineage cells based on our scRNA-seq analysis of 1-month-old mice.

3) The data supporting defective bone marrow hematopoiesis in Csf1 CKO mice are not particularly strong. They observed a reduction in bone marrow cellularity, but this was only associated with an expected reduction in macrophages and a mild reduction in overall HSPC populations. More in-depth analyses might be required to define mechanisms underlying reduced bone marrow cellularity in CKO mice.

We thank the Reviewer for this constructive comment. Accordingly, we performed a thorough analysis of bone marrow hematopoietic compartments and observed significant decreases of monocytes and erythroid progenitors in CKO mice compared to WT mice. These results are now included as Fig. 6E.

4) Some of the phenotypic analyses are still incomplete. The authors did not report whether CHet (Adipoq-Cre Csf1(flox/+)) showed any bone phenotype. Further, the authors did not report whether Csf1 mRNA or M-Csf protein is indeed expressed by MALPs, with current evidence solely reliant on scRNAseq and qPCR data of bulk-isolated cells. More specific histological methods will be helpful to support the premise of the study.

A pilot microCT study revealed the same femoral trabecular bone structure in WT and Adipoq-Cre Csf1flox/+ (Csf1 Het) mice at 3 months of age (Fig. R3). While the sample number for Het is low, we are confident about this conclusion.

Figure R3. MicroCT measurement of trabecular bone structural parameters from WT and Csf1 Het mice. BV/TV: bone volume fraction; BMD: bone mineral density; Tb.N: trabecular number; Tb.Th: trabecular thickness; Tb.Sp: trabecular separation; SMI: structural model index. n=3-8 mice/group.

Author Response

Reviewer #1 (Public Review):

This study provides evidence for previously unknown relationship between oncogenic protein kinase A (PKA) signaling and MYC family members. Specifically, the authors have employed a combination of systems biology and biochemical assays to capture mediators of oncogenic PKA signaling in a fibrolamellar carcinoma and melanoma cell line. This lead to identification of Aurora A and PIM kinases as potential effectors of constitutively active PKA. Aurora A and PIM kinases have been previously shown to stabilize MYC proteins. Accordingly, evidence is provided that the effects of PKA/Aurora A and PKA/PIM axis are mediated via MYC. Collectively, these findings suggest a model whereby the effects of aberrant PKA signaling are mediated via Aurora A and PIM kinases and related feedback mechanisms that ultimately result in stabilization of MYC proteins. Importantly, PKA-driven cancer cell lines exhibited high sensitivity to Aurora A kinase inhibitors in cell culture-based assays. These findings not only provide pioneering insights into oncogenic PKA signaling, but may also have implications for developing therapeutic approaches for neoplasia that harbor constitutively active PKA.

Strengths:

This study addresses the role of aberrant PKA signaling in cancer, which represents a major gap in knowledge in cancer biology. Systems biology approaches and dissection of signaling networks downstream of constitutively active PKA are found to be exciting in the context of this study and likely to provide a wealth of information for future studies. Results from samples obtained from fibrolamellar carcinoma patients partially confirmed correlations observed in cell lines, which was seen as an advantage. Notwithstanding that, it was thought that orthogonal genetic validation may in some cases be warranted, pharmacological approaches using e.g. Aurora A inhibitors hold a promise for accelerated translation of observed findings into the clinic.

We appreciate this positive assessment of our work and are hopeful that we have solidified the significance and potential impact of our findings through additional analysis.

Weaknesses:

The major drawback of the study is the lack of in vivo models to validate observations garnered from the cell lines. This is particularly important considering that experiments carried out in samples from fibrolamellar carcinoma patients suggested additional Aurora A and PIM kinase-independent mechanisms of PKA-driven increase in MYC levels and likely in neoplastic growth may be implicated in vivo. In addition, it was thought that more mechanistic evidence is required for linking PKA to PIM kinase, especially because different PIM kinases were implicated in stabilization of MYC in fibrolamellar carcinoma vs. melanoma cell lines. Finally, although pharmacological approaches were appreciated, due to potential issues with the specificity of the inhibitors, it was thought that orthogonal genetic approaches are warranted to further corroborate the proposed model.

We acknowledge the lack of in vivo treatment modeling in this manuscript. The work presented here provides motivation for these important experiments, but they remain outside the scope of this manuscript. The expansion of the manuscript in revision with new investigations into protein translation and several additional data sets creates a more complete systems biology analysis of PKA signaling and PKA-induced signaling dependencies. This expanded scope makes in vivo validation of specific treatments and treatment combinations an even larger undertaking. The text has been modified to emphasize this point. We further acknowledge the accuracy of the reviewer’s assessment of our findings on PIM2. The limited reagents to study PIM kinases made this relatively difficult to expand. We shifted the focus of the work to include assessment of PKA effects on mRNA translation as a mechanism of c-MYC regulation. We have strengthened our assessments with loss- and gain-of-function genetic and pharmacological models, which we believe will more completely answer the reviewer’s concerns.

Reviewer #2 (Public Review):

Protein kinase A (PKA) is often stimulated and contributes to cancer growth, yet the downstream kinase signaling cascades remain unclear. Here the authors use a global phosphoproteomics and kinome activity profile to show that not only is the RAS/MAPK pathway activated, as expected, but the authors also suggest Aurora kinase A (AURKA) and PIM kinases are activated to stabilize the expression of MYC expression; a potent oncoprotein associated with poor prognosis and aggressive disease. The authors use a number of different cell lines in this study, but focus on fibrolamellar carcinoma as PKA is known to contribute to this disease.

Strengths: It has been notoriously difficult to map kinases and their substrates as these protein-protein interactions are not always amenable to traditional biochemical techniques due to their labile nature, and kinase substrate consensus sites are often overlapping and not highly specific. Thus, the authors' pipeline to delineate such kinase cascades is quite novel and useful. They apply it here to determine PKA signaling in cancer using sophisticated computational strategies and then validate with classic molecular techniques.

We appreciate this positive assessment of our analytical tools and the importance of understanding oncogenic PKA signaling.

Weaknesses: The lack of mechanistic evidence linking aberrant PKA activation with regulation of MYC family members was considered to be a major weakness of the study. As it stands, it is hard to delineate whether observed changes in the levels of MYC family members are indeed a consequence of aberrant PKA signaling. It also remains unclear which MYC phosphorylation sites are implicated in the context of neoplastic PKA function and whether MYC family members are regulated at the level of protein stability or mRNA translation. Moreover, some methodological issues (e.g. using single siRNAs) were also observed. Collectively it was thought that these weaknesses should be addressed to corroborate author's conclusions.

We acknowledge these concerns about our initially submitted manuscript and present extensive data that advances the manuscript in answering the key questions posed by the reviewer. We note that with the development of data showing PKA-induced phosphorylation of translation initiation components and sensitivity of c-MYC levels to eIF4A inhibition, some detailed evaluations of c-MYC phosphorylation were not undertaken, although key c-MYC mutants were tested in the course of our study and are included for reviewer interest.

Author Response

Reviewer #1 (Public Review):

In the current study, the authors reanalyze a prior dataset testing effects of D2 antagonism on choices in a delay discounting task. While the prior report using standard analysis, showed no effects, the current study used a DDM to examine more carefully possible effects on different subcomponents of the decision process. This approach revealed contrasting effects of D2 blockade on the effect of reward size differences and bias. Effects were uncorrelated, suggesting separate mechanisms perhaps. The authors speculate that these opposing effects explain the variability in effects across studies, since they mean that effects would depend on which of these factors is more important in a particular design. Overall the study is novel and well-executed, and the explanation offers interesting insight into neural processes.

We thank the reviewer for judging our study as interesting and well-executed.

Reviewer #2 (Public Review):

The authors aim to test the hypothesis that dopamine mediates the evaluation of temporal costs in intertemporal choice in humans, with a specific goal of synthesizing the competing accounts and previous results regarding whether dopamine increases or decreases evaluation of delays in comparing differently delayed future rewards. To do this, they computationally dissect the impact of the drug amisulpride, a D2R antagonist, using a variant of a sequential sampling model, the drift-diffusion model (DDM), that is well established in decision-making literature as a cognitive process model of choice. This model allows the dissociation of starting bias from the rate at which decision evidence is integrated ('drift'), which the authors map to different accounts of the role of dopamine: the temporal proximity of an outcome is proposed to impact bias, while the cost of a delay to impact the drift rate of evidence evaluation/accumulation. Consistent with previous results, and perhaps integrating conflicting findings, the authors find that d2R blockade impacts both bias and drift rate in a cohort of 50 participants, demonstrating dopaminergic action at this receptor is implicated in dissociable components of intertemporal choice, with D2R block reducing the bias towards sooner, more temporally proximate rewards as well as enhancing the contrast between reward magnitudes irrespective of delay, effectively diminishing the effect of delay in the drug condition. These effects are consistent across a small subset of alternative models, confirming the multiple cognitive mechanisms through which D2R block impacts intertemporal choice is a robust feature of decisions on this task.

Overall, this study is a detailed dissection of the specific effects of amisulpride on a type of future-oriented, hypothetical intertemporal choice, and provides consistent evidence integrating conflicting accounts that implicate dopaminergic signaling on evaluation of the cognitive costs, such as a delay, on choice. However the specificity of the empirical intervention and the task design limits the interpretation of the broader dopaminergic mechanisms at play in intertemporal choice, especially given the complexity of receptor specificity of this drug, dopamine precursor availability and individual differences and the specifics of the intertemporal choice in this task. As it stands, the results contribute an interesting, synthesized account of how D2R manipulation can impact evaluation of delays in multiple ways, that will likely be useful for motivating future studies and more detailed computational assessments of the cognitive process-level components of intertemporal choice more generally.

We thank the reviewer for the positive overall evaluation of our study. We revised the manuscript according to the reviewer’s comments, addressing also the receptor specificity of amisulpride and the specifics of the administered intertemporal choice task, which further improved the quality of the manuscript.

The focus of this study is important, and delineating the role of DA in intertemporal choice is of high relevance given DA disfunction is prevalent in many psychiatric disorders and a key target of pharmacological treatment. While the hypotheses of the current study are framed with respect to "costs", the task used by the authors reduces these to evaluation of a hypothetical delay, one which the participants do not necessarily experience in the context of the task. In some respects this is reasonable, given the prevalence of this task paradigm in testing temporal aspects of choice in humans in an economic sense. However, humans are also notoriously subject to framing effects and the impact of instructions in cognitive tasks like these, which can limit the generality of the conclusions, and in particular the specific ways in which a delay can be interpreted as costly (for eg cost as loss of potential earnings, cost as effortful waiting, cost as computational/simulation cost in future evaluation). Given the hypothesis recruits the idea of cost in assessing the role of dopamine, testing for generality in the effects of amisulpride in related but differently framed tasks seems critical for making this link in a general sense, and in connecting it to the previous studies in the literature the authors point to as demonstrating conflicting effects.

We agree that it is important to discuss whether our findings for delay costs can be generalized to other costs types as well, such as risk, social costs, effort, or opportunity costs. Based on a recent literature review (Soutschek, Jetter, & Tobler, 2022), we speculate that dopamine may moderate proximity effects also for risk and social costs but not for effortful rewards, though we emphasize that these hypotheses still require more direct empirical evidence. We also discuss the issue that delays can be perceived as costly in different ways. While in some tasks participants actually experience the waiting time until reward delivery, such that delayed rewards are associated with opportunity costs, in our current task paradigm delayed rewards were virtually free of opportunity costs as participants could engage in other reward-related behaviors during the waiting time. Previous studies suggest that lower tonic dopamine levels reduce the sensitivity to opportunity costs (Niv et al., 2007), which seems in line with our finding that amisulpride decreases the influence of delays on the starting bias parameter. Nevertheless, we emphasize that further evidence is needed to decide whether dopamine shows similar effects for experienced and non-experienced waiting costs. In the revised manuscript, we discuss the cost specificity of our findings on p.22:

“An important question refers to whether our findings for delay costs can be generalized to other types of costs as well, including risk, social costs (i.e., inequity), effort, and opportunity costs. In a recent review, we proposed that dopamine might also moderate proximity effects for reward options differing in risk and social costs, whereas the existing literature provides no evidence for a proximity advantage for effort-free over effortful rewards (Soutschek et al., 2022). However, these hypotheses need to be tested more explicitly by future investigations. Dopamine has also been ascribed a role for moderating opportunity costs, with lower tonic dopamine reducing the sensitivity to opportunity costs (Niv et al., 2007). While this appears consistent with our finding that amisulpride (under the assumption of postsynaptic effects) reduced the impact of delay on the starting bias, it is important to note that choosing delayed rewards did not involve any opportunity costs in our paradigm, given that participants could pursue other rewards during the waiting time. Thus, it needs to be clarified whether our findings for delayed rewards without experienced waiting time can be generalized to choice situations involving experienced opportunity costs.”

Further, while the study aims to test the actions of dopamine broadly, the empirical manipulation is limited to the action of amisulpride, a D2R anatgonist. There is little to no discussion of, or control for, the relationship between dopaminergic action at D2 receptors (the site of amisulpride effects) and wider mechanisms of dopaminergic action at other sites eg D1-like receptors, and the interplay between activation at these two receptor types alongside baseline levels of dopamine concentration. This is necessary for a comprehensive account of dopamine effects on intertemporal choice as the authors aim to test, as opposed to a specific test of the role of the D2 receptor, which is what the study achieves. On a related note, in some preparations at least, amisulpride also acts at some of the 5-HT receptors, raising the possibility of a non-dopaminergic mechanism by which this drug might impact intertemporal decisions. This possibility, while it would not be expected to act without dopaminergic effects as well, is consistent with established effects of serotonin on waiting behaviors and patience. Granted, the limits of pharmacology in humans does not necessarily mean this can be controlled for, it should be kept in mind with a systemic manipulation such as this.

We agree with the reviewer that it is important to distinguish between the contributions of D1 and D2 receptors to decision making, given that these receptor families are hypothesized to have dissociable functional roles. We therefore re-analyzed also data on the impact of a D1 agonist on intertemporal decision making (previous findings for this data set were published in Soutschek et al., 2020, Biological Psychiatry). This analysis provided no evidence for significant effects of D1R stimulation on parameters from a drift diffusion model. This suggests that D2R, rather than D1R, activation mediates the impact of proximity on intertemporal choices.

In the revised manuscript, we report the findings for the D1 agonist study on p.16:

“To assess the receptor specificity of our findings, we conducted the same analyses on the data from a study (published previously in Soutschek et al. (2020)) testing the impact of three doses of a D1 agonist (6 mg, 15 mg, 30 mg) relative to placebo on intertemporal choices (between-subject design). In the intertemporal choice task used in this experiment, the SS reward was always immediately available (delay = 0), contrary to the task in the D2 experiment where the delay of the SS reward varied from 0-30 days. Again, the data in the D1 experiment were best explained by DDM-1 (DICDDM-1 = 19,657) compared with all other DDMs (DICDDM-2 = 20,934; DICDDM-3 = 21,710; DICDDM-5 = 21,982; DICDDM-6 = 19,660; note that DDM-4 was identical with DDM-1 for the D1 agonist study because the delay of the SS reward was 0). Neither the best-fitting nor any other model yielded significant drug effects on any drift diffusion parameter (see Table 4 for the best-fitting model). Also model-free analyses conducted in the same way as for the D2 antagonist study revealed no significant drug effects (all HDI95% included zero). There was thus no evidence for any influence of D1R stimulation on intertemporal decisions.”

We discuss the specificity of D2 receptors for moderating the proximity bias on p.17: “This finding represents first evidence for the hypothesis that tonic dopamine moderates the impact of proximity (e.g., more concrete versus more abstract rewards) on cost-benefit decision making (Soutschek et al., 2022; Westbrook & Frank, 2018). Pharmacological manipulation of D1R activation, in contrast, showed no significant effects on the decision process. This provides evidence for the receptor specificity of dopamine’s role in intertemporal decision making (though as caveat it is worth keeping the differences between the tasks administered in the D1 and the D2 studies in mind).”

We also agree that amisulpride acts also on 5-HT7 receptors, such that it remains unclear whether also such effects contribute to the observed result pattern. We discuss this limitation in the revised manuscript on p.21:

“Lastly, while the actions of amisulpride on D2/D3 receptors are relatively selective, it also affects serotonergic 5-HT7 receptors (Abbas et al., 2009). Because serotonin was related to impulsive behavior (Mori, Tsutsui-Kimura, Mimura, & Tanaka, 2018), it is worth keeping in mind that amisulpride effects on serotonergic, in addition to dopaminergic, activity might contribute to the observed result pattern.”

Overall the modeling methods are robust and appropriate for the specific test of decision impacts of D2R blockade, and include several prima facie variable alternative models for comparison. Some caution is warranted, since there are not many trials per subject, and some trials are discarded as well as outliers, which raises the question of power. Given the models are fit hierarchically, which gives both group-level and individual-level parameter estimates, the elements are there to probe more deeply into individual differences, and to test how reliably this approach can dissociate the dual effects of bias and drift rate at the individual level, and perhaps correlate it with other informative subject measures of either dopamine activity/capacity or other dopamine-dependent behaviors. Alternative DDMs might also capture some of this individual variation, with meaningful differences potentially in model comparison at the individual level. It should be noted that the scope of these models do not exhaust the ways in which proximity (here, temporal) of rewards and contrast between choice options might be incorporated into a cognitive process model account of choice; all alternatives here rest on the same implicit 2-alternative forced choice assumption of the DDM, and the assumptions of this model are not here tested against other accounts of choice, for example the linear ballistic accumulator (LBA) and its derivatives. Further, the concept of proximity as a global feature of a trial (on average, how soon are these options overall?) is never tested on my read of the alternative models.

We thank the reviewer for these interesting suggestions. First, to explore whether measures of dopaminerigc activity correlate with individual differences in drug effects on DDM parameters, we now report correlations between DDM parameters and performance in the digit span backward task as proxy for dopamine synthesis capacity (Cools et al., 2008). None of these correlation analyses showed significant results. In the revised manuscript, we report these analyses on p.13:

“However, we observed no evidence that individual random coefficients for the drug effects on the drift rate or on the starting bias correlated with body weight, all r < 0.22, all p > 0.10. There were also no significant correlations between DDM parameters and performance in the digit span backward task as proxy for baseline dopamine synthesis capacity (Cools, Gibbs, Miyakawa, Jagust, & D'Esposito, 2008), all r < 0.17, all p > 0.22. There was thus no evidence that pharmacological effects on intertemporal choices depended on body weight as proxy of effective dose or working memory performance as proxy for baseline dopaminergic activity.”

Regarding model comparisons on the individual level, we note that the hierarchical Bayesian modelling approach allows (to the best of our knowledge) computing indices of model fit like DIC only on the group, not the individual level (while accounting for individual differences). However, we agree with the reviewer that theoretically different models might work best in different individuals (depending, for example, on the individual sensitivity to proximity). While such fine-grained model comparisons on the individual level are beyond the scope of the current study (and might not yield robust results given the limited number of trials for each participant), we now discuss this limitation in the revised manuscript (p.17-18):

“We note that the hierarchical modelling approach allowed us to compare models on the group level only, such that in some individuals behavior might better be explained by a different model than DDM-1. Such model comparisons on the individual level, however, were beyond the scope of the current study and might not yield robust results given the limited number of trials per individual.”

Likewise, linear ballistic accumulator (LBA) models represent a further class of process models with different assumptions on the mechanisms underlying the choice process than DDMs. In LBAs, evidence is accumulated separately for each choice alternative, whereas DDMs assume only one accumulation process which integrates attributes from two choice options, limiting the use of DDMs to two-alternative forced-choice scenarios. Nevertheless, proximity effects might be incorporated also in LBA models via modulating the starting point of the option-specific accumulators as a function of proximity. To the best of our knowledge, there is no built-in function in JAGS that allows estimating LBA models in a hierarchical Bayesian fashion (in contrast to, e.g., STAN), such that in the context of the current study it is difficult to directly compare our DDM-based approach with LBA models. It is importance to emphasize, however, that similar to other studies we do not make any claims about whether the choice process per se is best explained by DDMs or LBA models; instead, we focus on how rewards and delay costs affect different components of the decision process within a class of decision models. Nevertheless, we discuss such alternative modelling approaches in the revised manuscript on p.18:

“We also emphasize that alternative process models like the linear ballistic accumulator (LBA) model make different assumptions than DDMs, for example by positing the existence of separate option-specific accumulators rather than only one as assumed by DDMs. However, proximity effects as investigated in the current study might be incorporated in LBA models as well by varying the starting points of the accumulators as function of proximity.”

Lastly, we thank the reviewer for the interesting suggestion to assess whether the starting bias parameter is affected by the overall proximity of offers (sum of delays) instead of by the difference in proximity between the options. We ran a further DDM to test this hypothesis, but this model explained the data worse (DIC = 9,492) than the original DDM (DIC = 9,478). Nevertheless, also the overall proximity DDM yielded a significant amisulpride effect on the impact of reward magnitude on the drift rate, HDImean = 0.83, HDI95% = [0.04; 1.75], underlining the robustness of this effect. In the revised manuscript, we report this analysis on p.12:

“In a further model (DDM-4), we explored whether the starting bias is affected by the overall proximity of the options (sum of delays, Delaysum) rather than the difference in proximity (Delaydiff; see Table 3 for an overview over the parameters included in the various models). Importantly, our original DDM-1 (DIC = 9,478) explained the data better than DDM-2 (DIC = 9,481), DDM-3 (DIC = 10,224), or DDM-4 (DIC = 9,492). Nevertheless, amisulpride moderated the impact of Magnitudediff on the drift rate also in DDM-2, HDImean = 0.86, HDI95% = [0.18; 1.64], and DDM-4, HDImean = 0.83, HDI95% = [0.04; 1.75], and amisulpride also lowered the impact of Delaydiff on the starting bias in DDM-3, HDImean = -0.02, HDI95% = [-0.04; -0.001]. Thus, the dopaminergic effects on these subcomponents of the choice process are robust to the exact specification of the DDM.”

Reviewer #3 (Public Review):

Soutschek and Tobler provide an intriguing re-analysis of inter-temporal choice data on amisulpride versus placebo which provides evidence for an as-yet untested hypothesis that dopamine interacts with proximity to bias choices.

The modeling methods are sound with a robust and reasonably exhaustive set of models for comparison, with good posterior predictive checks at the single subject level, and decent evidence of parameter recoverability. Importantly, they show that while there is no main effect of drug on the proportion of larger, later (LL) versus smaller, sooner (SS) choices, this obscures conflicting-directional effects on drift rate versus starting point bias which are under-the-hood, yet anticipated by the hypothesis of interest.

We thank the reviewer for judging our findings as intriguing and the modelling approach as robust and convincing.

While I have no major concerns about methodology, I think the Authors should consider an alternative interpretation - albeit an interpretation which would actually support the hypothesis in question more directly than their current interpretation. Namely, the Authors should re-consider the possibility that amisulpride's effects are mediated primarily by acting at pre-synaptic receptors. If the D2R antagonist were to act pre-synaptically, it would drive more versus less post-synaptic dopamine signaling.

There are multiple reason for this inference. First, the Authors observe that the drug increases sensitivity to differences in the relative offer amounts (in terms of effects on the drift rate). With respect to the canonical model of dopamine signaling in the direct versus indirect pathway, greater post-synaptic signaling should amplify sensitivity to reward benefits - which is what the Authors observe.

Second, the Authors also observe an effect on the starting bias which may also be consistent with an increase in post-synaptic dopamine signaling. Note that according to the Westbrook & Frank hypothesis, a proximity bias in delay discounting should favor the SS over the LL reward, yet the Authors primarily observe a starting bias in the direction of the LL reward. This contradiction can be resolved with the ancillary assumption that, independent of any choice attribute, participants are on average predisposed to select the LL option. Indeed, the Authors observe a reliable non-zero intercept in their logistic regression model indicating that participants selected the LL more often, on average. As such, the estimated starting point may reflect a combination of a heightened predisposition to select the LL option, opposed by a proximity bias towards the sooner option. Perhaps the estimated DDM starting point is positive because the predisposition to select the LL option has a larger effect on choices than the proximity bias towards sooner rewards does in this data set. To the extent that amisulpride increases post-synaptic dopamine signaling (by antagonizing pre-synaptic D2Rs) it should amplify the proximity bias arising from the differences in delay, shifting the starting bias towards the SS option. Indeed, this is also what the Authors observe.

Note that it remains unclear why an increase in post-synaptic dopamine signaling would amplify one kind of proximity bias (towards sooner over later rewards) without amplifying the other (towards a predisposition to select the LL option). Perhaps the cognitive / psychological nature of the sooner bias is more amenable to interacting with dopamine signaling than the latter. Or maybe proximity bias effects are most sensitive to dopamine signaling when they are smaller, and the LL predisposition bias is already at ceiling in the context of this task. These assumptions would help explain why a potential increase in post-synaptic dopamine signaling both amplified the proximity effect of delay when it was smallest (when the differences in delay were smaller), and also failed to amplify the predisposition to select the LL option (which may already be maxed out). More importantly, the assumption that there are opposing proximity biases would also help explain why there is a negative effect of delay magnitude on the estimated starting point on placebo. Namely - as the delay gets larger, the psychological proximity of sooner over later rewards grows, counteracting the proximity bias arising from choice predisposition / repetition.

We thank the reviewer for suggesting this alternative interpretation of our data. We agree that the administered dose of 400 mg amisulpride can show both postsynaptic (reducing D2R activation) and presynaptic effects (enhancing D2R activation), which in many studies makes it difficult to decide whether the observed behavioral effects are caused by presynaptic or postsynaptic mechanisms.

The reviewer suggests that the observed stronger influence of reward magnitudes on drift rates under amisulpride compared with placebo speaks in favor of presynaptic effects, because according to theoretical accounts higher dopamine levels should increase reward seeking (e.g., Frank & O’Reilly, 2006). On the other hand, Figure 2C suggests that amisulpride (compared with placebo) increased the preference only for relatively high, above-average rewards. If the difference between reward magnitudes was below average, amisulpride reduced rather than increased the preference for the larger reward. In our view, this is consistent with the hypothesis that D2R activation implements a cost control, with higher D2R activation increasing the attractiveness of costly rewards and lower D2R activation reducing it. In other words, under low dopamine levels individuals should decide for the costlier reward only if the magnitude of the costlier reward is sufficiently large compared with the lower, less costly reward. In fact, this is exactly what we find in our data according to Figure 2C. In our view, the amisulpride effect on drift rates is thus compatible with both presynaptic and postsynaptic mechanisms of action, depending on the underlying conceptual account of dopamine, as we now discuss in the revised manuscript.

According to the reviewer, also the observed influence of amisulpride on the starting bias speaks in favor of increased rather than reduced dopamine levels. We agree with the reviewer that the result pattern for the starting bias is somewhat complex and seems to combine the effects of two different biases: a general tendency to choose LL over SS rewards (intercept of starting bias where the difference in delays is close to zero), and a shift towards the SS option under placebo if one options has a strong (temporal) proximity advantage over the other. Amisulpride shows opposite effects on the two different biases, as it shifts the intercept of the starting bias further away from the LL option but also reduces the proximity advantage of the SS over the LL reward for larger differences in delay. The reviewer writes that “To the extent that amisulpride increases post-synaptic dopamine signaling (by antagonizing pre-synaptic D2Rs) it should amplify the proximity bias arising from the differences in delay, shifting the starting bias towards the SS option. Indeed, this is also what the Authors observe.” In contrast to that statement, in our study amisulpride reduced rather than increased the starting bias arising from delay (as in Figure 2K the regression line is flatter under amisulpride compared with placebo, despite the differences regarding the intercept). We believe that the amisulpride effects on both the intercept and the delay-dependent slope can be explained via postsynaptic effects: First, the shift of the intercept of the starting bias (small differences in proximity) from the LL towards the SS option under amisulpride is consistent with the assumption that lower dopamine reduces the preference for larger reward (e.g., Beeler & Mourra, 2018; Salamone & Correa, 2012). Second, the finding that amisulpride weakens the proximity advantage of SS over LL rewards (delay-dependent slope) is consistent with the proximity account by Westbrook & Frank (2018) according to which lower tonic dopamine should reduce proximity effects. Thus, if we assume that the result pattern for the starting bias parameter is driven by dopaminergic effects on two separate decision biases (as suggested by the reviewer), we believe that both effects can better be explained by pharmacologically reduced rather than increased dopamine levels.

In the revised manuscript, we extensively discuss the question as to whether the observed drug effects are caused by postsynaptic versus presynaptic effects. We clarify that the amisulpride effect on drift rates seems consistent with both presynaptic and postsynaptic effects (depending on the underlying conceptual account). We moreover discuss that the starting bias effects may reflect the interaction between two different bias types, and the drug effects on both bias types can more easily be reconciled with postsynaptic than presynaptic effects. On balance, we believe that the observed effects are more likely to reflect lower as compared to higher dopamine levels, but the extended discussion of this issue gives all readers the opportunity to weigh the arguments for and against these alternatives. If the reviewer should not agree with some aspects of our argumentation as outlined above, we would of course be happy to modify the discussion according to the reviewer’s advice.

In the revised manuscript, we modified the discussion of presynaptic versus postsynaptic effects as follows (p.20-21):

“While higher doses of amisulpride (as administered in the current study) antagonize post-synaptic D2Rs, lower doses (50-300 mg) were found to primarily block pre-synaptic dopamine receptors (Schoemaker et al., 1997), which may result in amplified phasic dopamine release and thus increased sensitivity to benefits (Frank & O'Reilly, 2006). At first glance, the stronger influence of differences in reward magnitude on drift rates under amisulpride compared with placebo might therefore speak in favor of presynaptic (higher dopamine levels) rather than postsynaptic mechanisms of action in the current study. On the other hand, one could argue that amisulpride reduced the preference for the LL reward if the gain from the costlier LL option compared with the SS option was small (as suggested by Figure 2C), which is consistent with the cost control hypothesis of dopamine (Beeler & Mourra, 2018). The impact of amisulpride on the drift rate thus appears ambiguous regarding the question of pre- versus postsynaptic effects. The result pattern for the starting bias parameter, in turn, suggests the presence of two distinct response biases, reflected by the intercept and the delay-dependent slope of the bias parameter (see Figure 2K), which are both under dopaminergic control but in opposite directions. First, participants seem to have a general bias towards the LL option in the current task (intercept), which is reduced under amisulpride compared with placebo, consistent with the assumption that dopamine strengthens the preference for larger rewards (Beeler & Mourra, 2018; Salamone & Correa, 2012; Schultz, 2015). Second, amisulpride reduced the proximity advantage of SS over LL rewards with increasing differences in delay, as predicted by the proximity account of tonic dopamine (Westbrook & Frank, 2018). On balance, the current results thus appear more likely under the assumption of postsynaptic rather than presynaptic effects. Unfortunately, the lack of a significant amisulpride effect on decision times (which should be reduced or increased as consequence of presynaptic or postsynaptic effects, respectively) sheds no additional light on the issue.”

Regardless of the final interpretation, showing that pharmacological intervention into striatal dopamine signaling can simultaneously modify a starting point bias and drift rate (in opposite directions - thus having systematic effects on choice biases without altering the average proportion of LL choices) provides crucial first evidence for the hypothesis that dopamine and proximity interact to influence decision-making. These results thereby enrich our understanding of the neuromodulatory mechanisms influencing inter-temporal choice, and take an important step towards resolving prior contradictions in this literature. They also have implications for how striatal dopamine might impact decision-making in diverse domains of impulsivity beyond inter-temporal choice, ranging from cognitive neuroscience (e.g. in numerous cognitive control tasks) to psychiatry (treating diverse disorders of impulse control).

We thank the reviewer for highlighting the importance of the current findings for understanding dopamine’s role in decision making.

Author Response

Reviewer #1 (Public Review):

Liau and colleagues have previously reported an approach that uses PAM-saturating CRISPR screens to identify mechanisms of resistance to active site enzyme inhibitors, allosteric inhibitors, and molecular glue degraders. Here, Ngan et al report a PAM-saturating CRISPR screen for resistance to the hypomethylating agent, decitabine, and focus on putatively allosteric regulatory sites. Integrating multiple computational approaches, they validate known - and discover new - mechanisms that increase DNMT1 activity. The work described is of the typical high quality expected from this outstanding group of scientists, but I find several claims to be slightly overreaching.

Major points:

The paper is presented as a new method - activity-based CRISPR scanning - to identify allosteric regulatory sites using DNMT1 as a proof-of-concept. Methodologically, the key differentiating feature from past work is that the inhibitor being used is an activity-based substrate analog inhibitor that forms a covalent adduct with the enzyme. I find the argument that this represents a new method for identifying allosteric sites to be relatively unconvincing and I would have preferred more follow-up of the compelling screening hits instead. The basic biology of DNMT1 and the translational relevance of decitabine resistance are undoubtedly of interest to researchers in diverse fields. In contrast, I am unconvinced that there is any qualitative or quantitative difference in the insights that can be derived from "activity-based CRISPR scanning" (using an activity-based inhibitor) compared to their standard "CRISPR suppressor scanning" (not using an activity-based inhibitor). Key to their argument, which is expanded upon at length in the manuscript, is that decitabine - being an activity-based inhibitor that only differs from the substrate by 2 atoms - will enrich for mutations in allosteric sites versus orthosteric sites because it will be more difficult to find mutations that selectively impact analog binding than it is for other active-site inhibitors. However, other work from this group clearly shows that non-activity-based allosteric and orthosteric inhibitors can just as easily identify resistance mutations in allosteric sites distal from the active site of an enzyme (https://www.biorxiv.org/content/10.1101/2022.04.04.486977v1). If the authors had compared their decitabine screen to a reversible DNMT1 inhibitor, such as GSK3685032, and found that decitabine was uniquely able to identify resistance mutations in allosteric sites, then I would be convinced. But with the data currently available, I see no reason to conclude that "activity-based CRISPR scanning" biases for different functional outcomes compared to the "CRISPR suppressor scanning" approach.

We appreciate the reviewer’s comments and thank them for their constructive feedback. We agree with the reviewer that our claims regarding the utility of activity-based CRISPR scanning would be more strongly supported with a head-to-head comparison against a non-covalent, reversible inhibitor. To address this point, we conducted a CRISPR scanning experiment on DNMT1 and UHRF1 using GSK3484862 (GSKi), which is shown in Fig. 1e–h. We observed that the top enriched sgRNA under GSKi treatment targets H1507, which directly interacts with the drug and contributes to compound binding. (Fig. 1e,h, Supplementary Fig. 1e). Our results are consistent with previous structural and biochemical studies of these inhibitors (reported in Pappalardi, M.B. et al., Nat. Cancer 2021), in which they demonstrate that the H1507Y mutation reduces GSK3685032 (a derivative of GSK3484862) inhibition of DNMT1 by >350-fold compared to wild-type DNMT1. By contrast, the top enriched sgRNA under decitabine (DAC) treatment targets D702 in the autoinhibitory linker region (Fig. 1c). Furthermore, comparison of sgRNA resistance scores across DAC and GSKi treatment conditions reveals highly distinct sgRNA enrichment profiles (Fig. 1g). Taken together, our data suggest that these two mechanistic classes of inhibitors may exert differential selective pressures that lead to unique enrichment profiles.

While we consider these data to strengthen our claim that activity-based CRISPR scanning can preferentially enrich for mutations in allosteric sites versus orthosteric sites, we also recognize that allosteric site mutations can be identified without the use of activity-based inhibitors, as the reviewer points out. To address this point, we have modified the text to suggest that the use of activity-based inhibitors may exert a greater bias for the enrichment of allosteric site mutations but clarifying that the enrichment of such mutations are not exclusive to the use of activity-based inhibitors.

How can LOF mutations from cluster 2 be leading to drug resistance? It is speculated in the paper that a change in gene dosage decreases the DNA crosslinks that cause toxicity. However, the immediate question then would be why do the resistance mutations cluster around the catalytic site? If it's just gene dosage from LOF editing outcomes, would you not expect the effect to occur more or less equally across the entire CDS?

This is an excellent point. As outlined previously above, we recognize that our gene dosage hypothesis regarding the mechanism of cluster 2 sgRNAs may lack sufficient explanation to convey our reasoning clearly, and we have added more text and data to clarify and support our claim.

Mutations that are highly likely to lead to a nonfunctional protein product (i.e., frameshift, nonsense, splice site disrupting) are annotated as “loss-of-function” (LOF) in the text, with all other protein coding mutations designated as “in-frame.” The key insight underlying our gene dosage hypothesis is that sgRNAs targeting essential protein regions and functional domains generate greater proportions of null (i.e., knockout) mutations and undergo stronger negative selection compared to sgRNAs targeting non-essential protein regions (see Shi, J. et al., Nat. Biotechnol. 2015). This is because in-frame coding mutations in protein regions that are functionally important (e.g., DNMT1 catalytic domain) are more likely to disrupt protein function than those in non-essential protein regions. As a result, sgRNAs targeting functional protein regions are more likely to generate in-frame mutations resulting in a null allele and are thus “effectively LOF.” Importantly, the observation that sgRNAs targeting specific protein regions are more likely to lead to null mutations also implies that 1. not all CDS-targeting sgRNAs are equivalent at inducing LOF effects and 2. sgRNAs that are more effective at generating null mutations may exhibit preferential clustering within functionally important protein regions.

In this context, we reasoned that cluster 2 sgRNAs, which target the essential catalytic domain, may be more effective at reducing DNMT1 gene dosage than other DNMT1-targeting sgRNAs because in-frame mutations generated by these sgRNAs are more likely to lead to nonfunctional DNMT1 protein. That is, cluster 2 sgRNAs may generate greater proportions of “effectively LOF” in-frame mutations that disrupt DNMT1’s essential function. Consequently, we posited that the observed clustering of these sgRNAs in the catalytic domain is likely a reflection of its functional importance. To test this idea, we transduced WT K562 cells with 6 individual sgRNAs targeting the N-terminus, RFTS domain, and catalytic domain of DNMT1, and performed genotyping on the cellular pools over 28 days (Fig. 4f). We observed that sgRNAs targeting outside of the catalytic domain exhibited increasing frequencies of in-frame mutations over time, consistent with the idea that these sgRNAs generate functional in-frame mutations that are not under strong negative selection. By contrast, catalytic-targeting sgRNAs exhibited significant depletion of inframe mutations over time, supporting the notion that in-frame mutations in essential regions are functional knockouts and thus negatively selected under normal growth conditions. Consequently, the ability of catalytic-targeting sgRNAs to generate greater proportions of null mutations would therefore make them more effective at conferring resistance through gene dosage reduction than other DNMT1-targeting sgRNAs.

Our hypothesis implies that a large proportion of in-frame mutations generated by cluster 2 sgRNAs are functionally equivalent to LOF mutations (i.e., frameshift, nonsense, splice site disruption), and therefore neither in-frame or LOF mutations should be preferentially selected for under DAC treatment, in contrast to the positive selection of gain-of-function (GOF) in-frame mutations in cluster 1 sgRNAs. Consistent with this idea, our data indicate that the relative proportions of in-frame and LOF mutations in cluster 2 sgRNAs remain comparable across vehicle and DAC treatments (Fig. 4b). Furthermore, since the selective pressure on in-frame and LOF mutations should be similar if they are functionally equivalent, the relative proportions of in-frame versus LOF mutations in cluster 2 sgRNAs should be primarily dictated by their frequencies as editing outcomes. Consistent with this idea, the observed proportions of in-frame versus LOF mutations in cluster 2 sgRNAs under DAC treatment do not deviate significantly from their expected proportions as predicted by inDelphi (Supplementary Fig. 4c). Conversely, cluster 1 sgRNAs exhibit greater ratios of in-frame versus LOF mutations under DAC treatment than their predicted ratios from inDelphi (Supplementary Fig. 4c,d). Altogether, these data are consistent with the notion that cluster 2 sgRNAs may operate through a gene dosage reduction effect.

In general, I found the screens, and integrative analyses, highly compelling. But the follow-up was rather narrow. For example, how much do these mutations shift the IC50 curves for DAC?

To address this point, we derived two clonal cell lines from the screen harboring endogenous DNMT1 mutations in either the autoinhibitory linker or the RFTS domain (Supplementary Fig. 3g). We treated these cell lines, in addition to WT K562 cells, with varying concentrations of DAC and observed a partial growth rescue in the mutant cell lines relative to WT K562 cells (Fig. 3i). We also show that these mutant cell lines exhibit DAC-mediated degradation of DNMT1, consistent with our fluorescent reporter results (Supplementary Fig. 3h). To further validate whether these endogenous DNMT1 mutations confer partial resistance to DAC, we transduced WT K562 cells with vectors encoding an shRNA targeting the 3' UTR of the endogenous DNMT1 transcript and a DNMT1 overexpression vector encoding WT and mutant DNMT1 constructs (Supplementary Fig. 3i). Upon treating these knockdown and overexpression cells with varying concentrations of DAC, we again observed a partial growth rescue in the presence of mutant versus WT DNMT1 (Fig. 3j).

What kinetic parameters have changed to increase catalytic activity?

We performed enzyme activity assays at various temperatures with recombinant DNMT1 protein for WT and mutant DNMT1 constructs, observing that mutant DNMT1 constructs exhibit varying degrees of overactivity relative to WT DNMT1 at different temperatures (Fig. 3h, Supplementary Fig. 4f). Whereas the autoinhibitory linker mutations display consistently higher levels of activity relative to WT DNMT1 at all temperatures tested, we observed that RFTS and CXXC mutants exhibited decreasing levels of overactivity with increasing temperature (Fig. 3h). Previous studies (see Berkyurek, A.C. et al., J. Biol. Chem. 2014) have observed similar behavior with RFTS mutations, suggesting that these mutations may disrupt critical hydrogen bonds at the autoinhibitory interface that reduce the activation energy required to release DNMT1 from an autoinhibited to active conformation. Our RFTS and CXXC mutations exhibit behavior that are consistent with this hypothesis, which may explain the decreasing levels of overactivity with increasing temperature.

Do the mutants with increased catalytic activity alter the abundance of methylated DNA (naively or in response to the drug)? It is speculated that several UHRF1 sgRNAs disrupt PPIs and not DNA binding, but this is never tested.

While we derived clonal cell lines containing DNMT1 mutations, as noted above, it proved too difficult to compare these drug-resistant cells to naïve cells because they were cultured in the presence of DAC for 2 months, leading to large changes in DNA methylation that may confound any conclusions about the effects of the mutations alone. Additionally, the reviewer also brings up valid limitations regarding our studies on UHRF1, which also proved very difficult to biochemically purify and beyond our expertise. After some initial studies, we chose not to pursue these additional experiments further but instead prioritized the GSKi CRISPR-suppressor scan and cluster 2 studies, as suggested by the reviewers. We acknowledge these limitations in the text.

Reviewer #2 (Public Review):

In this manuscript, Ngan and coworkers described a CRISPER-based screening approach to identify potential variants of DNMT1 and UHRF1 that can suppress the anti-proliferation role of decitabine. In theory, such an effect can be achieved by at least two types of gain-of-activity DNMT1/UHRF1 mutants by directly boosting the enzymatic activity or by indirectly abolishing the intrinsic inhibitory activity of the DNMT1-UHRF1 axis. Through systematically targeting the DNMT1-UHRF1 reading frames with a rationally designed sgRNA library, the authors identified and characterized a few potential hotspots within multiple autoinhibitory motifs. While the approach has its merits in regard to the unbiased screening of the target proteins in living cells, there are the following serious concerns in terms of how the data were interpreted and the limitation of the approach itself as detailed below.

(1) Although the authors identified multiple hotspots in the DNMT1-UHRF1 complex with their alterations associated with the resistance to decitabine, it is risky to argue these mutations increase DNMT1 activity simply because they are clustered within known auto-inhibitory regions. There are many alternative explanations for this observation. For instance, some mutants may allosterically alter how DNMT1 recognizes decitabine-containing vs native GpC motifs; others may recruit other proteins as modulators. The key gap here is to associate the decitabine-resistance phenotype to the loss of auto-inhibitory functions because multiple hotspots were in the auto-inhibitory regions.

In our original manuscript, we supported our claim that gain-of-function DNMT1 mutations enhance DNMT1 activity with experimental data using purified DNMT1 protein constructs in enzyme activity assays (Fig. 3g, Fig. 4g), so our conclusion was not solely inferred from sgRNA clustering at the autoinhibitory interface, but also experimentally validated. In our revised manuscript, we provide additional experimental biochemical characterization to further support the claim that autoinhibition is weakened in the DNMT1 mutants we identified (Fig. 3h, Supplementary Fig. 4f). Moreover, we provide cellular data using clonal cell lines harboring endogenous DNMT1 mutations in addition to knockdown/overexpression experiments, demonstrating that RFTS and autoinhibitory linker mutations confer partial growth rescue to DAC treatment (Fig. 3i,j). We agree that we cannot rule out the possibility that these mutations may exert other effects that independently contribute to the observed resistance phenotype (e.g., altered CpG recognition), and we have added a statement acknowledging this limitation.

(2) Lack of general biological relevance of the corresponding findings. Through this work, the author identified multiple DNMT1-UHRF1 variants that alter the anti-proliferation role of decitabine. However, the observation that the multiple mutants were clustered in a hotspot doesn't mean that these mutants have to act via the same mechanism. The authors seem to underestimate the complexity of how these mutants can render the same biological readouts and even haven't considered the possibility of transcriptional modulation of antagonists or agonists in the DNMT1-UHRF1. Therefore, the biological relevance of these findings remains unclear.

We agree that although the cluster 1 mutations share a common property of increased DNMT1 activity, it does not preclude alternative mechanisms. Indeed, it is likely that these mutations have complex and nuanced mechanistic differences in the biochemical alterations underlying their observed increases in DNMT1 activity. Indeed, we have included enzyme activity data suggesting that autoinhibitory linker mutations may exhibit a different biochemical basis for increased DNMT1 activity than RFTS and CXXC mutations. That said, we did not intend to make broader claims regarding biological relevance and were instead focused on conveying that this activity-based methodology can identify gain-of-function mutations, which we directly support with experimental data. To clarify these points, we have adapted the text to more precisely convey our intended claims and have acknowledged that other complex mechanisms may also be involved.

(3) Collectively for reasons (1) and (2), the mechanistic analysis seems only to associate the current findings with known regulatory pathways. Without detailed in vitro and in-cell characterization of the DNMT1-UHRF1 mutants, the novel regulatory mechanisms, which may exist, could be largely missed.

We have added some additional characterization of these mutations in the revised manuscript, which have been detailed above, and we would like to note that we identified new sites in DNMT1 and UHRF1 that may be functional based off our allele analysis. However, since this manuscript is intended more as a methodology, we believe that extensively exploring novel regulatory mechanisms and their mechanism is beyond the scope of this report.

(4) The current CRISPER-based screening approach has the technical limitation of mainly screen deletion with some exceptions for point mutations. As a result, the majority of loss/gain-of-function point mutations will be missed by the CRISPER-based screening method.

We acknowledge that a technical limitation of this Cas nuclease-based mutational scan is that it is biased toward insertion/deletion mutations versus point mutations. However, we disagree with the reviewer’s claim that this means that the majority of the loss-/gain-of-function mutations will be missed, since insertion/deletions are often larger perturbations than point mutations and thus have stronger effect sizes in many cases. In principle, the selection modalities (e.g., activity-based inhibitors) used here — which are the primary focus of the study — can also be combined with alternative genomic editing approaches to assess distinct mutational perturbations, such as base editing for point mutations (see Lue, N.Z. et al., Nat. Chem. Biol. 2022). To acknowledge the reviewer’s concern, however, we have added additional text explicitly stating that the screen is biased against point mutations and that future integration with base editing and other mutational modalities may be useful to complement our nuclease-based approach.

(5) The current CRISPER-based screening approach can work only in the context of living cells. As a result, robust cellular readouts are needed. The DNMT1-UHRF1 in combination with decitabine is among few suitable targets for such application.

While running CRISPR-based screens requires robust cellular assays, the main advantage of CRISPRbased mutational scanning is the ability to mutagenize the endogenous protein target in situ and assess the effect of the perturbation in the native cellular and genomic context. Resistance to activity-based probes — and small molecules more broadly — provides a robust phenotypic readout that has been extensively used by our group and many others. Alternatively, other types of phenotypic readouts that do not rely on cell viability can also be employed with these screens, including those used to assess DNA methylation (see Lue, N.Z. et al., Nat. Chem. Biol. 2022). Given the increasingly large body of literature applying CRISPR-based screens towards a multitude of biological pathways and diverse targets, we disagree with the reviewer’s claim that only a few targets can be evaluated in such a manner.

(6) Although the authors claim that their mutants are "gain-of-function" for DNMT1/UHRF1, they were indeed due to the loss of inhibitory regulation. It is a little disappointing because the screening outcomes still fall into the conventional expectation of the loss-of-function variants.

We agree that the mutations are not truly neomorphic, but instead likely hypermorphic due to loss of an autoinhibitory mechanism, resulting in gain-of-function increase in catalytic activity. While discovering neomorphic mutations would be extraordinary, we do not believe that our results are disappointing since the identification of autoinhibitory mechanisms is nevertheless impactful.

Collectively, the current status of the manuscript is short of merits in terms of the impacts of technology and biological findings.

We respectfully disagree with the reviewer’s comment as we believe that the experimental and computational methodology may be broadly useful for the field. Indeed, we have already implemented many of the tools developed here in our current ongoing work.

Author Response

Reviewer #2 (Public Review):

This manuscript presents a rather technical modelling analysis of the impact of local lockdowns on Covid-19 hospitalisations in the Netherlands. The major strength of the study is that the authors attempt to calibrate their model to a novel data source, a commercial database of mobility patterns between municipalities. The major weakness is that the model seems overly complicated, many parameters seem to have been 'guessed' without a formal uncertainty analysis, e.g. within a Bayesian framework, so that it is impossible to judge how robust the results and therefore the conclusions are.

Major points:

1) In some aspects the structure of the model presented seems overly complicated: It is not clear why the authors chose the 1:100 population scale and why they didn't go directly for modelling the full population. Artificially reducing the population size has important stochastic effects at the early phase of the epidemic. Also it is not clear what it means when 1:100 of one municipality mixes with 1:100 of another municipality? The authors should at least attempt to see what impact this has on output, i.e. conduct a sensitivity analysis.

The reason for choosing a 1:100 population scale instead of the full population is computational speed. Indeed, this (and its consequences) is not mentioned explicitly and will be added. Moreover, to identify the sensitivity of the results to population scale, we add runs on different population scales.

• Added reasoning and consequences associated with the 1:100 population scale in SI C.1.

• The sensitivity of the results to population scale is now discussed in SI C.1 using runs with other population resolutions.

2) On the other hand the model goes into (too) much detail regarding mixing behaviour and attempts to model processes during each hour of the day. This does not seem to be informed by actual data, but the data seems to be made up e.g. as in A.6. As an ex-student and a father of a teenager I can tell you that the susceptibility profile guessed in Table 3 does not seem to be very realistic. As it is stated in the appendix, the Mezuro data set only provides daily averages of travelling between communitities, so it is not clear why the hourly resolution is actually needed in the model.

Indeed, several aspects in the model are informed by “secondary statistics” which unfortunately add uncertainty. An example would be the normalization of the mobility matrices by means of data on how people spend their time (see SI A.3). Note that the example of the susceptibility profile that the reviewer mentions, however, does not involve such secondary statistics and happens to be exactly reported by the Dutch health agency (cited in SI A.5).

We agree that the model includes much detail, which potentially has weaknesses as the reviewer rightfully mentions. However, one of the main points of this paper is that in order to address the questions of local interventions, geographical spread and associated hospital admissions, we simply need this level of detail, or even higher. In other words, assessments of such mechanics would be even more uncertain if this level of detail is not included.

We agree that the motivation for hourly resolution is not well described in the manuscript – this will be added. The reasoning is that mixing of the population is highly heterogeneous throughout the day: clearly, seen in Fig. S5 (SI A.7), mixing at work is fully different from mixing at school or at home.

Moreover, people meet at work in different municipalities and then return to home to potentially spread the disease further. It is exactly such mechanics that we are after in our analysis.

• Added a more in-depth discussion of the mobility data in SI C.2.

• Added the motivation for hourly resolution in SI A.1-A.3.

3) It is not clear why the authors rely on only one short period of the Mezuro data set in March 2019 and not investigate the same data source during the actual lockdown in 2020, or even for the full year, as travelling is likely to be very season dependent. This would provide much better estimates of the effects of lockdown on travel patterns. The analysis presented and categorisation into frequent, regular and incidental also need further explanation. It is not clear how international travel is accounted for in the mobility data.

The reviewer is correct that using a longer mobility dataset or one that is exactly addressing the period of the actual lockdown would be beneficial. The reason we are not doing so is simply that this data is not available.

The model accounts for international travel by means of its initialization, but not further. In practise, international travel got severely reduced throughout this period. Hence, we deem the uncertainties associated with not accounting for international travel limited.

• Added a discussion on the effect of using this mobility dataset in SI C.2. • Added a further explanation of categorizing the movements (in SI C .2).

• Added a discussion on international travel in SI C.2.

4) Beyond the technical points on the modelling, the main hypothesis of whether local lockdowns may work has also not been sufficiently discussed outside of the Dutch context. The authors fail to mention that this was the approach chosen in Northern Italy at the start of the epidemic (https://en.wikipedia.org/wiki/COVID-19_lockdowns_in_Italy) where it didn't work, as we all know. On the other hand, more recent local lockdowns in China appeared to be successful, albeit at a great societal cost in terms of restrictions to freedom (https://en.wikipedia.org/wiki/COVID19_lockdown_in_China).

The reviewer is correct that we only show this in the Dutch context. We can reason about other situations, but clearly these situations differ vastly from country to country.

Reviewer #3 (Public Review):

This work uses an agent-based model of SARS-CoV-2 transmission (calibrated to the first wave in the Netherlands) to examine how the societal impact of interventions could have been reduced - while maintaining epidemiological impact - if they were implemented at a subnational (eg, municipality) rather than a national level. After more than two years of lockdowns and mobility restrictions, the societal cost of such measures is becoming better understood, and it is important to evaluate the effectiveness of such measures and reflect upon how they can be deployed in a minimally disruptive fashion. Mathematical and computational models are a natural choice for such investigations as they enable researchers to explore counter-factual scenarios ("what might have happened had we acted differently?")

The authors conclude that subnational interventions, triggered via prevalence in a particular municipality, could have controlled the first wave of SARS-CoV-2 in the Netherlands with minimal health cost but less societal disruption than national interventions. This claim is supported by reference to Figure 4 showing the impact on (a) hospital admissions and (b) municipalities without interventions through different phases of the outbreak. For more remote/rural municipalities, the use of interventions is delayed by ~1 week, although some (6%) of municipalities avoid interventions altogether.

Strengths:

As noted above, the general objective of this study is important and of potentially broad interest. The agent-based model is complex, but not unreasonably so, and makes good use of rich demographic, mobility, epidemiological/clinical, etc. data for calibration. The simulations conducted using the model support the specific conclusions of the manuscript.

Weaknesses:

While the motivation and approach are strong points of this work, the analysis and interpretation would benefit from further development. The robustness of model behaviour to the threshold used to trigger subnational interventions is explored; however, there are other aspects of the model that are not subjected to sensitivity analysis, including:

1) The impact of imperfect surveillance (eg, due to asymptomatic transmission, reporting delays, etc);

2) The impact of non-compliance, which could potentially differ for subnational versus national interventions;

3) The impact of pathogens/variants with transmission/severity characteristics different from the original SARS-CoV-2 strain.

In the absence of such analyses, it is difficult to generalise the findings beyond "this is how subnational interventions could have been used to control the first wave of SARS-CoV-2 in the Netherlands" to "this is how subnational interventions could be used effectively in the event of future outbreaks" (of a SARS-CoV-2 variant or other pathogen).

The discussion focuses on limitations associated with the model but does not consider other potential implications of subnational interventions. For example:

1) Subnational interventions may produce unintended consequences if populations respond by relocating from regions with interventions (high prevalence) to regions without interventions (low prevalence).

2) Subnational interventions would require extremely effective public health messaging to avoid confusing populations. Particularly in densely populated regions where municipalities may be small and tightly connected, the feasibility of communicating (and enforcing compliance with) interventions may be challenging.

3) A proposal to implement subnational interventions - following the results of this work - may raise ethical questions about cost-benefit trade-offs (eg, whether 355 additional hospital admissions is an acceptable price to pay for 36 million person-days without interventions; ie, two days per citizen, on average). The fact that such decisions would (in the even of a future outbreak) need to be made rapidly, in the face of potential uncertainty about pathogen characteristics, heightens the need for clear understanding of how situational factors may affect the likely effectiveness of interventions (at any scale).

Impact and broader utility:

As noted, the question addressed - how we can reduce the disruption caused by interventions for transmission control - is important. Thus, the work presented in this manuscript has the potential for broad utility. Currently, this is limited by the focus on specific outbreak instance.

In general terms, we agree with the reviewer. That said, the “possibility space” of policymaking is infinite dimensional, in the sense that the intervention measures can take an infinitely many forms, starting times and durations. The framework that we have built upon combining data sources such as demography, mobility, interactions and disease parameters now makes it possible to explore these possibilities. These will be explored in future work.

Author Response

Reviewer #1 (Public Review):

The data that is presented is quite clear, and expected given the prior in vitro work, as well as prior work in vivo with helminth infection and BCG vaccination. Overall, it is important to demonstrate that observations from in vitro experiments are relevant in vivo, however, there are concerns with the design of this study which limits its impact. In addition, the study confirms what is expected from prior work, but falls short of adding any new mechanistic insight.

We thank the Reviewer for evaluation of the manuscript and for the comments. Indeed, published studies have shown that helminth infection can impair the response to the BCG vaccine. However, this manuscript shows for the first time that IL-4 and helminth infection impair MINCLE expression in vivo. In addition, it is the first report demonstrating a negative effect of helminth infections on the antigen-specific Th1/Th17 response after vaccination with a MINCLE-dependent adjuvant.

Regarding mechanistic insight, we have employed mice deficient in IL-4/IL-13 to determine whether the thwarted Th1/Th17 response is caused by these Th2 cytokines in helminth-infected mice. New Figure 6 in the revised manuscript indeed demonstrates recovery of antigen-specific IFN and IL-17 production in the absence of IL-4/IL-13.

In terms of the in vivo experimental design, it is unclear why the authors chose to administer BCG IP, when the vaccine is given SC (and then based on more recent data, IV could be arguably interesting and relevant). The focus on the peritoneum limits the potential application of these findings to address the important question of the effects of helminth infection on BCG vaccine responses. The ultimate in vivo experiment to be able to demonstrate a physiological relevance of the mechanisms explored here would be to see what the effect was on Mtb infection in the lung.

BCG was injected i.p. to induce upregulation of MINCLE on peritoneal cells and to be able to ask whether IL-4 and/or helminth infection will lead to a down-regulation of MINCLE expression on myeloid cells in vivo. Thus, we were not interested in this context in the adaptive immune response to BCG. Instead, the peritoneal BCG injection provided access to myeloid cells exposed to Th2 immune condition in vivo for analysis of MINCLE protein levels on the surface. As stated in the Discussion section (lines 400-405 in the revised manuscript), detection of MINCLE by flow cytometry from tissue cells is complicated by the loss of cell surface protein during enzymatic organ digestion.

We agree that it would be interesting to study the impact of helminth infection on BCG-induced protection to Mtb challenge infection in the lung. As we have described here the impairment of Th1/Th17 immune responses after immunization with H1/CAF01 that induces protection (Werninghaus et al. 2009 J Exp Med), it would make most sense to perform such challenge infections first in this setting. However, Mtb infection requires a dedicated BSL3 animal facility, we therefore consider such challenge experiments beyond the scope of this manuscript

The authors do report different responses in the spleen and lymphnode, which is interesting, but lines 336-337 accurately point out that compartmentalized overexpression of IL-10 in the spleens but not the lymph nodes has been described in mice with chronic schistosomiasis. Mechanistic insight into this phenomenon was lacking, and the relevance to Mtb infection is still unknown.

We agree that the mechanism for the compartmentalized regulation of adaptive immune differentiation in helminth-infected mice is not clear.

Reviewer #2 (Public Review):

The manuscript entitled "IL-4 and helminth infection downregulate Mincle-dependent macrophage response to mycobacteria and Th17 adjuvanticity" by Schick et al. demonstrate the inhibitory activity of IL-4 and helminth infection on mycobacteria-mediated Th17 immunity. Overall, the authors reported interesting findings with solid data that advance our understanding of CLR function in fungal-bacterial co-infection.

We thank the Reviewer for the appreciation of our study.

Reviewer #3 (Public Review):

The authors first demonstrated in bone marrow-derived macrophages (BMMs) that IL-4 treatment of BMMs led to a significant reduction of BCG- and TDB-induced MINCLE expression (Fig. 1). While IL-4 treatment did not impact BCG phagocytosis by BMMs, it led to a reduced production of the cytokines G-CSF and TNF by BMMs (Fig. 2). In an elegant model using hydrodynamic injection of mini-circle DNA encoding IL-4, the authors show that IL-4 overexpression abrogated the increased MINCLE expression in monocytes upon BCG infection in vivo. Similar findings were observed in a co-infection model with the hookworm Nippostrongylus brasiliensis, where MINCLE expression on inflammatory monocytes from BCG-infected mice was reduced compared to control mice infected only with BCG (Fig. 3). The key findings of the manuscript include the two murine helminth infection models, S. mansoni as a chronic infection, and N. brasiliensis as a transient infection, in both of which the authors showed an organ-specific inhibition of the Th17 response in a vaccination setting with a MINCLE-dependent adjuvant (Fig. 4 and 5).

Data shown in the manuscript represents a major advance over previous studies because for the first time a relation between IL-4 and MINCLE expression and function is demonstrated in vivo in relevant co-infection models. All experiments have been done with care. Appropriate controls have been included and conclusions are largely supported by the data. Future studies in human patients will be needed to determine the clinical relevance of the findings observed in the murine helminth infection models.

We thank the Reviewer for the positive comments and agree that it will be interesting to study the impact of helminth infection on CLR expression and function in human infection and vaccination settings.

Author Response

Reviewer #1 (Public Review):

COVID-19 severity has been previously linked to a genetic region on chromosome 3 introgressed from Neandertals. The authors use several computational methods to, within this region, identify specific regions that putatively regulate gene expression, and to identify genes within these regions associated with COVID-19 severity. The use of several complementary computational approaches is a major strength of the paper as it bolsters confidence in the findings and narrows the search for significant genomic regions down to most likely candidates. They find 14 genes that exhibit expression regulated by the identified introgressed genomic regions. Among these are several chemokine receptors including two - CCR1 and CCR5 - whose upregulation is associated with severe COVID-19. The authors then use functional genomics to determine whether the identified regions do regulate gene expression.

We thank this Reviewer for highlighting these strengths.

In contrast to the robustness of the computational findings, the authors' MPRA results are less robust with respect to the significance of the paper to clinical severity of COVID-19. The MPRA shows that the computational methods were reasonably effective at identifying regulatory elements within the introgressed region (53%). The authors then focus on emVars where the H.n. allele differentially regulates expression and identify 4 putative emVars that may regulate expression of CCR1 and CCR5. However, the authors found in their MPRA that these emVars downregulate reporter gene expression, whereas the genes of interest CCR1 and CCR5 are upregulated during severe COVID.

This result highlights the principal weakness of using the MPRA in this context, as it assumes that reporter gene expression using a minimal promoter has identical regulatory determinants as expression of the gene of interest. Its strength is the high-throughput nature of the assay, but its weakness is the lack of specificity with respect to the question at hand. This lack of specificity mitigates the impact of the functional aspect of the work. The authors' computational findings certainly bolster previous work that H.n. introgressed alleles are associated with COVID-19 severity and that this association may be at least partly dependent on gene expression differences between the archaic and modern alleles. However, the specific question at hand, whether chemokine receptor expression is linked to the clinical phenotype, remains unaddressed.

Ultimately the authors results support the conclusions that the 4 emVars identified do regulate gene expression. However, the hypothesis that these specific regions are linked to COVID-19 severity is not supported. The authors' speculation as to why their results may differ from the observed upregulation during disease is intriguing, but lacks support.

We thank the Reviewer for providing these important points and we hope through our new experimental approach we helped to strengthen our findings. However, we also have modified the manuscript to also be more critical of our findings in the context of the issues Reviewer has brought up. This is shown in our updated Discussion, whose parts are provided above in the section addressed to the Editor, as well as in the newly revised manuscript.

Reviewer #2 (Public Review):

Previous research using GWAS and population genetics approach identified a genetic haplotype on chromosome 3 derived from Neanderthals as the major risk factor for severe COVID-19. However, the specific variants that are causative of the severe COVID-19 phenotype remain unknown. Here, Jagoda et al. aim to identify the causative variants for the severe COVID-19 by leveraging eQTL analysis followed by Massively parallel reporter assays (MPRA). Their datasets and results are unique and novel. Their research is well designed, and will serve as a model strategy for future studies of functional annotation of disease-associated variants.

We thanks Reviewer #2 for these compliments.

However, there are following critical weaknesses in this manuscript that reduce the impact of this work; (1) The quantitativity of the MPRA output is questionable because of their incomplete definition of MPRA activity, which is based on absolute barcode counts without comparing negative controls. (2) Molecular mechanisms (binding transcription factors, etc.) of causative variants that underlie the regulation of CCR1/5 expression and COVID19 severity are not analyzed and validated.

We hope that below we have addressed these comments through our analyses and new experiments.

Reviewer #3 (Public Review):

This manuscript by Jagoda et al. addresses the genetic mechanism of the haplotype at chromosome 3 where introgressed from Neanderthals shows the strong association with COVID-19 severity in Europeans. They re-evaluate the adoptively introgressed segment using Sprime and U and Q95 methods and analyze cis- and trans- eQTLs based on the whole blood dataset. All the 361 Sprime-identified introgressed variants act as eQTLs in the whole blood and alter the expression of 14 genes including seven chemokine receptor genes. Then they tested the 613 variants using a Massively Parallel Reporter Assay (MPRA) in K562 cells and narrow downed the 20 emVars. In the end, they selected the four variants based on four criteria regarding the association of COVID-19 severity, eQTL data, chromosomal interaction, and epigenetic marks in immune cells. They highlighted variant rs35454877 (CCR5 regulation), rs71327024, rs71327057, and rs34041956 (CCR1 regulation).

Narrowing down the four critical variants from the around 800 kb introgressed region is impressive work. However, MPRA and eQTL data are not consistent, and these data don't support clinical gene expression data (increased expression of CCR1 in severe COVID-19 patients).

We thank this Reviewer for noting our impressive work, we have now addressed these concerns.

Author Response

Reviewer #1 (Public Review):

This is an interesting and timely paper investigating the impact on participation in cancer screening programs across Italy during the COVID-19 pandemic where there was massive disruptions to health services. What is of particular interest in this analysis was the investigation of social, educational and cultural factors that might have impacted access and participation to screening.

• In the present study, the authors analyzed data collected by PASSI between 2017 and 2021, from interviews of more than 106,000 people, a representative sample of the Italian population aged 25-69 was selected but its not clear what was the representativeness by region, gender and age educational attainment? Also what is the total population (so I don't have to look it up). I am wondering if participation differed by characteristics and what approach to achieving the representative sample was made (e.g. replacement of individuals or oversampling certain strata where participation was lower).

PASSI is one of the two routinely collected Italian National Health Interviews. It has been described in several papers and there is a website reporting in detail methods, percentage of refusals, and numbers of interviews. Nevertheless, we agree with the reviewer that a brief summary of the methods is needed, and we added some details on data collection. Furthermore, details on the number of interviews according to the selected period, age, and sex strata cannot be found in the general description of the survey. Therefore, we gave more details also on the sample used for this study in supplementary table 1.

• For figures 5-8 what is the N for the different groups not just the %?

We agree with the reviewer that giving the actual numbers on which the percentages are computed is necessary. Nevertheless, as with any stratified sample, estimates from PASSI are computed using weights, therefore percentages cannot be computed directly from the observed numbers. We decided to add supplementary table 1, which reports the number of valid interviews on which percentages are estimated.

• Table 2 to me is a key piece of information and very interesting can the authors formally test if there are significant differences between the time periods?

Thank the reviewer for this suggestion. Firstly, we added a table in which we analyzed all the data together and we included the calendar period, categorized as before and after the pandemic, among covariates. Secondly, we checked if any of the differences between the prevalence ratios observed in the two periods were significantly different at a 0.05 alpha error threshold and we added a comment in the text: “Nevertheless, the differences could be due to random fluctuations”. We did not add p-values for the interaction of all the variables in each cancer screening because the table is already very complex, and three more columns would make it difficult to read.

Reviewer #3 (Public Review):

This study is primarily a descriptive analysis that provides a clear and accessible account of how screening activity varied across Italy and between groups. While primarily a simple descriptive account such work is important to document what were the impacts of the pandemic on preventative health services and to understand how they differed across groups. The combination of survey responses from regional screening programmes and individuals is a useful use of two data sources. The study is very clearly written and does not over-interpret the presented data.

The methods description states that the analysis presents the "standard months" required for the programmes to recover from the service delays. The subsequent reporting of these delays in the results section did not use the same terminology and I see scope for clarification by using common language regarding this assessment throughout the paper. I see scope for further disaggregation of the regional results within the study but equally I understand why the authors might not wish to report outcomes for specific regions. I see scope for improvement in the figures within the manuscript but this is a relatively presentational matter. I would like to see some further description of the Poisson regression analysis as what is included within the manuscript appears rather brief. There is also one section of the methods that seems as if it would better belong in the introduction, but overall the manuscript was very clearly structured.

We thank the reviewer for his encouraging comments. We checked all the manuscript and we tried to use always the same name for each concept.<br /> We expanded the method section giving more details on models and statistical analysis. We decided not to report data at the regional level but the variability within macro areas.

The analysis presented achieves the authors' stated aims in my view. I see a useful contribution in documenting the impact of the COVID-19 pandemic on screening in Italy. This may inform further work on assessing the eventual health impact of delays as well as work considering how best to make screening programmes more resilient to such shocks. Ultimately it will take time to observe just how significant the impacts of service interruptions were on cancer prevention. Readers should remember that many screening services may still provide good protection against cancer as long as the interruptions are limited to simply to delays in coverage rather than the longer-term loss of participation, especially for those with incomplete screening histories or of otherwise elevated risk of disease.

Further work may wish to consider how programmes prioritised capacity or what efforts have been made to restart screening. Similarly, there is scope for more detailed disaggregation assessment of who received screening as programs restarted. Both these issues are beyond the scope of the present study however. The present submission provides a good basis for any further such exploration.

We thank the reviewer for these comments. We tried to capture some of the concepts in our discussion.

Author Response

Reviewer #3 (Public Review):

The authors explore the use of SRT as a host-directed therapy for use in combination with other first-line TB antibiotics. This manuscript is of substantial importance since TB is a major world health concern, and there is growing interest in the development of host-directed therapies to augment existing therapies for TB. Demonstrating the effectiveness of adding an FDA-approved drug to existing cocktails of anti-TB drugs has potentially exciting implications.

The manuscript is bolstered by their use of multiple in vitro and in vivo models of infection, as well as a clinically relevant strain of TB. While their findings generally support the use of SRT as an effective HDT/treatment, the mechanistic details underlying the effectiveness of SRT remain somewhat obscure, and as presented, the in vitro experiments support more limited conclusions.

Major concerns:

In vitro studies (i.e. bacterial culture) were only performed with SRT up to 6 uM while the cultured cell experiments used a range up to 20 uM. 5 uM had almost no effect on the viability/growth of Mtb in macrophages. The authors should use the same concentrations in vitro as their macrophage studies to test whether SRT directly impacts Mtb viability to be able to rule in/out that SRT does not impact Mtb viability when cultured.

We haven’t seen any appreciable decrease in the growth of Mtb at upto 20M in in vitro experiments, nearly 30-40% restriction after 8 days of culture. We used in combination of HR a lower dose of 6mM in combination with HR to offset the effect of minimal SRT inhibitory effects so that only the effect of SRT is understood.

The mechanism of action of SRT during TB infection and the conclusions drawn by the authors are not supported by the limited experimentation. SRT is presented as an antagonist of polyI:C-induced type I IFNs, but during TB infection, cytosolic DNA sensing via the cGAS/STING axis constitutes the major pathway through which type I IFNs are induced in macrophages.

To offer more support that SRT inhibits type I IFN, the authors should consider measuring the the actual amount of type I IFN using an IFNb ELISA. Additionally, the authors should use human/mouse primary macrophages (not just THP1 reporter cells) and measure transcript levels (at key time points post infection) and protein levels of type I IFN and other proinflammatory mediators (e.g. TNFa, IL-1, IL-6) +/- SRT to determine if SRT is specific to the type I IFN response. If this is indeed the case, other NFkB genes/cytokines should not be impacted.

Moreover, to draw the conclusion that "augmentation property of SRT is due to its ability to inhibit IFN signalling" a set of experiments using an IFN blocking antibody would enhance Figure 2, as both cGAS and STING KO macs have significant differences in basal gene expression and their ability to respond to innate immune stimuli.

Because the first half of the paper focuses on type I IFNs during macrophage infection to explain the mechanism of action for SRT, additional analysis of the mouse infections to examine levels of type I IFNs, as well as IL-1B and IFN-g (in serum/tissues?), is important for connecting the two halves of the manuscript. The in vivo data would also be strengthened by quantitative analysis of histological changes by, for example, blinded pathology scoring. This type of quantitation would also permit statistical analyses of this important pathology readout.

We have performed analyse of tissue cytokine levels and did not see stark differences in the levels between HRZE and HRZES at two time points of 4 and 8weeks post treatment (Figure below). We feel that such studies would need a more comprehensive analyses of the immunological response induced in the host by the treatment at multiple time points. Such studies would be part of a more focussed plan in the future proposals and manuscripts. We have also conducted a manual scoring of the lesions between the groups and have recorded this data in the manuscript (Fig.4-figure supplement 1)

The authors conclude that SRT functions through an inflammasome-related function, but this conclusion requires further support of actual inflammasome activation, such as IL-1B secretion by ELISA or IL-1B processing by western blot analysis, rather than Il1b gene expression alone. Additional functional readouts of inflammasome activation like cell death assays would also strengthen this conclusion.

We thank the reviewer for these suggestions. These studies are currently underway and will be part of a future manuscript detailing the mechanistics of SRT mediated increase in antibiotic efficacy.

What strain of TB was used in these studies? The results and methods do not indicate the strain used, which is critical to know since different strains have varying pathogenesis phenotypes.

We have used Mtb Erdman for routine drug sensitive and N73 for the drug tolerant studies. This has been added in the text.

Minor concerns:

It might be worth consistently using the more common INH and RIF abbreviations to increase the clarity/readability of the MS and figures.

We have used the conventional clinical abbreviations used for INH and Rifampicin What is the physiological concentration of SRT when taken for depression and how does that compare to the concentrations used in vitro? Are the in vitro concentrations feasible to achieve in patients?

In Figure 3B, why is there a spike in TNF-a in the HRS treated cells only at 42h?

The authors wish to thank the reviewer for this query. We have reanalysed the data and have depicted the modified figures in the current text version. The spike at 42H for TNF was an oversight and due to an erroneous representation of the values in the figure.

Was statistical analysis performed on the data in Figure 3B and D?

Yes, we have incorporated this information in the modified figure.

A description/discussion of the different mouse strains use in infection - what benefits each has as a model and why several were used - would help convey the impact of the in vivo studies.

These have been incorporated in the text. A discussion of the mouse strains and their immunopathology in infection has been included in the text.

Since antibiotics and SRT were administered ad libitum, how did the authors ensure that mice took enough of the antibiotics and especially SRT? Is it known whether these drugs affect the water taste enough to affect a mouse's willingness to drink them?

We preferred the use of ad libitum delivery of TB drugs in drinking water as used in the previous studies by Vilchèze et .al, 2018 Antimicrob Agents Chemother 23;62(3):e02165-17. To avoid non drinking, we used 5% glucose in the water of all animals including the non-antibiotic treated groups. We also followed the uptake of water during the treatment and found comparable levels of usage between the groups.

Was statistical analysis performed on time-to-death experiments?

Because of the inherent differences in the susceptibility and response between males and females C3HEBFEJ mice, we did not perform statistical analyses between the groups.

Were CFUs measured in mice from Figure 4 to determine empirically how effective the antibiotic treatments were? And if SRT impacted their effectiveness?

We have not tested the effect of SRT on bacterial burdens on bacteria treated with HR alone as these studies were aimed at deciphering chronic pathology. We have tested the effect on bacterial loads in the C3HEBFEJ model with the four-drug therapy and the C57BL6 and Balbc models of infection.

The H&E images could use some additional labels to more easily discern what groups they belong to.

These have been incorporated in the figure.

Author Response

eLife assessment

The purpose of this study was to determine whether heme oxygenase -2 deficiency translates to deficiencies in motor neuron function. This paper plays a plausible mechanism by which heme oxygenase-2 deficiency can lead to obstructive apneas. Indeed, this is among the first papers to comprehensively describe a signaling pathway in motor neurons and the consequences of its deficiency. Furthermore, the work completed here may be relevant to other diseases in which motor neuron signal transmission is a key contributor.

We thank for their assessment and constructive comments. Based on their input below we performed additional analyses focused on the impact of HO-2 dysregulation on the rhythmogenesis from the preBötC.

Author Response

Reviewer #1 (Public Review):

The manuscript discussed the combination use of pyrotinib, tamoxifen, and dalpiciclib against HER2+/HR+ breast cancer cells. Through a series of in vitro drug sensitivity studies and in vivo drug susceptibility studies, the authors revealed that pyrotinib combined with dalpiciclib exhibits better therapeutic efficacy than the combination use of pyrotinib with tamoxifen. Moreover, the authors found that CALML5 may serve as a biomarker in the treatment of HER2+/HR+ breast cancer.

The authors provide solid evidence for the following:

1) The combination use of pyrotinib with dalpiciclib exhibits better therapeutic efficacy than the combination use of pyrotinib with tamoxifen.

2) Nuclear ER distribution is increased upon anti-HER2 therapy and could be partially abrogated by the treatment of dalpiciclib.

3) CALML5 may serve as a putative risk biomarker in the treatment of HER2+/HR+ breast cancer.

The manuscript has significant strengths and several weaknesses. The strengths include the identification of the novel role of dalpiciclib in the treatment of HER2+/HR+ breast cancer. Moreover, the authors provide solid evidence that the combined use of dalpiciclib with pyrotinib significantly decreased the total and nuclear expression of ER. The main weakness of the manuscript is that the manuscript is difficult to read due to language inconsistency. In addition, some figure captions and figure legends should be carefully amended.

Thanks for your comments on our manuscript. We feel sincerely sorry for the inconsistency of the manuscript due to poor language. We have improved our manuscript as well as the figures according to your valuable suggestions.

Reviewer #2 (Public Review):

The authors performed preclinical studies to investigate the underlying mechanism of how the combination of pyrotinib, letrozole and dalpiciclib achieved satisfactory clinical outcomes in the MUKDEN 01 clinical trial (NCT04486911). Mechanistically, using anti-HER2 drugs such as pyrotinib and trastuzumab could degrade HER2 and facilitate the nuclear transportation of ER in HER2+HR+ breast cancer, which enhanced the function of ER signaling pathway. The introduction of dalpiciclib partially abrogated the nuclear transportation of ER and exerted its canonical function as cell cycle blockers, which led to the optimal cytotoxicity effect in treating HER2+HR+ breast cancer. Furthermore, using mRNA-seq analysis and in vivo drug susceptibility test, the authors succeeded in identifying CALML5 as a novel risk factor in the treatment of HER2+HR+ breast cancer.

Thanks for your comments and valuable suggestions, we’ve improved our manuscript according to your suggestions.

Reviewer #3 (Public Review):

In this research, the authors explore a novel mechanism of CDK4/6 inhibitor dalpiciclib in HER2+HR+ breast cancers, in which dalpiciclib could reverse the process of ER intra-nuclear transportation upon HER2 degradation. The conclusions are significant to gain insight into the biological behavior of TPBC and provided a conceptual basis for the ideal efficacy in the published clinical trial. The findings are supported by supplemented in vivo assay and transcriptomic analysis.

Thanks for your comments and valuable suggestions to us so that we could improve this manuscript.

Author Response

Reviewer #2 (Public Review):

The majority of genetic effects discovered in genome-wide association studies (GWAS) of common human diseases point to non-coding variants with putative gene regulatory effects. In principle, studying genetic effects on gene expression phenotypes, as mediators between genotype and disease, can help understand the underlying function of GWAS variants.

Lafferty et al., set to study the regulation of microRNA (miRNA) levels in mid-gestation human neocortical tissues as a potential contributor to brain-related phenotypes. To this end they performed miRNA expression profiling via small-RNA sequencing, followed by assaying expression quantitative trait loci (eQTLs) that locally regulate miRNA genes.

In addition to reporting some properties of miRNA-eQTLs, e.g., their tissue-specificity, the authors searched for potential overlap or "colocalization" between these eQTL loci and GWAS loci for several putatively brain-related phenotypes. They reported colocalization at the locus containing the SNP rs4981455 which is an eQTL for miR-4707-3p and is also associated with global cortical surface area (GSA) and educational attainment phenotypes in GWAS. They further showed that exogenously increased expression of miR-4707-3p in primary human neural progenitor cells (as a model to study neurogenesis) derives an increased rate of proliferation.

The reported results are interesting and important, particularly for the understanding of miRNA biology. That said, as I detail below, the claim that miR-4707-3p expression modulates brain size and thus cognitive ability, although potentially consistent with the data, is not unequivocally supported by the analyses. As such, considering the potential social impact of the misinterpretations of these results, I believe the authors should explicitly discuss caveats, alternative explanations consistent with the data, and broader implications:

We thank the reviewer for their positive evaluation of our work and detailed comments. We agree that misinterpretation of our results could have negative social impacts, and now have added caveats and alternative explanations to our discussion section.

1) The colocalization analysis used effectively tests whether miRNA-eQTL and GWAS variants are in linkage disequilibrium (LD), and does not formally test whether the miRNA-eQTL and GWAS signals are explained by the same genetic variant which is necessary for establishing causality. In this study, a formal test of colocalization is challenging given that the LD patterns in the eQTL data (from mixed ancestries) are dissimilar to the GWAS data (from European-descent samples). Furthermore, even if GWAS and miRNA-eQTL signals are explained by the same variant, this could be due to confounding (a confounder affecting both), or pleiotropy (genotype independently affecting both), and not necessarily that the miRNA-eQTL signal mediates the GWAS signal. These are also true for colocalization analyses of miRNA-eQTLs with mRNA-eQTLs or splicing-QTLs. One practical suggestion is whether authors can perform the colocalization analysis better, e.g., with methods such as SMR (https://yanglab.westlake.edu.cn/software/smr/#Overview).

As the reviewer mentioned, testing colocalized genetic signals using the eQTL dataset presented in this study remains challenging given the mixed-ancestry of the samples. We believe our primary test for colocalization, conditioning the miRNA-eQTL association using a secondary signal index variant, is sufficient evidence for a shared genetic signal (Nica et al., 2010). This is particularly true when looking for colocalizations between the miRNA-eQTLs and mRNA-e/sQTLs because both datasets used largely the same samples for expression quantification. However, the colocalization between the miRNA-eQTL for miR-4707-3p expression and the GWAS signal for educational attainment warrants greater scrutiny because the GWAS signal was discovered in European-descent samples.

To address this concern, we have conducted an additional colocalization test using the SMR and HEIDI methods as suggested by the reviewer (Zhu et al., 2016). We have updated the results section, “Colocalization of miR-4707-3p miRNA-eQTL with brain size and cognitive ability GWAS”:

"In addition to the HAUS4 mRNA-eQTL colocalization, the miRNA-eQTL for miR-4707-3p expression is also co-localized with a locus associated with educational attainment (Figure 5A)(2). Conditioning the miR-4707-3p associations with the educational attainment index SNP at this locus (rs1043209) shows a decrease in association significance, which is a hallmark of colocalized genetic signals (Figure 5-figure supplement 2A)(58,59). Additionally, the significance of the variants at this locus associated with miR-4707-3p expression are correlated to the significance for their association with educational attainment (Pearson correlation=0.898, p=5.1x10-7, Figure 5-figure supplement 2B). To further test this colocalization, we ran Summary-data-based Mendelian Randomization (SMR) at this locus which found a single causal variant to be associated with both miR-4707-3p expression and educational attainment (p=7.26x10-7)(60). Finally, the heterogeneity in dependent instruments test (HEIDI), as implemented in the SMR package to test for two causal variants linked by LD, failed to reject the null hypothesis that there is a single causal variant affecting both gene expression and educational attainment when using the mixed-ancestry samples in this study as the reference population (p=0.159). The HEIDI test yielded similar results when estimating LD with 1000 Genomes European samples (p=0.120). All this evidence points to a robust colocalization between variants associated with both miR-4707-3p expression and educational attainment despite the different populations from which each study discovered the genetic associations."

To strengthen the argument for colocalization, we added Figure 5-figure supplement 2.

Given the unique problem of colocalizing genetic signals from datasets with different LD patterns, we also attempted to colocalize the miRNA-eQTL for miR-4707-3p and educational attainment GWAS using eCAVIAR and coloc (Hormozdiari et al., 2016; Wang et al., 2020). Neither of these methods produced a significant colocalization between these two genetic signals at this locus. However, neither of these methods were designed or tested using mix-ancestry reference populations, and therefore we are still confident in declaring a shared genetic signal at this locus.

2) Although possible, there is no direct evidence that the GWAS signals at rs4981455 for educational attainment and GSA are driven by variation in miRNA levels in the studied tissue. As the authors noted, rs4981455 is also an eQTL for the gene HAUS4. Furthermore, rs4981455 is a significant e/sQTL across almost all adult tissues in GTEx, and so likely has regulatory activity across wide ranges of cell or tissue types. Therefore, pinpointing the causal contexts mediating the effect in GWAS is impossible with the current data.

We agree that fully understanding the causal relationship, or mechanism, between rs4981455 and educational attainment is impossible with the current data. However, we believe the miRNA-eQTL at rs4981455, discovered in developing brain tissue, provides clues as to the causal context of this locus on educational attainment. We have updated the language throughout the manuscript to temper the notion that expression differences in miR-4707-3p is causal for changes in educational attainment (discussed below), yet we maintain that the evidence provided is consistent with miR-4707-3p playing a role in brain development ultimately leading to changes in adult educational attainment. The updated hypothesized causal relationship is shown in Figure 6H and expanded discussion on the caveats of this study, addressed in the next section, also serve to mitigate this concern.

3) Orthogonal to the issues above, the genotype-to-phenotype pathway as hypothesized, i.e., genotype → miRNA levels → brain structure → educational attainment, is oversimplistic and rests on an implicit prior belief that genetic associations with educational attainment can be trivially mapped to fundamental brain features that determine cognitive ability. To illustrate the problem with this prior I refer to an old example by Christopher Jencks: in a society that prevents red-hair kids to go to school, genetic effects on hair color would be associated with educational attainment, despite having no intrinsic biological relationship with cognition. I give two scenarios consistent with the specific case of rs4981455 that are fundamentally different from what is implied in the paper: (i) The case of indirect genetic effects (see Kong et al., Science 2018). In this case, rs4981455 affects the nurturing behavior of an individual's parents, which in turn influences the individual's educational achievements and consequently brain structure features. (ii) The case of confounding. In this case, the genetic effects on brain structure are shared with another feature, such as facial shape (see Naqvi et al., Nature Genetics 2021). Variation in facial shape in a discriminatory educational environment can covary with educational attainment.

The causal pathway presented in the original version of this manuscript was indeed too simplistic and inferred a causal pathway between rs4981455 and educational attainment that was not fully backed by our data nor could be fully proved experimentally. The point we had hoped to make, and which is better represented by the updated version of Figure 6H, is that if there is a causal relationship between rs4981455 and educational attainment mediated by miR-4707-3p expression, we may be able to detect the influence of miR-4707-3p on a cellular phenotype that would explain the association of rs4981455 with cortical surface area, intracranial volume, and head size.

An updated discussion summarizes how we were not able to find evidence for a molecular mechanism consistent with the radial unit hypothesis, but that a biological link between the miRNA-eQTL and GWAS phenotypes may yet be uncovered:

"We did find one colocalization between a miRNA-eQTL for miR-4707-3p expression and GWAS signals for brain size phenotypes and educational attainment. This revealed a possible molecular mechanism by which genetic variation causing expression differences in this miRNA during fetal cortical development may influence adult brain size and cognition (Figure 6H). Experimental overexpression of miR-4707-3p in proliferating phNPCs showed an increase in both proliferative and neurogenic gene markers with an overall increase in proliferation rate. At two weeks in differentiating phNPCs, we observed an overall increase in the number of cells upon miR-4707-3p overexpression, but we did not detect a difference in the number of neurons at this time point. Based on the radial unit hypothesis (26,73), we expected to find an overall decrease in proliferation or increase in neurogenesis upon miR-4707-3p overexpression which would explain decreased cortical surface area. However, our in vitro observations with phNPCs do not point to a mechanism consistent with the radial unit hypothesis by which increased miR-4707-3p expression during cortical development leads to decreased brain size. This has also been seen in similar studies using stem cells to model brain size differences linked with genetic variation (74). Nevertheless, the transcriptomic differences associated with overexpression of miR-4707-3p in differentiating phNPCs suggest this miRNA may influence synaptogenesis or neuronal maturation, but these phenotypes may be better interrogated at later differentiation time points, by jointly expressing HAUS4 and mir-4707, or with assays to directly measure neuronal migration, maturation, or synaptic activity."

We believe the two cases addressed by the reviewer of indirect genetic effects and confounding which may actually explain the association between rs4981455 and educational attainment are less likely to be influencing the miRNA expression of miR-4707-3p measured in developing cortical tissue. This is combined with a discussion on the caveats of our findings and is addressed in the next section.

4) The paper lacks a discussion on caveats to protect against potential misinterpretation of findings, especially considering the troubled history of linking facial shape and head morphology to human behavior and intelligence. I refer to the last paragraph of Naqvi et al., Nature Genetics 2021, as an example of such discussion. This is particularly crucial given that the frequency of rs4981455 varies across human populations. For example, it is important to state that the GSA and education attainment GWAS findings are in individuals of European descent, and may not necessarily point to an effect in other ancestries or even in European-descent individuals that differ from the GWAS samples in various ways, e.g., socioeconomic status (see Mostafavi et al., eLife 2020). In other words, these findings pertain to variation within the studied samples. On this note, it is important to state the amount of variation in multiple phenotypes explained by rs4981455 (which is likely tiny), and that it by no means determines the phenotype.

We have added a paragraph to the discussion highlighting the caveats of our analysis and protecting from overinterpretation of our findings:

"Here we have proposed a biological mechanism linking genetic variation to inter-individual differences in educational attainment. Given the important societal implications education plays on health, mortality, and social stratification, a proposed causal mechanism between genes and education warrants greater scrutiny (75,76). Any given locus associated with educational attainment may be driven by a direct effect on brain development, structure, and function, an indirect genetic effect such as parental nurturing behavior, or confounding caused by discriminatory practices or societal biases (77,78). Given that expression was measured in prenatal cortical tissue, where confounding societal biases are less likely to drive genetic associations and that experimental overexpression of miR-4707 affected molecular and cellular processes in human neural progenitors, the evidence at this locus is consistent with a direct effect of genetic variation on brain development, structure, and function rather than being driven by confounding or indirect effects. However, there are some important caveats to this statement. First, our study only provides evidence for the direct effect on the brain at this one educational attainment locus. Our study does not provide evidence for the direct brain effects of any other locus identified in the educational attainment GWAS. Second, common variation at this locus explains a mere 0.00802% of the variation in educational attainment in a population, so this locus is clearly not predictive or the sole determinant of this phenotype. Third, the GWAS for educational attainment and brain structure were conducted in populations of European ancestry, and allele frequency differences at these loci cannot be used to predict differences in educational attainment or brain size across populations. Finally, though both experimental and association evidence suggests a causal link between this locus and educational attainment mediated through brain development, we are unable to directly test the influence of miR-4707-3p expression during fetal cortical development on adult brain structure and function phenotypes. Therefore, we cannot rule out the possibility that the causal mechanism between rs4981455 and adult cognition may be a result of genetic pleiotropy rather than mediation at this locus. Despite these caveats, identifying the mechanisms leading from genetic variation to inter-individual differences in educational attainment will likely be useful for understanding the basis of psychiatric disorders because educational attainment is genetically correlated with many psychiatric disorders and brain-related traits (2,79)."

We hope that this paragraph contextualizes our results sufficiently to emphasize the high bar that must be surpassed to propose a biological link between a miRNA-eQTL and a risk loci for brain related traits while maintaining that we can not completely rule out the possibility of genetic pleiotropy.

5) The main colocalization signal is tentatively shown for GSA. However, the authors casually refer to links with "brain size" or "head size" throughout the paper.

In addition to the locus showing a sub-genome wide significant association to global cortical surface area (GSA) presented in Figure 5, a GWAS for head size (Knol et al., 2020) and a GWAS for intracranial volume (Nawaz et al., 2022) (recently published since submitting the original manuscript) both show genomic associations at this locus for miR-4707-3p expression. The index variants for both traits colocalize with the miRNA-eQTL for miR-4707-3p and their effect directions match: alleles increasing expression of miR-4707-3p show association to decreased head size and decreased intracranial volume. For both of these studies, the summary data is not yet publicly available, preventing us from constructing plots at this locus (similar to those shown in Figure 5) or conducting additional colocalization analyses. To be more consistent throughout the paper, we have replaced many “head size” references with “brain size” when talking about this locus.

Author Response

Reviewer #2 (Public Review):

I am not a specialist in cryo-EM, so cannot comment on the technicalities of the structure reconstruction or methods used. I thus focus on the conclusions and observations that the authors provide in the manuscript and their relevance to functional photosynthesis.

The authors attempt to resolve the structure of PSII from Dunaliella and noticed that three types of PSII could be identified: two conformational states, and a stacked configuration. There is no doubt that these structures add to our current knowledge of PSII and that they exist in abundance upon solubilisation of the sample. My main issue however is the relevance to in vivo conditions, and the efforts to exclude the possibility that pigment loss and conformational states and stacking are a reflection of ex-vivo manipulations.

Our compact model contains 202 Chls molecules while the stretched conformation contains 206 Chls. All of the differences in Chl binding are attributed to CP29. We have compiled a table enumerating the different CP29 structures currently available from plants and green alga at similar resolution to our work (Supplementary table 2). In the larger plant complexes (C2S2M2) CP29 contains 14 chls, while CP29 in smaller C2S2 complexes contains 10-13 chls, so it appears the some chl loss from CP29 is associated with the release of LHCIIM. In the green alga structures, CP29 contains less chls in general and shows a similar trend. The currently published structure most relevant to our work contains 8 chls (6KAC), a somewhat lower amount then both the compact and stretched models (9 and 11 chls, respectively). The stretched orientation, which is the closest match to the known PSII core arrangement, therefore contains more chls than comparable models. While the in-vivo configuration is not known in the sense that it could contain more chls, the current structure is apparently the closest representation of it.

The presence of CP29 with lower chls content in the chlamy C2S2 (6KAC, which is in a stretched orientation) supports a conclusion that pigment loss from CP29 alone is not sufficient to trigger the stretch to compact transition although it is associated with it. In general, the precise orientation of CP29 is variable and seem to depend on the binding of additional LHCII, it is possible that some chl loss is accompanied with these changes in vivo.

I see a number of questions pertaining to this work. Starting from the two conformations of PSII, compact and stretched, the authors say that both are highly active based on oxygen measurements at a saturating light intensity. In the meantime, they report large variations in the chl content and positions of the chlorophyll molecules in these structures (also compared to other known PSIIs). This gives the impression that one can lose two chlorophylls, and freely modify the distance between others without losing efficiency, certainly a risky conclusion. Are the samples highly active also in light-limiting conditions? It is thought that even tiny movements and alterations in chl-chl distances alter their coupling and spectral properties, how come the variations in this report are so huge? In other words, the assay tests the charge separation activity of the PSII RC in the preps, but not the light-harvesting efficiency.

The chl content differences reported in this work amounts to 2%. In our opinion this represents quite a low variation in pigment content, which exist in virtually any experiment involving large complexes. We agree that measurements of activity in limiting light conditions are interesting, however this goes beyond the scope of the current work. Light harvesting efficiency in PSII is known to vary substantially as a result of additional mechanisms (NPQ in some of its forms), not associated with chl loss or gain. While the formation of quenching centers is attributed to small structural changes within specific pigment protein complexes, what we are showing in this work are structural changes between pigment protein complexes. These can affect transfer rates between the different complexes but are distinct from the structural changes thought to accompany the formation of quenching centers within specific pigment protein complexes.

How does one ascertain that the lost chlorophyll molecules in CP29 are not a preparation error? Does slightly increasing the detergent concentration impact the proportion of stretched:compact forms?

The effect of detergent concentration on the proportion of the different forms was not tested directly. However, we do not detect many differences in lipids or bound detergent molecules content between the two conformations, suggesting that for these “ligands” the differences are not substantial. We can only distinguish these two forms at the very last stages of data processing, at the present state of cryoEM cost and time availability, mapping the effect of detergent concentration on the different orientations is outside our reach.

On a similar note, how do the authors exclude that a certain interaction with this type of grid impacts the distribution of these complexes? Is it identical to a biologically separate preparation of algae? In case of discoveries of this type, it is of high importance to exclude as many possibilities of non-native conditions or influences on the structure.

It’s hard to completely exclude grid and sample preparation issues. However, we employed relatively standard grids and vitrification conditions. The observed complexes are embedded in vitrified ice and do not interact with the grid directly. The differences we observed are mainly in the orientations of the PSII cores, all the interactions between PSII subunits within each core are preserved and agree with previously published structures. Since the interactions within the core and between cores involve the same physical principles, we think its fairly conservative to think that the observed core orientations are not an artefact of sample preparation.

I would further like to encourage the authors to elaborate on the CP29 phosphorylation. What is the proportion of PSIIcomp that are phosphorylated? I assume it is not 100%, as in this case, the authors would propose that this is the effect that modulates between compact and stretched architectures.

Its difficult to estimate the proportion of observed phosphorylation/sulfinylation. To be detected in maps, most of the residues (above 50%) are probably modified. We attempted to estimate this by refining the atom occupancies of the Pi molecule on Ser84 and the oxygens attached to Cys218, both values suggested that about 70% of the complexes are modified. With regards to the possibility that these modifications can promote the formation of the compact state, we think that this is certainly a possibility, since these modifications were detected in this state and are in close proximity to each other. However, this can also result from the resolution differences of the maps and the structural implications of both modifications are hard to predict. At this point we prefer to note their existence without further interpretations.

In line 290, the authors highlight the structural heterogeneity within the two groups' PSII conformations. I would like to see how does the distribution look like for all the structures together: are the two (stretched and compact) specifically forming two heterogenous distributions? Or is it possible that the distribution between the two is quasi-continuous? In other words, if the structures are not perfectly defined, how do the authors decide that two- and not more or less subtypes exist?

We went back and refined the initial particle group (containing both compact and stretched orientations) using multibody with masks defining the two PSII monomers. This analysis showed the expected two peaks only in the first Principal components which accounted for ~38% of the variance in the dataset.

Multibody refinement carried out on the combined particle dataset shows one very large PC accounting for about 38% of the variance and the presence of two distinct peaks in the particle distribution of the first PC.

From this analysis it’s clear that there are two distinct classes in this particle set (as expected), as none of the other PC’s shows any signs of multiple peaks, this analysis suggests that two distinct models are the best representation of this eukaryotic PSII. Whether these are quasi continuous or distinct is more complex. There is continuity in this representation (particle distributions along PC), a different picture may appear if characters such as CP29 state are considered, but the size of CP29 and the remaining heterogeneity does not provide enough signal to carry out this classification at the moment.

Considering the stacked PSII, I also have a few concerns. Contrary to previous studies the authors do not assign a functional role to the stacking beyond the structural aspect. This could be better backed by a discussion about the closest chlorophyll a molecules across the stacked PSII, which given the rather large distance shown in fig. 4L seems to be too large for any EET across the stromal gap.

The closest chl-chl distance that we can measure in the stacked PSII dimer is ~54 Å, with most distances at the ~70 Å range, making EET between staked complexes very slow. We have added a statement clarifying this to our manuscript. In our opinion a structural role for the staked PSII dimer is more likely.

There is a report that suggests the presence of some density between the stacked PSII - could the authors comment on the differences between it and their work? Are the angles and positions conserved between these types of stacks? https://doi.org/10.1038/s41598-017-10700-8

We referred to Albanese et al, in our manuscript. We isolated the C2S2 complex from green alga, the analysis in Albanese et al was done on C2S2M1 complexes from pea and this can account for some of the differences. At any rate, our conclusion that we don’t find any evidence for protein linkers in the stacked complex is stated clearly. The angles described in Albanese et al are consistent with our analysis.

Line 387, the authors state that due to the transient nature of the interactions across the stromal gap, the stacks could be "under-detected" in cryo-ET data. This statement is in my opinion misformulated. For once, the transient interaction argument would apply the same (if not more due to changing conditions induced by the purification process) to the single particle analysis performed in this paper. Second, tomographic volumes detect hundreds of PSII in a suspended state. Any transient interaction that adds up to 25% of particle population in a steady state cell should be clearly visible, while the in situ data suggests not more than random cross-stromal-gap orientations. Of course, this can be a specificity of Chlamydomonas or a particular growth condition. The statement used by the authors could be indeed converted into: the PSII stacks are over-detected in vitro, and it is certainly a simpler explanation for their presence. It is also important to mention that PSII stacking alone is not the only reason for grana architecture - stacking with the antenna of larger complexes, absent in the authors' preparation could also contribute to grana maintenance; and auxiliary proteins such as CURT help with this issue as well. Here a recent demonstration of the importance of minor antenna should probably be also cited: https://doi.org/10.1101/2021.12.31.474624

We used the term “flexible” rather than “transient” to describe the interactions within the stacked PSII dimer. Our data (and tomographic data) do not contain any temporal component. When we used the term under-detected we refer to the fact that PSII is mainly detected by the luminal extrinsic subunits. The flexibility detected in our analysis may affect the concurrent visibly of these features in the PSII complexes making up an individual PSII stack. Specifically, Wietrzynski et al mainly analyze C2S2M2L2 complexes while our analysis only contained C2S2 complexes. It is likely that the different amount of bound LHCII affect PSII stacking as well. For example, Wietrzynski et al, show some overlap between LHCII complexes and little overlap between cores in the larger complexes they analyzed. We observe mainly core to core overlap with little LHCII overlap in the smaller C2S2, although we did not observe any states where LHC’s were not included in what appear to be the binding interface. We agree with the reviewer on the relevance Lhcb’s and CURT contributions to stacking but prefer to focus on what was directly demonstrated in our data. We clearly note that we are discussing in-vitro results.

Taking these last thoughts, I would like to finish by mentioning one more thing - almost philosophical. The authors are certainly at the forefront of the booming cryoEM revolution in biology which is profoundly changing the way we understand the living. There is absolutely zero doubt that this powerful technique is of the highest interest. But a growing number of structures of photosynthetic complexes remain puzzling, in particular with regard to their abundance in vivo (such as the PSII stacks) and functional relevance. How do we ascertain that these interactions are not due to in vitro preparation (isolation from cells, solubilisation)? Which ways can we use to try to exclude this (simple) hypothesis? I suggest that at least a small extent of biological replicas - experiments performed on separate batches, in different technical conditions, with slightly altered solubilization conditions, and so on - could shed light on the nature of these structures and their occurrence in vivo. Technical reps of the freezing+analysis pipeline could also be tried to see the variability. This would strongly reinforce this manuscript and its conclusions, and while not completely unequivocal (the stacked PSII, for example, could form upon each purification), a quantification of the effects would be of high interest.

We certainly share the reviewer hope of being able to conduct cause and effect cryoEM experiments covering a complete set of experimental parameters. This is still beyond reach in terms of time and cost. Within each cryoEM experiment, however, all the analysis is consistent and, more importantly, transparent with regards to image analysis, which is the most important factor in our opinion. Preparation artefacts are always a possibility but, in our opinion, cryoEM is not affected by them differentially compared to other techniques. As we mentioned above, the particles are being observed suspended in vitreous ice, this is not different, and one can say even better, then numerous low temperature spectroscopic observations on samples suspended in glass state or crystals obtained in the presence of high concentrations of various agents. One thing that validates structural studies are the chemical details (bond lengths and angles etc…) underlying every model which are consistence with known values to close tolerances.

Reviewer #3 (Public Review):

In this manuscript, Caspy et al. present a detailed structural analysis of eukaryotic photosystem II (PSII) isolated from the green alga Dunaliella salina. By combining single-particle cryo-EM with multibody refinement, the authors not only reveal a high-resolution (2.4Å) structure of the eukaryotic PSII, but also demonstrate alternate conformations and intrinsic flexibility of the overall complex. Stretched and compact conformations of the PSII dimer were readily identified within the single-particle dataset. From this structural analysis, the authors propose that excitation energy transfer properties may be modulated by changes in transfer distance between key chlorophyll molecules observed in different conformational states of the PSII dimer. Due to the high resolution of the maps obtained, the authors identify post-translational modifications and a sodium binding site based on the observed cryo-EM maps. Additionally, the authors analyze PSII complexes in stacked and unstacked configurations, and find that compact and stretched states also exist within the stacked PSII complexes. From their cryo-EM maps, the authors demonstrate that there is no direct protein-protein interaction between stacked PSII complexes, and rather propose a model wherein long-range electrostatic interactions mediated by divalent cations such as magnesium, can facilitate PSII stacking.

The conclusions and models presented in the manuscript are mostly well justified by the data. The cryo-EM maps are high quality and the models appear generally well refined. However, some aspects of data processing and analysis, as well as the resultant conclusions need to be clarified.

1) In general, it is not clear from the cryo-EM processing workflow (suppl. Fig 1) or the methods section when exactly symmetry was applied during 3D classification and refinement. In the case of C2S2 unstacked particles, when was symmetry first applied in the overall processing workflow? To identify the compact and stretched configurations of C2S2, did the 3D classification without alignment (and/or the refinement preceding this classification) have C2 symmetry applied? If so, have you considered the possibility that some particles may actually be asymmetric in some regions?

We modified figure S1 to clearly indicate the use of symmetry and particle expansion. In general, we refined most of the particle sets without symmetry (C1). At the final processing stage of the unstacked PSII sets, after we separated both conformations, we used C2 symmetry to expand the data, this was followed by multibody refinement. No symmetry or symmetry expansion was used for the stacked PSII particle sets.

2) Following multibody refinement in Relion individual maps and half-maps for each body will be generated. There is no mention in the methods of how these individual maps for each C2S2 "monomer" were combined to produce an overall map of the dimer following multibody refinement. There are several methods currently used to combine such maps, including taking the maximum or average of the two maps or using a model-based approach in phenix. The authors should be explicit about the method they used, any potential artifacts that may develop from this map combination process, and/or the interface between masks used in multibody refinement.

We used phenix.combined_focused_maps to combine the maps. This is now indicated in the method section.

3) In addition to the point raised above, following multibody refinement there will be an individual FSC curve and resolution for each body. However, in supplemental figure 2 and supplemental table 1, only a single FSC curve and resolution are reported. Are these FSC curves/resolutions only reported for the better of the two bodies? If not, how was a single resolution calculated for the overall map of combined bodies?

Both FSC curves were calculated and were highly similar, as expected following C2 expansion. This can also be evaluated from the local resolution maps which are highly similar between the two bodies. The reported resolutions are all taken from the displayed FSC curves generated through relion PostProcess.

4) One of the major conclusions from the 3D classification and multibody refinement is that conformational changes and inherent flexibility of the PSII dimers have the potential to change distances between cofactors in the complex, ultimately leading to altered excitation energy transfer. However, it is unclear whether or not the authors believe one conformation over another may more readily support the evolution of oxygen. It would be nice if the authors could elaborate slightly upon this topic in the discussion.

As discussed above the structural changes associated with the formation of quenching centers are not expected to be detected in the current work. The changes we observe can however affect the transfer to such centers and by doing so can play an important part in PSII biology. We do not detect any changes around the OEC and we don’t find any reason to think the two conformations are different with respect to their ETC.

5) Along the lines of point 4 above, on line 95 the authors claim that "the high specific activity of 816 umol O2/ (mg Chl * hr) suggest that" both the C2S2 compact and stretched conformation are highly active. However, it is not clear to me why this measure of specific activity would suggest that both PSII conformations should have "high" activity. Maybe a reference here would help guide readers to previous measures of specific activity?

Looking at specific activity from previously published structural studies on eukaryotic PSII we find that Sheng et al, 2019 reported on a specific activity of 272 mol O2/ (mg Chl * hr), this difference can stem partially from the presence of larger complexes in their preparation and is comparable to the activity that we measured in our As fraction (276 mol O2/ (mg Chl * hr), Figure 1-figure supplement 9). Reported specific activity values from plants (Pisum sativum) are also similar, Su et al, reported on a maximal value of 288 mol O2/ (mg Chl * hr), again, for larger complexes which can explain some of the difference. However, the specific activity measured for the C2S2 PSII isolated in the current study is 2.8 X higher than this value, more than the differences in chl content which ranges between 1.5 X to 2 X in favor of the larger complexes. If either one of the conformations is not as active, it would only mean that the other conformation will display even higher specific activity which seems less likely. In addition, we find no difference around the oxygen evolution center or in the peripheral luminal subunits in both the shape or map strength so both orientations show highly similar structures around these regions which determine the oxygen evolution activity.

6) It is claimed that "more than 2100 water molecules were detected in the C2S2 compressed model", and the water distribution is shown in Figure 3. Obtaining resolutions capable of visualizing waters with cryo-EM is still a significant challenge. Upon visual inspection of the map supplied, it appears that several of the waters that were built into the atomic model simply do not have supporting peaks in the coulomb potential map above the level of noise. While some of the modeled waters are certainly supported by the map, in my opinion, there are many waters that simply are not, or at best are questionable. What method or tool was originally used to build waters into the model, and how were these waters subsequently validated during structure refinement?

We followed standard methods for water placement and refinement in the preparation of the model, in addition to manually curating the water structure. However, in light of the reviewer comment we undertook additional rounds of refinement and inspection of the water molecules in the model. We removed a few hundred water molecules so that the total number of water molecules is now around 1700. All the water molecules in the present model should be well supported at maps values higher then 2.5 sigma and in our opinion the current water model should be regarded as conservative and underestimates the number of bound water molecules. This also led to some improvements in additional validation statistics of the model which are listed in the Table 1. The new model has been deposited in the PDB and the new PDB validation report is included in our resubmission.

7) The authors claim to identify several unique map densities during model building. One of these is a sodium ion close to the OEC, which is coordinated by D1-His337, several backbone carbonyls, and a water molecule. When looking closely at the cryo-EM map supplied, it appears that the coulomb potential map is quite weak for this sodium, and is only visible at quite low contour levels. In fact, the features for the coordinating water, and chloride ions located ~7-9A away are much stronger than the sodium. Do the authors have any explanation for why the cryo-EM map is significantly weaker for the sodium compared to the coordinating water or chloride ions in the same general vicinity? Similar to what they did for the other post-translational modifications, the authors should consider showing the actual cryo-EM map for the bound sodium in supplemental Figure 10 a,b.

Our main support for the placement of a Na+ ion in this location stems from the analysis of Wang et al. Our maps show the presence of a density which is discernible at 4 σ with an elongated shape suggesting the presence of multiple atoms/waters. Although in principle positive ions should have very strong densities in cryoEM maps due to their interactions with electrons, other factors such as occupancy, coordination and b-factor also play a role making the distinction between water and sodium complicated and case specific. The sodium peak is not observed in unsharpened maps (as do most of the water molecules which occupy conserved positions).

  We collected a few examples from comparable cases (cryo-EM maps of similar resolution ranges) where the presence of sodium ions is highly probable based on additional evidence. These maps densities highlight the factors we discussed above. In cases ‘a’ (dual oxidase 1 prepared in high sodium conditions) and ‘b’ (human voltage-gated sodium channel), Na+ is observed in a highly coordinated states and especially in ‘a’ shows the expected increase density values compared to water molecules. However, cases ‘d’ (human Na+/K+ P type Atpase) and ‘e’ (voltage-gated sodium channel) appear very similar to the proposed Na+ assignment in PSII. We conclude that map density alone is not enough to distinguish between Na+ and water molecules and rely on the additional experiments described by Wang et al. which show increase PSII activity in elevated Na+ levels in basic conditions.

8) The cryo-EM maps showing CP29-Ser84 phosphorylation and CP47-Cys218 sulfinylation are quite convincing. However, it is interesting that these modifications are only observed in the compact conformation, and not in the stretched conformation. Can the authors elaborate on whether or not they believe the compact and stretched conformations could be a result of these posttranslational modifications, or vice versa?

This is an interesting suggestion. In our opinion it is less likely that the modification themselves trigger the transition between compact and stretched states. It is not clear how these modifications will stabilize the compact vs the stretched states. It is equally likely that these modifications are somehow triggered by the structural change. We cannot be certain that these modifications are not present in the stretched orientation as well but remain unobserved due to resolution differences. The correlation between the states and post translation modifications should be verified before a discussion on their possible roles in the transitions.

9) Do the authors believe that PSII dimers in the solution can readily interconvert between compact and stretched conformations? Or is the relative ratio of these conformations fixed at the time of membrane solubilization with decyl-maltoside?

We think that its more probable that the transition between these states occur in the membrane phase. The main reason for this will be that pigment loss and structural transitions in CP29 are more likely to occur in the membrane rather than in aqueous/micelle environments.

10) The model proposed for divalent cation-mediated stacking of PSII dimers is compelling, and seems to be in agreement with previous investigations that observed a lack of stacked dimers in cryo-EM preparations lacking calcium/magnesium. However, my understanding from reading the methods section is that the observed lack of density between the stacked PSII dimers was inferred from maps obtained after multibody refinement. Based on the way the masks to define bodies were created for multibody refinement (Fig. 4A), the region between stacked dimers would be highly prone to map artifacts following multibody refinement. Have the authors looked closely at the interfacial region between stacked dimers following conventional 3D classification/refinement to ensure that there are indeed no features observed in the interfacial region even at low contour levels?

We’ve made several attempts to resolve differences in the space between the stacked PSII dimer. These include focused classification with masks containing selected volumes from this regions and masks that include only one of the stacked PSII dimers to avoid signal subtraction in this region. All of these did not reveal any discernible features in this region. In addition, any stable binding of a bridging protein across the stacked dimer will probably be at least partially visible as additional density over the unstacked PSII. We searched for such features and found none.

Author Response

Reviewer #2 (Public Review):

Weaknesses

The author's approach, as with traditional approaches to molecular identification of vector species, relies on expert entomologists capable of identifying mosquitoes in the field which is rare in most places. The authors do not provide citations for the taxonomic keys used for morphological identification, which in many places are outdated or unavailable for specific locations.

We have added references for taxonomic identification keys in lines 677–679.

The authors give no explanation as to why they chose rRNA-seq as their method of next-generation sequencing, which is most commonly used for transcriptomics, instead of traditional DNA-based metagenomics which is more commonly used to define community relationships as would be more appropriate for this study.

We have added a sentence in the Introduction (lines 65–66) to explain why RNA-seq is a frequent choice for surveillance and virus discovery in mosquitoes.

Author Response

Reviewer #1 (Public Review):

This paper shows that nuclear pore complex components are required for Kras/p53 driven liver tumors in zebrafish. The authors previously found that nonsense mutation in ahctf1 disrupted nuclear pore formation and caused cell death in highly proliferative cells in vivo. In the absence of this gene, there are multiple mitotic functions involving the nuclear pore that are defective, leading to p53 dependent cell death. Heterozygous fish are viable but have reduced kras/p53 liver tumor growth, and this is associated with multiple nuclear and mitotic defects that lead to cancer cell death/lack of growth. This therapeutic window suggests targetability of this pathway in cancer. I think the data are robust, rigorous, and clearly presented. I believe this in vivo work will encourage therapeutic targeting of NPCs in cancer.

We are pleased that this reviewer believes that our data are robust, rigorous, and clearly presented and that our in vivo work will encourage therapeutic targeting of NPCs in cancer.

Reviewer #2 (Public Review):

Overall this is a very interesting and important paper that demonstrates a novel synthetic interaction between nucleoporin inhibition and oncogene-driven hyperproliferation. This work is especially significant because of the paucity of effective treatments for hepatocellular carcinoma (HCC). The authors' demonstration that the Nup inhibitor Selinexor decreases larval liver size in KRAS-overexpressing zebrafish but does not cause toxicity in wild-type animals lays the groundwork for exploiting this class of drugs in HCC treatment. This paper represents an elegant demonstration of the utility of zebrafish models in cancer studies. The relevance of this work to human cancer is supported by the authors' studies using TCGA data, wherein they demonstrate that decreased NUP expression is associated with increased survival in HCC.

Other major strengths of the paper include beautiful pictures demonstrating that ahctf1+/- decreases the density and volume of nuclear pores in TO(kras) larvae and increases the rate of multipolar spindle formation, misaligned chromosomes, and anaphase bridges. The experiments are very well-controlled, including detailed analysis of the effects of ahctf1 heterozygosity and Selinexor on wild-type animals. The inclusion of distinct methods for disruption nucleoporins (ranbp2 heterozygosity and drug treatment) bolsters the authors' conclusion that this represents a viable drug target in HCC.

My major concerns are as follows:

1) The authors state that "the beneficial effect of ahctf1 heterozygosity to reduce tumour burden persists in the absence of functional Tp53, due to compensatory increases in the levels of tp63 and tp73". However, tp63 and tp73 appear similarly upregulated in ahctf1 heterozygotes regardless of tp53 status. The authors do not provide enough evidence that tp63 and tp73 are compensating for tp53 loss. An alternative possibility based on the data presented is that the effects of ahctf1+/- are independent of tp53 family members, and the effects on apoptosis go through a different pathway.

We agree with this reviewer that we did not provide enough evidence that tp63 and tp73 are compensating for tp53 loss. Accordingly, we have addressed this issue comprehensively.

2) The authors state in multiple locations that nucleoporin inhibition decreases tumor burden. In my opinion, this is not strictly correct. The TO(kras) model clearly results in HCC in adults, but it's a little unclear whether the larval liver overgrowth is truly HCC or not based on the original paper by Nguyen et al. (2012 Dis Model Mech).

We agree with these comments and accordingly, we performed several new experiments in adult fish.

Reviewer #3 (Public Review):

The nuclear transport machinery is aberrantly regulated in many cancers in a context-dependent fashion, and mounting evidence with cultured cell and animal models indicates that reducing the activity or expression of certain nuclear transport proteins can selectively kill cancer cells while sparing nontransformed cells. Here the authors further explore this concept using a zebrafish model for hepatocellular carcinoma (HCC) induced by liver-specific transgenic expression of oncogenic krasG12V. The transgene causes greatly increased liver size by day 7 in larvae, associated with a gene expression profile that resembles early-stage human HCC. This study focuses on Ahctf1, a nuclear pore complex (NPC) protein known to be essential for postmitotic NPC assembly. Using the krasG12V background, the authors analyze animals that are heterozygous for a recessive mutation in the ahctf1 gene that leads to ~50% reduction in ahctf1 mRNA (and likely the encoded protein). The authors show that the ~4-fold increase in liver volume of krasG12V animals is reduced by ~1/3 in the ahctf1 heterozygous mutants. This is associated with increased apoptosis, decreased DNA replication, up-regulation of pro-apoptotic and cdk-inhibitor genes, and down-regulation of anti-apoptotic gene. These effects found to be substantially Tp53-dependent. Consistent with previous Ahctf1 depletion studies, hepatocytes of ahctf1 heterozygotes show decreased NPC density at the nuclear surface, elevated levels of aberrant mitoses and increased DNA damage/double stranded breaks. Finally, the authors show that combining the achtf1 heterozygous mutant with a heterozygous mutation in another NPC protein- RanBP2- or treating animals with a chemical inhibitor of exportin-1 (Selinexor) can further reduce liver volume. Overall they suggest that combinatorial targeting of the nuclear transport machinery can provide a therapeutic approach for targeting HCC.

This is an interesting study that bolsters the notion that reduction in the levels of discrete nucleoporins (and/or inhibiting specific nuclear transport pathways) can result in cancer cell-selective killing. Moreover, the work extends previous studies involving cultured cell and mouse xenografts to a new cancer model (HCC) and nucleoporin (Ahctf1). Whereas the authors describe multiple aberrant cellular phenotypes associated with the dosage reduction in ahctf1, the exact causes for reduction in liver size in the krasG12V model remain unclear. Although it would be desirable to parse effects of Ahctf1 related to NPC number, aberrant mitoses, licensing of DNA replication and chromatin regulation, this is a tall order at present, given the limited understanding of Ahctf1. However, useful insight on these and related questions could be gained with further analysis of the system as outlined below.

We are pleased this reviewer thinks this is an interesting study that bolsters the notion that reduction in the levels of discrete nucleoporins (and/or inhibiting specific nuclear transport pathways) can result in cancer cell-selective killing. This reviewer also suggests that useful insight on these and related questions could be gained with further analysis of the system as outlined below:

1) In the krasG12V model, it would be helpful to distinguish the contribution of increased cell death vs decreased cell proliferation to the change in liver size seen with heterozygous ahctf1. Is this predominantly due to decreased proliferation?

We think this question is difficult to address, because the relative contributions of the two processes may vary with time. Our data show definitively that by 7 dpf, the impact of ahctf1 heterozygous mutation has disrupted multiple cellular processes, leading to a 40% increase in the number of hepatocytes expressing Annexin 5 (dying cells), and a 40% decrease in the number of hepatocytes incorporating EdU over a 2 h incubation (fewer cells in S-phase). Both responses are likely to contribute to the reduction in liver volume observed in response to ahctf1 heterozygosity. It is worth stating that in our experiments, we captured snapshots of apoptosis and DNA replication in the livers of larvae at 7 days post-fertilisation after 5d of dox treatment/KrasG12V expression. To answer the Reviewer’s question properly, we would need to monitor the behaviour of individual cells over time. If such experiments were technically possible, we think that some cells that undergo growth arrest in response to dox treatment might ultimately succumb to apoptosis (unless dox treatment is withdrawn) while other cells might enter into a state of prolonged senescence. However, given the technical challenges, we did not attempt to test this in the current manuscript.

2) It would be good to know whether the heterozygous ahctf1 state blunts the overall level of Ras activity in krasG12V animals.

We have addressed this interesting question thoroughly in new Fig. 1g, h. To do this, we used a commercial RAS-RBD pulldown kit followed by western blot analysis to determine the levels of activated GTP-bound Kras protein. Our results demonstrate that the levels of GTP-bound Kras protein, expressed as a proportion of total Kras protein, do not change in response to ahctf1 heterozygosity. We conclude from these data that the potentially therapeutic value of reduced ahctf1 expression in a cancer setting is not caused by inhibiting Kras activity.

3) Notwithstanding the analysis of Tp53 target genes presented in this study, it would be helpful to see detailed transcriptional profiling of hepatocytes in the krasG12V model with the heterozygous ahctf1 mutation, and to assess the effects of Selinexor. GSEA type analysis offers a way to start untangling the effects of these pathways. Moreover this analysis could provide insight on the relevance of this model to human HCC.

We used RNAseq to address the relevance of our larval model to human HCC. Specifically, we performed differential gene expression analysis to identify up- and downregulated genes in cohorts of ahctf1+/+ (WT) larvae versus dox-treated ahctf1+/+(WT);krasG12V larvae. We used gene set enrichment analysis to compare these differentially regulated transcripts with the gene expression signature of 369 patient samples in the Liver hepatocellular carcinoma (LIHC) dataset versus healthy liver samples in the TCGA. These analyses revealed a significant association between the patterns of gene expression in our larval model of zebrafish HCC and those of human HCC (Fig. 1-figure supplement 1c, d).

The genetic experiments we report in Figures 4, 5, 6 show that WT Tp53 is required for the reductions in liver enlargement (Fig. 4), apoptosis (Fig. 5) and DNA replication (Fig. 6) that occurs in response to ahctf1 heterozygosity in dox-treated krasG12V larvae. We also used RT-qPCR to show that a Tp53-mediated transcriptional program was activated in these ahctf1 heterozygous livers (Fig. 5). Similarly, in adult livers, ahctf1 heterozygosity triggered the upregulation of Tp53 target genes, including pro-apoptotic genes (pmaip1, bbc3, bim and bax) and cell cycle arrest genes (cdkn1a and ccng1) (new Fig. 6-figure supplement 1). These results show that to obtain the full potential of ahctf1 heterozygosity in reducing growth and survival of KrasG12V-expressing hyperplastic hepatocytes requires activation of WT Tp53. This is an important conclusion from our paper that is likely to be relevant in a clinical setting, for instance in patient selection, if ELYS inhibitors are developed for the treatment of HCC in which the KRAS/MAPK pathway is activated.

Also, one reviewer mentions performing genome-wide transcriptional profiling of hepatocytes in the krasG12V model in response to ahctf1 heterozygosity and the presence and absence of Selinexor treatment. While these are potentially interesting experiments, they are substantial in nature and not crucial for the main messages of our paper. Therefore, we respectively contend that they are beyond the scope of the current manuscript.

4) Functions of Achtf1 in regard to chromatin regulation could be compromised in this model. Scholz et al (Nat Gen 2019) report that Ahctf1 is involved in increasing Myc expression via gene gating mechanism. It would be good to know what the effects are in this system.

The Scholz, 2019 and Gondor, 2022 papers from the same group, are very interesting in that they demonstrate a novel role for the ELYS protein in addition to the ones we pursued in our paper. The authors showed that in HCT116 cells, a human colorectal cancer cell line in which proliferation is driven by aberrant WNT/CTNNB1 signalling, the longevity of nascent MYC mRNA was increased by accelerating its movement from the nucleus to the cytoplasm, thereby preventing its degradation by nuclear surveillance mechanisms. The authors showed that siRNA knockdown of AHCTF1 in HCT-116 cells reduced the rate of nuclear export of MYC transcripts without changing the transcriptional rate of the MYC gene. They proposed a mechanism that depended on the formation of a complex chromatin architecture comprising transcriptionally active MYC and CCAT1 alleles plus proteins including β-Catenin, CTCF and ELYS. Together these interacting components guided nascent MYC mRNA molecules to nuclear pores, enhanced their export to the cytoplasm to be translated, resulting in activation of a MYC transcriptional program that induced expression of pro-proliferation genes.

In theory, this role of ELYS in protecting MYC from nuclear degradation might extrapolate to other cancer settings where MYC expression is elevated. While interplay between MYC and mutant KRAS to enhance cancer growth has been previously reported, to date, most emphasis on this interaction has focused on the role of mutant KRAS in increasing the stability of the MYC protein, for example via RAS effector protein kinases (ERK1/2 and ERK5) that stabilise MYC by phosphorylation at S62 (Farrell and Sears, 2014: https://doi.org/10.1101/cshperspect.a014365) (Vaseva and Blake 2018: DOI:https://doi.org/10.1016/j.ccell.2018.10.001). While we appreciate the novelty of the recent papers, the current findings are limited to -Catenin activated HCT-116 cells and may not be relevant to our zebrafish model of mutant Kras-driven HCC. Accordingly, we have not allocated a high priority to following this up in our current manuscript.

6) The synthetic lethality argument pressed in this manuscript seems exaggerated. Standard anti-cancer treatments typically target several cellular pathways, and nucleoporins directly affect a multiplicity of pathways besides nuclear transport.

While we do not disagree that standard anti-cancer treatments may target several cellular pathways, we believe our data are consistent with the accepted definition of a synthetic lethal interaction whereby single mutations in two separate genes (kras and ahctf1) cooperate to cause cell death, whereas cells harbouring just one of these mutations are spared.

Author Response

Reviewer #1 (Public Review):

1) Context and definitions for stochasticity and heritability: The authors provide well-referenced introductions and explanations throughout the manuscript. However, key understanding of concepts for their central hypothesis on transient heritability are not shared until well into the results sections (Lines 215-227), leaving the introduction somewhat unclear on the authors thinking and motivation. The manuscript would benefit by including clear definitions of "stochastic", "transiently heritable", and "heritable" and their relationships to "intrinsic" and "deterministic" in the introduction.

Regarding the first point, we agree it is important to include clear definitions timely. Therefore, we added much more detail to the introduction (see tracked changes), and added the following definitions and additional explanations:

Multilayered stochasticity: “stochasticity originating from different levels over the course of an infection.“

“Importantly, although the terms stochasticity and determinism seem highly dichotomous, deterministic features (e.g., epigenetic regulation) are often, if not always, stochastically regulated (Zernicka-Goetz and Huang, 2010). However, in cellular decision-making, the major difference between a stochastic process and a deterministic process boils down to the effects of (varying) inputs on dictating (varying) outputs. In fact, a stochastic process in characterized by the exact same stimulus leading to varying response outcomes, often as a result of varying host-intrinsic factors (Symmons and Raj, 2016). In contrast, a deterministic process is characterized by an outcome (e.g., IFN-I production) that is fixed, or at least to a large degree, while the input can be variable. How cells are epigenetically predispositioned, in turn, can again be a stochastic process, similar to the fundamentals of developmental biology in which cells are randomly pushed towards deterministic outcomes (Zernicka-Goetz and Huang, 2010).”

“Transient heritability refers to heritable epigenetic profiles [e.g., profiles encoding cellular fates for the production IFN-Is] that only transfer over a couple of generations, as observed across cell types and systems including cancer drug resistance (Shaffer et al., 2020), cancer fitness (Fennell et al., 2022; Oren et al., 2021), NK cell memory (Rückert et al., 2022), HIV reactivation in T cells (Lu et al., 2021), epithelial immunity (Clark et al., 2021), and trained immunity (Katzmarski et al., 2021).”

“Besides a growing body of evidence on the role of transient heritable fates dictating cellular behaviors, the effects of population density, often referred to as quorum sensing, are getting more established for immune (signaling) systems (Antonioli et al., 2019; Polonsky et al., 2018; Van Eyndhoven and Tel, 2022). On top of the intrinsic features characterized by stochasticity and determinism, individual immune cells can communicate in various ways to elicit appropriate systemic immune responses. Typically, cytokine-mediated communication is categorized into two types: autocrine and paracrine signaling. Autocrine signaling is defined by cells secreting signaling molecules while simultaneously expressing the cognate receptor. Paracrine signaling is defined by cells either secreting signaling molecules without expressing the cognate receptor, or cells expressing the receptor without secreting the molecule. In essence, quorum sensing can be considered a phenomenon in which autocrine cells determine their population density based on cells engaging in neighbor communication, but without self-communication (Doğaner et al., 2016; Van Eyndhoven and Tel, 2022). Especially in the presence of other competitive decision makers [i.e., cytokine consumers and producers], it is critical for individual cells to assess cellular density, and act accordingly (Oyler-Yaniv et al., 2017).”

2) Generalizability of findings to other cell types, systems, and triggers: The cell line and Poly(I:C) delivery method used by the authors lacks sufficient characterization to extend the conclusions derived from its use. Notably, the NIH3T3-IRF7-CFP cell line expresses IRF7 constitutively and thus may only be a good model for cells with similar expression levels; many primary cells only express IRF7 at low levels or not at all until stimulated (PMID: 2140621). The conclusions would be greatly strengthened by demonstrating similar first responder dynamics/heritability in other cell types. The experiments measuring the efficiency of Poly(I:C) delivery by transfection lack sufficient resolution to determine if the Poly(I:C) is intracellular or membrane bound. IFN-I response kinetics, and potentially quality, would likely be distinct between cytosolic and endosomal sensing and may impact the likelihood of becoming a first responder.

Regarding the generalizability of findings to other cell types, systems, and triggers, we thank reviewer 1 for binging up this crucial point. About the IRF7 expression, IRF7 is expressed at a low amount in most cells and is strongly induced by type I IFN-mediated signaling (Marie et al., 1998; Sato et al., 1998b; Honda et al., 2006). How we used the word “constitutively” refers to the IRF7 molecules always being fluorescent, not that IRF7 is always highly expressed in these cells. Therefore, NIH3T3 is similar to all other cells, except for plasmacytoid dendritic cells, which are known for their high background levels of IRF7. We changed the revised manuscript accordingly:

“Accordingly, we used a NIH3T3:IRF7-CFP reporter cell line, expressing low, physiological background levels of IRF7-CFP fusion proteins, to monitor signaling dynamics during early phase IFN-I response dynamics (Figure 1b).”

Regarding the comparison with other cell types, we emphasized the similar responders numbers observed in plasmacytoid dendritic cells (an argument that the intrinsic factor of IRF7 background differences is not determining responders). We changed the revised manuscript accordingly:

“In short, IFN-I responses are elicited by fractions of so-called first responding cells, also referred to as ‘precocious cells’ or ‘early responding cells’, which start the initial IFN-I production upon viral detection, both validated in vitro, in vivo, and across cell types (Bauer et al., 2016; Hjorton et al., 2020; Patil et al., 2015; Shalek et al., 2014; Van Eyndhoven et al., 2021a; Wimmers et al., 2018).”

“This percentage is in line with what has been found across literature, species [i.e., human and mice] and cell types [i.e., fibroblasts, monocyte derived dendritic cells, plasmacytoid dendritic cells], which ranges from 0.8 to 10% of early responders, emphasizing the elegant yet robust feature of only a fraction of first responding cells driving the population-wide IFN-I system (Bauer et al., 2016; Drayman et al., 2019; Patil et al., 2015; Shalek et al., 2014; Van Eyndhoven et al., 2021a; Wimmers et al., 2018).”

Regarding the hypothesis brought up by the reviewer on the role of cytosolic versus endosomal sensing impacting IFN-I response kinetics, and potentially quality, we hypothesize otherwise. Shalek and colleagues tested LPS (TLR4 ligand), PIC (TLR3 ligand, endosomal), and PAM (TLR2 ligand), all eliciting similar early responding cells, which they called precocious cells. This argues that the phenomenon of first responders is independent of the type of stimulation. Besides, for plasmacytoid dendritic cells, both R848 (TLR7/8 ligand), and CpG-C (TLR9 ligand) elicit very similar early IFN-I responses. In contrast, R848 and CpG-C elicit very different late IFN-I response dynamics, reflected by the fraction and activation dynamic of second responders (yet unpublished). We clarified accordingly:

“Moreover, various stimuli (live and synthetic) targeted membrane, cytosolic, and endosomal receptors, arguing that the mode of activation is not driving the discrepancies in responder fates.”

3) Epigenetic regulation of transient heritability: To test the contribution of epigenetic regulation on first responder fate, the authors treat their cells with DNMTi. While treatment with this drug does increase the proportion of first responder cells, the authors don't provide evidence that the mechanism of action is mediated by inhibiting DNA methylation. This is further confounded by the reduced responder frequencies in DNMTi treated cells transduced with Poly(I:C) (Fig 4g). The authors offer an explanation for this observation, but their reported data (Fig 4h) doesn't measure whether DNMTi, leads to latent retrovirus activation, broader demethylation, or a combination of the two.

We are well aware that the hypothesis on retrovirus activation are inconclusive. Unfortunately, we currently do not have the ability to utilize the tools to properly assess this hypothesis. Instead, we can only speculate. However, we were able to assess the effects of a different epigenetic drug [i.e., HDACi], as suggested later by the reviewer. Therefore, to strengthen our data interpretation, we added the following additional information and experimental data to the revised manuscript:

“Also the treatment with varying dosages and durations of Trichostatin A, an histone deacetylase inhibitor (HDACi), increased the number of responding cells (Supplementary Figure 5).”

“The rather long timescales of switching from responders to non-responders, and the other way around, imply epigenetic mechanisms at play, and indeed, prior work has indicated an important role for epigenetics dictating IFN-I response dynamics (reviewed in (Barrat et al., 2019)).”

“Both methylation and histone acetylation have been suggested in dictating transient heritable cellular fates (Clark et al., 2021; Lu et al., 2021; Shaffer et al., 2020).”

4) Temporal experimental data to validate and extend transient heritability and quorum sensing: Developing a model for cellular-decision making during early IFN-I responses, the authors formalize and test the hypothesis of transient heritability. While the data largely fit the model proposed (Fig 6D-F), the reported data points lack sufficient temporal resolution to validate the model during the earlier and more variable generations. Given that by generation 9 variability in first responder frequency has almost stabilized, there is only one data point (generation 6) to evaluate the fit of the ODE described. More densely sampled data points below generation 10 are necessary to validate the model. Moreover, a discussion of Kon calculation/observation, meaning, and validation is missing. To partially test their claim that Kon is a function of density (i.e., quorum sensing), the authors plate cells at different densities and measure the responder frequency at generation 6. This analysis lacks contextualization of other autocrine and paracrine signals potentially impacting IFN-I response. Moreover, these signals will be diverse in different cell types and could impact Kon and/or the overall model.

We agree that our first model validation was suboptimal, indeed because of lacking sufficient temporal resolution. Therefore, we performed additional experiments on clones of generation 1, 2, 3, 4, 5, of which the results turned out to be remarkably robust. We changed the revised manuscript accordingly:

“Surprisingly, the data obtained from clones of generation one through nine resulted in a mean higher than 2.134% (Figure 6d; Supplementary Figure 9), and a fluctuating CV (Figure 6e). From generation 13 onwards, both the mean and the CV start to meet the data obtained from the regular cultures again, which are similar to the theoretical outcomes of a stochastic process. Accordingly, we modeled first responders as a binary switch, where individual cells are either responding (ON) or nonresponding (OFF), similar to the transient heritable fates characterized and modeled before (Shaffer et al., 2020). Details on the ODE model are provided in the Materials and Methods section. We could fit the transient heritability model to the data when starting from 100% responders at generation zero [i.e., a single cell isolated from the regular culture]. Cells switch OFF after 5 generation on average, with a constant kon rate throughout. Interestingly, in generation zero we observed (nearly) only IFN-I responders, which we believe might be caused by single cells being deprived from any paracrine cues, which could include inhibitory factors that normally limited responsiveness. However, single IFN-I-producing cells [i.e., plasmacytoid dendritic cells and monocyte derived dendritic cells] encapsulated in picoliter droplets or captured in small microfluidic chambers did not display this behavior (Shalek et al., 2014; Wimmers et al., 2018). Instead, one could argue that single cells establish a different microenvironment, compared to a situation in which cells are close to neighboring cells, which elicits behavioral changes accordingly. The dimensions of microfluidic droplets and chambers are in the same range of cell-to-cell contacts in vitro, while single cells seeded for cloning are surrounded by rather massive areas and volumes without other cells present. Therefore, we hypothesize that these single cells lack biochemical, and perhaps biomechanical cues provided by dense cell populations, which result in behavioral changes in these cells, in our case, making them more responsive. Similarly, in quorum sensing, cells secrete soluble signaling molecules (called autoinducers), which enables cells to get a sense of their cell density (Postat and Bousso, 2019; Waters and Bassler, 2005). Without signaling of these molecules, cells perceive being isolated from the rest. In microfluidic droplets and chambers, these molecules accumulate, given the relatively small volumes.”

Regarding the contextualization of autocrine and paracrine signaling impacting IFN-I response dynamics in these studies, we added the following additional information:

“On top of the intrinsic features characterized by stochasticity and determinism, individual immune cells can communicate in various ways to elicit appropriate systemic immune responses. Typically, cytokine-mediated communication is categorized into two types: autocrine and paracrine signaling. Autocrine signaling is defined by cells secreting signaling molecules while simultaneously expressing the cognate receptor. Paracrine signaling is defined by cells either secreting signaling molecules without expressing the cognate receptor, or cells expressing the receptor without secreting the molecule. In essence, quorum sensing can be considered a phenomenon in which autocrine cells determine their population density based on cells engaging in neighbor communication, but without self-communication (Doğaner et al., 2016; Van Eyndhoven and Tel, 2022).”

Regarding the point that signals will be diverse in different cell types and could impact Kon and/or the overall model, yes, but we expect this to be only minor. Besides, the model can be easily adjusted to the different parameters per cell type (see Saint-Antoine et al., 2022).

Reviewer #3 (Public Review):

1) For the small fraction of cells that respond in the absence of Poly(I:C), are these cells just showing IRF7 translocation or are they fully responding with IFNB production? Has this been observed in other experimental systems or contexts? Do you also observe secondary responders in the unstimulated samples (as shown in the stimulated in Fig. 2G-I)?

Regarding the first point on the unstimulated translocated cells, excellent point. Although we have not experimentally validated it, we hypothesize that cells are able to produce constitutive levels of IFN-Is, as thoroughly described in literature, so we assume that these translocated cells produce IFN-Is. We provided additional speculation in the revised manuscript:

“Besides, the background numbers of translocated cells possibly reflect the intrinsic feature of the IFN-I system to ensure basal IFN-I expression and IFNAR signaling to equip immune cells to rapidly mobilize effective antiviral immune responses, and homeostatic balance through tonic signaling (Gough et al., 2012; Ivashkiv and Donlin, 2014).”

2) Do the second responders only arise through direct IFN-I production by first responders? Is it possible that this response has any relationship with the initial transfection with Poly(I:C)?

From the droplet-based experiments with plasmacytoid dendritic cells performed before (Wimmers et al., 2018; Van Eyndhoven et al., 2021), we could conclude that the second responders indeed required the activation and subsequent early IFN-I production of first responders. Whereas droplet-based microfluidics is a very stable, and controlled method, producing thousands of homogeneous droplets, we concluded that the difference between first and second responders is not elicited upon variations in activation (e.g., transfection discrepancies).

Author Response

Reviewer #1 (Public Review):

The authors use their expertise in live-cell imaging and mathematical modeling to further explore the relationship between chromatin structure, gene positioning and transcriptional coregulation. One of the strengths of the manuscript arises from the authors analysis of two publicly available datasets encompassing chromatin tracing and transcriptional activity. Using spatial analysis and modeling, the authors have impressively extended the findings of Su et. al, Cell 2020, who generated the analyzed dataset. A number of important concepts were explored including 1.) do genes re-position upon activation and 2.) can spatial proximity be correlated with transcriptional co-regulation. In general the authors conclusions are supported by their findings and should provide a blueprint for analysis of additional related big imaging datasets in the future.

However there are a number of weaknesses including lack of statistical analysis or incomplete description (e.g. bootstrapping parameters, statistical methods, number of genes/cells/measurements, etc.) on some figures that make it difficult to interpret the significance of the trends. In addition, the modeling using live-cell studies is generalized based on a behavior (e.g. diffusion) of a single gene. The manuscript is densely written in a way that may be inaccessible for non-specialists. A final schematic model that summarizes biological findings would help alleviate this weakness.

We are glad that the reviewer considers the observed phenomenon important and that our overall findings are consistent with our results. We implemented changes in response to each of the above requests:

1) we added additional explanation of test statistics;

2) we analyzed diffusion of additional genes;

3) we tried to simplify the text;

4) we added a final schematic.

Reviewer #2 (Public Review):

In their manuscript, Bohrer and Larson reanalyse previously published imaging datasets in order to tackle a long-standing question in modern genome biology: does the physical proximity of transcribed genes correlate with their co-expression?

The authors start off by reanalysing fixed-cell data, in which they find that active genes (i.e., any gene with RNA FISH signal) often repositions towards the centroid of the imaged chromatin environment one transcriptionally active. The analysis is straightforward, but the notion of "closer to the centroid" remains a bit vague to me, and is not well defined as regards its functional significance. There is no doubt of the clear trend in the analysed data -- but the interpretation could be strengthened.

We tried to clarify this part of the text and also added a schematic illustration to the discussion to help clarify this important point (Fig. 5).

Then, using the same dataset, the question on physical gene proximity is addressed. This is not only an important and timely question, but also one which the authors address very nicely. They deduce that when a pair of loci are brought within sufficiently low physical 3D proximity (unrelated to their genomic distance) they are more likely than expected to be co-expressed. In cis, this distance can be defined to approx. <2.5 Mb of genomic separation. They also looked in trans, via a complex transfer of knowledge from live-cell imaging to the fixed-cell dataset, to show that genes brought within approx. 400 nm from one another display quite a high coexpression correlation. Despite the parsimonious nature of the model and assumptions that the authors use for this (testing more complex parameters might prove beneficial here), their postulations can quite adequately explain observations published by others that were previously left largely without interpretation.

In my opinion, the main strength of this manuscript lies with the initial analysis of the fixed-cell data and the clear trends therein. The latter part, which nicely identifies caveats in available data and analyses and which makes a solid effort to combine live-cell with fixed-cell data, leaves more scenarios to be tested. Nevertheless, based on the outcome of this analysis (mostly found in Fig. 4), the value of ~400 nm as a physical proximity cutoff for co-expression is reasonable (based on previous knowledge) and does provide a solid first step in the direction of deciphering the rules that allow coordinated gene expression in mammalian cells.

We agree that the modelling section is more of a first step and that future work will need to be done to investigate further. In the revision, we make this point explicit within the main text (See below).

Overall, this is a conceptual advance of merit that can re-shape ways of approaching the stillopen issue of gene co-bursting in light of novel (mostly imaging) technologies.

We appreciate the comment.

Author Response

Reviewer #2 (Public Review):

This paper by Angueyra, et al., adds to the field’s current understanding of photoreceptor specification and factors regulating opsin expression in vertebrates. Current models of specification of vertebrate photoreceptors are largely based on studies of mammals. However, a great number of animals including teleosts express a wider array of photoreceptor subtypes. Zebrafish for example have 4 distinct cone subtypes and rods. The approach is sound and the data are quite convincing. The only minor weaknesses are that the statistical analyses need to be revisited and the discussion should be a bit more focused.

To identify differentially expressed transcription factors, the authors performed bulk RNA-seq of pooled, hand-sorted photoreceptors. The selection criterion was tightly controlled to limit unhealthy cells and cellular debris from other photoreceptors subtypes. The pooling of cells provided a considerable depth of sequencing, orders of magnitude better than scSeq. The authors identified known transcription factors and several that appear to be novel or their role has not been determined. The data are made available on the PIs website as is a program to access and compare the gene expression data.

The authors then used CRISPR/Cas9 gene targeting of two known and several novel factors identified in their analysis for effects on cell fate decisions and opsin expression. Phenotyping performed on the injected larvae is possible, and the target genes were applied and sequenced to demonstrate the efficiency of the gene targeting. Targeting of 2 genes with know functions in photoreceptor specification in zebrafish, Tbx2b and Foxq2 resulted in the anticipated changes in cell fate, albeit, the strength of the alterations in cell fate in the F0 larvae appears to be less than the published phenotypes for the inherited alleles. Interestingly, the authors also identified the expression of an RH2 opsin in the SWS2 another cone type. The changes are subtle but important.

The authors then targeted tbx2a, the function of which was not known. The result is quite interesting as it matches the increase of rods and decrease of UV cones observed in tbx2b mutants. However, the injected animals also showed RH2 opsin expression but are now in the LWS cone subtype. These data suggest that Tbx2 transcription factors repress misexpression of opsins in the wrong cell type.

The authors also show that targeting additional differentially expressed factors does not affect photoreceptor fate or survival in the time frame investigated. These are important data to present. For these or any of the other targeted genes above, did the authors test for changes in photoreceptor number or survival?

We have attempted to address this point, but the answer is not clear cut. We used activated caspase-3 inmmunolabeling as a marker of apoptosis (Lusk and Kwan 2022). At 5 dpf, the age we chose to make quantifications, we don’t see an increase in activated caspase-3 positive cells when we compare control and tbx2a F0 mutants (Reviewer Figure 1A-B). Labeled cells are very rare and located near the ciliary marginal zone irrespective of genotype. This suggests that there is no detectable active death at this late stage of development in tbx2 F0 mutants. Earlier in development, at 3 dpf, when photoreceptor subtypes first appear, there is also a normal wave of apoptosis in the retina (Blume et al. 2020; Biehlmaier, Neuhauss, and Kohler 2001), resulting in many cells positive for activated caspase-3; our preliminary quantifications don’t show a marked increase in the number of labeled cells in tbx2a F0 mutants, but we consider that it’s likely that subtle effects might be obscured by the physiological wave of apoptosis (Reviewer Figure 1C-D).

Reviewer Figure 1 - Assessment of apoptosis in tbx2a F0 mutants. (A-B) Confocal images of 5 dpf larval eyes of control (A and A’) and tbx2a F0 mutants (B and B’) counterstained with DAPI (grey) and immunolabeled against activated Caspase 3 (yellow) show sparse and dim labeling, restricted to cells located in the ciliary marginal zone, without clear differences between groups. (C-D) Confocal images of 3 dpf larval eyes of control (C and C’) and tbx2a F0 mutants (D and D’) immunolabeled against activated Caspase 3 show many positive cells, located in all retinal layers, as expected from physiological apoptosis at this stage of development and without clear differences between groups.

Furthermore, the additional single-cell RNA-seq datasets we have reanalyzed suggest that tbx2a and tbx2b are expressed by other retinal neurons and progenitors and not just photoreceptors (Reviewer Figure 2), further confounding attempts at the quantification of apoptosis specifically in photoreceptor progenitors.

Reviewer Figure 2 – Expression of tbx2 paralogues across retinal cell types. The transcription factors tbx2a and tbx2b are expressed by many retinal cells. Plots show average counts across clusters in RNA-seq data obtained by Hoang et al. (2020).

At this stage, we consider that fully resolving this issue is important and will require considerably more work, which we will pursue in the future using full germline mutants and live-imaging experiments.

Reviewer #3 (Public Review):

Angueyra et al. tried to establish the method to identify key factors regulating fate decisions in the retinal visual photoreceptor cells by combining transcriptomic and fast genome editing approaches. First, they isolated and pooled five subtypes of photoreceptor cells from the transgenic lines in each of which a specific subtype of photoreceptor cells are labeled by fluorescence protein, and then subjected them to RNA-seq analyses. Second, by comparing the transcriptome data, they extracted the list of the transcription factor genes enriched in the pooled samples. Third, they applied CRISPR-based F0 knockout to functionally identify transcription factor genes involved in cell fate decisions of photoreceptor subtypes. To benchmark this approach, they initially targeted foxq2 and nr2e3 genes, which have been previously shown to regulate S-opsin expression and S-cone cell fate (foxq2) and to regulate rhodopsin expression and rod fate (nr2e3). They then targeted other transcription factor genes in the candidate list and found that tbx2a and tbx2b are independently required for UV-cone specification. They also found that tbx2a expressed in the L-cone subtype and tbx2b expressed in L-cones inhibit M-opsin gene expression in the respective cone subtypes. From these data, the authors concluded that the transcription factors Tbx2a and Tbx2b play a central role in controlling the identity of all photoreceptor subtypes within the retina.

Overall, the contents of this manuscript are well organized and technically sound. The authors presented convincing data, and carefully analyzed and interpreted them. It includes an evaluation of the presented data on cell-type specific transcriptome by comparing it with previously published ones. I think the current transcriptomic data will be a valuable platform to identify the genes regulating cell-type specific functions, especially in combination with the fast CRISPR-based in vivo screening methods provided here. I hope that the following points would be helpful for the authors to improve the manuscript appropriately.

1) The manuscript uses the word “FØ” quite often without any proper definition. I wonder how “Ø” should be pronounced - zero or phi? This word is not common and has not been used in previous publications. I feel the phrase “F0 knockout,” which was used in the paper cited by the authors (Kroll et al 2021), is more straightforward. If it is to be used in the manuscript, please define “FØ” and “CRISPR-FØ screening” appropriately, especially in the abstract.

We have made changes to replace “FØ” to “F0.” In our other citation (Hoshijima et al., 2019), “F0 embryo” was used throughout the paper. Following our references and Dr Kojima’s suggestion, we adopted “F0 mutant larva” as the most straightforward and less confusing term. We have also made changes in the abstract to define our approach more clearly and made appropriate changes throughout the manuscript.

2) Figure 1-supplement 1 shows that opn1mw4 has quite high (normalized) FPKM in one of the S-cone samples in contrast to the least (or no) expression in the M-cone samples, in which opn1mw4 is expected to be detected. The authors should address a possible origin of this inconsistent result for opn1mw4 expression as well as a technical limitation of using the Tg(opn1mw2:egfp) line for detection of opn1mw4 expression in the GFP-positive cells.

In Figure 1 - Supplement 1, we had attempted to provide a summarized figure of all phototransduction genes, but the big differences in expression levels — in particular, the high expression of opsins genes — forced us to use gene-by-gene normalization for display. Without normalization, the expression of opn1mw4 is very low across all samples, and its detection in that sole S-cone sample can likely be attributed to some degree of inherent noise in our methods. We have revised Figure 1 - Supplement 1: we find that we can avoid gene-by-gene normalization and still provide a good summary of the expression of phototransduction genes if the heatmap is broken down by gene families, which have more similar expression levels. In addition, we have added caveats to the use of the Tg(opn1mw2:egfp) line as our sole M-cone marker in the results section describing our RNA-seq approach, including our inability to provide data on Opn1mw4-expressing M cones.

3) The manuscript lacks a description of the sampling time point. It is well known that many genes are expressed with daily (or circadian) fluctuation (cf. Doherty & Kay, 2010 Annu. Rev. Genet.). For example, the cone-specific gene list in Fig.2C includes a circadian clock gene, per3, whose expression was reported to fluctuate in a circadian manner in many tissues of zebrafish including the retina (Kaneko et al. 2006 PNAS). It appears to be cone-specific at this time point of sample collection as shown in Fig.2, but might be expressed in a different pattern at other time points (eg, rod expression). The authors should add, at least, a clear description of the sampling time points so as to make their data more informative.

We have included this information in the materials and methods. We collected all our samples during the most active peak of the zebrafish circadian rhythm between 11am and 2pm (3h to 6h after light onset) to avoid the influence of circadian fluctuations in our analysis.

Author Response

Reviewer #1 (Public Review):

The authors set out to develop an in vitro model of multiple species representing diversity in the CF airway as a platform for a range of studies on why polymicrobial communities resist therapy. The rationale for their design is sound and the methods appear justifiable and reproducible. The major strength of this work is in producing a method for a range of future work, ideally for multiple groups in the field. The primary findings are interesting but not groundbreaking. One weakness in the method of reporting interspecies interactions and another in evaluating alternative causes of lasR advantages present opportunities for a stronger research contribution beyond this terrific method.

We thank the reviewer for this accurate summary of the data presented in our manuscript. We have addressed the raised concerned in the revised document. The modifications and comments can be seen in the “Essential Revisions” section above.

Reviewer #2 (Public Review):

Differences between the infection environment and in vitro model systems likely contribute to disconnects between the antimicrobial susceptibility profile of bacterial isolates and the clinical response of patients. The authors of this paper focus on a specific aspect of the infection environment, the polymicrobial nature of some chronic infections like those in people with Cystic Fibrosis (CF), as a factor that could impact antibiotic tolerance. They first use published genomic datasets and computational techniques to identify a clinically relevant, four-member polymicrobial community composed of Pseudomonas aeruginosa, Staphylococcus aureus, Streptococcus spp., and Prevotella spp. They then develop a high throughput methodology in which this community grows and persists in a CF-like environment and in which antibiotic susceptibility can be tested. The authors determine that living as a member of this community decreases the antibiotic tolerance of some strains of biofilm-associated P. aeruginosa and increases the tolerance of most strains of planktonic and biofilm-associated S. aureus and planktonic and biofilm-associated Streptococcus. They focus on the decreased tolerance of P. aeruginosa and determine that a ΔlasR mutant of P. aeruginosa does not display increased tobramycin susceptibility in the mixed community. One of the phenotypes associated with a ΔlasR mutant is an overproduction of phenazines. The authors find that by deleting the phenazine biosynthesis genes from ΔlasR, they can restore community-acquired susceptibility. They further investigate this phenomenon by showing that a specific type of phenazine, PCA, is significantly increased in mixed communities with the ΔlasR mutant compared to WT. Finally, they demonstrate that adding a specific phenazine, pyocyanin, to mixed communities can restore the tolerance of WT P. aeruginosa.

Strengths:

With this study the authors address a very important problem in infectious disease microbiology - our in vitro drug susceptibility assays do a poor job of mimicking the infection environment and therefore do a poor job of predicting how effective particular drugs will be for a particular patient. By demonstrating how an infection-relevant community modifies tolerance to a clinically relevant drug, tobramycin, the authors identify specific interactions that could be targeted with therapeutics to improve our ability to treat the chronic infections associated with CF. In addition, this study provides a framework for how to effectively model polymicrobial infections in vitro.

The experiments in the paper are very rigorous and well-controlled. Statistical analysis is appropriate. The paper is very well-written and clear.

The authors do an admirable job of using in silico analysis to inform their in vitro studies. Specifically, they provide a comprehensive rationale for why they chose and studied the specific community they did.

The authors provide a very robust dataset which includes determining how strain differences of each of their four community members affect community dynamics and antibiotic tolerance. These types of analyses are laborious but very important for understanding how broadly applicable any given result is.

We appreciate the reviewer’s thorough summary of our work and their positive comments.

Weaknesses:

The authors very clearly and convincingly demonstrate that WT P. aeruginosa becomes more susceptible to tobramycin in their mixed community. Our ability to turn these types of observations into therapeutic development depends on mechanistic insight. That said, it is unclear if the authors can make any solid conclusions about what specific aspects of the polymicrobial environment cause WT P. aeruginosa to become more susceptible. The authors make a compelling case that increased phenazine production by the ΔlasR mutant restores tolerance in the mixed community and that exogenous phenazine addition increases the survival of WT P. aeruginosa in the mixed community. However, it remains a plausible explanation that the effects of phenazines on tobramycin susceptibility are independent of the initial observation that WT. P. aeruginosa becomes susceptible to tobramycin in the mixed community.

We agree with the reviewer’s comment here as it pertains to the initial observation of P. aeruginosa becoming more susceptible to tobramycin in the mixed community. However, as mentioned by the reviewer, we provide several lines of evidence that phenazines play a key role in the tolerance of the lasR mutant tobramycin, including genetic studies and feeding studies wherein exogenous addition of this molecule to WT P. aeruginosa phenocopies the lasR mutant exposed to tobramycin. Why the community impacts phenazine production of the WT strain is an open question, and the subject of future work. We have modified the abstract of the manuscript as follows at Lines 41–43:

“Our data suggest that the molecular basis of this community-specific recalcitrance to tobramycin for the P. aeruginosa LasR mutant is increased production of phenazines.”

Some aspects of the methodology are unclear. Specifically, the authors note that they use a specific sealed container system to grow their strains in anoxic conditions, which mimic portions of CF sputum. However, it is unclear how the authors change medium over the course of their experiments, or how they test susceptibility to tobramycin, without exposing the cells to oxygen. It is well understood that oxygen exposure impacts the susceptibility of P. aeruginosa to tobramycin, so it is very important that the methodology involving oxygen deprivation and exposure is described in detail.

We have made the necessary modifications to the manuscript as indicated in the “Essential Revisions” section to address these concerns (see Comment #3). Furthermore, new validation experiments were performed in a controlled anoxic environmental chamber that yielded observations similar to the data presented in the original manuscript, thereby confirming that we were using anoxic conditions with the GasPak anaerobic jar system (see Figure 1 - figure supplement 2 and Figure 2 - figure supplement 7).

Lines 198–204: “The impact of residual oxygen negatively influencing the growth of P. melaninogenica in monoculture was ruled out by performing these experiments using an anoxic environmental chamber (Figure 1 – figure supplement 2). That is, we did not detect CFU counts for either planktonic or biofilm populations of P. melaninogenica when grown in ASM in the anaerobic chamber, but as a positive control, significant growth was detected when using a medium shown previously to support growth of this microbe (10) (Prevotella Growth Medium, or PGM) (Figure 1 – figure supplement 2).”

Lines 406–414: “Also, we ruled out the possibility of remaining oxygen in ASM negatively impacting the viability of P. melaninogenica by reproducing our results using an anoxic chamber (Figure 1 – figure supplement 2). That is, we observed that P. melaninogenica can robustly grow as a planktonic or biofilm monospecies community in a medium capable of sustaining its growth (PGM) while this microbe fails to grow in ASM (Figure 1 – figure supplement 2). Thus, we argue that the mixed-community-specific growth of Prevotella spp. we observed across several conditions (Figure 1C, Figure 1 – figure supplement 5, Figure 2 – figure supplement 6) is not due to residual oxygen.”

Lines 290–293: “Growing and replenishing the preformed biofilm communities with fresh ASM supplemented or not with tobramycin using an anoxic environmental chamber resulted in similar phenotypes for all tested microorganisms (Figure 2 – figure supplement 7), indicating that the use of the GasPak system provides a robust anoxic environment.”

Lines 533–540: “Plates were incubated using an AnaeroPak-Anaerobic container with a GasPak sachet (ThermoFisher) at 37 °C for 24 hours. Then, unattached cells were aspirated with a multichannel pipette and the pre-formed biofilms replenished with 100 µl of fresh ASM on the bench and incubated for an additional 24 hours at 37 °C using an AnaeroPak-Anaerobic container with a GasPak sachet (ThermoFisher). Similar experiments were performed using an anoxic environmental chamber (Whitley A55 - Don Whitley Scientific, Victoria Works, UK) with 10% CO2, 10% H2, 80% N2 mixed gas at 37 °C, yielding results identical to those observed for the GasPak system.”

Reviewer #3 (Public Review) :

This manuscript by Jean-Pierre et al. describes the creation and experimentation with a model CF lung community in an artificial sputum medium. The group uses data from 16S rRNA sequencing studies to select organisms for creating the model and then performs experiments to determine outcomes of growth competition and antibiotic tolerance in a community context. The main finding of the manuscript is that P. aeruginosa, notorious for its antimicrobial resistance phenotypes, is more susceptible to tobramycin in the community context than when grown alone. The manuscript is well prepared and follow-up experiments with mutant strains and phenazines greatly strengthen the project overall. The initial results paragraph where the authors go through the rationale for selecting the different organisms is perhaps a bit overkill, the organisms selected make sense based on their prevalence in CF airways, which in and of itself is a strong enough rationale. This aspect of the manuscript could be minimized to focus more on the exciting culture experiments in the latter parts of the results. Overall, this is a strong and well-crafted manuscript that will have a broad interest in the CF and microbial ecology fields.

We thank the reviewer for this thoughtful review of our manuscript. We have not minimized the “front-end” of the paper because we believe the rationale for selecting the community and its members, and the validation of the model system are key for placing the resulting observations in a robust context, and for providing the underlying rationale to support the relevance of the findings.

Major Critiques. I have two major critiques of this study.

(1) Prevotella growth in monoculture. After reading the methods section it appears that the cultures were extensively washed and prepped prior to the inoculation into ASM. Prevotella did not grow alone, is this due to oxygen penetration of the cells during preparation? Perhaps oxygen is present in ASM prior to placement in an anaerobic bag? It is interesting, and perhaps worth exploring, whether the mixed community draws down oxygen from the media explaining the ability of Prevotella to grow. I suspect this is the case, but more detail is needed in the methods and this experiment would help us understand this interesting result.

As presented in the “Essential Revisions” section (Comment #3), we have repeated the experiment using fully anoxic conditions (i.e., using an anoxic environmental chamber where the cultures were grown, washed and mixed before incubation) and still observed absence of growth of Prevotella cultivated in ASM in both biofilm and planktonic populations. Moreover, including a positive control, Prevotella Growth Medium, resulted in robust growth of this microbe. Taken together, our data suggest that residual oxygen in ASM is not the driver of the community-specific growth of P. melaninogenica.

(2) Dilution of the community reproducing toby tolerance of P. aeruginosa. In supplemental figures, the replication of the 1:1000 dilution of the mixed community with P. aeruginosa shows poor replication and very large error bars. This experiment should be repeated to ensure it is reproducible.

The diluted mixed community experiment was repeated a fourth time, yielding the same statistical conclusions. An updated “Figure 2 – figure supplement 1” was added to the paper. The highest (1:1000) dilution still yielded high variation which could perhaps be explained by low (i.e., ~103 CFU/mL) inoculum for S. aureus, S. sanguinis and P. melaninogenica used in these experiments; see updated “Microbial assays” paragraph of the “Materials and Methods” section). Thus, the variation at low inoculum is robust and reproducible. The Materials and Methods section was also updated to clarify the CFU counts used for those experiments. We have added modifications to the text as follows to address this critique:

Lines 526–532: “The optical density (OD600) was then measured for each bacterial suspension and diluted to an OD600 of 0.2 in ASM. Monocultures and co-culture conditions were prepared from the OD600 = 0.2 suspension and diluted to a final OD600 of 0.01 for each microbial species in ASM corresponding to final bacterial concentrations of 1x107 CFU/mL, 3.5x106 CFU/mL, 1.2x106 CFU/mL and 4.6x106 CFU/mL of P. aeruginosa, S. aureus, Streptococcus spp. and Prevotella spp. respectively. A volume of 100 µl of bacterial suspension all at a final OD600 of 0.01 each in the mix was added to three wells.”

Lines 558–570: “For experiments with varying concentrations of S. aureus, S. sanguinis and P. melaninogenica in monocultures and co-cultures, the organisms were grown from bacterial suspensions adjusted to an OD600 = 0.8 in ASM. Suspensions were further diluted in ASM to an OD600 of either 0.1, 0.001, 0.0001 or 0.00001 while maintaining P. aeruginosa at OD600 = 0.01 (approximating 1x107 CFU/mL) in all conditions. The OD600 = 0.1 dilution factor resulted in CFU/mL count average of 3.8x108 CFU/mL for S. aureus, 1.6x108 CFU/mL for S. sanguinis and 1.0x108 CFU/mL for P. melaninogenica. The OD600 = 0.001 dilution factor resulted in a CFU/mL count average of 6.7x105 CFU/mL for S. aureus, 1.1x105 CFU/mL for S. sanguinis and 1.4x105 CFU/mL for P. melaninogenica. The OD600 = 0.0001 dilution factor resulted in a CFU/mL count average of 4.2x104 CFU/mL for S. aureus, 3.3x104 CFU/mL for S. sanguinis and 4.6x104 CFU/mL for P. melaninogenica. The OD600 = 0.00001 dilution factor resulted in a CFU/mL count average of 5.6x103 CFU/mL for S. aureus, 4.4x103 CFU/mL for S. sanguinis and 6.2x103 CFU/mL for P. melaninogenica.”

Author Response

Reviewer #4 (Public Review):

The study employs a number of methods, including TEM morphometric analysis, immunochemistry, western blotting, genomics, genetically modified models, whole heart measurements.

However, the manuscript seems to be a collection of two unfinished works: one on the transition p20-p60 in post-natal development of the heart, second about the role of ephrinB1 in the maturation of the crests of the sarcolemma. Otherwise, it is not clear why in the first figure there is no staining for ephB1, and why there is staining for claudin 5 instead.

The reason is clearly explained in the text on page 6. The first figure explores the postnatal maturation of the CM crests and their molecular determinants and our previous paper described Claudin-5 as the first molecular determinant of the crests (Guilbeau-Frugier et al, Cardiovasc Research 2019). Based on our previous demonstration of ephrin-B1 as a direct claudin-5 partner and regulator (Genet et al, Circulation Research 2012), we thus intuitively proposed ephrin-B1 as another potential molecular determinant of the crests that we explored for the first time in our current paper in revision. Moreover, ephrin-B1 is part of a large family of direct physical cell-cell communication proteins (Eph-Ephrin system), its role in the lateral crest-crest interaction was also obvious.

This is why at the beginning of the paper we explored claudin-5 and thereafter ephrin-B1 to explore more the functional role of the crests using Efnb1 KO mouse model we had already established in the lab.

The authors are trying to defend the idea that development of the heart in rats doesn't finish on postnatal day 20 and goes on for up to day 60. However, it is not convincing.

It is no surprise transcription profile is different between day 20 and day 60, I am sure as life goes on development continues into aging and any comparison of samples collected with sufficient time lapse will give transcriptional differences. Whether these differences represent a truly separate development stage is not a clear-cut story.

Most of the argument is based on morphometric study of TEM images.

But also on confocal microscopy studies and more importantly on transcriptomic data.

Whether it was evident that transcription profile is different between day 20 and day 60, then most of the studies in this postnatal field would have extended their study window over P20 which is not the case. As we mentioned it in the manuscript, most people in the field were assuming terminal maturity of the CM based essentially on its typical rod-shape which is already acquired at P20. Then growth of the heart between P20 and P60 was assumed to rely only on an increase in tissue quantitative content and not on transcriptomic changes, i.e. in qualitative content.

However, the method is not described at all. There is reference to another paper by the authors, but this paper doesn't provide a concise description of the morphometry either. It is unclear how randomisation of images and fields of view has been achieved and what statistical methods has been implemented. In TEM it is often possible to find all sorts of oddities depending on how you choose the images.

We agree with the author that TEM is often associated with “all sorts of oddities” and that‘s the reason our recent paper (Guilbeau-Frugier et al, Cardiovasc Research 2019) was dedicated to the analysis of technical pitfalls and analysis. All this paper relies on that: How to proceed the cardiac tissue to avoid artifacts on the crests/SSM visualization and how to quantify them?.

Now, instead of only citing our previous paper, we have implemented the “Material and methods” / “Transmission electron microscopy (TEM) and quantitative analysis” section (Main manuscript, page 20-21) by highly detailing all the TEM observation/quantification.

The question of randomization of images of the number fields of view is a general question in all imaging techniques and not specific at all with our TEM study. In imaging, there is no randomization.

All statistical analysis of TEM data quantifications are accurately described in all figure legends. For instance, in the figure 1: (B) Quantification of crest heights / sarcomere length (left panel), SSM number / crest (middle panel) and SSM area (right panel) from TEM micrographs obtained from P20- or P60 rat hearts (P20 n=6, P60 n=6; 4 to 8 CMs/rat, ~ 70 crests/rat). However, to better clarify the “P20 n=6, P60 n=6”, we have now specified “P20 or P60 n=6 rats”. This have been now specified in the figure legends for all statistical analysis (highlighted in yellow in the revised manuscript).

Why didn't the authors use microscopy of live isolated cells, which may be more relevant to study crest height?

We clearly explained it at the very beginning of the results section of our paper (first paragraph, second sentence (i, ii). The use of living CMs is a non-sense based on our two previous papers on this topic (Dague et al JMCC 2014 and Guilbeau-Frugier et al, Cardiovasc Research 2019). Our first paper was essentially based on AFM studies using isolated CMs and we found that rapidly after isolation, CM surface crests/SSM have a high tendency to shrink and disappear in control mice. This is why the second paper was based on an extensive characterization of the crests within the tissue using TEM experiments and the comparison of CM crests between tissue and living cells is also highlighted in this paper. More importantly, in this recent paper, we have described for the first time using high resolution imaging techniques (TEM and STEAD), the existence of intermittent physical interactions between neighboring CMs on their lateral side through crest-crest interaction via the extracellular domain of claudin-5. This crest-crest physical interaction can only be observed within the tissue since isolated adult CMs remain isolated and do not reproduce CM-CM physical interactions (through lateral or physical interactions at the longitudinal level, i.e. the intercalated disk level).

Both claudin5 and EphrinB1 seem to be expressed highly after p5, which doesn't correlate with the proposed maturation of crests at days 20 to 60.

Many processes do not rely only on gene/protein expression but on post-translational processes and localization/trafficking of proteins within the cell. This is exactly what we show with ephrin-B1 and claudin-5 proteins that traffic from the cytoplasm to the lateral membrane at the surface of the CMs between P20 and P60, as shown by our confocal images of the cardiac tissue while the global expression level of these two proteins doesn’t change (western blot results).

There is no causative relationship between the lack of ephrinb1 and crest maturing, at least to my mind.

Comparing the cardiac tissue between P20 an P60 and showing both ephrin-B1 trafficking at the CM lateral surface and crest maturation is obviously not a criterion of any relationship between these two events. However, when you delete a specific protein, i.e ephrin-B1, from a specific cell, i.e. the CM, and the phenotype of the KO mice is again a lack of crest maturation, you can at least deduce that ephrin-B1 is involved, directly or indirectly we don’t know, in the maturation process of the crests in the CM.

Now, because of the constitutive deletion of Efnb1, we couldn’t completely exclude that the phenotype of the constitutive Efnb1 CM-KO mice we described at the adult stage was directly related to specific alteration of CM surface crest/diastolic function at the adult stage or more likely related to other earlier developmental defects (secondary mechanisms). Also, to discriminate between these two possibilities, we have now used in the revision process a tamoxifen-inducible conditional-knockout (Mer-Cre-Mer) of Efnb1 in the CM (MHC promotor). This mouse model has never been reported before but its characterization (new Supplementary Figure 16) indicated that tamoxifen injection can lead to up to 50 % of Efnb1 deletion in CMs. In these conditions, deletion of Efnb1 (tamoxifen injection) was initiated at the young adult stage (2-month old) and the systolic and diastolic function (echo Doppler and LV-catheterism) but also CM crest phenotype (TEM) were examined one month later. As shown in the new Figure 7, deletion of efnb1 at the adult stage led to partial loss of CM surface crests (New Fig 7B), agreeing with the partial deletion of Efnb1, associated with a significant increase in the IVRT (echo-doppler), LVEDP (LV catheterism) with no modification of the ejection fraction (echo) compared to the control mouse littermates (tamoxifen injected) (New Fig. 7C, D). Thus, these data clearly demonstrate that ephrin-B1 is a specific determinant of the crest architecture at the CM surface and of the diastolic function at the adult stage.

Author Response

Reviewer #3 (Public Review):

The manuscript by Le T.D.V. et al used in vitro cell culture and inhibitors for cellular signaling molecules and found that GLP-1 receptor activation stimulated the phosphorylation of Raptor, which was PKA-mediated and Akt-independent. The authors reported the physiological function of this GLP-1R-PKA-Raptor in liraglutide stimulated weight loss. This timely study has high significance in the field of metabolic research for the following reasons.

(1) The authors' findings are significant in the field of obesity research. GLP-1 receptor (GLP-1R) is a successful target for diabetes (and weight loss) therapeutics. However, the mechanisms of action for the weight-loss effect of GLP-1 agonists are not fully understood. Therefore, mechanistic studies to elucidate the signaling pathways of GLP-1 receptors pertaining to weight loss at the cellular level are timely.

(2) G protein-coupled receptors (GPCRs) induces various signaling activities, which could be cellular and tissue specific. As these are an important protein family for drug targeting, understanding the basic biology of these receptors is of interest to a broad readership.

(3) The authors have made important discoveries that Exendin-4 stimulated mTORC1 signaling was essential for the anorectic effect induced by Exendin-4. The study reported in this current manuscript provides more details of brain GLP-1R signaling pathways and is innovative.

Overall, the authors have presented sufficient background in a clear and logically organized structure, clearly stated the key question to be addressed, used the appropriate methodology, produced significant and innovative main findings, took potential caveats into consideration, and made a justified conclusion.

Recommendations for the authors:

The manuscript can be further strengthened with more clarification on the following points.

1) In Figure 1 panels B and C, please provide the quantification for pCREB/CREB. In Figure 1 panel D, please provide the quantification for pAkt/Akt.

We thank the Reviewer for this suggestion. We now provide quantification of pCREB and pAkt expression in Supp. Fig. 1.

2) The western blots to assess the signaling activities revealed the phosphorylation status of the key signaling molecules at a single time point. Whether the overall signaling dynamics have been affected is unclear.

We agree with the reviewer on this point. We conducted initial time course experiments to identify a suitable time point for the subsequent experiments conducted in the present studies. The 1h time point presented in our results was chosen because it was the earliest time point at which both liraglutide stimulated mTORC1 signaling and this effect was inhibited by the various pharmacological inhibitors. We agree with the reviewer that at this point it is not clear whether the various inhibitors or the Ser791Ala mutation in Raptor modifies the dynamics of mTORC1 signaling. Although we have preliminary data in CHO-K1 cells suggesting that the temporal dynamics of these signaling events are not affected, this does not necessarily translate to the in vivo setting. Once we identify the key target tissue/cell type(s) mediating the weight loss effect of liraglutide via the PKA-Raptor interaction and generate the necessary mutant mice, we will test whether this affects signaling dynamics in vivo.

3) Figure 3 panels A and B demonstrated the remarkable importance of the Ser791 Raptor. However, this PKA-resistant mutant did not completely abolish the weight loss effect of liraglutide. The authors pointed out the importance of AMPK in mTORC1 signaling. Other pathways that may complement GLP-1R-PKA-Raptor signaling can be further discussed.

We agree with the reviewer that other signaling pathways are likely involved that contribute to the remaining weight-lowering effect of liraglutide. Besides AMPK, we have also included a discussion of Akt being a potential molecule that interacts with these pathways in vivo (lines 218-225). The word limitations of a Short Communication prevent us from further expanding on these possible mechanisms.

4) Food intake was decreased on day 2 in Figure 3D but became comparable between WT and S791A Raptor groups on the following days. Could this be due to some compensatory mechanisms?

This pattern of food intake response to GLP-1R agonists has been previously reported by our group and others (please see Brown JD et al. Am J Physiolo Regul Integr Comp Physiol 2018 and Adams JM et al. Diabetes 2018). The reason for this is unclear at this moment, but we can speculate that the rebound in food intake is a compensatory mechanism to prevent the organism from continuously losing weight. We now also present also showing an initial drop in energy expenditure with liraglutide treatment that progressively increases to pre-treatment levels.

Author Response

Reviewer #3 (Public Review):

The size of the excitation region and the size of the aster are linearly correlated but are drastically different in size. This provokes several questions.

• Why does only one aster form if the region of excitation is over 10x the size? Why are there not multiple asters formed within this activation region?

• A much larger excitation diameter than the size of the resultant structure suggests the amount of dimeric motor is not limiting. Why then does the size of the aster increase with excitation diameter?

• A linear relationship between excitation region and aster size may suggest a constant density of material within the aster. While the intensity profile of a single aster is given in Fig 1C, the magnitude of intensity versus the estimated size of the aster would determine whether the system is reduceable purely to changes in size/radial distribution.

We thank the reviewer for the careful consideration of our work. In the experiments performed for this study, we were careful to be in a regime in which a single aster formed within the excitation region. However, by varying the concentration of components in the system, it is possible for multiple asters to form. See Figure R2 for example images of cases in which multiple asters formed.

The increase in aster size with excitation region was also described previously in Ross, et al. 2019. In this, we found that the aster size scales with the volume of the excitation region, suggesting that the number of microtubules is limiting to aster size. This supports the hypothesis that there may be a density limit to the microtubules, likely due to steric interactions between the microtubules. We clarified this and added reference to the Ross, et al. findings in lines 115-118, as follows:

“In Ross, et al., it was determined that the aster size roughly scaled with the volume of the excitation area, suggesting that the number of microtubules limits the size of the aster. This hints that there may be a density limit to the microtubules in an aster.”

Is dimerization reversible after activation? If the motors cannot unbind from each other, and act as crosslinkers (for as long as they remain bound) are they likely to accumulate within the aster over time? This may challenge the steady state assumption.

We thank the reviewer for the thoughtful analysis. Dimerization is reversible after activation - the lifetime of the optogenetic bond is about 20 seconds (Guntas et al., 2015). In order to form an aster, we repeatedly activate the sample at 20 second intervals, so there is a balance between motors unbinding from each other and ones becoming dimerized. This balance can create a non-equilibrium steady state. We have clarified this in lines 78-80, as follows:

“The optogenetic bond lasts for about 20 seconds before reverting to the undimerized state, thus in our experiments, we repeatedly illuminate the sample every 20 seconds (Guntas, et al. 2015).”

Author Response

Reviewer #3 (Public Review):

Gomolka et al. are trying to establish how aquaporin-4 (AQP4) water channels, a key component of the glymphatic system, facilitate brain-wide movement of interstitial fluid (ISF) into and through the interstitial space of the brain parenchyma. Authors employ a number of advanced non-invasive techniques (diffusion-weighted MRI and high-resolution 3D non-contrast cisternography), invasive dynamic-contrast enhanced (DCE-) MRI along with ex-vivo histology to build a robust picture of the effects of the removal of AQP4 on the structure and the fluid dynamics in the mouse brain. This work is a further step for the implementation of non-invasive tools for studying the glymphatic system.

The main strengths of the manuscript are in the extensive brain-wide and regional analysis, interrogating potential changes in the structural composition, tissue architecture, and interstitial fluid dynamics due to the removal of AQP4. The authors demonstrate an increase in the interstitial fluid volume space, an increase in total brain volume, and a higher brain water content in AQP4 knockout mice. Importantly, an increase in apparent diffusion coefficient (ADC) was reported in most brain regions in the AQP4-KO animals which would suggest an increase in the movement of the fluid, which is supported by an increase in interstitial fluid space measures by real-time iontophoresis with tetramethylammonium (TMA). There is a reduction in the ventricular CSF space compartment while the perivascular space remains consistent. A reduction in gadolinium-based MRI tracer influx into many regions of the AQP4 KO mouse brain parenchyma is found, which supports conclusions of slowing down of fluid transfer while noting that the tracer dynamics in the main CSF compartments show no significant differences.

The interpretation of non-invasive measures of the interstitial fluid dynamics in relationship to regional AQP4 expression is less well supported. The regional AQP4 channel expression in WT mice positively correlates with the ADC and extravascular diffusivity (D) measures. However, their finding that regional ADC also increases when AQP4 is removed weakens the conclusion that the removal of AQP4 leads to interstitial fluid stagnation.

We are thankful to the reviewer for the positive feedback. Indeed, we aimed to provide the scientific field with the most detailed and objective assessment on effect of congenital loss of AQP4 channel on the brain water homeostasis and glymphatic transport. Therefore, we predominantly employed MRI techniques enabling non-invasive assessment, while superimposing obtained findings to standard DCE-MRI and physiological evaluation in-vivo and ex-vivo.

In response to the remark, it is indeed difficult to discuss this phenomena other than relating the regional AQP4 expression to a specific metabolic or morphological structure in WT mice brain, thus associating AQP4 channel expression with regional water distribution. This would have a background not only in to date report highlighting upregulation of AQP4 in response to fluid stagnation, but also in possibility of rapid AQP4 relocalization after acute water intoxication (as comprehensively reviewed by Salman et al. 2022). This would also not reject the possibility that AQP4 is by default expressed more in the regions of functionally higher water content, reflected by higher ADC measures.

In KO mice, we found deletion of AQP4 channel affecting mainly the brain water homeostasis (Figure 1), and thus increased slow MR diffusion metrics would be related to increased brain swelling and increased ISF space compared to WT littermates (Figure 2). However, it is not excluded that this might be rather a superposition of two opposing effects: decrease in measured ADC due to decrease water exchange, and even larger increase in ADC as a manifestation of increased ISF space volume resulting from prior phenomenon. Such explanation was previously presented based on estimation using Latour’s model of long-time diffusion behavior (Pavlin et al. 2017, https://pubmed.ncbi.nlm.nih.gov/28039592/) and connected to rather to enlarged interstitial space Urushihata et al. 2021, https://pubmed.ncbi.nlm.nih.gov/34617156/) that are not paralleled by changes in blood perfusion between genotypes (Zhang et al. 2019, https://pubmed.ncbi.nlm.nih.gov/31220136/).

Author Response

Reviewer #1 (Public Review):

In this manuscript, the authors investigate the genes involved in the retention of eggs in Aedes aegypti females. They do so by identifying two candidate genes that are differentially expressed across the different reproductive phases and also show that the transcripts of those two genes are present in ovaries and in the proteome. Overall, I think this is interesting and impressive work that characterizes the function of those two specific protein-coding genes thoroughly. I also really enjoyed the figures. Although they were a bit packed, the visuals made it easy to follow the authors' arguments. I have a few concerns and suggested changes, listed below.

1) These two genes/loci are definitely rapidly evolving. However, that does not automatically imply that positive selection has occurred in these genes. Clearly, you have demonstrated that these gene sequences might be important for fitness in Aedes aegypti. However, if these happen to be disordered proteins, then they would evolve rapidly, i.e., under fewer sequence constraints. In such a scenario, dN/dS values are likely to be high. Another possibility is that as these are expressed only in one tissue and most likely not expressed constitutively, they could be under relaxed constraints relative to all other genes in the genome. For instance, we know that average expression levels of protein-coding genes are highly correlated with their rate of molecular evolution (Drummond et al., 2005). Moreover, there have clearly been genome rearrangements and/or insertion/deletions in the studied gene sequences between closely- related species (as you have nicely shown), thus again dN/dS values will naturally be high. Thus, high values of dN/dS are neither surprising nor do they directly imply positive selection in this case. If the authors really want to investigate this further, they can use the McDonald Kreitman test (McDonald and Kreitman 1991) to ask if non- synonymous divergence is higher than expected. However, this test would require population-level data. Alternatively, the authors can simply discuss adaptation as a possibility along with the others suggested above. A discussion of alternative hypotheses is extremely important and must be clearly laid out.

We agree with the reviewer’s point that rapid evolution is not the same as positive selection. We also agree with the reviewer’s point that McDonald-Kreitman test (MK test) is more powerful than dN/dS analysis. We took advantage of a large population dataset from Rose et al. 2020. After filtering the data, we kept 454 genomes for MK tests. We found both genes are marginally significant or insignificant (tweedledee p = 0.068; tweedledum p = 0.048), despite that these are small genes and have low Pn values. This suggests that it is likely the genes evolve under positive selection.

In line with the reviewer’s suggestion, we performed another analysis using a large amount of population data. We asked if the SNP frequencies of tweedledee and tweedledum are correlated with environmental variables. We found that when compared to a distribution of 10,000 simulated genes with randomly-sampled genetic variants, both tweedledee and tweedledum showed significant correlation to multiple ecological variables reflecting climate variability, such as mean diurnal range, temperature seasonality, and precipitation seasonality (p<0.05). These results are now incorporated into the manuscript in Figure 5 and Figure 5 – Figure supplement 1.

2) The authors show that the two genes under study are important for the retention of viable eggs. However, as these genes are close to two other conserved genes (scratch and peritrophin-like gene), it is unclear to me how it is possible to rule out the contribution of the conserved genes to the same phenotype. Is it possible that the CRISPR deletion leads to the disruption of expression of one of the other important genes nearby (i.e., in a scratch or peritrophin-like gene) as the deleted region could have included a promoter region for instance, which is causing the phenotype you observe? Since all of these genes are so close to each other, it is possible that they are co-regulated and that tweedledee and tweedledum and expressed and translated along with the scratch and peritrophin-like gene. Do we know whether their expression patterns diverge and that scratch and peritrophin-like genes do not play a role in the retention of viable eggs?

This is a fair criticism; however, we think the chance that the phenotypes are caused by interrupting nearby genes is very low. First, peritrophin-like acts in the immune response, and scratch is a brain-biased transcription factor. Neither of the genes show expression in the ovary before or after blood feeding (TPM <1 or 2 are generally considered unexpressed, while scratch and peritrophin-like expression levels are overall lower than 0.1 TPM).

This suggests that peritrophin-like and scratch are not likely to function in the ovary. Thus, although we cannot completely rule out the gene knockout impacts regulation of very distant genes, it is unlikely. Since the mounting evidence we show in this manuscript that tweedledee and tweedledum are highly translated in the ovary after blooding feeding, under the principle of parsimony, we expect the phenotypes came from knocking out the highly expressed and translated genes.

Reviewer #2 (Public Review):

This manuscript is overall quite convincing, presenting a well- thought-out approach to candidate gene detection and systemic follow- ups on two genes that meet their candidate gene criteria. There are several major claims made by the authors, and some have more compelling evidence than others, but in general, the conclusions are quite sound. My main issues stem from how the strategy to identify genes playing a role in egg retention success has led to very particular genes being examined, and so I question some of the elements of the discussion focusing on the rapid evolution and taxon- uniqueness of the identified genes. In short, while I believe the authors have demonstrated that tweedledee and tweedledum play an important role in egg retention, I'm not sure whether this study should be taken as evidence that taxon-specific or rapidly evolving genes, in general, are responsible for this adaptation, or simply play an important role in it.

We have revised the paper to make it clearer that the focus is indeed on these two genes on not on the greater question of taxon-specific or rapidly-evolving genes.

First, the authors present evidence that Aedes aegypti females can retain eggs when a source of fresh water is lacking, confirming that females are not attracted to human forearms while retaining eggs and that up to 70% of the retained eggs hatch after retaining them for nearly a week. This ability is likely an important adaptation that allows Aedes aegypti to thrive in a broad range of conditions. The data here seem fairly compelling.

Based on this observation, the authors reason that genes responsible for the ability to retain eggs must: 1) be highly expressed in ovaries during retention, but not before or after. 2) be taxon-specific (as this behavior seems limited to Aedes aegypti). While this approach to enriching candidate genes has proven fruitful in this particular case, I'm not sure I agree with the authors' rationale. First, even genes at a low expression in the ovaries may be crucial to egg retention. Second, while egg-laying behavior is vastly varied in insects, I'm not sure focusing on taxon-restricted genes is necessary. It is entirely possible that many of the genes identified in Figure 2E play a crucial role in egg retention evolution. These are minor issues, but they are relevant to some later points made by the authors.

We regret framing the discovery of tweedledee and tweedledum in the original submission using this somewhat artificial set of filtering criteria. The reality is that the genes caught our attention for their novel sequence, tight genetic linkage, and interesting expression profile. That really is the focus of the paper, not these other peripheral questions that have been the focus of attention of the reviews. We really do apologize for all of the confusion about what this paper is about.

Nonetheless, the authors provide very compelling evidence that the two genes meeting their criteria - tweedledee and tweedledum, play an important role in egg retention. The genes seem to be expressed primarily in ovaries during egg retention (some observed expression in brain/testes is expected for any gene), and the proteins they code seem to be found in elevated quantities in both ovaries and hemolymph during and immediately after egg retention. RNA for the genes is detected in follicles within the ovary, and CRISPR knockouts of both the genes lead to a large decrease in egg viability post retention.

My earlier qualms about their search strategy relay into some issues with Figure 4, which describes how the two genes are 1) taxon- restricted and 2) have evolved very rapidly. Neither of the two statements is unexpected given the authors' search strategy. Of course, the genes examined precisely for their lack of homologs do not have any homologs. Similarly, by limiting themselves to genes that show a lack of homology (i.e. low sequence similarity) to other genes as well as genes with high expression levels in the ovaries, a higher rate of evolution is almost inevitable to infer (as ovary expressed genes tend to evolve more rapidly in mosquitoes). I agree with the authors that inferences of the evolutionary history of these genes are quite difficult because of their uniqueness, and I especially appreciate their attempts to identify homologs (although I really dislike the term "conceptualog").

We have removed our term “conceptualog” and replaced with the mor conventional “putative ortholog”

This leads to my main (fairly minor) issue of the paper - the discussion on the evolutionary history of these genes and its implications (sections "Taxon-restricted genes underlie tailored adaptations in a diverse world" and "Evolutionary histories and catering to different natural histories"). As noted, inferring this history is very difficult because the authors have focused on two rapidly evolving, taxon-restricted genes. The analyses they have performed here definitely demonstrate that the genes play an important role in egg retention, however, they do not show that taxon-restricted genes play a disproportionate role in egg retention evolution. Indeed, the only data relevant to this point would be the proportion of genes in Figure 2E that are taxon-restricted (3/9), but I'm not sure what the null expectation for this proportion for highly expressed ovary genes is to begin with. Furthermore, the extremely rapid evolution of this gene makes it hard to judge how truly taxon-restricted it is. My own search of tweedle homologs identified multiple as previously having been predicted to be "Knr4/Smi1-like", and while no similar genes are located in a similar location in melanogaster, there is generally little synteny conservation in Drosophila (for instance Bhutkar et al 2008), so I'm unsure what can really be said about their evolutionary origins/lack of homologs in Drosophila.

In short - the manuscript makes clear that tweedledee and tweedledum play an important role in egg retention in A. aegypti, nonetheless, it is not clear that this is a demonstration of how important taxon- restricted genes are to understanding the evolution of life-history strategies.

Again, we should have never framed the paper the way we did in the original version. We make no claims whatsoever that taxon-restricted genes in general should play a role in this biology, only that the two candidate genes under study influence egg viability after extended retention. We hope that the framing is clearer in this revision.

Author Response

Reviewer #1 (Public Review):

Understanding the evolution of broadly neutralizing influenza antibodies is key to developing a more universal vaccine. In this study, Phillips et al. performed a comprehensive analysis of the evolutionary pathway of CH65, which is an H1-specific broadly neutralizing antibody. The authors generated a combinatorial mutant library with 2^16 members that contained all possible evolutionary intermediates between the unmutated common ancestor (UCA) and CH65, less two mutations that did not affect binding. The binding affinity of each member in the library was measured against HAs from MA90 and SI06, which were isolated 16 years apart, as well as MA90 with a UCA escape mutation G189E. The binding affinity was measured using a high-throughput approach that combined yeast display and Tite-Seq, with careful experimental validation. The results showed that epistasis between mutations within the heavy chain and also across heavy and light chains plays an important role in CH65 to evolve breadth. Although this study highly resembles a previous study by the authors that focused on another broadly neutralizing influenza antibody called CR9114 (Phillips et al., eLife 2021), there are several key differences. Firstly, CR9114 is a HA stem-directed antibody, whereas CH65 binds to the receptor-binding site of HA. Secondly, their previous study only studied the mutations in the heavy chain, whereas the present study looked at mutations in both heavy and light chains. Lastly, the present study provided a structural mechanism of epistasis by solving crystal structures. Such investigation of structural mechanisms was absent in their previous study. Overall, the data quality in this study is very high. In addition, the results have important implications for vaccine development.

We thank Reviewer #1 for their review of our work and have implemented each of their suggestions to improve the clarity of our manuscript.

Reviewer #2 (Public Review):

Although many broadly-neutralizing antibodies were discovered against virus accumulating mutations such as HIV, Influenza, and Sars-CoV-2, the methodology to induce such antibodies or design to generate them is highly demanded. The authors take the broadly-neutralizing antibody, CH65 as a model antibody and try to recapitulate the generation of the broadly-neutralizing antibody from an unmutated common ancestor over time. By performing Tite-Seq assays, Epistasis analysis, Pathway analysis, and Affinity measurement, and structural study, the authors proposed a scenario of the evolution of CH65.

Strengths

Combining the models and affinity/structure data, the authors enable us to show the possible track of gaining the breadth of the CH65 antibody from the unmutated repertoire. Using the Tite-Seq assay, the authors took a forward genetics approach which is high-throughput and non-bias and mimics the situation of the evolution of a B cell repertoire in an individual over time. The data is robust, and its outcome will provide an opportunity to build a prediction model to design the antibody in silico. Especially their identification of amino acid positions important for epistasis mode in antibody evolution is valuable. Antigen selection scenarios are decisive in this study.

Weakness

The proposed scenarios cannot be tested using human CH65. The readers would have great interest in how these hypothetical scenarios are fitting to the evolution occurring in vivo situation, especially in a quantitative way. The broadly neutralizing antibodies often react with self-antigens as the authors cite previous work(ref 19). How do these environmental factors affect the evolution of the antibody? These already-known facts could be mentioned and discussed in detail.

We thank Reviewer #2 for these comments and agree that applying these insights to understand in vivo antibody affinity maturation would be fascinating. As the Reviewer points out, our study is limited to examining antigen affinity and neglects other properties that are known to impact antibody affinity maturation (e.g., autoreactivity). As we mention in the Discussion, our work shows how the acquisition of breadth is shaped by mutations that interact epistatically to determine binding affinity, and future work is required to understand how these mutations and interactions may also impact the myriad other properties relevant to antibody maturation.

Author Response

Reviewer #2 (Public Review):

The paper has two key messages: the discovery and the function of LncSox17. Claims of gene discovery are today untrivial, given the large number of genome-wide datasets. Of course, I understand the authors cannot check everything but I feel some more clear and deep analysis of current databases is lacking.

The reviewer is right when stating that there is an extremely high number of publicly available datasets and resources. In the current manuscript, we used Ensembl genes, Genecode V36 and Genecode V36 lncRNAs (commonly used datasets for gene and transcript annotation) and could not find reports of long non-coding RNAs with similar location, length and strand of T-REX17 (see Fig. 1). To further ensure that we did not overlook it, during the revision we inspected these datasets again, coming to the same conclusion that T-REX17 has not been previously reported at this locus.

As we show, T-REX17 is only very transiently expressed in definitive endoderm and given that there are few available RNA-seq datasets covering this developmental transition from hiPSCs it is not entirely surprising that it has been missed in the past.

Also, the exact coordinates of the lncRNA are not easy to find in the manuscript.

This is certainly an important annotation we missed in the manuscript. We now updated the legend of Figure 1A to include the exact genomic location of T-REX17.

Many statistical analyses are rather lacking. In particular I did not find details of how the DEGs were identified during differentiation (FDR? How many replicates?).

We thank the reviewer for pointing this out. We now specify in the Methods section (page 42, lines 1037-1039) and in the figure legends (page 54, lines 1269-1271) how the DEGs have been identified, which thresholds have been used, and number of replicates performed.

The results of the smFISH are surprising, since the level of expression seems rather low in comparison to the qPCR (only 4 times less expressed than Sox17) or the RNA-seq.

Direct quantitative comparisons between smFISH and qPCR (or RNA-seq) assays are in general quite hard since the two technologies rely on different biochemical principles. qPCR and RNA-seq include an amplification step, and therefore their interpretation should be considered as relative rather than absolute. On the other hand, smFISH offers a more absolute quantitative information and provides clues about the subcellular localization of the investigated target. At the same time, in smFISH experiments, individual foci could represent the accumulation of more than one molecule, making it hard to accurately infer gene expression levels from images. Throughout the manuscript we combine the two assays in an attempt to provide more robust information about T-REX17 expression dynamics.

We would also like to note the high specificity of our smFISH signal, given that we do not observe any detectable foci for T-REX17 in undifferentiated cells (Fig. 2C) or T-REX17 depleted endoderm cells (Fig. 3C).

Author Response

1) Response to the Editor

We thank the Editor and the Reviewers for the kind words, the helpful suggestions, and the points of critique, which have all helped us substantially strengthen the manuscript in this revised version. Regarding the 3 general critiques highlighted by the Editor:

Essential Revisions:

1) Some hypothesis, and in particular the one that all individuals have the same inter-burst interval distribution should be tested/justified/discussed.

(a) We have generalized the theory to directly address this point by relaxing the assumption of an identical inter-burst interval for all individuals. In short: the main insights continue to hold and we discuss the nuances in the text.

(b) Experimentally, the hypothesis that all single fireflies isolated from the group exhibit the same interburst interval (IBI) distribution could not be rigorously tested. The main reason is practical: in order to compare IBI distributions across individuals, we would need to collect a large number of fireflies and track them for long durations, which was not realistic given our experimental setup and the short window of firefly emergence. In addition, external environmental factors might slightly alter behaviors as well, making comparisons even more complex. Thus, due to paucity of field data, we eventually use the assumption that all individual fireflies follow the same IBI distribution.

2) Comparison between the models and the data must be improved, in particular through a quantification of the differences between distributions and sensitivity analysis of the numerical results.

(a) Regarding the comparison of the agent-based simulations with experimental data, in Fig. 7, we compare the underlying distributions using the two-sided Kolgomorov-Smirnov statistical test for goodness-of-fit. These appear to us the most straightforward and informative approaches, without over-fitting.

(b) Regarding sensitivity analysis for the agent-based simulations, for each β value from 0 to 1 we statistically compared simulations to the experimental distributions to find the most well-fitted β.

(c) Finally, owing to experimental constraints leading to sparsity of available data in characterizing the interburst distribution, we strive to strike a delicate balance between sophisticated statistical tools to compare theoretical and simulation distributions (with unrestricted access to large sample sizes) to the finite samples in the empirical distributions. As such, we think it is the apposite to use the first two moments of respective distributions In Fig. 3 to show the striking similarity of trends.

3) More discussion of the modeling in connection to past theoretical results and existing literature is necessary to better contextualize the present work and assess its originality.

We have done this closely following the specific suggestions from reviewers.

2) Revised terminology: removing usage of “model”

Since unintended ambiguity may be caused by use of the word “model”, which could refer to either (1) the theoretical framework, principle of emergent periodicity, and attendant analytic calculation , or (2) the agent-based simulation in the computational realization, we have removed all instances of the word “model” from the results presented in the paper, and replaced by the specific meaning (theory or simulation) in each context.

Similarly, in responding to Reviewers’ comments, we clarify what we understand by their use of the word “model” in each case.

3) Addressing an error in the agent-based simulation code

We (OM and OP) have now addressed an inadvertent unit typo in the agent-based simulation code. The discharging time (Td) before the typo was fixed was set to 10000ms. After the fix, the Td value was correctly set to 100ms. This caused very slow discharges, keeping the voltage high until any beta addition was received, resulting in more frequent bursts than we’d actually expect from the model dynamics. This has been fixed, and in our responses to the reviewers, we address the results of this fix by referring to the “unit typo”. We corrected the panels corresponding to agent-based simulation in Figs. 3 and 5 to reflect the new numerical simulation results, as well as the corresponding sections in the text of the paper.

4) Addressing changes to experimental dataset

We increased the size of our N=1 dataset (N is number of fireflies) to correctly match what was reported in the original text of 10 samples. Additionally, we have added characterization of the size of the datasets for N=5, 10, 15, and 20 fireflies.

5) Response to Reviewer 1

We thank the Reviewer for kind remarks, and the highlights of the strengths of the paper.

Regarding concerns raised, point by point:

Reviewer #1 (Public Review):

Weaknesses:

The work presented here is an excellent start at understanding the collective behavior of this particular species of firefly. However, the model does not apply to other species in which individual males are intrinsically rhythmic. So the model is less general than it may appear at first.

We take the Reviewer’s point well. We have added text to the paper to clearly highlight this point.

The modeling framework is also developed under the very stylized conditions of experiments conducted in a small tent. While that is a natural place to begin, future work should consider the conditions that fireflies encounter in the wild. Swarms that are spread out in space would require a model with a more complicated structure, perhaps with network connectivity and coupling strengths that both change in time as fireflies move around. This is not so much a weakness of the present work as a call to arms for future research.

We agree with the Reviewer that this is an exciting call to arms for future research!

Other comments:

This assumption that all individuals have the same IBI distribution could be directly tested. Has this been done? If not, why not? e.g. Are there difficulties with letting one firefly flash long enough to collect sufficient data to fill out the distribution?

1. We have generalized the theory to directly address this point by relaxing the assumption that all individuals exhibit the same inter-burst interval distribution. In short: the main insights continue to hold and we discuss the nuances in the text.

2. Experimentally, hypothesis that all single fireflies isolated from the group exhibit the same interburst interval (IBI) distribution could not be rigorously tested. The main reason is practical: in order to compare IBI distributions across individuals, we would need to collect a large number of fireflies and track them for long durations, which was not realistic given our experimental setup and the short window of firefly emergence. In addition, external environmental factors might slightly alter behaviors as well, making comparisons even more complex. Thus, due to paucity of field data, we eventually use the assumption that all individual fireflies follow the same IBI distribution.

The derivation given in 6.2.1 is clearer than the approach taken here, which unnecessarily introduces Q, q, and c and then never uses them again.

We agree with the Reviewer and have accordingly revised the manuscript.

We have also implemented the suggested edits in the marked up manuscript. We are grateful for the detailed feedback, which helped us substantially extend results, and improve presentation and clarity.

6) Response to Reviewer 2

We thank the Reviewer for their thorough feedback. We provide point by point responses below.

Reviewer #2 (Public Review):

1) The biological relevance of certain hypotheses is insufficiently discussed. This is important because if the observed behaviour is a universal one, alternative models may explain it as well.

We thank the reviewer for raising this point. The main hypotheses underlying our models are: 1) individual fireflies in isolation flash at random intervals; 2) these random intervals are drawn from the empirical distribution reported (implicitly: all fireflies follow the same distribution); 3) once a firefly flashes, it triggers all others. Hypothesis 1) is directly supported by the data presented. Hypothesis 2) is comprehensively addressed in the revised manuscript, as discussed previously. Hypothesis 3) is central to the proposed principle, and enables intrinsically non-oscillating individuals to oscillate periodically when in a group. The resulting phenomenon has been compared to experimental data and extensively discussed in the manuscript. Further, we have also simulated the effect of changing the strength of coupling between fireflies based on this hypothesis in the revised section on agent-based simulation.

2) Comparison between the models and the data could be improved, in particular through quantification of the differences between distributions and sensitivity analysis of the numerical results.

1. Regarding the comparison of the agent-based simulations with experimental data, in Fig. 7, we compare the underlying distributions using the two-sided Kolgomorov-Smirnov statistical test for goodness-of fit. These appear to us the most straightforward and informative approaches, without over-fitting.

2. Regarding sensitivity analysis for the agent-based simulations, for each β value from 0 to 1 we statistically compared simulations to the experimental distributions to find the most well-fitted β.

3. Finally, owing to experimental constraints leading to sparsity of available data in characterizing the interburst distribution, we strive to strike a delicate balance between sophisticated statistical tools to compare theoretical and simulation distributions (with unrestricted access to large sample sizes) to the finite samples in the empirical distributions. As such, we think it is the apposite to use the first two moments of respective distributions In Fig. 3 to show the striking similarity of trends.

Reviewer #2 (Recommendations for the authors):

A. The assumption that single-firefly spikes obey the same distribution (there is no individual variation in the frequency, or even of the composing number of bursts, of the flash) does not seem to have been verified on the data, that are instead pulled together in one single distribution (Fig. 1D). Moreover, the main feature of such distribution is that it has a minimum at 12 secs (discarding the faster bursts that are not considered in the model) and that it is sufficiently skewed so that it takes a minimal coupling for collective synchrony to emerge. I think that the agreement between the distributions for different N would be more meaningfully discussed having previous work as a reference, whereas now this is relegated to the discussion, so that it is unclear how much of the theoretical results are novel and/or unexpected. Quantification of the distance between distributions would also be interesting: it looks like the two models (analytical and simulations) disagree more among themselves than with the data.

Regarding the hypothesis that all individual fireflies exhibit the same interflash interval, please see our response to Main Point 1. Regarding comparing the analytical theory and numerical simulation analysis, Figs. 3 and 5 have been revised after a unit typo was found in the code (see Section 2). Following the update, the analytical and numerical models agree in (1) the location of the peak in Fig. 3 for all N values, and (2) the peak approaches the minimum of the input distribution as N increases.

B. If I understand correctly, simulations are introduced as a way to get a dependence on the intensity of the coupling (\beta). There are several issues here. First, I do not see how the coupling constant could change in the present experimental setup, where all fireflies presumably see each other (different from when there is vegetation). Second, looking at Fig. 3, the critical coupling strength appears to depend very weakly from N, and it is not clear how the 'detailed comparison' that leads to the fit is realized (in fact, the fitted \betas look larger that those at which the transition occurs in Fig. 3A). I think a sensitivity analysis is needed in order to understand how do results change when \beta is changed, and also what is the effect of the natural Tb distribution (Fig. 2 F). Results of the simulations might be clearer if instead of using the envelope of the experimental results, the authors tried to fit it to a standard distribution (ex. Poisson) so that it can be regularized. This should allow to trace with higher resolution the boundary between asynchronous and synchronous firing.

We have included agent-based numerical simulations as a way to provide a concrete instantiation of the theory principle and analytical results in the preceding section. While the analytic theory results are fitting parameters free, in the agent-based simulations, we introduce an additional fitting parameter, to see what happens when we relax one hypothesis of the analytical theory: the instantaneous triggering of all fireflies upon an initial flasher. Additionally, the agent-based simulations pave the way for future work, allowing for convenient exploration of the connectivity between individuals and analysis of the behavior of individual fireflies. in this context, please note that Fig. 5 has been corrected (see above), leading to a stronger co-dependence of β and N. In addition to the envelopes, we also report the trends in the first empirical moments (mean and STD) for comparison and tracking of the transition to synchrony.

C. More care should be put in explaining what are the initial conditions hypothesized for the different models. For instance, the results of paragraph 3 are understandable if all fireflies are initialized just after firing, something that is only learnt at the end of the paragraph. I also wonder whether initial conditions may be involved with T_bs in the low-coupling region of Fig. 3A not being uniformly distributed, as I would have expected for a desynchronized population.

We have clarified that, indeed, all fireflies are re-initialized after firing. The initial conditions then become a new random vector of interflash intervals. Importantly, we found after receiving the reviews that, due to inconsistent units in our numerical simulation code, Fig. 5 was incorrect. With proper units, the new results show a much more widespread distribution at low coupling, as expected by the Reviewer.

D. I found that equations were hard to understand either because one of the variables was not precisely (or at all) defined, or because some information was missing: Eq. 1: q is not defined Eq. 2: explain what it means: the prob. that others have not flashed times that that one flashes. Also, say explicitly what is the 'corresponding PDF. Eq. 3: the equation for \epsilon(t) to which this is coupled is missing Why introduce \beta_{i,j} and T_bi if they are then taken independent of the indexes? Definitions of collective and group burst interval should be provided. It would be clearer if t_b0 was defined in the first paragraph of the results, so as to clarify as well its relation with T_b. Define T^i_b in the caption of Fig. 3 (they are defined later than the figure is first discussed). The definition of 'the vertical axis label' (maybe find a word for that...) is pretty cumbersome. I could imagine that other definitions would allow the lines in Fig. 3 E to converge to the same line for large betas, which would make more sense, considering that in the strong coupling limit I see no reason why the collective spiking should not be the same for different N (the analytical model could help here).

Thank you for these comments; we have incorporated these and related changes.

E. I think that the author's reading of the two 'dynamical quorum sensing' papers they cite is incorrect: De Monte et al. was not about the Kuramoto model, but the same limit cycle oscillators as in Strogatz; Taylor et al. considers excitable systems, potentially closer to noisy integrate-and-fire, at least in that they do not have self-sustained oscillations. Both papers show that oscillations appear above a certain density threshold, and that the frequency of oscillations increases with density, as found in this work. A more accurate link to previous publications in the field of synchronization theory, including the models by Kurths and colleagues for fireflies, would be useful both in the introduction and in the discussion, and would help the reader to position this work and appreciate its original contributions.

1. Thank you for pointing out an inaccuracy in our literature citations regarding synchronization. We have now made corrections to address this point.

2. While we take the Reviewer’s points well, our theory framework (“model”), building off of the principle of emergent periodicity we propose here, is fundamentally different in the nature of individuals from extant “models”. The reference in question has individuals as oscillators, and the fastest frequency is the frequency of the fastest individual oscillator. In contrast, in our work there is no fastest individual oscillator and the “fastest frequency” has a completely different meaning, since individuals do not have a particular frequency associated with them. In this sense, our work is not inspired by theirs. That said, we have included citations as suggested by the Reviewer.

F. The authors say that part of the data is unpublished. I guess they mean that the whole data set will be published with this manuscript. I think the formulation is ambiguous.

Thank you for this comment. We have now clarified that the data will indeed be published with the manuscript.

Author Response

Reviewer #1 (Public Review):

This paper tests whether people vary their reliance on episodic memory vs. incremental learning as a function of the uncertainty of the environment. The authors posit that higher uncertainty environments should lead to more reliance on episodic memory, and they find evidence for this effect across several kinds of analyses and across two independent samples.

The paper is beautifully written and motivated, and the results and figures are clear and compelling. The replication in an independent sample is especially useful. I think this will be an important paper of interest to a broad group of learning, memory, and decisionmaking researchers. I have only two points of concern about the interpretation of the results:

1) My main concern regards the indirect indicator of participants' use of episodic memory on a given trial. The authors assume that episodic memory is used if the value of the chosen object (as determined by its value the last time it was presented) does not match the current value of the deck it is presented in. They find that these mismatch choices happen more often in the high-volatility environment. But if participants simply choose in a more noisy/exploratory way in the high volatility environment, I believe that would also result in more mismatched judgments. What proportion of the trials labeled as episodic should we expect to be a result of noise or exploration? It seems conceivable that a judgment to explore could take longer, and result in the observed RT effects. Perhaps it could be useful to match up putative episodic trials with later recognition memory for those particular items. The across-subjects correlations are an indirect version of this, but could potentially be subject to a related concern if participants who explore more (and are then judged as more episodic) also simply have a better memory.

Thank you for this important suggestion. We agree that noisy/exploratory choices could potentially masquerade as episodic on the episodic-based choice index used as one of our behavioral measures. As pointed out, this is because participants may be more likely to make noisier incremental value-based decisions in the high volatility compared to the low volatility environment. In our revision, we provided a new analysis that shows that, as the reviewer predicted, choices are indeed more noisy in the high volatility environment. We answer this concern in two ways. First, we took this noise into account in our analysis of the episodic/incremental tradeoff and show that it does not account for the main findings. And second, we provided a new analysis of subsequent memory that shows that choices that are defined as episodic during the decision-task are also associated with better recognition memory later on. These new analyses are described below as well.

We used a mixed-effects logistic regression model to test for an interaction effect of environment and model-estimated deck value on whether the orange deck was chosen. We fit this model only to trials without the presence of a previously seen object in order to achieve a more accurate measure of noise specific to incremental learning. In both the main and replication samples, participants did indeed make noisier incremental decisions in the high compared to the low volatility environment (Main: 𝛽 = −1.589, 95% 𝐶𝐼 = [−2.091, −1.096], Replication: 𝛽 = −1.255, 95% 𝐶𝐼 = [−1.824, −0.675]). To account for the possibility that the measured difference between environments in our episodic-based choice index may be related to this difference in incremental noise between the environments, we included each participant’s random effect of the environment by deck value interaction from this model as a covariate in our analysis of the effect of environment on the episodic-based choice index. While each participants’ propensity to choose with greater noise in the high volatility environment did have an effect on the episodic-based choice index (Main: 𝛽 = 0.042, 95% 𝐶𝐼 = [0.012, 0.072], Replication: 𝛽 = 0.055, 95% 𝐶𝐼 = [0.027, 0.082]), the effect of environment was similar to that originally reported in the manuscript for both samples following this adjustment. The reported effects (lines 178 and Appendix 1) and methods (lines 643-655) have been updated to reflect these changes.

We applied a similar logic to the reaction time analysis, to address the possibility that decisions based on exploration may take longer compared to decisions based on exploitation of learned deck value. We included a covariate in the analysis of the effect of episodic-based choices on reaction time that captured possible slowing due to switching from choosing one deck to the other (lines 656-662) and found that the slower reaction times on episodic choices are not fully explained by exploration. Because in this task a decision to explore is captured by switching from one deck to another, the effect of episodic-based choices on reaction time reported in the manuscript should account for this behavior. We have clarified this reasoning in the methods (lines 661-662).

Finally, thank you for the idea to sort objects in the recognition memory test by whether they were from episodic- or incremental-based choice trials to provide a further test of whether our approach for sorting episodic decisions withstands an independent test. We performed this analysis and found that, in both samples, participants had better memory for objects from episodic-based choice trials. This result provides further support for the putative episodic nature of these trials and is now reported in the Results (lines 300-304 and Appendix 1), Methods (lines 737-742) and appears as a new panel in Figure 5 (Figure 5A).

2) The paper is framed as tapping into a trade-off between the use of episodic memory vs. incremental learning, but it is not clear why participants would not use episodic memory in this particular task setup whenever it is available to them. The authors mention that there is "computational expense" to episodic memory, but retrieval of an already-established strong episodic memory could be quite effortless and even automatic. Why not always use it, since it is guaranteed in this task to be a better source of information for the decision? If it is true that RT is higher when using episodic memory, that is helpful toward establishing the trade-off, so this links to the concern above about how confident we can be about the use of episodic memory in particular trials.

Thank you for raising this important point and for giving us the opportunity to clarify. We now address this point in two ways: first, we provide a new analysis of episodic memory and choice behavior and we address this point explicitly in the discussion.

As now emphasized in the paper (lines 118-122 and lines 384-388), in this task, it is true that an observer with perfect episodic memory should always make use of it whenever available (i.e. on trials featuring previously seen objects). However, human memory is fallible and resourcelimited, and we find that participants with less reliable episodic memory overall actually relied less on this strategy and more on incremental learning throughout the task (Figure 5C and 5D). In other words, there is noise and uncertainty also in the episodic memory trace. While it is not the main focus of our study, the noise in episodic memory is indeed another reason why trading off between episodic memory and incremental learning is advantageous for behavior. We further agree that while the RT effects show that, relative to using incremental value, episodic memory retrieval takes longer, we cannot make strong statements about effort or “computational expense” per se from our data. Accordingly, we have removed the “computational expense” phrase (line 491), as well as our suggestion that episodic retrieval is “perhaps more effortful overall” (line 181), from the paper.

Reviewer #2 (Public Review):

This manuscript addresses the broad question of when humans use different learning and memory systems in the service of decision-making. Previous studies have shown that, even in tasks that can be performed well using incremental trial-and-error learning, choices can sometimes be based on memories of individual past episodes. This manuscript asks what determines the balance between incremental learning and episodic memory, and specifically tests the idea that the uncertainty associated with each alters the balance between them in a rational way. Using a task that can separate the influence of incremental learning and episodic memory on choice in two large online samples, several lines of evidence supporting this hypothesis are reported. People are more likely to rely on episodic memory in more volatile environments when incremental learning is more uncertain and during periods of increased uncertainty within a given environment. Individuals with more accurate episodic memories are also more likely to rely on episodic memory and less likely to rely on incremental learning. These data are compelling, even more so because all of the main findings are directly replicated in a second sample. These data extend the notion of uncertainty-based arbitration between different forms of learning/memory, which has been proposed and evaluated in other contexts, to the case of episodic memory versus incremental learning.

The weaknesses in the paper are mostly minor. One potential weakness is the nature of the online sample. Many participants apparently did not respond to the volatility manipulation, making it impossible to test whether this altered their choices. It is unclear whether this is a feature of online samples (where people can be distracted, unmotivated, etc.) or of human performance more generally.

Thank you for your comments. Indeed, we also found it interesting that many participants were insensitive to the manipulation of volatility in our study, as assessed and filtered based on the initial deck learning task. As you note, our study is not positioned to determine the cause and whether this is due to the online population or human performance more generally, and we added a discussion of this point to the paper (lines 477-485). Also, fractions exceeding 1/3 apparently inattentive participants are very much the norm in our experience with other online studies across many tasks. While there is much to say about the implications of this (see e.g. Zorowitz, Niv & Bennett PsyArXiv 2021), our basic philosophy (which we follow here) is that it is best practice, and conservative, to exclude aggressively so as to focus analyses on those participants for whom the experimental questions can meaningfully be asked.

Reviewer #3 (Public Review):

The purpose of this work is to test the hypothesis that uncertainty modulates the relative contributions of episodic and incremental learning to decisions. The authors test this using a "deck learning and card memory task" featuring a 2-alternative forced choice between two cards, each showing a color and an object. The cards are drawn from different colored decks with different average values that stochastically reverse with fixed volatility, and also feature objects that can be unfamiliar or familiar. Objects are not shown more than twice, and familiar objects have the same value as they did when shown previously. This allows the authors to construct an index of episodic contributions to decision-making: in cases where the previous value of the object is incongruous with the incrementally observed value, the subject's choice reveals which strategy they are relying on.

The key manipulation is to introduce high- and low- volatility conditions, as high volatility has been shown to induce uncertainty in incremental learning by causing subjects to adopt an optimal low learning rate. The authors find that the subjects show a higher episodic choice index in the high-volatility condition, and in particular immediately after reversals when the model predicts uncertainty is at a maximum. The authors also construct a trial-wise index of uncertainty and show that episodic index correlates with this measure. The authors also find that at the subject level, the overall episodic choice index correlates with the ability to accurately identify familiar objects, and the reason that this indicates higher certainty in episodic memory is predicting the usage of episodic strategies. The authors replicate all of their findings in a second subject population.

This is a very interesting study with compelling results on an important topic. The task design was a clever way to disentangle and measure different learning strategies, which could be adopted by others seeking to further understand the contributions of different strategies to decision-making and its neural underpinnings. The article is also very clearly written and the results clearly communicated.

A number of questions remain regarding the interpretation of the results that I think would be addressed with further analysis and modeling.

At a conceptual level, I was unsure about the equivalence drawn between volatility and uncertainty: the main experiments and analyses all regard reversals and comparisons of volatility conditions, but the conclusions are more broadly about uncertainty. Volatility, as the authors note, is only one way to induce uncertainty. It also doesn't seem like the most obvious way to intervene on uncertainty (eg manipulated trial-wise variance seems more obvious). The trial-wise relative uncertainty measurements in Fig 4 speak a bit more to the question of uncertainty more generally, but these were not the main focus and also do not disambiguate between trial-wise uncertainty derived from reversals versus within block variation.

Thank you for your comments. We agree that this distinction was unclear and appreciate the opportunity to clarify. We hope the manuscript is now clear about the conceptual distinction between uncertainty as the construct of theoretical interest vs. volatility as the operational manipulation being used to access it. We have adjusted the presentation and added discussion to clarify this, and also enhanced the trial-wise analyses to strengthen the interpretation of results in terms of uncertainty more generally. Regarding obviousness, we think perhaps there is a difference between areas of study on this point. While trial-wise outcome variance (which we call stochasticity) has been widely used to manipulate uncertainty in perceptual and sensorimotor studies, it has been more rarely manipulated in reward learning studies, where instead the volatility manipulation we use has predominated. We have a recent paper reviewing examples of both and arguing that the field has underemphasized the importance of stochasticity, so we are sympathetic here (Piray and Daw, Nature Communications 2021).

In any case, to address these points on revision, we have reframed the first section of the results, where we look at effects of environment on episodic-based choice, to focus primarily on volatility. Specifically, we have expanded on our explanation of how volatility induces uncertainty, changed the subtitle of the section from ‘uncertainty’ to ‘volatility’, and have specified that the prediction in this section is primarily about volatility (lines 97 and 116-123). We also reframed the second section of the results to be primarily about the uncertainty induced by volatility: while differences between the environments capture coarse effects of volatility, trialwise uncertainty should be present following reversals across both environments. We have now focused our explanation in this section on trial-wise uncertainty within the environments rather than volatility between the environments (lines 184-192). Further, we agree that there are other sources of uncertainty besides volatility that we did not manipulate in the paper, and that it remains for future work whether their manipulation would produce similar results. To amend this, we have added a new paragraph to the discussion covering these alternative sources and further qualifying the scope of our conclusions (lines 434-446).

We also agree that our analyses in Figure 4 did not yet speak to differences in episodic-based choice that may arise due to blockwise volatility (as captured by the categorical effect of environment) vs. trial-to-trial fluctuations in uncertainty (as captured by relative uncertainty, over and above the blockwise effect). We have addressed this by adding an additional, separate effect of the interaction between environment and episodic value to our combined choice models which is explained in more detail in the recommendations for the authors portion of our response. These changes and results are described in the Methods (lines 686-694) and Results (lines 276-277; Figure 4C).

Another key question I had about design choice was the decision to use binary rather than drifting values. Because of this, the subjects could be inferring context rather than continuously incrementing value estimates (eg Gershman et al 2012, Akam et al 2015): the subjects could be inferring which context they are in rather than tracking the instantaneous value + uncertainty. I am not sure this would qualitatively affect the results, as volatility would also affect context confidence, but it is a rather different interpretation and could invoke different quantitative predictions. And it might also have some qualitative bearing on results: the subjects have expectations about how long they will stay in a particular environment, and they might start anticipating a context change after a certain amount of time which would lead to an increase in uncertainty not just immediately after switches, but also after having stayed in the environment for a long period of time. Moreover, depending on the variance within context, there may be little uncertainty following context shifts.

Thank you for raising this important point. To address the possibility that the task structure could have encouraged participants to infer context rather than engage in incremental learning, we added an alternative contextual inference (CI) model, based on a hidden Markov model with two hidden states (e.g. that either the red deck is lucky and the blue deck unlucky or vice versa). This model is now described in the Results of the main text (lines 226-228), listed in the Methods (line 674), and explained in detail in Appendix 3 alongside the computational models of incremental learning. Following model comparison, we found that this model provided a worse fit than the incremental learning models we previously presented in both samples, suggesting that incremental learning is a better descriptor of participants’ choices in this task than contextual inference. The results of this comparison are reflected in an updated Figure 3A.

Author Response

Reviewer #1 (Public Review):

Slusarczyk et al present a very well written manuscript focused on understanding the mechanisms underlying aging of erythrophagocytic macrophages in the spleen (RPM) and its relationship to iron loading with age. The manuscript is diffuse with a broad swath of data elements. Importantly, the manuscript demonstrates that RPM erythrophagocytic capacity is diminished with age, restored in iron restricted diet fed aged mice. In addition, the mechanism for declining RPM erythrophagocytic capacity appears to be ferroptosis-mediated, insensitive to heme as it is to iron, and occur independently of ROS generation. These are compelling findings. However, some of the data relies on conjecture for conclusion and a clear causal association is not clear. The main conclusion of the manuscript points to the accumulation of unavailable insoluble forms of iron as both causing and resulting from decreased RPM erythrophagocytic capacity.

We are proposing that intracellular iron accumulation progresses first and leads to global proteotoxic damage and increased lipid peroxidation. This eventually triggers the death of a fraction of aging RPMs, thus promoting the formation of extracellular iron-rich protein aggregates. More explanation can be found below. Besides, iron loading suppresses the erythrophagocytic activity of RPMs, hence further contributing to their functional impairment during aging.

In addition, the finding that IR diet leads to increased TF saturation in aged mice is surprising.

We believe that this observation implies better mobilization of splenic iron stores, and corroborates our conclusion that mice that age on an iron-reduced diet benefit from higher iron bioavailability, although these differences are relatively mild. More explanation can be found in our replies to Reviewer #2.

Furthermore, whether the finding in RPMs is intrinsic or related to RBC-related changes with aging is not addressed.

We now addressed this issue and we characterized in more detail both iron and ROS levels in RBCs.

Finally, these findings in a single strain and only female mice is intriguing but warrants tempered conclusions.

We tempered the conclusions and provided a basic characterization of the RPM aging phenotype in Balb/c female mice.

Major points:

1) The main concern is that there is no clear explanation of why iron increases during aging although the authors appear to be saying that iron accumulation is both the cause of and a consequence of decreased RPM erythrophagocytic capacity. This requires more clarification of the main hypothesis on Page 4, line 17-18.

We thank the reviewer for this comment. It was previously reported that iron accumulates substantially in the spleen during aging, especially in female mice (Altamura et al., 2014). Since RPMs are those cells that process most of the iron in the spleen, we aimed to explore what is the relationship between iron accumulation and RPM functions during aging. This investigation led us to uncover that indeed iron accumulation is both the cause and the consequence of RPM dysfunction. Specifically, we propose that intracellular iron loading of RPMs precedes extracellular deposition of iron in a form of protein-rich aggregates, driven by RPMs damage. To support this, we now show that the proteome of RPMs overlaps with those proteins that are present in the age-triggered aggregates (Fig. 3F). Furthermore, corroborating our model, we now demonstrate that transient iron loading of RPMs via iron-dextran injection (new Fig. 3G) leads to the formation of protein-rich aggregates, closely resembling those present in aged spleens (new Fig. 3H). This implies that high iron content in RPMs is indeed a major driving factor that leads to aggregation of their proteome and cell damage. Importantly, we now supported this model with studies using iRPMs. We demonstrated that iron loading and blockage of ferroportin by synthetic mini-hepcidin (PR73)(Stefanova et al., 2018) cause protein aggregation in iRPMs and lead to their decreased viability only in cells that were exposed to heat shock, a well-established trigger of proteotoxicity (new Fig. 5K and L). We propose that these two factors, namely age-triggered decrease in protein homeostasis and exposure to excessive iron levels, act in concert and render RPMs particularly sensitive to damage during aging (see also Discussion, p. 16).

In parallel, our data imply that the increased iron content in aged RPMs drives their decreased erythrophagocytic activity, as we now better documented by more extensive in vitro experiments in iRPMs (new Fig 6E-H). We cannot exclude that some of the senescent splenic RBCs that are retained in the red pulp and evade erythrophagocytosis due to RPM defects in aging, may also contribute to the formation of the aggregates. This is supported by the fact that mice that lack RPMs as well exhibit iron loading in the spleen (Kohyama et al., 2009; Okreglicka et al., 2021), and that the proteome of aggregates overlaps to some extent with the proteome of erythrocytes (new Fig. 3F).

We believe that during aging intracellular iron accumulation is chiefly driven by ferroportin downregulation, as also suggested by Reviewer#3. We now show that ferroportin drops significantly already in mice aged 4 and 5 months (new Fig. 4H), preceding most of the other impairments. This drop coincides with the increase in hepcidin expression, but if this is the sole reason for ferroportin suppression during early aging would require further investigation outside the scope of the present manuscript.

In sum, to address this comment, we now modified the fragment of the introduction that refers to our hypothesis and major findings to be more clear (p. 4), we improved our manuscript by providing new data mentioned above and we added more explanation in the corresponding sections of the Results and Discussion.

2) It is unclear if RPMs are in limited supply. Based on the introduction (page 4, line 13-15), they have limited self-renewal capacity and blood monocytes only partially replenished. Fig 4D suggests that there is a decrease in RPMs from aged mice. The %RPM from CD45+ compartment suggests that there may just be relatively more neutrophils or fewer monocytes recruited. There is not enough clarity on the meaning of this data point.

Thank you for this comment. We fully agree that %RPMs of CD45+ splenocytes, although well-accepted in literature (Kohyama et al., 2009; Okreglicka et al., 2021), is only a relative number. Hence, we now included additional data and explanations regarding the loss of RPMs during aging.

It was reported that the proportion of RPMs derived from bone marrow monocytes increases mildly but progressively during aging (Liu et al., 2019). This implies that due to the loss of the total RPM population, as illustrated by our data, the cells of embryonic origin are likely even more affected. We could confirm this assumption by re-analysis of the data from Liu et al. that we now included in the manuscript as Fig. 5E. These data clearly show that the representation of embryonically-derived RPMs drops more drastically than the percent of total RPMs, whereas the replenishment rate from monocytes is not affected significantly during aging. Consistent with this, we have not observed any robust change in the population of monocytes (F4/80-low, CD11b-high) or pre-RPMs (F4/80-high, CD11b-high) in the spleen at the age of 10 months (Figure 5-figure supplement 2A and B). We also have detected a mild decrease, not an increase, in the number of granulocytes (new Figure 5-figure supplement 2C). Furthermore, we measured in situ apoptosis marker and found a clear sign of apoptosis in the aged spleen (especially in the red pulp area), a phenotype that is less pronounced in mice on an IR diet (new Fig. 5O). This is consistent with the observation that apoptosis markers can be elevated in tissues upon ferroptosis induction (Friedmann Angeli et al., 2014) and that the proteotoxic stress in aged RPMs, which we now emphasized better in our manuscript, may also lead to apoptosis (Brancolini & Iuliano, 2020). Taken together, we strongly believe that the functional defect of embryonically-derived RPMs chiefly contributes to their shortage during aging.

3) Anemia of aging is a complex and poorly understood mechanistically. In general, it is considered similar to anemia of chronic inflammation with increased Epo, mild drop in Hb, and erythroid expansion, similar to ineffective erythropoiesis / low Epo responsiveness. It is not surprising that IR diet did not impact this mild anemia. However, was the MCV or MCH altered in aged and IR aged mice?

We now included the data for hematocrit, RBC counts, MCV, and MCH in Figure 1-figure supplement 5. Hematocrit shows a similar tendency as hemoglobin levels, but the values for RBC counts, MCV, and MCH seem not to be altered. We also show now that the erythropoietic activity in the bone marrow is not affected in aged versus young mice. Taken together, the anemic phenotype in female C57BL/6J mice at this age is very mild, which we emphasized in the main text, and is likely affected by other factors than serum iron levels (p. 6).

4) Page 6, line 23 onward: the conclusion is that KC compensate for the decreased function of RPM in the spleen, based on the expansion of KC fraction in the liver. Is there evidence that KCs are engaged in more erythrophagocytosis in aged mice? Furthermore, iron accumulation in the liver with age does not demonstrate specifically enhanced erythrophagocytosis of KC. Please clarify why liver iron accumulation would not be simply a consequence of increased parenchymal iron similar to increased splenic iron with age, independent of erythrophagocytic activity in resident macrophages in either organ.

Thanks for these questions. For the quantification of the erythrophagocytosis rate in KC, we show, as for the RPMs (Fig. 1K), the % of PKH67-positive macrophages, following transfusion of PKH67-stained stressed RBCs (Fig. 1M). The data implies a mild (not statistically significant) drop (of approx. 30%) in EP activity. We believe that it is overridden by a more pronounced (on average, 2-fold) increase in the representation of KCs (Fig. 1N). The mechanisms of iron accumulation between the spleen and the liver are very different. In the liver, we observed iron deposition in the parenchymal cells (not non-parenchymal, new Fig. 1P) that we currently characterizing in more detail in a parallel manuscript. Our data demonstrate a drop in transferrin saturation in aged mice. Hence, it is highly unlikely that aging would be hallmarked by the presence of circulating non-transferrin-bound iron that would be sequestered by hepatocytes, as shown previously (Jenkitkasemwong et al., 2015). Thus, the iron released locally by KCs is the most likely contributor to progressive hepatocytic iron loading during aging. The mechanism of iron delivery to hepatocytes from erythrophagocytosing KCs was demonstrated by Theurl et al.(Theurl et al., 2016), and we propose that it may be operational, although in a much more prolonged time scale, during aging. We now discussed this part better in our Results sections (p. 7).

5) Unclear whether the effect on RPMs is intrinsic or extrinsic. Would be helpful to evaluate aged iRPMs using young RBC vs. young iRPMs using old RBCs.

We are skeptical if the generation of iRPMs cells from aged mice would be helpful – these cells are a specific type of primary macrophage culture, derived from bone marrow monocytes with MCSF1, and exposed additionally to heme and IL-33 for 4 days. We do not expect that bone marrow monocytes are heavily affected by aging, and would thus recapitulate some aspects of aged RPMs from the spleen, especially after 8-day in vitro culture. However, to address the concerns of the reviewer, we now provide additional data regarding RBC fitness. Consistent with the time life-span experiment (Fig, 2A), we show that oxidative stress in RBCs is only increased in splenic, but not circulating RBCs (new Fig. 2C, replacing the old Fig. 2B and C). In addition, we show no signs of age-triggered iron loading in RBCs, either in the spleen (new Fig. 2F) or in the circulation (new Fig. 2B). Hence, we do not envision a possibility that RPMs become iron-loaded during aging as a result of erythrophagocytosis of iron-loaded RBCs. In support of this, we also have observed that during aging first RPMs’ FPN levels drop, afterward erythrophagocytosis rate decreases, and lastly, RBCs start to exhibit significantly increased oxidative stress (presented now in new Fig. 4H, J and K).

6) Discussion of aggregates in the spleen of aged mice (Fig 2G-2K and Fig 3) is very descriptive and non-specific. For example, if the iron-rich aggregates are hemosiderin, a hemosiderin-specific stain would be helpful. This data specifically is correlatory and difficult to extract value from.

Thanks for these comments. To the best of our knowledge Prussian blue Perls’ staining (Fig. 2J) is considered a hemosiderin staining. Our investigations aimed to better understand the nature and the origin of splenic iron deposits that to some extent are referred to as hemosiderin. Most importantly, as mentioned in our reply R1 Ad. 1. to assign causality to our data, we now demonstrated that iron accumulation in RPMs in response to iron-dextran (Fig. 3G) increases lipid peroxidation (Fig. 5F), tends to provoke RPMs depletion (Fig. 5G) and triggers the formation of protein-rich aggregates (new Fig. 3H). Of note, we assume that the loss of embryonically-derived RPMs in this model may be masked by simultaneous replenishment of the niche from monocytes, a phenomenon that may be addressed by future studies using Ms4a3-driven reporter mice (as shown for aged mice in our new Fig. 5E).

7) The aging phenotype in RPMs appears to be initiated sometime after 2 months of age. However, there is some reversal of the phenotype with increasing age, e.g. Fig 4B with decreased lipid peroxidation in 9 month old relative to 6 month old RPMs. What does this mean? Why is there a partial spontaneous normalization?

Thanks for this comment and questions. Indeed, the degree of lipid peroxidation exhibits some kinetics, suggestive of partial normalization. Of note, such a tendency is not evident for other aging phenotypes of RPMs, hence, we did not emphasize this in the original manuscript. However, in a revised version of the manuscript, we now present the re-analysis of the published data which implies that the number of embryonically-derived RPMs drops substantially between mice at 20 weeks and 36 weeks (new Fig. 5E). We think that the higher proportion of monocyte-derived RPMs in total RPM population later in aging (9 months) might be responsible for the partial alleviation of lipid peroxidation. We now discussed this possibility in the Results sections (p. 12).

8) Does the aging phenotype in RPMs respond to ferristatin? It appears that NAC, which is a glutathione generator and can reverse ferroptosis, does not reverse the decreased RPM erythrophagocytic capacity observed with age yet the authors still propose that ferroptosis is involved. A response to ferristatin is a standard and acceptable approach to evaluating ferroptosis.

We fully agree with the Reviewer that using ferristatin or Liproxstatin-1 would be very helpful to fully characterize a mechanism of RPMs depletion in mice. However, previous in vivo studies involving Liproxstatin-1 administration required daily injections of this ferroptosis inhibitor (Friedmann Angeli et al., 2014). This would be hardly feasible during aging. Regarding the experiments involving iron-dextran injection, using Liproxstatin-1 would require additional permission from the ethical committee which takes time to be processed and received. However, to address this question we now provide data from iRPMs cell cultures (new Fig.5 K-L). In essence, our results imply that both proteotoxic stress and iron overload act in concert to trigger cytotoxicity in RPM in vitro model. Interestingly, this phenomenon does not depend solely on the increased lipid peroxidation, but when we neutralize the latter with Liproxstatin-1, the cytotoxic effect is diminished (please, see also Results on p. 13 and Discussion p. 15/16).

9) The possible central role for HO-1 in the pathophysiology of decreased RPM erythrophagocytic capacity with age is interesting. However, it is not clear how the authors arrived at this hypothesis and would be useful to evaluate in the least whether RBCs in young vs. aged mice have more hemoglobin as these changes may be primary drivers of how much HO-1 is needed during erythrophagocytosis.

Thanks for this comment. We got interested in HO-1 levels based on the RNA sequencing data, which detected lower Hmox-1 expression in aged RPMs (Figure 3-figure supplement 1). We now show that the content of hemoglobin is not significantly altered in aged RBCs (MCH parameter, Figure 1-figure supplement 5E), hence we do not think that this is the major driver for Hmox-1 downregulation. Likewise, the levels of the Bach1 message, a gene encoding Hmox-1 transcriptional repressor, are not significantly altered according to RNAseq data. Hence, the reason for the transcriptional downregulation of Hmox-1 is not clear. Of note, HO-1 protein levels in the total spleen are higher in aged versus young mice, and we also detected a clear appearance of its nuclear truncated and enzymatically-inactive form (see a figure below, we opt not to include this in the manuscript for better clarity). The appearance of truncated HO-1 seems to be partially rescued by the IR diet. It is well established that the nuclear form of HO-1 emerges via proteolytic cleavage and migrates to the nucleus under conditions of oxidative stress (Mascaro et al., 2021). This additionally confirms that the aging spleen is hallmarked by an increased burden of ROS. Moreover, we also detected HO-1 as one of the components of the protein iron-rich aggregates. Thus, we propose that the low levels of the cytoplasmic enzymatically active form of HO-1 in RPMs (that we preferentially detect with our intracellular staining and flow cytometry) may be underlain by its nuclear translocation and sequestration in protein aggregates that evade antibody binding [this is also supported by our observation that the protein aggregates, despite the high content of ferritin (as indicated by MS analysis) are negative for L-ferritin staining. Of note, we also cannot exclude that other cell types in the aging spleen (eg. lymphocytes) express higher levels of HO-1 in response to splenic oxidative stress.

Fig. Total splenic levels of HO-1 in young, aged IR and aged mice.

Reviewer #2 (Public Review):

Slusarczyk et al. investigate the functional impairment of red pulp macrophages (RPMs) during aging. When red blood cells (RBCs) become senescent, they are recycled by RPMs via erythrophagocytosis (EP). This leads to an increase in intracellular heme and iron both of which are cytotoxic. The authors hypothesize that the continuous processing of iron by RPMs could alter their functions in an age-dependent manner. The authors used a wide variety of models: in vivo model using female mice with standard (200ppm) and restricted (25ppm) iron diet, ex vivo model using EP with splenocytes, and in vitro model with EP using iRPMs. The authors found iron accumulation in organs but markers for serum iron deficiency. They show that during aging, RPMs have a higher labile iron pool (LIP), decreased lysosomal activity with a concomitant reduction in EP. Furthermore, aging RPMs undergo ferroptosis resulting in a non-bioavailable iron deposition as intra and extracellular aggregates. Aged mice fed with an iron restricted diet restore most of the iron-recycling capacity of RPMs even though the mild-anemia remains unchanged.

Overall, I find the manuscript to be of significant potential interest. But there are important discrepancies that need to be first resolved. The proposed model is that during aging both EP and HO-1 expression decreases in RPMs but iron and ferroportin levels are elevated. In their model, the authors show intracellular iron-rich proteinaceous aggregates. But if HO-1 levels decrease, intracellular heme levels should increase. If Fpn levels increase, intracellular iron levels should decrease. How does LIP stay high in RPMs under these conditions? I find these to be major conflicting questions in the model.

We thank the Reviewer for her/his valuable feedback. As we mentioned in our replies we can only assume that a small misunderstanding in the interpretation of the presented data underlies this comment. We show that ferroportin levels in RPMs (Fig. 1F) are modulated in a manner that fully reflects the iron status of these cells (both labile and total iron levels, Figs. 1H and I). FPN levels drop in aged RPMs and are rescued when mice are maintained on a reduced iron diet. As pointed out by Reviewer#3, and explained in our replies we believe that ferroportin levels are critical for the observed phenotypes in aging. We now described our data in a more clear way to avoid any potential misinterpretation (p.6).

Reviewer #3 (Public Review):

This is a comprehensive study of the effects of aging of the function of red pulp macrophages (RPM) involved in iron recycling from erythrocytes. The authors document that insoluble iron accumulates in the spleen, that RPM become functionally impaired, and that these effects can be ameliorated by an iron-restricted diet. The study is well written, carefully done, extensively documented, and its conclusions are well supported. It is a useful and important addition for at least three distinct fields: aging, iron and macrophage biology.

The authors do not explain why an iron-restricted diet has such a strong beneficial effect on RPM aging. This is not at all obvious. I assume that the number of erythrocytes that are recycled in the spleen, and are by far the largest source of splenic iron, is not changed much by iron restriction. Is the iron retention time in macrophages changed by the diet, i.e. the recycled iron is retained for a short time when diet is iron-restricted (making hepcidin low and ferroportin high), and long time when iron is sufficient (making hepcidin high and ferroportin low)? Longer iron retention could increase damage and account for the effect. Possibly, macrophages may not empty completely of iron before having to ingest another senescent erythrocyte, and so gradually accumulate iron.

We are very grateful to this Reviewer for emphasizing the importance of the iron export capacity of RPMs as a possible driver of the observed phenotypes. Indeed, as mentioned above, we now show in the revised version of the manuscript that ferroportin drops early during aging (revised Fig. 4). Importantly, we now also observed that iron loading and limitation of iron export from iRPMs via ferroportin aggravate the impact of heat shock (a well-accepted trigger of proteotoxicity) on both protein aggregation and cell viability (new Fig. 5K and L). Physiologically, recent findings show that aging promotes a global decrease in protein solubility [BioRxiv manuscript (Sui X. et al., 2022)], and it is very likely that the constant exposure of RPMs to high iron fluxes renders these specialized cells particularly sensitive to proteome instability. This could be further aggravated by a build-up of iron due to the drop of ferroportin early during aging, ultimately leading to the appearance of the protein aggregates as early as at 5 months of age in C57BL/6J females. Based on the new data, we emphasized this model in the revised version of the manuscript (please, see Discussion on p. 16)

Author Response

Reviewer #1 (Public Review):

This manuscript clearly demonstrates that murine malaria infection with Plasmodium chabaudi impairs B cells' interaction with T cells, rather than DCs interaction with T cells. The authors elegantly showed that DCs were activated, capable of acquiring antigens and priming T cells during P. chabaudi infection. B cells are the main APC to capture particulate antigens such as infected RBC (iRBC), while DCs preferentially take up soluble antigens. This study is important to understand how ongoing infections such as malaria may negatively affect heterologous immunizations.

Overall, the experimental designs are straightforward, and the manuscript is well-written. However, there were several limitations in this study.

Specific comments:

1) The mechanism of how the prior capture of iRBC by B cells lead to the impairment of B-T interaction was not understood. It is unclear whether the impairment of B-T cell interaction is due to direct BCR interaction with iRBC, or an indirect response to extrinsic factors induced by malaria infection.

We believe we have carefully demonstrated that impairment of B-T interactions does not require specific BCR-antigen interactions between B cells and iRBCs (for a complete explanation of this point, please see the response to the next comment). However, the question remains whether direct, antigen-nonspecific iRBC-B cell interactions (i.e., not mediated by the BCR) or additional extrinsic factors, or a combination, are responsible for the observed defects in Tfh and GC B cell populations.

Existing studies from other infection models are informative in answering this question. Daugan et al (Front Immunol 2016; PMID 27994594) previously published experiments similar to ours, but used LCMV instead of Plasmodium. That is, they immunized uninfected or LCMV-infected mice with the well-studied immunogen NPP-CGG and measured NP-specific antibody production and other parameters. They found that LCMV infection concurrent with immunization (or 4-8 days before) significantly decreased the numbers of NP-specific splenic antibody-secreting cells and IgG1 titers, and caused major disruptions to splenic architecture. These defects were shown to require type I interferon (T1IFN) signaling in B cells. However, T1IFN is unlikely to be solely responsible for the observed phenotypes, because simultaneous infection with VSV, another virus that also induces T1IFN, did not cause any defects in NP-specific antibody production. Contrasting with the work of Daugan et al, Banga et al (PloS One 2015; PMID 25919588) found that infecting with LCMV (or with Listeria monocytogenes) two days after heterologous immunization did not disrupt immunogen-specific responses, whereas P. yoelii did. Examining both these studies, we hypothesize that both LCMV and Plasmodium infections can disrupt humoral responses, but that LCMV does so within a narrower time frame, thereby yielding different results depending on whether infection comes a few days before or a few days after immunization.

Complementing these studies of heterologous immunization, additional publications have reported that cytokines induced by several different pathogenic infections drive disruption of germinal centers and decreases in antibody titers specific for the pathogen itself, often correlated with disordered splenic architecture. Glatman Zaretsky et al. (Infect Immun 2012; PMID 22851754) showed that Toxoplasma gondii infection causes transient disruption of splenic architecture and loss of defined GCs by microscopy. These defects were partially due to decreased lymphotoxin expression by B cells, and were rescued by a lymphotoxin receptor agonist. Similarly, we previously reported that blood-stage Plasmodium infection disrupted germinal center responses to a Plasmodium liver-stage antigen (Keitany et al. Cell Rep 2016; PMID 28009289). In this context, however, the same lymphotoxin receptor agonist had no effect on GCs; instead, blockade of the pro-inflammatory cytokine interferon gamma partially restored antibody responses to the liver-stage antigen. Overall, we favor the hypothesis that several different pathogens can disrupt GCs and antibody responses indirectly by inducing inflammation and a disordered splenic environment; however, the precise mechanisms of disruption likely differ from infection to infection, with different cytokines or other effectors playing key roles in some but not other settings. Importantly, not all pathogens disrupt antibody production, since again, infection with VSV or L. monocytogenes did not affect immunogen-specific titers in immunized mice (Daugan Front Immunol 2016; Banga et al. 2015). We have now addressed this topic at length in the Discussion (lines 399-418).

The existence of indirect, inflammation- or cytokine-related mechanisms that may interfere with germinal center formation and antibody production does not preclude additional direct interactions between B cells and iRBCs that might also affect B cell function. We address this possibility more fully in the response to the next comment.

2) Would malaria infection in MD4 mouse that carries transgenic BCR that does not recognize malaria parasite impair subsequent B cell response to HEL immunization? This may clarify whether the impairment of subsequent B cell response is BCR-specific. If malaria impairs subsequent B cell response to HEL in MD4 mouse, it might suggest that other cell types and B cell-extrinsic factors might be involved in causing the impaired B cell responses, instead of malaria affecting B cells directly.

The question of whether the impairments we observe require BCR-specific interactions with iRBCs is an important one. However, we believe that the experiment the reviewer proposes to address this question has technical limitations; further, we assert that we have already provided data to address a requirement for BCR specificity.

With regard to the proposed experiment of immunizing MD4 mice with HEL in the presence or absence of malaria infection: MD4 mice, in which B cells express a transgenic receptor specific for HEL, can be expected to mount a massive, monoclonal response to direct immunization with HEL that would be very different from the physiological context of a polyclonal B cell population. We are doubtful that this experimental setup would be informative for the question at hand, especially because we are studying the effects of B-Tfh interactions, which are already limiting in the physiological setting of a polyclonal B cell response, but would be massively unbalanced in an MD4 mouse where all B cells express the receptor for HEL.

Usually, investigators studying MD4 B cell responses generate a more physiological setting by adoptively transferring a small but detectable number of MD4 transgenic B cells into a mouse with a normal polyclonal B cell population, and immunizing that mouse. We maintain that this approach is essentially what we have done in our study, except that instead of using transferred. transgenic cells to identify a B cell population of known specificity, we have used tetramers to detect a specific population of endogenous B cells in a polyclonal setting. By examining GP-specific B cells in our immunization experiments, we restricted our analysis to B cells that could not have had any BCR-mediated, antigen-specific interactions with iRBCs (because the GP antigen is not present in the iRBCs; it is delivered as a soluble protein antigen, 5 days after initiation of infection). Because we see dysfunction in the GP-specific T and B cell populations despite the absence of this antigen within iRBCs, we can conclude that the disruptions to these populations are not due to antigen-specific iRBC-BCR interactions.

We do also show (using MD4 B cells in Fig. S1B) that selective interactions between iRBCs and B cells do not require an antigen-specific BCR. Thus, it is still possible that direct interactions between iRBCs and B cells (that are independent of antigen binding to the BCR) are responsible for disrupting subsequent adaptive responses, perhaps in addition to the more indirect factors that we discuss in the response to Comment #1 above. We are very interested in this possibility, which is discussed in lines 428-436 of the manuscript. But the use of MD4 B cells would not address this specific question. Instead, we would need to identify an alternative pathway or receptor that mediates the iRBC-B cell interaction, and study the effects of blocking that pathway on downstream adaptive responses. We have spent considerable time and energy on this question, but have not yet been able to identify such a pathway; this remains a matter for further study.

3) MD4 mice were mentioned in the Methods in vitro RBC binding, although none of the figures described the usage of MD4 mice. This experiment data might be important to show whether RBC binding to B cells is mediated through BCR.

Cells from MD4 mice were used in Figure S1B to show that in vitro binding of iRBCs to B cells did not require interaction with an antigen-specific BCR. We agree that this is an important point and have revised the text (lines 152-156) to outline it more clearly.

4) Does P. chabaudi infection have any effects on B cell uptake of subsequent antigens, such as soluble antigen PE or particulate antigen CFSE-labeled P. yoelii iRBC?

We examined uptake of PE by B cells in P. chabaudi-infected mice (5 days post-infection) compared to naïve mice. There was a trend towards increased uptake in the infected mice, but this difference was not significant. These data are taken from the same samples that did reveal a significant increase in PE uptake by DCs in infected mice (Fig. 3C). We have now included the B cell data in the paper as Figure 3D, and discussed them in lines 231-232.

5) Is this phenomenon specific to malaria infection? Does malaria-irrelevant particulate immunization affect T-B interaction of subsequent heterologous immunization?

We do not believe this phenomenon is specific to malaria infection; please see the extensive discussion of this point in the response to Comment #1 above. We would hypothesize that malaria-irrelevant particle immunization (as with nanoparticles) would not affect T-B interactions for subsequent heterologous immunizations, however, since the disruption seems to be associated with the massive inflammation and splenic disorganization that occurs following certain infections.

6) Despite the impaired Tfh and GC 8 days after immunization following malaria infection, Fig. 5F showed GP-specific IgG eventually increased to the same level as the uninfected immunized mice on day 23. Did the authors check whether these mice had a delayed Tfh and GC response that eventually increase on day 23? Are these antibody responses derived from GC, or GC-independent response?

We have now examined GP-specific T cell numbers and polarization between days 23 and 35 post-immunization. We found that although a defect persists in the percentage of GP66-specific T cells that exhibit a GC Tfh phenotype at later timepoints, the absolute number of GC Tfh cells is not significantly defective in infected mice at these times. Concurrently there is a slight (though nonsignificant) increase in the total numbers of GP66+ T cells in the infected mice; we believe that this modest overall expansion permits recovery of the GC Tfh population numbers despite the continued defect in their frequency. These findings are consistent with our observation that antibody levels recover in infected mice by 3 weeks post-infection. We have added these data to Figure 4 (E-G) and discuss them in lines 283-293.

7) Does recovery from malaria infection by antimalarial treatment rescue the B cell response to subsequent heterologous immunization?

We have shown previously that drug-mediated clearance of blood-stage Plasmodium infection restores GC and antibody responses to a liver-stage-specific antigen, which normally are disrupted by emergence of the blood-stage (Keitany et al. Cell Rep 2016). We have also shown that antimalarial drug treatment restores GC responses in mice lacking the innate immune sensor CGAS, which have higher parasitemia, exacerbated splenic disruption, and diminished GC responses following P. yoelii infection (Hahn et al., JCI Insight 2018). Based on these results we hypothesize that drug-mediated clearance of blood-stage infection would also rescue B cell responses to heterologous immunization.

8) Fig. 1C shows more nRBC was taken up than iRBC in B cells, but Line 142 states that "B cells bound significantly more iRBC than nRBC. Is there a mistake in the figure arrangement? Why do B cells take up for naïve RBC than iRBC?

The symbols in the figure legend were switched in error; the filled circles are actually iRBC+ and the outlined circles are nRBC+. We regret the error and appreciate the reviewer bringing it to our attention. We have corrected the figure.

9) Fig. S1 C and D are confusing. CD45.1+ CD45.2+ mouse did not receive labeled iRBC, but why iRBC was detected as much as 40% in the spleen of this naïve mouse?

The experiment depicted in Figs. S1 C and D was designed to test whether B cells actually bound injected iRBCs in vivo, or whether the binding occurred during processing of the tissue. With this experimental setup (injecting labeled iRBCs into CD45.2+ mice, then excising and disrupting the spleen together with an untreated CD45.1+ CD45.2+ spleen), iRBC signal from in vivo uptake should be observed only in CD45.2+ splenocytes, whereas iRBC binding that occurs during tissue processing will be distributed between the two genotypes. Thus, the ~40% of iRBC signal observed in CD45.1+ CD45.2+ B cells leads us to conclude that much of the observed B cell binding from our in vivo experiments occurs during processing, as we state in the text (lines 151-152). Even so, in vitro experiments clearly show that B cells selectively bind iRBCs over naïve RBCs in a setting where processing is not a confounder (Fig. S1B). To clear up any confusion, we have expanded the description of the experiment and its interpretation in the Supplemental Figure Legend.

Reviewer #2 (Public Review):

The data presented support the conclusions of the paper, and my concerns are largely conceptual in how we understand this data in the context of malaria infection in vaccination in endemic areas

1) The data is presented based on the idea that antigen uptake and presentation differ between particle and soluble antigens, and that during malaria infection particle uptake is more important due to circulating iRBCs. However, during parasite invasion of RBCs, the parasite sheds large amounts of antigen into the circulation, at least some of which would then be found in a soluble form in the circulation. Can the authors comment on this aspect of infection and if/how this may impact the interpretation of results? Do authors assume that any soluble antigen taken up and presented (via DCs?) during infection would be impacted as for GP66 soluble antigen? Or could an interaction on immune responses where the antigen is presented via both particle and soluble pathways?

This is an important point and we have now discussed it further in the text (lines 111-115, 204-210, and 356-357). In our previously published study, where we extensively characterized CD4 T cell responses to the GP66 epitope expressed by P. yoelii, the epitope was fused to a parasite protein (Hep17) that localizes to the parasitophorous vacuole membrane, and so we do assume that the majority of this antigen is encountered by APCs in the context of an iRBC, rather than shed in soluble form. In contrast, some merozoite surface antigens such as cleaved MSP1 are shed copiously from the parasite coat upon RBC invasion, and therefore would be expected to exist in soluble as well as parasite-associated form.

Unfortunately, our laboratory does not currently have tetramer reagents or access to transgenic mice that would allow us to assess T cell responses specific for shed or soluble parasite antigens. But a previous study from Stephens et al. (Blood 2005; PMID 15890689) reported that T cells with a transgenic TCR specific for an epitope in the shed portion of MSP1 could boost antibody production when transferred into T cell-deficient mice infected with P. chabaudi, suggesting that at least some fraction of the MSP1-specific T cells differentiate into T helper cells capable of supporting B cell activity. However, antibody production was significantly delayed in this setting compared to its usual kinetics in wild-type mice. Further side-by-side characterization would be needed to assess differentiation of these MSP1-specific transgenic T cells during infection, and determine whether they are primed from B cells or from DCs (or a combination).

We will note that we have extensively characterized B cell responses to MSP1 during both infection and immunization. While robust and T-dependent, MSP1-specific B cell responses in infected mice are delayed relative to their kinetics in mice immunized with recombinant MSP1 or other protein antigens. This may indicate that MSP1-specific T cell activation or cognate B-T interactions are defective in infected mice relative to immunized mice, despite the presumed presence of soluble (shed) MSP1 during infection. If this is the case, it suggests that the defects we describe in the current manuscript exist for both particle-associated and soluble parasite antigens. However, as we mentioned above, a careful characterization of MSP-1-specific T cell differentiation is needed to really understand this, and that requires additional tools that we can’t easily access at this time.

2) Impact of particle antigen opsonisation on antigen uptake and presentation. The authors use parasites isolated from mice who have been infected for 6-7 days to investigate the ability of different subsets to update particle antigens. At this time point, have mice developed antibody responses that opsonise these parasites, or are antibody levels low and parasites opsonised? Would opsonised parasites, such as those coated with sera from children in a setting of chronic infection, have a different pattern/ability to be opsonised by different immune cell subsets? And/or would opsonisation change how the DC and other cell types are processing/presenting antigens? While these issues could be addressed experimentally either now or in the future, the manuscript should at least consider this issue because, during a human infection in areas of high exposure, individuals are likely to have reasonable levels of antibodies with opsonised parasites circulating.

We ourselves have been very interested in the question of whether host antibodies (or other host factors such as complement) might affect uptake of iRBCs. As the reviewer notes, the iRBCs we use in our experiments are taken from mice 6-7 days post-infection, at which time some amount of anti-parasite antibody has developed. We spent a considerable amount of time trying to measure effects of opsonizing antibody, or even deposited complement, on uptake of iRBCs. However, we did not see any change in B cell binding of iRBCs in vitro when we blocked complement receptor with anti-CD21; blocked antibody receptors (Fc receptors) with anti-CD16/CD32 or excess normal mouse serum; or used iRBCs taken from complement-depleted mice (treated with cobra venom factor) or from uMT mice (which entirely lack B cells and antibody). Thus, we have not been able to find any role for opsonizing antibody (or complement) in iRBC uptake. We have not included these experiments in the manuscript because they yielded only negative data, and we were not able to design positive controls robust enough to give us confidence that the experiments were technically sound (and therefore that the negative results were due to the underlying biology and not to experiment failure). We have added a discussion point about this issue (lines 438-442).

3) While authors show that malaria infection disrupts the response to soluble antigens, the relevance directly to vaccination should be considered carefully, specifically because vaccines of soluble antigens are largely given alongside adjuvants which also will modulate DC function. Again, this could be addressed experimentally now or in the future, but definitely should be mentioned and considered when interpreting the results.

Whenever we performed soluble protein immunizations to examine adaptive immune responses in this study, the immunogen was delivered in adjuvant, specifically Sigma Adjuvant System (SAS), as described in the Methods. This adjuvant contains the Monophosphoryl Lipid A component from Salmonella in an oil-water emulsion, and as such, its formulation is at least roughly similar to the AS01 adjuvant used in Mosquirix (RTS,S), the only licensed anti-malaria vaccine, as well as other vaccines currently used in humans. SAS has been shown to elicit very high titers of neutralizing antibodies in mice (Sastry et al., PloS One 2017, PMID 29073183). Therefore our results should have relevance for vaccination in humans. We have modified the manuscript text (lines 454-455 to highlight that in this study, protein immunogens were administered with adjuvant.

Author Response

Reviewer #1 (Public Review):

The study by Xie et al., investigates whether the entorhinal-DG/CA3 pathway is involved in working memory maintenance. The main findings include a correlation between stimulus and neural similarities that was specific for cued stimulus and entorhinal-DG/CA3 locations. The authors observed similar results (cuing and region specificity) using inverted encoding modeling approach. Finally, they also showed that trials in which participants made a smaller error showed a better reconstruction fidelity on the cued side (compared to un-cued). This effect was absent for larger-error trials.

The study challenges a widely held traditional view that working memory and episodic memory have largely independent neural implementations with the MTL being critical for episodic memory but not for working memory. The study adds to a large body of evidence showing involvement of the hippocampus across a range of different working memory tasks and stimuli. Nevertheless, it still remains unclear what functions may hippocampus play in working memory.

We thank the reviewer’s positive appraisal of the current research, which adds to the growing research interest in the MTL’s contribution to WM.

Reviewer #2 (Public Review):

Xie et al. investigated the medial temporal lobe (MTL) circuitry contributions to pattern separation, a neurocomputational operation to distinguish neutral representations of similar information. This presumably engages both long-term memory (LTM) and working memory (WM), bridging the gap between the working memory (WM) and long-term memory (LTM) distinction. Specifically, the authors combined an established retro-cue orientation WM task with high-resolution fMRI to test the hypothesis that the entorhinal-DG/CA3 pathway retains visual WM for a simple surface feature. They found that the anterior-lateral entorhinal cortex (aLEC) and the hippocampal DG/CA3 subfield both retained item-specific WM information that is associated with fidelity of subsequent recall. These findings highlight the contribution of MTL circuitry to item-specific WM representation, against the classic memory models.

I am a long-term memory researcher with expertise in representational similarity analysis, but not in inverted encoding modeling (IEM). Therefore, I cannot verify the correctness of these models and I will leave it to the other reviewers and editors. However, after an in-depth reading of the manuscript, I could evaluate the significance of the present findings and the strength of evidence supporting these findings. The conclusions of this paper are mostly well supported by data, but some aspects of image acquisition and data analysis need to be clarified.

We thank the reviewer for positive appraisal of the current study.

I would like to list several strengths and weaknesses of this manuscript:

Strengths:

• Methodologically, the authors addressed uncertainty in previous research resulting from several challenges. Namely, they used a high-resolution fMRI protocol to infer signals from the MTL substructures and an established retro-cue orientation WM task to minimize the task load.

• The authors selected a control ROI - amygdala - irrelevant for the experimental task, and at the same time adjacent to the other MTL ROIs, thus possibly having a similar signal-to-noise ratio. The reported effects were observed in the aLEC and DG/CA3, but not in the amygdala.

• Memory performance, quantified as recall errors, was at ceiling - an average recall error of 12 degrees was only marginally away from the correct grating towards the closest incorrect grating (predefined with min. 20 degrees increments). However, the authors controlled for the effects of recall fidelity on MTL representations by comparing the IEM reconstructions between precise recall trials and imprecise recall trails (resampled to an equal number of trials). The authors found that precise recall trails have yielded better IEM reconstruction quality.

• The author performed a control analysis of time-varying IEM to exclude a possibility that the mid-delay period activity in the aLEC-DG/CA3 contains item-specific information that could be attributed to perceptual processing. This analysis showed that the earlier TR in the delay period contains information for both cued and uncued items, whereas the mid-delay period activity contains the most information related to the cued, compared to uncued, item.

We thank the reviewer for highlighting the multiple strengths of the current study.

Weaknesses:

• The authors formulate their main hypothesis building on an assumption related to the experimental task. This task requires correctly selecting the cued grating orientation while resisting the interference from internal representations of the other orientation gratings. The authors hypothesize that if this post-encoding information selection function is supported by the MTL-s entorhinal-DG/CA3 pathway, the recorded delay-period activity should contain more information about the cued item that the uncued item (even if both are similarly remembered). Thus, the assumption here is that resolving the interference would be reflected by a more distinct representation in MTL for the cued item. Could it be the opposite, namely the MTL could better represent the unresolved interference, for example by the mechanism of hippocampal repulsion (Chanales et al., 2017). It could strengthen the findings if the authors comment on the contrary hypothesis as well.

We thank the reviewer for pointing out this interesting alternative hypothesis. Because of the different task design (e.g., over the course of learning vs. WM) and stimuli (e.g., spatial memory vs. orientation grating), it is hard to directly compare Chanales et al.’s findings with the current results. That said, we think the idea that the representation of similar information would lead to greater task demand on the MTL is consistent with our intuition regarding the role of the MTL in supporting the qualitative aspect of WM representation. We have now further discussed this issue in our revised manuscript to invite further consideration of the suggested alternative hypothesis,

“Our data suggest that this process would result in more similar and stable representations for the same remembered item across trials, as detected by multivariate correlational and decoding analyses in the current study. However, under certain task conditions (e.g., learning spatial routes in a naturalistic task over many repetitions), the MTL may maximally orthogonalize overlapping information to opposite representational patterns (hence “repulsion”) to minimize mnemonic interference (Chanales et al., 2017). It remains to be determined how these learning-related mechanisms in a more complex setting are related to MTL’s contributions to WM of simple stimulus features.”

• It is not clear for me why the authors chose the inverted encoding modelling approach and what is its advantage over the others multivoxel pattern analysis approaches, for example representational similarity analysis also used in this study. How are these two complementary? Since the IEM is still a relatively new approach, maybe a little comment in the manuscript could help emphasizing the strength of the paper? Especially that this paper is of interest to researchers in the fields of both working memory and long-term memory, the latter being possibly not familiar with the IEM.

We thank the reviewer for this suggestion. In principle, the IEM is a multivariate pattern classification analysis based on an encoding model. There is no fundamental difference between this approach and other machine-learning or classification approaches, except that the IEM is a more model-based approach and therefore can be more computationally efficient (see Xie et al., 2023 for a conceptual overview for multivariate analysis of high-dimensional neural data). The relationship between IEM and representational similarity is grounded in item-specific information that could lead to shared neural variance. How these two analyses are complimented each other is well characterized by a recent theoretical review (Kriegeskorte & Wei, 2021). The rationale is that trial-wise RSA reveals shared neural variance between items, implying the presence of item-specific information in the recorded neural data. And the IEM approach or other classification algorithms can more directly test this item-specific information under a prediction-based framework (e.g., train the data and test on a hold-out set). As a result, the findings of these two methods are correlated at the subject-level (Figure S4), which is important to note for the purpose of analytical reliability. Furthermore, using the IEM also allows us to compare our current findings with that from the previous research (Figure S3), addressing some replicability questions in the field (e.g., Ester et al., 2015).

We have clarified more on this issue in the paragraph when we first introduce IEM,

“To directly reveal the item-specific WM content, we next modeled the multivoxel patterns in subject-specific ROIs using an established inverted encoding modeling (IEM) method (Ester et al., 2015). This method assumes that the multivoxel pattern in each ROI can be considered as a weighted summation of a set of orientation information channels (Figure 3A). By using partial data to train the weights of the orientation information channels and applying these weights to an independent hold-out test set, we reconstruct the assumed orientation information channels to infer item-specific information for the remembered item – operationalized the resultant vector length of the reconstructed orientation information channel normalized at 0° reconstruction error (Figure S2). As this approach verifies the assumed information content based on observed neural data, its results can be efficiently computed and interpreted within the assumed model even when the underlying neuronal tuning properties are unknown (Ester et al., 2015; Sprague et al., 2018). This approach, therefore, complements the model-free similarity-based analysis by linking representational geometry embedded in the neural data with item-specific information under a prediction-based framework (Kriegeskorte and Wei, 2021; Xie et al., 2023). Based on this method, previous research has revealed item-specific WM information in distributed neocortical areas, including the parietal, frontal, and occipital-temporal areas (Bettencourt and Xu, 2015; Ester et al., 2015; Rademaker et al., 2019; Sprague et al., 2016), which are similar to those revealed by other multivariate classification methods (e.g., support vector machine, SVM, Ester et al., 2015). We have also replicated these IEM effects in the current dataset (Figure S3).”

Overall, this work can have a substantial impact of the field due to its theoretical and conceptual novelty. Namely, the authors leveraged an established retro-cue task to demonstrate that a neurocomputational operation of pattern separation engages both working-memory and long-term memory, both mediated by the MTL circuitry, beyond the distinction in classic memory models. Moreover, on the methodological side, using the multivariate pattern analyses (especially the IEM) to study neural computations engaged in WM and LTM seems to be a novel and promising direction for the field.

Thanks for the reviewer for this positive appraisal of the current study.

Reviewer #3 (Public Review):

This work addresses a long-standing gap in the literature, showing that the medial temporal lobe (MTL) is involved in representing simple feature information during a low-load working memory (WM) delay period. Previously, this area was suggested to be relevant for episodic long-term memory, and only implicated in working memory under conditions of high memory load or conjunction features. Using well-rounded analyses of task-dependent fMRI data in connection with a straightforward behavioural experiment, this paper suggests a more general role of the medial temporal lobe in working memory delay activity. It also provides a replication of previous findings on item-specific information during working memory delay in neocortical areas.

We thank the reviewer for highlighting the contribution of the current study to fill a gap in the literature.

Strengths:

The study has strengths in its methods and analyses. Firstly, choosing a well-established cueing paradigm allows for straightforward comparison with past and future studies using similar paradigms. The authors themselves show this by replicating previous findings on delay-period activity in parietal, frontal, and occipito-temporal areas, strengthening their own and previous findings. Secondly, they use a template with relatively fine-grained MTL-subregions and choose the amygdala as a control area within the MTL. This increases confidence in the finding that the hippocampus in particular is involved in WM delay-period activity. Thirdly, their combined use stimulus-based representational similarity analysis as well as Inverted Encoding Modeling and the convergence on the same result is encouraging. Finally, despite focusing on the delay period in their main findings, extensive supplementary materials give insight into the time-course of processing (encoding) which will be helpful for future studies.

We thank the reviewer for highlighting multiple strengths of this current study.

Weaknesses:

While the evidence generally supports the conclusions, there are some weaknesses in behavioural data analysis. The authors demonstrated fine stimulus discrimination in the neural data using Inverted Encoding Modeling (IEM), however the same standard is not applied in the behavioural data analysis. In this analysis, trials below 20 degrees and trials above 20 degrees of memory error are collapsed to compare IEM decoding error between them. As a result, the "small recall error" group encompasses a total range of 40 degrees and includes neighbouring stimuli. While this is enough to demonstrate that there was information about the remembered stimulus, it does not clarify whether aLEC/CA3 activity is associated with target selection only or also with reproduction fidelity. It leaves open whether fine-grained neural information in MTL is related to memory fidelity.

We thank the reviewer for this cautious note. As the current task is optimized to reveal the neural representation during visual WM and as our participants are cognitively normal college students, participants’ behavioral performance in the current experiment tends to be very good (Figure 1). This leaves us relatively small variation to further probe the behavioral outcomes of the task. We have recently generalized our findings using intracranial EEG and confirmed that trial-by-trial mnemonic discrimination during a short delay is indeed associated with the fidelity of item-specific WM representation (Xie, Chapeton, et al., in press).

We have further discussed this issue in the revised Discussion,

“… These two approaches are therefore complementary to each other. Nevertheless, these analyses are correlational in nature. Hence, although fine-grained neural representations revealed by these analyses are associated with participants’ behavioral outcomes (Figure 4), it remains to be determined whether the entorhinal-DG/CA3 pathway contributes to the fidelity of the selected WM representation or also to the selection of task-relevant information. Strategies for resolving this issue can involve generalizing the current findings to other WM tasks without an explicit requirement of information selection (e.g., intracranial stimulation of the MTL in a regular WM task without a retro-cue manipulation, Xie et al., in press) and/or further exploring how the frontal-parietal mechanisms related to visual selection and attention interact with the MTL system (Panichello and Buschman, 2021).”

Moreover, the authors could be more precise about the limitations of the study and their conclusions. In particular, the paper at times suggests that the results contribute to elucidating common roles of the MTL in long-term memory and WM, potentially implementing a process called pattern separation. However, while the paper convincingly shows MTL-involvement in WM, there is no comparison to an episodic memory condition. It therefore remains an open question whether it fulfils the same role in both scenarios. Moreover, the paradigm might not place adequate pattern separation demands on the system since information about the un-cued item may be discarded after the cue.

We thank the reviewer for this cautious note. We have now included a more detailed discussion on this issue.

In the Discussion,

“To more precisely reveal the MTL mechanisms that are shared across WM and long-term memory, future research should examine the extent to which MTL voxels evoked by a long-term memory task (e.g., mnemonic similarity task, Bakker et al., 2008) can be directly used to directly decode mnemonic content in visual WM tasks using different simple stimulus features.”

Author Response

Reviewer #2 (Public Review):

In regions that implement an elimination strategy prolonged periods of no local transmission mean that there is no data available to estimate Reff using the currently available methods. Transmission rates from travellers to community members, and between community members, are different when border restrictions occur, as is frequently the case when implementing an elimination strategy. When cases are low and importation risk is high, a reasonable estimation method must acknowledge this transmission heterogeneity, for example, as shown in equations 5-8 and 10-11 of this paper.

The calculation of transmission potential adds significant data requirements (summarized in Figure 1), such that some regions where the methodology would be valuable may lack the data to estimate the macro- and micro-distancing parameters. In the paper, such parameters are estimated from weekly surveys performed by market research groups and the University of Melbourne. In contrast, using existing methods in regions where local spread does occur, Reff can be calculated and generate reasonable insight with relatively little data. Due to the additional data requirements, the calculation of transmission potential is less accessible than some current approaches to calculate Reff in regions with local spread.

We agree with these comments about the need for behavioural data. We believe this point is made clearly in our existing discussion text, copied below:

Despite its demonstrated impact, there are limitations to our approach. Firstly, it relies on data from frequent, population-wide surveys. In Australia, these data are collected for government and made available to our analysis team by a market research company which has access to an established “panel” of individuals who have agreed to take part in surveys of public opinion. Researchers and governments in many other countries have used such companies for rapid data collection to support pandemic response [23, 25]. However, these survey platforms are not readily available in all settings.

We also believe it is clear throughout the manuscript that transmission potential provides complementary information to Reff, and unlike Reff can be calculated in the absence of transmission.

The authors describe "macro-distancing": the rate of non-household contacts; and "micro-distancing": the transmission probability per non-household contact. This terminology "micro-distancing" gives the false impression that transmission probability depends solely on distance. In the paper, transmission probability is estimated from survey responses to the question 'are you staying 1.5m away from people who are not members of your household?'. This data is limited to estimate the transmission probability and overlooks the impact of mask use, improved ventilation, and meeting outdoors (all non-distance-based approaches). The paper mentions that self-reported hand hygiene could be used to estimate micro-distancing. COVID-19 spreads through airborne transmission, but the paper gives no mention of ventilation or mask-wearing.

We agree with these important points and have adjusted the terminology for micro-distancing behaviour to improve clarity. We now refer to it as “precautionary micro-behaviour” since adherence to the 1.5 metre rule is used as a proxy/indicator for change over time in all behaviours that influence transmission (other than those reducing the number of contacts). This includes behaviours such as mask-wearing, preference for outdoor gatherings, hand hygiene etc .

In addition to changing the terminology for this metric throughout the manuscript, we have added the following explanation to the “Model” section of the manuscript (lines 100-105):

The modelling framework uses adherence to the 1.5 metre rule as a proxy for all behaviours (other than those reducing the number of contacts) that may influence transmission, and so is intended to capture the use of masks, preference for outdoor gatherings, and hand hygiene, among other factors. The 1.5 metre rule was a suitable proxy because it was consistent public health advice throughout the analysis period and time-series data were available to track adherence to this metric over time.

Some of the writing lacks precision around the descriptions of Reff. Notably, Reff is not a rate because it does not have units 'per time'. There is a lack of clarity that Reff is infections generated over an individual's entire infectious period. Other metrics of outbreak growth are rates, for example, an exponential growth rate parameter. This lack of clarity in the writing does not impact the methodology.

Thank you for pointing out this lack of clarity, we have removed references to Reff as a ‘rate’ throughout. We have added to our initial definition of Reff (lines 29-32) that the infections cover the entire infectious period:

A key element of epidemic response is the close monitoring of the speed of disease spread, via estimation of the effective reproduction number (Reff) — the average number of new infections caused by an infected individual over their entire infectious period, in the presence of public health interventions and where no assumption of 100% susceptibility is made.

In the paper, model parameters are estimated from multiple independent data sources using carefully derived inference models that include complex considerations such as right-censoring of reported cases. While data availability may be a limitation to calculating the transmission potential, the modelling and statistics in the paper are rigorous, and calculation of the transmission potential fills a gap by allowing regions that implement elimination strategies to estimate a quantity similar to Reff.

We thank the reviewer for their positive feedback.

Author Response

Reviewer #2 (Public Review):

In the current manuscript, Feng et al. investigate the mechanisms used by acute leukemia to get an advantage for the access to the hematopoietic niches at the expense of normal hematopoietic cells. They propose that B-ALLs hijack the niche by inducing the downmodulation of IL7 and CXCL12 by stimulating LepR+ MSCs through LTab/LTbR signaling. In order to prove the importance of LTab expression in B-ALL growth, they block LTab/LTbR signaling either through ligand/receptor inactivation or by using a LTbR-Ig decoy. They also show that CXCL12 and the DNA damage response induce LTab expression by B-ALL. They finally propose that similar mechanisms also favor the growth of acute myeloid leukemia.

Although the proposed mechanism is of particular interest, further experiments and controls are needed to strongly support the conclusions.

1/ Globally, statistics have to be revised. The authors have to include a "statistical analysis" section in the Material and Methods to explain how they proceeded and specify for each panel in the figure legend which tests they used according to the general rules of statistics.

We apologize for the lack of details. This has been corrected in the revised manuscript.

2/ The setup of each experiment is confusing and needs to be detailed. Cell numbers are not coherent from one experiment to the other. As an example, there are discrepancies between Fig1 and Fig2. Based on the setup of the experiment in Fig.2 (Injection of B-ALL to mice followed by 2 injections of treatment every 5 days), mice have probably been sacrificed 12-14 days post leukemic cell injection. However, according to Fig.1, B cells and erythroid cells at this time point should be decreased >10 times while they are only decreased 2-4 times in Fig.2. This is also the case in Fig.4B-J or Fig.5D with even a lower decrease in B cells and erythroid cells despite a high number of leukemic cells. Please explain and give the end point for each experiment in each figure (main and supplemental).

We understand the reviewer concern but we’d like point out the following: kinetic experiments such as these were reproduced multiple times in the laboratory. However, when comparing side-by-side experiments performed over the course of several months discrepancies in the exact days when leukemia shuts-down hematopoiesis are bound to happen. This is because there are numerous variables at play that we can minimize to the extent possible, but we cannot completely eliminate. For example, we took all possible steps to work with stable batches of preB-ALL cells. However, it is impossible to be absolutely certain that the batch in one experiment is identical to another experiment. Cells have to be expanded for adoptive transfer, which inevitably carries some variability (all biological systems undergo random mutations, including purchased C57Bl6/J from reputable vendors); slight differences in ALL engraftment (i.e. injection variability) can occur such that kinetics may change by a couple of days, etc. The findings we reported here are highly reproducible: ALL shuts down lymphopoiesis and erythropoiesis acutely, less so myelopoiesis; that LTbR signaling is the major mechanism shutting down lymphopoiesis but not erythropoiesis; that ALLs up-regulate LTbR ligands when compared to non-leukemic cells of the same lineage and at a similar developmental stage; that CXCR4 and DSB pathways both promote lymphotoxin a1b2 expression. The exact kinetics of these experiments will vary, or at least carry a margin of error that is to the best of our capability impossible to eliminate.

3/ To formally prove that the observed effect is really due to LTab/LTbR signaling, the authors must perform further control experiments. LTbR signaling is better known for its positive role on lymphocyte migration. They cannot rule out by blocking LTbR signaling, that they inhibit homing of leukemic cells into the bone marrow through a systemic/peripheral effect, more than through an impaired crosstalk with BM LepR+ cells. They must confirm for inhibited/deficient LTbR signaling conditions, as compared to control, that similar B-ALL numbers home to the BM parenchyma at an early time point after injection. Furthermore, they cannot exclude that the effect on the expression of IL7 (and other genes), and consequently the effect on B cell numbers, is not simply due to the tumor burden. Indeed, B-ALL numbers/frequencies are different between control and inhibited/deficient signaling conditions at the time of analysis. The analyses should thus be performed at similar low and high tumor burden in the BM for both control and inhibited/deficient LTbR signaling conditions.

We performed ALL homing experiments into control and LTbR∆ and found no significant differences in ALL frequency or number in BM 24h after transplantation. These data have been included in Figure 4A.

We also performed experiments to control for the number of ALL cells in the bone marrow. Briefly, we compared the impact of 3 million WT ALLs with that of 3 and 9 million Ltb-deficient ALLs on Il7-GFP expression in BM MSCs. The number of Ltb-deficient ALLs in the BM of mice recipient of 9 million ALLs was equivalent to that of mice that received 3 million WT ALLs 7 days after transplantation. Importantly, Il7 was only downregulated in mice transplanted with WT ALLs. These data have been included in Figure 4R and 4S.

4/ LT/LTbR signaling is particularly known for its capacity to stimulate Cxcl12 expression. How do the authors explain that they see the opposite?

The reviewer is alluding to a well-known role of LTbR signaling as an organizer of immune cells in secondary lymphoid organs such as spleen and lymph nodes, and particularly its role in promoting CXCL13, CCL19, CCL21 production by fibroblastic reticular cells of these organs. Both the B cell follicle and the T-zone do not express CXCL12 abundantly. Furthermore, in the B cell follicle niche, LTbR signaling is critical for the maturation of Follicular Dendritic Cells, yet FDCs hardly produce CXCL12 as well. So, while LTbR is a well-known regulator of cell organization through the production of homeostatic chemokines and lipid chemoattractants, CXCL12 itself is not one of the major chemokines controlled by this pathway. In summary, we do not think our data is in any way incompatible with prior studies on the LTbR pathway, and even if it was, to our knowledge this is the first study on cell-intrinsic effects of LTbR signaling in BM MSCs.

5/ The authors show that CXCL12 stimulates LTa expression in their cell line. They then propose that CXCR4 signaling in leukemic cells potentiates ALL lethality by showing that a CXCR4 antagonist reverses the decrease in IL7 and improves survival of the mice. This experiment is difficult to interpret. CXCL12 has been shown to be important for migration/retention of B-ALL in the BM and the decreased tumor burden is probably linked to a decreased migration more than an impaired crosstalk with LepR+ cells (see also point 3). If CXCL12 increases LTab expression, CXCR4 blockade should do the opposite. This result should be presented. The contradiction is that if B-ALLs induce a decrease in CXCL12 in the BM (in addition to IL7) and that CXCL12 regulates LTab levels, leukemic cells should be exhausted. Similarly, IL7 has been previously shown to stimulate LTab expression and B-ALL cells express the IL7R. Again, a decrease in IL7 should be unfavorable to B-ALL. How do they explain these discrepancies?

We thank the reviewer suggestion of testing the impact of CXCR4 blocking in vivo on LTa1b2 expression. We performed these experiments which have now been included in the revised manuscript (Fig. 5C and 5D). In summary, we observed reduced LTa1b2 on ALLs transplanted into mice treated with AMD3100, a well-known CXCR4 antagonist. These data also show that CXCR4 signaling is not the only mechanism driving LTa1b2. These results further strengthen the main conclusions of the manuscript. Finally, to our knowledge no study has reported Lymphotoxin a1b2 upregulation in B-ALLs by IL-7.

6/ In Supp 4A, RAG-/- mice are blocked at the pro-B cell stage and do not have pre-B cells. Please compare LTa and LTb expression by Artemis deficient pre-B cell to wt pre-B cells. In this experiment, the authors show that similarly to B-ALL artemis-/- pre-leukemic pre-B cells express high levels of LTab and induce IL7 downmodulation. Using mice deficient for LTbR in LepR+ cells, they show that IL7 expression is increased. However, in opposition to leukemic cells (see Figure 4F), pre-leukemic cells are increased in absence of LTab/LTbR signaling. Please explain this discrepancy. The authors use only one B-ALL model cell line for their demonstration (BCR-ABL expressing B-ALL). Another model should be used to confirm whether LTab/LTbR signaling does favor leukemic/pre-leukemic B cell growth.

We apologize for the confusion. The mice that were used in this study were initially described by Barry Sleckman and colleagues (Bredemeyer et al. Nature 2008). Briefly, they crossed Artemis-deficient mice with VH147 IgH transgenic and EμBcl-2 transgenic mice to generate mice in which B cell development is arrested at the preB cell stage. The Vh147 heavy chain allows their development to the pre-BCR+ preB cell stage but Artemis deficiency prevents Rag protein re-expression and hence B cell can’t recombine light chain genes. The EμBcl-2 transgene allows preB cells to survive despite carrying unrepaired double-strand DNA breaks (DSB).

Regarding the discrepancy noted by the reviewer we argue that this is not a discrepancy. While ALLs can grow in vitro and in vivo in the absence of IL7, non-leukemic developing B cells are strictly IL7 dependent. PreB cells carrying unrepaired DSBs still express IL7 receptor and although no data is currently available on whether these cells are also IL7-dependent, we speculate that they are. Because up-regulation of Lymphotoxin a1b2 in preB cells carrying unrepaired DSBs promotes IL7 downregulation we speculate that this mechanism may contribute to the efficient elimination of pre-leukemic preB cells in vivo. We revised the manuscript to include this explanation of the mouse model and discussion on how we think the LTbR pathway may play a role in pre-leukemic states.

Finally, the data presented in this study includes two distinct leukemia mouse models. It also includes data from human B-ALL and AML samples that is in agreement with the mouse data presented here. We respectfully disagree with the reviewer that a third model is needed to confirm a role for the LTa1b2/LTbR pathway in leukemia.

7/ Pre-B cells are composed of large pre-B cells (pre-BCR+) and small pre-B cells (pre-BCR-). BCR-ABL B-ALL cells express the pre-BCR. What is the level of expression of LTa and LTb by each of these 2 subsets as compared to BCR-ABL B-ALL?

This is a misconception. The difference between large and small preB cells is simply that large preB cells are in S/G2 phase of the cell cycle. Their increased size is a mere consequence of doubling DNA, protein, membrane content, etc.

Author Response

Reviewer #1 (Public Review):

In this study, they demonstrate that neonatal mice produce more CD43- B cellderived IL-10 following anti-BCR stimulation than adult mice. This is due to autocrine mechanisms whereby anti-BCR stimulation leads to pSTAT5 upregulation and production of IL-6 which then enhances IL-10 production via pSTAT3. These are interesting results for the regulatory B cell field, demonstrating that signaling is different in adult vs neonatal B cells and in particular for researchers studying the mechanisms underpinning the enhanced susceptibility to infection. The authors in the main achieved their aim and the results support their conclusions. However, considering that other studies have previously addressed the mechanisms contributing to enhanced IL-10 production in neonates, in the manuscript, there are some experimental decisions and data presentation decisions that at times need more explanation. An important additional comment is that the introduction/discussion is at times insufficiently referenced to put the data in context for non-experts in this field and that numbers in general are low for an in vitro study.

We have now updated the introduction and discussion to provide more insight into our study. We hope that our study is now more understandable for non-experts.

Reviewer #2 (Public Review):

This paper reports that neonatal CD43- B cells produce IL-10 upon BCR stimulation, which inhibits TNF-alpha secretion from the peritoneal macrophage. In the neonatal CD43- B cells, the BCR-mediated signal transmitted Stat5 activation and induced IL-6 production, and subsequently, the secreted IL-6 activated Stat3 finally leading to IL-10 production. The authors identified a unique signaling pathway leading to IL-10 production and revealed the different responses between CD43+ and CD43- B cells against BCR crosslinking. A weakness of this study is that the neonatal CD43- B cell subset secreting IL-10 has not been characterized and discussed as well. BCR expression levels between adult CD43- B cells and neonatal CD43- B cells have been overlooked to explain the different reactivity. Clarity on these points would substantially enhance the impact of the manuscript.

We thank the reviewer for the suggestion to measure BCR levels. We now measured the IgM and IgD levels on neonatal and adult B cell C43+ and CD43- subsets (Figure 1figure supplement 5).

Author Response

Reviewer #1 (Public Review):

This is an exciting paper describing the development of a robust differentiation of the common marmoset induced pluripotent stem cells (iPSCs) into primordial germ cell-like cells and subsequently into spermatogonia-like cells when combined with testis somatic cells. The work is of high quality, but some experimental details and protocols are missing which are necessary for a new protocol development - for example, reconstitution methods and protocols are missing completely in the manuscript and additional details in various aspects of the differentiation and cell maintenance are missing. Despite this, the work is valuable and would be of interest to the germ cell and in vitro gametogenesis communities. The data suggest that marmosets are very similar to humans and macaques, and indeed previously established protocols for PGCLC induction and likely previously published testis reconstitution methods/differentiation were employed here to generate the spermatogonia-like cells.

We greatly appreciate the positive comments of the reviewer on our manuscript. We have added experimental details of our germ cell differentiation schemes in Materials and Methods.

Reviewer #2 (Public Review):

This paper identifies the need for improved pre-clinical models for the study of human primordial germ cells (PGCs) and suggests the common marmoset (Callithrix jacchus) as a suitable primate model. In vitro gametogenesis offers an alternative method to generate germ cells from pluripotent stem cells for study and potential pre-clinical applications. Therefore, the authors aimed to take the first steps toward developing this technology for the marmoset. Here, iPSCs have been derived from the marmoset and differentiated to PGC like-cells (PGCLCs) in vitro that have similarities in gene expression with PGCs identified from single-cell studies of marmoset embryos, as demonstrated through immunofluorescence and RT-qPCR approaches, as well as RNA-sequencing.

The authors have successfully developed a protocol that produces PGCLCs from marmoset iPSCs. These are shown to express key germline gene markers and are further shown to correlate in gene expression with PGCs from the marmoset. This study uses a 2D culture system for further expansion of the PGCLCs. When cultured with mouse testicular cells in a xenogeneic reconstituted testis culture, evidence is provided that cjPGCLCs have the capacity to develop further, expressing marker genes for later germline differentiation. However, the efficiency of generating these prospermatogonia-like cells in culture is unclear. Nonetheless, with the importance of developing protocols across species for in vitro gametogenesis, this paper takes a key step towards generating a robust preclinical system for the study of germ cells in the marmoset.

We thank the reviewer for the encouraging comment. By IF analyses, we identified 0.89 and 3.3% of DAZL or DDX4 positive cells, respectively (DDX4+TFAP2C+ cells [4/123, 3.3% among all TFAP2C+ cells] and DAZL+TFAP2C+ cells [2/232, 0.86% among all TFAP2C+ cells]). Overall scarcity of cells and lack of fluorescence reporters (DAZL and DDX4 are cytoplasmic proteins necessitating technically challenging intracellular staining procedure to be assessed by flow cytometry), we were not able to provide the flow cytometric plots in this study. This has been described in the revised manuscript (page 11, Results, “Maturation of cjPGCLCs into early prospermatogonia-like state”).

The claims of the authors are generally justified by the data provided; however, some conclusions should be clarified. In particular, the authors have failed to show convincingly that cjPGCLCs are a distinct cell type to the iPSCs that generated them. cjiPSCs cultured in feeder conditions (OF) with IWR1 are reported to cluster closely with the derived cjPGCLCs using principal component analysis of RNA-Seq data. This contrasts with the cjiPSCs cultured in feeder-free (FF) conditions which maintain a more undifferentiated/less primed state, and are not capable of differentiating to the germline lineage. Therefore, the OF/IWR1 cjiPSCs could rather be an intermediate cell-state between iPSCs and cjPGCLCs.

Although OF/IWR1 cjiPSCs are closer to cjPGCLCs than cjiPSCs cultured in other conditions, they are pluripotent (as evidenced by trilineage differentiation assay, morphological assessment, and expression of pluripotency markers, Figure 3–figure supplement 2) and do not express most of key germ cell markers (Figure 6–figure supplement 1C). Our newly added scRNA-seq analyses also highlighted the differences between OF/IWR1 and cjPGCLCs and the molecular dynamics associated with the transition.

The reasons behind improved germline competence of iPSCs in the different media conditions are unclear. The authors reject the idea that this is due to the presence of IWR1, since this condition has not affected FF iPSCs. However, the efficiency of differentiation was greatly increased in OF conditions when IWR1 was used, indicating inhibition of WNT does indeed have a positive effect on induction to the germline lineage. This area requires further clarification.

As the reviewer pointed out, inclusion of IWR1 in cultures of OF cjiPSCs upregulates some pluripotency markers (SSEA3, SSEA4) and reduces meso/endodermal differentiation. Thus, the undifferentiated/less primed state under the Wnt inhibition might positively affect germ cell differentiation of OF cjiPSCs. However, FF cjiPSCs are pluripotent and are not germline competent, even in the presence of IWR1, suggesting that there are factors in FF culture conditions that make them incompetent for germline differentiation. Because FF cultures utilize PluriStem™ medium, a proprietary product of MilliporeSigma, we were unable to define the factor that confers such germline incompetence.

Another area requiring clarification is the reporting of RNA sequencing data as representative of a developmental trajectory, without defining which cell lines produced clusters, or defining the stages of this trajectory. The authors refer to the identification of four clusters representative of a developmental trajectory, however, they provide unclear information as to what this refers to. Importantly, detailed transcriptomic comparisons between in vivo-derived PGCs and in vitro PGCLCs are not provided.

Our original analysis revealed which cell lines produced clusters (Figure 6A) and defined the stages of the trajectory (iPSCs feeder free, iPSCs on feeder, PGCLCs, expansion, Figure 6C). The four clusters to which the reviewer refers are gene clusters that are defined by unsupervised clustering analysis of variably expressed genes across the samples (Figure 6D). As it is defined computationally, it is not possible to unequivocally define gene clusters by particular cell types. However, we found that these gene clusters revealed insightful patterns (1, genes higher in cjiPSCs; 2, genes higher in cjPGCLCs; 3, genes higher in expansion culture cjPGCLCs; 4, genes higher in d2 cjPGCLCs). We have added sample information to the Figure 6D to further clarify the meaning of the data and a brief explanation of gene clusters in the figure legend. To define the trajectory in a more unbiased manner, we performed scRNA-seq and have added additional trajectory analyses (Figure 7A-K in the revised manuscript). Moreover, we also added the transcriptomic comparison as the reviewer suggested (Figure 7L, M in the revised manuscript).

Functional validation of iPSC lines generated in the study is not provided besides confirming that the cells express pluripotency markers OCT3/4, SOX2, and NANOG. It is important to confirm tri-lineage differentiation of iPSCs, e.g., through an embryoid body assay. Since FF cjiPSCs were unable to differentiate into cgPGCLCs, it is even more important to confirm cells are genuine iPSCs.

We performed a trilineage differentiation assay and confirmed that they can generate three germ layers.

In summary, although there are issues surrounding clarity, this paper is generally justified in its conclusions. The authors present an optimised protocol for the derivation of PGCLCs from marmoset iPSC-like cells, with defined expansion conditions and evidence of further differentiation to prospermatogonia-like cells.

We thank the reviewer for the encouraging comment.

Author Response

Reviewer #1 (Public Review):

Sayin et al. sought to determine if bacterial drug resistance has impact on drug efficacy. They focused on gemcitabine, a drug used for pancreatic cancer that is metabolized by E. coli. Using an innovative combination of genetic screens, experimental evolution, and cancer cell co-cultures to reveal that E. coli can evolve resistance to gemcitabine through loss-of-function mutations in nupC, with potential downstream consequences for drug efficacy.

Major strengths include:

• Paired use of genetic screens and experimental evolution

• The spheroid model is a creative approach to modeling the tumor microbiome that I hadn't seen before

• Rigorous microbiology, including accounting for mutation rate in both selective and non-selective conditions

• Timely research question

Major weaknesses of the methods and results include the following:

1) Limited scope of the current work. Just a single drug-bacterial pair is evaluated and there are no experiments with microbial communities, animal models, or attempts to test the translational relevance of these findings using human microbiome datasets.

We agree with the reviewer that uncovering evidence from human microbiome datasets will be very exciting and complementary to our study. However, since gemcitabine is administered intravenously it’s unclear whether it will impose a considerable selective pressure on the gut microbiome. Therefore, it also remains unclear if adaptive mutations, as those we identified, are expected to be found in datasets for the gut microbiome. While metagenomics datasets that are bacterial-centric of infected pancreatic tumors will be ideal for addressing the reviewer’s suggestion, they do not exist to the best of our knowledge. It should be noted however, that our work generated hypotheses that can be tested in pancreatic tumor tissues infected with gammaproteobacteria and can be tested in the future by targeted sequencing for the specific genes of interest (e.g, nupC and cytR).

2) No direct validation of the primary genetic screen. The authors use a very strict cutoff (16-fold-change) without any rationale for why this was necessary. More importantly, a secondary screen is necessary to evaluate the reproducibility of the results, either by testing each KO in isolation or by testing a subset of the library again.

We used a strict cutoff to allow the reader to focus on a manageable list of gene names in the main figure (2E). To partly address this limitation in scope, we also included results from pathway enrichment analysis in the same figure (2F). This analysis utilizes all enrichment values and is therefore independent from any choice of cutoff value. We also now refer the reader to explore more of the hit genes in the supplementary information (line 152).

As the reviewer suggested we evaluated the reproducibility of the results by performing two validation screens. The first validation screen was performed as a biological replicate of the original screen and relied on the original collection of knockouts strains. The second validation screen was performed with a knockout strain collection that was cloned independently from the strains used in our original screen. The results from these two completely independent biological replicates are presented on supp. figure 1D. The results (resistance/sensitivity) from the two screens are highly correlated. We refer to this comparison in the main text (lines 142-147).

3) Some methodological concerns about the spheroid system. As I understood it, these cells are growing aerobically, which may not be the best model for the microbiome. Furthermore, bacterial auxotrophs are used and only added for 4 hours, which will really limit their impact. It also was unclear if the spheroids are truly sterile. Finally, the data lacks statistical analysis, making it unclear which KOs are meaningful. Delta-cdd looks clearly distinct by eye, but the other two genes are more subtle.

The 4 hour time interval chosen to address two opposing requirements of the co-culture system – mitigate overgrowth of the bacterial cultures (which hinders spheroid growth irrespective of the drug) while still allowing enough incubation time to allow for drug degradation. As the reviewer notes, removal after 4 hours may limit the bacteria impact. However, such a limitation will only result in underestimation the bacterial impact (but will have no impact on how we evaluate how strains compare to one-another). We now comment on this in the methods section (lines 699-705).

We do not expect the spheroid to remain infected after bacterial removal since we treat spheroids with antibiotics. We didn’t not detect any bacterial growth in the 7 days post infection in the microscope and we did not observe influence on spheroid growth when compared to spheroid that were not infected. Growth of spheroid before infection was performed w/o antibiotics and we did not detect any evidence of bacterial growth prior to introducing the bacteria intentionally (the cell-line itself was also tested for animal pathogens and bacterial contamination prior to the experiments).

We repeated the spheroid experiments and observed similar shifts in the EC50 fronts. We now include these replicates as supplementary figure 7. We comment on these replicates in the main text (lines 273-274).

Author Response

Reviewer #1 (Public Review):

This is an elegant and fascinating paper on individuality of structural covariance networks in the mouse. The core precepts are based on a series of landmark papers by the same authors that have found that individuality exists in inbred mice, and becomes entrenched when richer environments are available. Here they used structural MRI to provide whole brain analyses of differences in brain structure. They first replicated brain (mostly hippocampal) effects of enrichment. Next, they used their roaming entropy measurements to show that, after dividing their mice into two groups based on their roaming entropy, that there were no differences in brain structure between the two groups yet significant differences in brain networks as measured by structural covariance. Overall I enjoyed this paper, though am confused (and possibly concerned) about how they arrived at their two groups and have some less important methods questions.

The division of mice into two groups (down and flat) is confusing. The methods appear to suggest that k-means clustering combined with the silhouette method was used (line 380). The actual analyses used involves 2 groups of 15 mice each. The body of the manuscript suggests that 10 intermediate mice were excluded (line 100), but the methods (line 390) suggest that 8 mice were excluded, 2 for having intermediate results and 6 for having high RE slope values.

This leads to a series of questions:

• How many mice were excluded and for what reasons, given the discrepancy between body and methods?

The discrepancy was an oversight that has been corrected. The statement with the exclusion of six upward sloping and two intermediates is correct. For the rationale see above and the inserted text in the discussion.

• Was the k-means clustering actually used? It appears that the main division of mice was based on visual assessments.

The superfluous paragraph in the method section was removed.

• If the clustering was used, did it result in 2 or 3 groups?

Slope distribution did not reveal clear groups, so it did not offer an advantage over the arbitrary decision based on slope values and described above. We have now added a graphic depiction of the slope values next to the ‘flat’ or ‘down’ matrices for greater clarity (Fig. 3B).

• The intermediate group bothers me (if it was indeed 10 intermediate mice as indicated by the body rather than 2 as indicated in the methods): if these are indeed intermediate shouldn't they be analyzed and shown to be intermediate on the graph or other measures?

These were only 2 mice, for which the matrix cannot be calculated.

• Please explain the reasoning for excluding mice for having too high of a slope (if there were indeed mice excluded for having too high of a slope).

We went to long discussions among the authors and finally decided in favor of two equally-sized groups with homogenous patterns. The effect that we observed is so large and obvious that it survives all sorts of regrouping. We have also followed the suggestion to present the continuous correlation across the whole range of animals (Fig. 2)

I'd also appreciate more discussion about the structural covariance differences between flat and down mice. It is not clear what the direction of effects are - it appears that flats show mostly increases in covariance?

Yes, covariance is greater in the top (flat) than bottom (down) group.

The structural covariance matrix for those mice with a ‘flat’ RE suggests a much higher degree of inter-regional correlation in comparison to ‘down’ or STD mice, findings confirmed and extended by the NBS analysis.

Reviewer #2 (Public Review):

Lopes et al. use genetically identical mice to address a topic of broad interest: how does variation in roaming behaviour across individuals (here, quantified via the roaming entropy) arise over time when exposed to an enriched environment, and how does this variation in behaviour relate to brain structure and networks. Specifically, by examining the roaming entropy of mice and the sizes of brain structures, the authors convincingly show 1) an increase in variability in roaming behaviour over a period of 12 weeks, 2) that mice that roam more contain an increased number of doublecortin positive cells in the dentate gyrus (indicating higher levels of neurogenesis), and 3) that roaming is associated with widespread differences in neuroanatomy. The authors additionally partition mice into two groups characterized by roaming trajectories (continuous "flat" roamers and habituating "down" roamers), construct structural covariance networks for these groups, and show that the structural covariance network for "down" roamers is similar to mice housed in standard conditions and contrasts that of "flat" roamers.

A major strength of this study is the wealth of roaming data generated by the RFID setup; the high temporal resolution, fair spatial resolution, and long period of observation (3 months) allow for measures such as roaming entropy to be precisely quantified and tracked over time. Coupled with high-resolution whole brain structural MRI and histological measurements of neurogenesis in the dentate gyrus, the dataset generated is an incredibly valuable one to probe brain-behaviour relationships. Importantly, this study showcases the power of animal studies--because the subject mice are inbred, they are virtually identical in their genetics, and therefore any variation in the data collected should arise from the non-shared environment.

An area of improvement for this study follows from its strength: the dataset collected here contains far more information on mouse behaviours than is analyzed. For instance, the sizes of a broad set of regions were found to be statistically associated with roaming behaviour, but determining how much of this anatomical variation is specifically related to differential exploration of the static environment as opposed to social contact with other animals (which could presumably be determined from the RFID data) would make this study much more impactful and interesting to the community.

An important limitation in the network analyses performed is the small number of mice. Due to sampling variation, a large number of individuals are required to estimate correlation coefficients with reasonable precision. While large-scale similarities and differences between the structural covariance (correlation) matrices are visually apparent and quite striking, confidence in these results would be increased with the inclusion of more subjects, and/or a replication cohort.

We fully agree to this judgement. It is not straightforward, however, to further increase N in these studies, both for cost and logistic reasons. Rather than investing into further improving this current study, we decided to learn from our findings and design follow-up studies that take the next steps.

Finally, while both roaming behaviour and brain structure are assessed, relationships between these measures are associative. Since brain structure was only examined at one timepoint (post-enrichment), the direction of causation cannot be assessed. It remains to be seen if behavioural variation drives anatomical variation through plasticity, or whether anatomical variation present before enrichment is predictive of future behaviours. To their credit, the authors are careful not to make causal inferences. In the context of this brain-behaviour studies, this is an important limitation to recognize, but this does not detract from the important relationships between roaming behaviour and brain structure found by the authors in this study.

In summary, while there is much more to do in studying relationships between the environment, brain structure, and behaviour, Lopes et al. take an important step ahead in describing relationships between individual roaming behavioural trajectories, brain structure, and structural covariance networks.

Author Response

Reviewer #1 (Public Review):

This study elucidates a role of EHD2 as a tumor/metastasis promoting protein. Prior work has found varying results indicating that high expression of EHD2 is either associated with good or poor outcomes. In this work the authors find that EHD2 is expressed in both the nucleus and cytoplasm, and that high cytoplasmic to nuclear expression is associated with a poor prognosis. Using WT and either shRNA knockdown or CRISPR KO cells, they show that EHD2 promotes 3D growth, migration and invasion in vitro, and tumor growth and metastasis in vivo. Importantly, re-expression of EHD2 in KO cells rescues the loss of function phenotype. Mechanistically, the investigators show that the loss of EHD2 decreases the calveoli and that this decreases the Orai1/Stim induced calcium influx. Finally, they show that inhibitors of store operated calcium entry (SOCE) phenocopies the loss of EHD2. Together the data support a protumorigenic role for EHD2 via store-operated calcium entry and reinforce the utility of targeting calveoli and SOCE in tumors with high cytosolic EHD2. This study provides a rationale for using SOCE inhibitors in a subset of breast cancers, and a potential predictive biomarker for using SOCE inhibitors based on high expression of EHD2.

We are grateful for the positive comments. Since this paragraph is to be published in the event of our manuscript being accepted, we request the correction of one typo in the paragraph: “calveoli” should be “caveolae”.

Reviewer #2 (Public Review):

The manuscript by Luan et. al. describes the role of EHD2 in promoting breast tumor growth. They showed that EHD2 cytoplasmic staining predicts poor patient outcome. Both EHD2 KO or knockdown cells showed decreased cell migration/invasion abilities and significant reduction of tumor growth and metastasis in mice. The authors further showed that the levels of EHD2 and Cav1/2 correlate with each other. EHD2 KO cells showed defects on Ca2+ trafficking. Overexpressing the SOCE factor STIM1 partially rescued SOCE defects in EHD2 KO cells. Treatment of the SOCE inhibitor SKF96365 inhibited tumor cell migration in vitro and tumor growth in vivo.

Major strengths: The authors showed that EHD2 cytoplasmic levels predict patient survival and provided strong evidence that EHD2 knockout or knockdown inhibits tumor cell migration in vitro and tumor growth in vivo. The authors also showed that SKF96365, which inhibits SOCE, suppresses tumor growth in vivo.

Major weaknesses: The connection between EHD2 and SOCE is weak.

We are thankful to the reviewer for her/his assessment of the strengths in our manuscript and appreciate her/his pointing to its weaknesses. We agree that more studies will be needed to fully establish the connection of EHD2 to SOCE and have appropriately moderated our statements in the results and discussion sections of the manuscript. We have also added statements about the need for such future studies.

Author Response

Reviewer #1 (Public Review):

In this manuscript by Ramaprasad et al., the authors report on the functional characterization of the P. falciparum glycerophosphodiesterase to assess its role in phospholipid biosynthesis and development of asexual stages of the parasite. The authors utilized loxP strategy to conditionally knock-out the target gene, they also carried out complementation assays to show recovery of the knock-out parasites. They further show that Choline supplementation is also able to rescue the knock-out phenotype. Quantitative lipidomic analyses show effect on majority of membrane phospholipids. In vitro activity assays and metabolic labelling assays shows role of GDPD in metabolism of exogenous lysoPC for PC synthesis. The manuscript deciphers the functional role of an important component of lipid metabolism and phospholipid synthesis in the parasite, which are crucial metabolic pathways required for replication of the parasite in the host cell.

We thank the Reviewer for assessing our work and for the following helpful suggestions.

Reviewer #2 (Public Review):

The authors use a conditional Lox/Cre knock-out system to test and confirm the essentiality of glycerophosphodiester phosphodiesterase (GDPD) for blood-stage parasites and a key role in mobilizing choline from precursor lysophosphocholine (LPC) for parasite phospholipid synthesis. Prior works had identified serum LPC as the key choline source for parasites, localized this enzyme in parasites, and suggested an essential function in releasing choline, but this key function had remained untested in parasites. This manuscript critically advances mechanistic understanding of parasite phospholipid metabolism and its essentiality for blood-stage Plasmodium and identifies a potential new drug target.

Overall, this study is well constructed and rigorously performed, and the data provide strong support for the central conclusions about GDPD essentiality and functional contribution to parasite phosphocholine metabolism. The observation that exogenous choline largely rescues parasites from lethal deletion of GDPD is especially compelling evidence for a critical and dominant role in choline mobilization. A few aspects of the paper, however, are not fully supported by the current data and/or need clarification.

We thank the reviewer for this very positive assessment and the helpful suggestions below.

1) GDPD localization

a) The authors conclude that GDPD is localized to the parasitophorous vacuole (PV) and parasite cytoplasm (lines 114-115), which is consistent with the prior 2012 Klemba paper. However, the data in the present paper (Figures 2A and 2E) only seem to support cytoplasmic localization but don’t obviously suggest a population in the PV, in part because no co-staining with a PV marker is shown. The legend for Fig. 2E indicates staining with the PV marker, SERA5, but such co-stain is not shown in the figures or figure supplements. This data should ideally be included and described.

We apologise for this error and omission in our original submission. In response to this suggestion, we have now generated new data that demonstrate co-localisation of the PV marker SP-mScarlet (Mesen-Ramirez et al., 2019) with GDPD in our GDPD-GFP line. In the revised manuscript we now include those new data in Fig 2A and we have also corrected the legend of the revised Fig 2E to reflect what is being shown.

b) How do the authors explain cytoplasmic localization for GDPD? This protein contains an N-terminal signal peptide, which can account for secretion to the PV but would contradict a cytoplasmic population. The 2012 Klemba paper suggested that internal Met19 might provide an alternate site for translation initiation without a signal peptide and thus result in cytoplasmic localization. Some discussion of this ambiguity, its relation to understanding GDPD function, and a possible path to resolve experimentally seem necessary, especially as the authors suggest from data in Fig. 7 that this enzyme may have functions beyond choline mobilization, which may relate to distinct forms in different sub-cellular compartments.

The Reviewer raises an excellent point here. We agree that the apparent dual localization of GDPD and the question of its potential function in both compartments is intriguing. Since lysoPC is efficiently internalised into the parasite, one simple possible explanation (which we failed to state earlier) is that GDPD performs a similar enzymatic function in both compartments. Given the importance of choline for parasite membrane biogenesis, it would not be surprising for GDPD activity to be required at high abundance in order to maintain sufficient choline levels in the parasite. We have now modified lines 403 onwards in the revised Discussion to provide more perspective on this point, as follows: “Based on protein localisation, ligand docking and sequence homology analyses, we can further speculate regarding aspects of PfGDPD function not explored in this study. It has been previously suggested that the gene could use alternative start codons via ribosomal skipping to produce distinct PV-located and cytosolic variants of the protein (Denloye et al., 2012). PfGDPD could potentially perform similar functions in both compartments by facilitating the breakdown of exogenous lysoPC both within the PV and within the parasite cytosol (Brancucci et al., 2017). This scale of enzyme activity may be essential for the parasite to meet its choline needs, given the high levels of PC synthesis during parasite development and its crucial importance for intraerythrocytic membrane biogenesis. PfGDPD may also have other roles during asexual stages such as temporal and localised recycling of intracellular PC or GPC by the PfGDPD fraction expressed in the cytosol. Finally, our ligand docking simulations also do not rule out catalytic activity towards additional glycerophosphodiester substrates such as glycerophosphoethanolamine and glycerophosphoserine (Figure 6-figure supplement 1A and B). Further investigation that involves variant-specific conditional knockout of the gdpd gene could help us further dissect the role of PfGDPD in the parasite.”

2) The phenotypes depicted by representative microscopy images in panel 4E (especially for choline rescue) should be supported by population-level analysis by flow cytometry or microscopy of many parasites to establish generality.

We agree that this would be informative, and in the revised manuscript we have now added a representative microscopy image as source data (Figure 4E_G1+Cho48h-sourcedata.png). It is also worth pointing out that G1 is a clonal line generated from the RAP+ Choline+ parasite population. Both population-level analysis by flow cytometry (Fig 4A) and microscopic images (Fig 4D) are therefore also applicable to the G1 line.

3) The analysis in the last results section (starting on line 296) seems preliminary.

a) For panel 7B, a population analysis of many parasites, with appropriate statistics, is important to establish a generalizable defect beyond the single image currently provided.

b) The data here would seem to be equally explained by an alternative model that GDPD∆ parasites are competent to form gametocytes but their developmental stall (due to choline deficiency) prevents progression to gametocytogenesis. The authors speculate that GDPD may play other roles in phospholipid metabolism beyond choline mobilization that are essential for gametocytogenesis. Their model, if correct, predicts that a GDPD deletion clone from +RAP treatment that is rescued by exogenous choline should not form gametocytes. Testing this prediction would be important to strongly support the conclusion of broader roles for GDPD in sexual development beyond choline mobilization.

We interpreted our results precisely as the reviewer suggests here – that the developmental stall during trophozoite stages is severe enough to prevent sexual differentiation. A priori, we have no reason to suspect that GDPD plays other roles that are selectively essential for gametocyte development. We speculated that GDPD might have other roles in asexual stages but not necessarily based on this experiment. In the revised manuscript we have modified line 313 accordingly to remove ambiguity: “This result implies that the loss of PfGDPD causes a severe block in PC synthesis resulting in the death of asexual parasites before they get to form gametocytes.”

We have also altered line 411 in the Discussion to: “PfGDPD may also have other roles during asexual stages such as temporal and localised recycling of intracellular PC or GPC by the PfGDPD fraction expressed in the cytosol.”

We agree with the reviewer that the analysis is preliminary. Since we lose RAP-treated GDPD:HA:loxPintNF54 populations after cycle 1, we were unable to do more detailed analysis with the line. We also wished to carry out the experiment that the reviewer suggests here to analyze choline-rescued mutants. However, we would be unable to test for this as choline supply alone would suppress sexual differentiation in these parasites (as shown in Brancucci et al., 2017).

Reviewer #3 (Public Review):

In this work, Ramaprasad et al. aimed to investigate the roles of a glycerophosphodiesterase (PfGDPD) in blood stage malaria parasites. to determine its role, they generated a conditional disruption parasites line of PfGDPD using the DiCre system. RAP-induced DiCre-mediated excision results in removal of the catalytic domain of this protein. Loss of this domain leads to a significant reduction of parasite survival, specifically affecting trophozoite stages. They suggest that there is an invasion defect when this protein domain is deleted. They additionally show the introduction of an episomal expression of PfGDPD can rescue the activity of the protein and restore parasite development. Interestingly, exogenous choline can rescue the effects of the loss of PfGDPD. Lipidomic analyses with labelled LPC show that choline release from LPC is severely reduced upon protein ablation and in turn prevents de novo PC synthesis. These experiments also show increase in DAG levels suggesting a defect in the Kennedy pathway. The authors purified PfGDPD and enzymatically show that this protein facilitates the release of choline from GPC. Additionally, the paper also briefly looks at the effects of the protein during sexual blood stages and show this is unlikely to be involved in sexual differentiation.

This paper is of interest to the community since the breakthrough paper of Brancucci et al. (2017), which showed us that decreased LPC levels induce sexual differentiation. This work brings novel insight into a GDPD responsible for the release of choline from GPC which actual seems more relevant to asexual stages and not sexual stage parasites. The authors have been extremely thorough in their experimentations on parasite viability and the exact role of this protein.

We thank the reviewer for this positive assessment and the helpful comments.

Author Response

Reviewer #1 (Public Review):

It is a strength of the current manuscript that it provides a near-complete picture of how the metamorphosis of a higher brain centre comes about at the cellular level. The visualization of the data and analyses is a weakness.

I do not see any point where the conclusions of the authors need to be doubted, in particular as speculations are expressly defined as such whenever they are presented.

The fact that molecular or genetic analyses of how the described metamorphic processes are organized are not presented should, I think, not compromise enthusiasm about what is provided at the cellular level.

We appreciate the comments and guidance that Reviewer #1 has given us on data presentation. We have tried to simplify figures and make the images larger. For the developmental figures, a couple of illustrative examples are provided in the main figure with the remainder given in “figure supplements”

Reviewer #2 (Public Review):

This very nice piece of work describes and discusses the developmental progression of larval neurons of the mushroom body into those in the adult Drosophila brain. There are many surprising findings that reveal a number of strategies for how brain development has evolved to serve both the early functions specific to the larval brain and then their eventual roles in the adult brain. I think it is fascinating biology and I was educated while reviewing the paper.

Line 115-116. 'Output from PPL1 compartments direct avoidance behavior, while that from PAM compartments results in attraction'. This is not correct and is actually reversed. The learning rule is depression so that aversive learning reduces the drive to approach pathways whereas appetitive learning reduces the drive to avoidance pathways. This should be corrected and reference made to studies demonstrating learning-directed depression.

Line 222. It provides feed-forward inhibition from y4>2>1. I could be wrong but I'm not aware that there is functional evidence for this glutamatergic neuron being inhibitory. It's currently speculation.

We have noted that this function was proposed by Aso et al.

Line 242. I think it would be nice if the authors focused on extreme changes and showed larger and nicer images. The rest can be summarized but why not pick a few of the best examples to illustrate the strategies they consider in the discussion?

We have reduced the number of neurons shown in the new Figs 5 and 6. Hopefully, the images are now large enough to appreciate. Data for the remaining neurons are now in Figure Supplements for Figs 5 and 6.

Line 249 'became sexually dimorphic'. I may have missed it somewhere but this immediately made me think about the sex of all the images that are shown. Is this explicitly stated somewhere? Was it tracked in all larvae, pupae, and adults?

We now begin the Methods addressing this point. We did an initial screen and found sex-specific differences only in MBIN-b1 and -b2. After this time, we kept no records as to the sex of the fly that was used except for the latter cells.

Reviewer #3 (Public Review):

Truman et al. investigated the contribution and remodeling of individual larval neurons that provide input and output to the Drosophila mushroom body through metamorphosis. Hereto, they used a collection of split-GAL4 lines targeting specific larval mushroom body input and output neurons, in combination with a conditional flip-switch and imaging, to follow the fates of these cells.

Interestingly, most of these larval neurons survive metamorphosis and persist in the adult brain and only a small percentage of neurons die. The authors also elegantly show that a substantial number of neurons actually trans-differentiate and exert a different role in the larval brain, compared to their final adult functionality (similar to their role in hemimetabolous insects). This process is relatively understudied in neuroscience and of great interest.

Using the ventral nerve cord as a proxy, the authors claim that the larval state of the neuron would be their derived state, while their adult identity is ancestral. While the authors did not show this directly for the mushroom body neurons under study, it is a very compelling hypothesis. However, writing the manuscript from this perspective and not from the perspective of the neuron (which first goes through a larval state, metamorphosis, and finally adult state), results in confusing language and I would suggest the authors adjust the manuscript to the 'lifeline' of the neuron.

We have tried to be more “linear” in our presentation. This should make the text less confusing.

In general, this manuscript does not explain how the larval brain has evolved as the title suggests but instead describes how the larval brain is remodeled during metamorphosis. It thus generates perspectives on the evolution of metamorphosis, rather than the larval state. Additionally, this manuscript would benefit from major rearrangements in both text and figures for the story to be better comprehended.

We think that the end of the Discussion does relate to how a larval brain evolves. The evolution of the larval brain is faced with constraints related to the shortened period of embryonic development and the highly conserved temporal and spatial mechanisms that insects use to generate their neuronal phenotypes. These constraints result in a potential mismatch between the neurons that are needed and those that are actually made (revealed by the adult phenotypes of these neurons). The larva then turns to trans-differentiation to temporarily transform unneeded (or dead) neurons into the missing cell types to build its larval circuits.

We think that these ideas provide some new insights into how a larval brain may have evolved and that our title is appropriate.

The introduction is very focused on the temporal patterning of the insect nervous system, while none of the data collected incorporate this temporal code. Temporal patterning comes back in the discussion but is purely speculative.

The Speculation about the importance of temporal patterning is now brought in late in the Discussion in reference to Figure 12

Furthermore, the second part of the introduction describes one strategy for remodeling and why that strategy is not likely but does not present an alternative hypothesis. The first section of the results might serve as a better introduction to the paper instead, as it places the results of the paper better and concludes with the main findings. The accompanying Figure 1 would also benefit from a schematic overview of the larval and adult mushroom bodies as presented in Fig. 2A (left).

This has been revised in the spirit of these comments

In the second results section, the authors show the post-metamorphic fates of mushroom body input and output neurons and introduce the concept of trans-differentiation. Readers might benefit from a short explanation of this process. I also encourage the authors to revisit this part of the text since it gives the impression that the neurons themselves undergo active migration (instead of axon remodeling).

We have tried to make it clear that there is no cell migration. Rather there is retraction/fragmentation of larval arbors followed by outgrowth to new, adult targets

The discussion starts with a very comprehensive overview of the different strategies that neurons could use during metamorphosis (here too, re-writing the text from the neurons' perspective would increase the reflection of what actually happens to them).

The Discussion now begins by dealing with gross changes in the MB, with reference to the compartments and eventually moves to changes in individual cells. We have reduced our discussion of the metamorphic strategies of cells and no longer have Fig 8A

The discussion covers multiple topics concerning trans-differentiation, metamorphosis, memory, and evolution and is often disconnected from the results. It could be significantly shortened to discuss the results of the paper and place them in current literature. Generally, the figures supporting the discussion are hard to comprehend and often do not reflect what the text is saying they are showing.

The Discussion is still long, but, hopefully, our organization now makes it much easier to read and comprehend.

Author Response:

Reviewer #1 (Public Review):

Monfared et al. construct a three-dimensional phase-field model of cell layers and use it to examine cellular extrusion by independently tuning cell-substrate and cell-cell adhesion. In line with earlier studies (in some of which some of the authors were involved), they find that extrusion is linked to topological defects in cellular arrangement and relieving stress.<br /> The authors claim that their development of the three-dimensional phase field model is crucial for understanding cell extrusion (which I agree with the authors is inherently three-dimensional). However, I don't think the data they currently present clearly demonstrate that the three-dimensional model adds significantly more to our understanding of extrusion events than earlier two-dimensional models.

In the end, I think that the more important achievement of this work -- and one that is likely to be more influential -- is developing a three-dimensional phase field model for cell monolayers rather than any specific result regarding extrusion.

We sincerely thank the reviewer for their time examining our manuscript and providing critical feedback. We are confident that our detailed response provided below and additional analyses have further highlighted the importance of three-dimensional stresses.

Reviewer #2 (Public Review):

The paper provides a natural extension of 2D multiphase field models for cell monolayers to 3D, addressing cell deformations, cell-cell interaction, cell-substrate interactions and active components for the cells. As known from 2D, the cell arrangement leads to positional (hexatic) defects and if the elongation of the cells is coarse-grained to define a global nematic order also to orientational (nematic) defects. These defects are characterized, see Figure 2. However, this is done in 2D and it remains unclear if the projected basal or apical side is considered in this figure and the following statistics. The authors identify correlations between orientational defects and extrusion events. In terms of positional defects such statistics seem not to be considered and the relation between positional defects and cell extrusion events remains vague. Also in-plane and out-of-plane stresses are computed. These results confirm a mechanical origin for cell extrusions. However, these are the only 3D information provided. The final claim that the results clearly demonstrate the existence of a mechanical route related with hexatic and nematic disclinations is not clear to me. 3D vertex models for such systems e.g. showed the importance of different mechanical behavior of the apical and basal side and identified scutoids as an essential geometric 3D feature in cell monolayers. These results are not discussed at all. A comparison of the 3D multiphase field model with such results would have been nice.

We thank the reviewer for bringing to our attention the work on scutoids, which we now discuss in the manuscript as an important geometric feature of 3D layers on curved surfaces. We shall, however, emphasize that scutoids are specific to monolayers on curved surfaces, while we focus on a cell monolayer on flat substrates here. Moreover, we shall argue that the difference between apical and basal sides is just one element of the 3D complexity of cell layers. Here, we focus on another aspect of 3D complexity that is not accessible in 2D: the development of 3D mechanical stress and its role in an inherently 3D problem of cell extrusion. Nevertheless, as discussed in detail responses below we have now added additional analyses varying the monolayer interaction with the substrate on the basal side.

Reviewer #3 (Public Review):

In this paper, the authors studied the influence of topological defects on extrusion events using 3D multi-phase field simulations. By varying cell-cell and cell-substrate parameters, this study helps to better understand the influence of mechanical and geometrical parameters on cell extrusion and their linkage to topological defects.

First the authors show that extrusion events and topological defects of nematic and hexatic order are typically found in their system, and then that extrusions occur, on average, at a distance of a few cell sizes from a + and - 1/2 defects. Next, the author analyse at extrusion events the temporal evolution of the local isotropic stress and the local out-of-plane shear stress, showing that near the instant of extrusion, the isotropic stresses relax and the shear stresses fluctuate around a vanishing value. Finally, the authors analyse both the distribution of isotropic stress and the average isotropic stress pattern near +1/2 defects.

We are grateful to the reviewer for their time examining our manuscript and providing critical feedback that has certainly improved our manuscript. In what follows, we provide detailed responses to each comment, including additional statistics that we have computed and now include in the manuscript for completion.

Author Response

Reviewer #1 (Public Review):

Junctophilin is mostly known as a structural anchor to keep excitation-contraction (E-C) proteins in place for healthy contractile function of skeletal muscle. Here the authors provide a new interesting role in skeletal muscle for Junctophilin (44 kD segment, JPh44), where it translocates to the nuclei and influences gene transcription. Also, the authors have shown that Calpain 1 can digest junctophilin to generate the 44 kDa segment. The field of skeletal muscle generally knows little about how E-C coupling proteins have dual role and influence gene regulation that subsequently may alter the muscle function and metabolism. This part of the manuscript is solid, informative, and novel. The authors use advanced imaging and genetic manipulations of junctophilin etc to support their hypothesis. The authors then also aim to link this mechanism to hyperglycemia in individuals susceptible for malignant hyperthermia as they have elevated levels of the 44kDa segment. However, the power of the analyses are low and the included data comparisons complicates the possibility to interpret the results and its relevance. Nevertheless, the data supporting the novel dual role of junctophilin would likely be appreciated and gain attention to the muscle field.

Thanks for your constructive reading. We agreed (in our answer to Item 1) to your concern regarding power of the tests. To improve it we would need many more individual patients (which, after the pandemic peaks, are starting to be recruited again, although at a pace of no more than 2 per month). We are committed to updating the present report as soon as we obtain, say, 20 more MHS and MHN patients –a minimum to impact power of the tests. In any case, we claim that power is not an acute concern, as this communication deals mainly with positive results, where significance is of the essence.

We have established significance in most of the observations communicated here; in the few cases where p is marginal, significance is inferred by correlations.

Reviewer #2 (Public Review):

Skeletal muscle is the main regulator of glycemia in mammals and a major puzzle in the field of diabetes is the mechanism by which skeletal muscle (as well as other tissues) become insensitive to insulin or decrease glucose intake. the authors had proposed in a previous publication that high intracellular calcium, by means of calpain activation, could cleave and decrease the availability of GLUT4 glucose transporters. In this manuscript, the authors identify two additional targets of calpain activation. One of them is GSK3β, a specialized kinase that when cleaved, inhibits glycogen synthase and impairs glucose utilization. The second target is junctophilin 1, a protein involved in the structure of the complex responsible for E-C coupling in skeletal muscle. The authors succeeded in showing that a fragment of junctophilin1 (JPh44) moves from the triad to other cytosolic regions including the nuclei and they show changes in gene expression under these conditions, some of them linked to glucose metabolism.

Overall, the manuscript shows a novel and audacious approach with a careful treatment of the data (that was not always easy nor obvious) that allow sensible conclusions and definitively constitutes a step forward in this field.

Thanks for the generous report.

Reviewer #3 (Public Review):

First, we express utmost gratitude for your critical work on our manuscript. Your concerns made us perform additional experiments and validations, eventually forcing us to abandon a couple of erroneous notions and therefore improving our understanding and interpretations. Because your concerns were already in the “Essentials” list assembled by the Editor, our responses here will mostly refer to our earlier answers to the items in that list.

1) Figure 1 A and B show a western blot of proteins isolated from muscles of MHN and MHS individuals decorated with two different antibodies directed against JPH1. According to the manufacturer, antibody A is directed against the JPH1 protein sequence encompassing amino acids 387 to 512 while antibody B is directed against a no better specified C-terminal region of JPH1. Surprisingly, antibody B appears not to detect the full-length protein in lysates from human muscles, but recognizes only the 44 kDa fragment of JPH1. However, to the best of the reviewer's knowledge, antibody B has been reported by other laboratories to recognize the full-length JPH1 protein.

The reason for the failure of ab B to recognize the full human protein may be that it was raised against a murine immunogen (this interpretation was communicated to us by G.D. Lamb, who co-authored the 2013 paper by Murphy et al. where the failure was noted). It recognizes both JPh1 and JPh44 of murine muscle in our hands.

Thus, is not obvious why here this antibody should recognize only the shorter fragment.

We agree entirely. In spite of the difficulties in interpretation, the recognition of human JPh44 by the ab is, however, a fact, repeatedly demonstrated in the present study, which can be used to advantage.

In addition, in MHS individuals there is no direct correlation between reduction in the content of the full-length JPH1 protein and appearance of the 44 kDa JPH1fragment, since, as also reported by the authors, no significant difference between MHN and MHS can be observed concerning the amount of the 44 kDa JPH1.

Tentative interpretations of the lack of correlation have been presented in the response to Item 14, above.

Based on the data presented, it is very difficult to accept that antibody A and B have specific selectivity for JPH1 and the 44 kDa fragment of JPH1.

Indeed, we now acknowledge that Ab A reacts equally with JPh1 and the 44 kDa fragment (and provide quantitative evidence for it in Supplement 1 to Fig. 8). We also provide conclusive evidence of the specificity of ab B (e.g., Supplement 2 to Fig. 1).

2) In Figure 2B staining of a nucleus is shown only with antibody B against the 44 kDa JPH1 fragment, while no nucleus stained with antibody A is shown in Fig 2A. Images should all be at the same level of magnification and nuclear staining of nuclei with antibody A should be reported. In Figure 2Db labeling of JPH1 covers both the nucleus and the cytoplasm, does it mean that JPH1 also goes to the nucleus? One would rather think that background immunofluorescence may provide a confounding staining and authors should be more cautious in interpreting these data.

These items are fully covered in our response to Item 16.

Images in 2D and 2E refer to primary myotubes derived from patients. The authors show that RyR1 signals co-localizes with full-length JPH1, but not with the 44 kDa fragment, recognized by antibody B. How do the authors establish myotube differentiation?

Myotubes are studied 5-10 days after switching cells to differentiation medium, which is DMEM-F12 supplemented with 2.5% horse serum, as explained in Figueroa et al 2019. Cells with more than 3 nuclei were considered myotubes. Myotubes with similar degree of maturation (number of nuclei) were selected for experimental comparisons.

3) Figure 3 A-C. The authors show images of a full-length JPH1 tagged with GFP at the N-terminus and FLAG at the C- terminus. In Figure 3Ad and Cd the Flag signal is all over the cytoplasm and the nuclei: since these are normal mouse cells and fibers, it is surprising that the FLAG signal is in the nuclei with an intensity of signal higher than in patient's muscle.

Can the authors supply images of entire myotubes, possibly captured in different Z planes? How can they distinguish between the cleaved and uncleaved JPH1 signals, especially in mouse myofibers, where calpain is supposed not to be so active as in MHS muscle fibers?

Answer fully provided to Items 16b and 17 in Essentials list.

4) If the 44 kDa JPH1 fragment contains a transmembrane domain, it is difficult to understand the dual sarcoplasmic reticulum and nuclear localization. To justify this the authors, in the Discussion session, mention a hypothetical vesicular transport of the 44 kDa JPH1 fragment by vesicles. Traffic of proteins to the nucleus usually occurs through the nuclear pores and does not require vesicles. Even if diffusion from the SR membrane to the nuclear envelope occurs, the protein should remain in the compartment of the membrane envelope. There is no established evidence to support such an unusual movement inside the cells.

In agreement with the criticism, we have removed the speculation from the Discussion.

5) In Figure 5, the authors show the effect of Calpain1 on the full-length and 44 kDa JPH1 fragment in muscles from MHS patients. Can the authors repeat the same analysis on recombinant JPH1 tagged with GFP and FLAG?

We agree that confirmatory evidence of the calpain effect on dual-tagged recombinant JPh1 would be desirable. However, we think an in-depth study is required to follow up on the number of JPh1 fragments generated by calpain (or by different calpain isoforms) and their positions, similar to the detailed study of JPh2 fragmentation Wang et al. in 2021 (5).

Can the authors provide images from MHN muscle fibers stained with JPH1 and Calpain1.

We complied with the request.

6) In Figure 6, the authors show images of MHS derived myotubes transfected with FLAG Calpain1 and compare the distribution of endogenous JPH1 and RYR1 in two cells, one expressing FLAG Calpain1 (cell1) and one not expressing the recombinant protein. They state that cell1 shows a strong signal of JPH1 in the nucleus, while this is not observed in cell2. Nevertheless, it is not clear where the nucleus is located within cell2 since the distribution of JPH1 is homogeneous across the cell. Can the authors show a different cell?

In agreement, we now show a comparison between cultures with and without transfection in Supplement 1 to Fig. 6.

7) In Figure 7, panels Bb and Db: nuclei appear to stain positive for JPH1. It is not clear why in panels Ac, Bc they show a RYR1 staining while in panels Cc and Dc they show N-myc staining. The differential localization to nuclei appears rather poor also in these panels.

We have entirely removed from the manuscript the description of experiments of exposure to extracellular calpain, including Fig. 7 and three associated tables.

8) The strong nuclear staining in Figure 8, panels C and D is very different from the staining observed in Fig. 2 and Fig. 3. Transfection should not change the ratio between nuclear and cytoplasmic distribution.

Transfection is an intrusive procedure, which requires production and trafficking of an exogenous protein. This protein, furthermore, is an artificial construct (in this case, a “stand-in”, which adds to the native protein and therefore is akin to overexpression). For the above reasons, we believe that differences in intensity of nuclear staining may obey to multiple causes and should not be especially concerning.

Author Response

Reviewer #1 (Public Review):

1) This study performs an interesting analysis of evolutionary variation and integration in forelimb/hand bone shapes in relation to functional and developmental variation along the proximo-distal axis. They found expected patterns of evolutionary shape variation along the proximo-distal axis but less expected patterns of shape integration. This study provides a strong follow-up to previous studies on mammal forelimb variation, adding and testing interesting hypotheses with an impressive dataset. However, this study could better highlight the relevance of this work beyond mammalian forelimbs. The study primarily cites and discusses mammalian limb studies, despite the relevance of the suggested findings beyond mammals and forelimbs. Furthermore, relevant work exists in other tetrapod clades and structures related to later-developing traits and proximo-distal variation. Finally, variations in bone size and shape along the proximo-distal axis could be affecting evolutionary patterns found here and it would be great to make sure they are not influencing the analysis/results.

We appreciate the reviewer’s comments, and we acknowledge the importance of including examples of non-mammalian lineages in our study. We attended to the recommendation and included more examples of other tetrapod taxa in our text and in our references, providing a more inclusive discussion of limb bone diversity beyond mammals. We also explain below why the results obtained are not inflated by variation of bigger versus smaller sizes of bones.

Reviewer #2 (Public Review):

10) Congratulations on producing a very nice study. Your study aims to examine the morphological diversity of different mammalian limb elements, with the ultimate goal seemingly to test expectations based on the different timing of development of the limb bones. There's a lot to like: the sample size is impressive, the methods seem appropriate and sound, the results are interesting, the figures are clear, and the paper is very well written. You find greater diversity and integration in distal limb segments compared to proximal elements, and this may be due to the developmental timing and/or functional specialization of the limb segments. These are interesting results and conclusions that will be of interest to a broad readership. And the large dataset will likely be valuable to future researchers who are interested in mammalian limb morphology and evolution. I have one major concern with how you frame your discussion and conclusions, which I explain below. But I think you can address this issue with some text edits.

We sincerely thank the reviewer for his constructive recommendations and for his appreciation of our work. We addressed the issue raised as detailed below.

11) Major concern - is developmental timing the best hypothesis?

You discuss two potential drivers for the relatively greater diversity in distal elements: 1) later development and 2) greater functional specialization. Your data doesn't allow you to fully test these two hypotheses (e.g. you don't have detailed evo-devo data to infer developmental constraints), and I think you realize this - you use phrases like "consistent with the hypothesis that ...". You seem to compromise and conclude that both factors (development + function) are likely driving greater autopod diversity (e.g. Lines 302-306). Being unable to fully test these hypotheses weakens the impact of your conclusions, making them a bit more speculative, but otherwise, it isn't a critical issue.

But my concern is that you seem to favor developmental factors over functional factors as the primary drivers of your results, and that seems backwards to me. For instance, early in the Abstract (Line 32) and early in the Discussion (Line 201) you mention that your results are consistent with the developmental timing hypothesis, but it's not until later in the Abstract or Discussion that you mention the role of functional diversity/specialization/selection. The problem with favoring the development hypothesis is that your integration results seem to contradict that hypothesis, at least based on your prediction in the Introduction (Line 126; although you spend some of the Discussion trying to make them compatible). Later in the paper, you acknowledge that functional specialization (rather than developmental factors) might be a better explanation for the integration results (Lines 282-284, 345-347), but, again, this is only after discussions about developmental factors.

When you first start discussing functional diversity, you say, "high integration in the phalanx and metacarpus, possibly favoured the evolution of functionally specialized autopod structures, contributing to the high variation observed in mammalian hand bones." (Line 282). This implies that integration led to functional diversity in the autopod. But I'd flip that: I think the functional specialization of the hand led to greater integration. Integration does not result solely from genetic/developmental factors. It can also result from traits evolving together because they are linked to the same function. From Zelditch & Goswami (2021, Evol. & Dev.): "Within individuals, integration is customarily ascribed to developmental and/or functional interdependencies among traits (Bissell & Diggle, 2010; Cheverud, 1982; Wagner, 1996) and modularity is thus due to their developmental and/or functional independence."

In sum, I think your results capture evidence of greater functional specialization in hands relative to other segments. You're seeing greater 1) disparity and 2) integration in hands, and both of those are expected outcomes of greater functional specialization. In contrast, I think it's harder to fit your results to the developmental timing hypothesis. Thus, I recommend that throughout the paper (Abstract, Intro, Discussion) you flip your discussion of the two hypotheses and start with a discussion on how functional specialization is likely driving your results, and then you can also note that some results are consistent with the development hypothesis. You could maintain most of your current text, but I'd simply rearrange it, and maybe add more discussion on functional diversity to the Intro.

Or, if you disagree and think that there's more support for the development hypothesis, then you need to make a better case for it in the paper. Right now, it feels like you're trying to force a conclusion about development without much evidence to back it up.

We thank the reviewer for his thoughtful and thorough comment. We agree that the results provided, particularly those of integration, support the hypothesis that functional specialization contributes to the uneven diversity of limb bones. We addressed the concerns by substantially changing our discussion, particularly moderating (and removing) sections on the developmental constraints and adding new arguments for other possible drivers for the diversity of limb bones, such as function. However, the goal of the paper was to test whether the data corroborate - or not - the predictions derived from the developmental hypothesis, and they largely do. Therefore, we decided to keep the developmental hypothesis presented first in the introduction and in the discussion section, as we believe this sequence provides more coherence considering the hypothesis tested (we believe that detailing the role of functional specialization particularly in the introduction would mislead the reader to think that we directly tested for these parameters). Following the discussion of the integration results, we then go on to discuss the possible role of functional specialization on the results obtained (lines 262-285, see also lines 216-234). Yet, these are not tested in this paper and remain to be tested in a future analysis focusing specifically on the role of ecology and function in driving variation in the mammalian limb.

12) Limitations of the dataset

Using linear measurements is fine, but they mainly just capture simple aspects of the elements (lengths and widths). You should acknowledge in your paper the limitations of that type of data. For example, the deltoid tuberosity of the humerus can vary considerably in size and shape among mammals, but you don’t measure that structure. The autopod elements don’t have a comparable process, meaning that if you were to measure the deltoid tuberosity then you’d likely see a relative increase in humerus disparity (although my guess is that it’d still be well below that of the autopod). And you omit the ulna from your study, and its olecranon process varies considerably among taxa and its length is a very strong correlate of locomotor mode. In other words, your finding of the greatest disparity in the hand might be due in part to your choice of measurements and the omission of measurements of specific processes/elements. I recommend that you add to your paper a brief discussion of the limitations of using linear measurements and how you might expect the results to change if you were to include more detailed measurements and/or more elements.

We followed the recommendation and included a discussion about the dataset limitations, acknowledging for the possible impact of the measurements and the bones chosen in the results obtained (Lines 235-260).

Reviewer #3 (Public Review):

32) This paper uses a large (638 species representing 598 genera in 138 families) extant sample of osteologically adult mammals to address the question of proximodistal patterns of cross-taxonomic diversity in forelimb bony elements. The paper concludes, based on a solid phylogenetically controlled multivariate analysis of liner measurements, that proximal forelimb elements are less morphologically diverse and evolutionarily flexible than distal forelimb elements, which the paper concludes is consistent with a developmental constraint axis tied to limb bud growth and development. This paper is of interest to researchers working on macroevolutionary patterns and sources of morphological diversity.

Methodological review Strengths:

The taxonomic dataset is very comprehensive for this sort of study and the authors have given consideration to how to identify bony elements present in all mammalian taxa (no small task with this level of taxonomic breadth). Multivariate approaches as used in this study are the gold standard for addressing questions of morphological variations.

The authors give consideration to two significant confounders of analyses operating at this scale: phylogeny and body size. The methods they use to address these are appropriate, although as I note below body size itself may merit more consideration.

We sincerely thank the reviewer for his appreciation of our study. We addressed the main concerns pointed out below.

Weaknesses:

33) The authors assume a lot of knowledge on the part of the reader regarding their methods. Given that one of their key metrics (stationary variance) is largely a property as I understand it of OU models, more explanation on the authors' biological interpretation of stationary variance would help assess the strength of their conclusions, especially as OU models are not as straightforward as they first appear in their biological interpretation (Cooper et al., 2016).

We acknowledge that this may not be straightforward and now include a more extensive explanation of the approach and the metrics used. We detailed the explanation about the stationary variances in the methods, contextualizing the biological meaning (lines 456-469).

34) It is unclear what the authors mean when they say they "simulated the trait evolution under OU processes on 100 datasets". Are the 100 datasets 100 different tree topologies (as seems to be the case later "we replicated the body mass linear regressions with 100 trees from Upham et al (2019)." If that is so, what is the rationale for choosing 100 topologies and what criteria were used to select the 100 topologies?

We understand the explanation may have been confusing. Globally, we used a parametric bootstrap approach to assess the uncertainty around point estimates for morphological diversity and integration. That is, we first simulated 100 datasets on the maximum clade credibility tree (MCC tree, that summarizes 10,000 trees from Upham et al. 2019) – using the best fit model on our original data (i.e., an OU process) with parameters estimates from this model fit. The model (an OU process) was then fit to these 100 simulated traits, and the distribution of parameters estimates obtained was used to assess the variability around the point estimate (for the determinant, the trace, and the measure of integration) obtained on empirical data. We did not used the simulated dataset to estimate the significance of the stationary variances. We fitted the empirical datasets with 100 trees randomly sampled from the credible set of 10,00 trees of Upham et al (2019) – instead of using the MCC – to further assess the variability due to the tree topology and branching times uncertainties. We included this expanded explanation in the methods in lines 421-428 and 471.

35) The way the authors approach body mass and allometry, while mathematically correct, ignores the potential contribution of body mass to the questions the authors are interested in. Jenkins (1974) for example argued that small mammals would converge on similar body posture and functional morphology because, at small sizes, all mammals are scansorial if they are not volant. Similarly, Biewener (1989) argued that many traits we view as cursorial adaptations are actually necessary for stability at large body sizes. Thus size may actually be important in determining patterns of variation in limb bone morphology.

We agree with the observation. We believe that categorizing the groups according to size would provide a meaningful overview on the effect of size on the diversity and evolution of limb bones. Although insightful and worthy of investigation, we were particularly interested in understanding whether developmental timing corresponds to bone diversification more broadly across Mammalia and thus considered only the size residual values. This issue will be addressed in our future works. We discussed in the lines 329-341 the potential contribution of body size to limb segment diversification and the importance of considering this aspect in future studies.

36) Review of interpretation.

The authors conclude that their result, in showing a proximo-distal gradient of increasing disparity and stationary variance in forelimb bone morphology, supports the idea that proximo-distal patterning of limb bone development constrains the range of morphological diversity of the proximal limb elements. However, this correlation ignores two important considerations. The first is that the stylopod connects to the pectoral girdle and the axial skeleton, and so is feasibly more constrained functionally, not developmentally in its morphological evolution. The second, related, issue arises from the authors' study itself, which shows that the lowest morphological integration is found in the stylopod and zeugopod, whereas the autopod elements are highly integrated. This suggests a greater tendency towards modularity in the stylopod and zeugopod, which is itself a measure of evolutionary lability (Klingenberg, 2008). And indeed the mammalian stylopod is developmentally comprised of multiple elements (the epiphyses and diaphysis) that are responding to very different developmental and biomechanical signals. Thus, for example, the functional signal in stylopod (Gould, 2016) and zeugopod (MacLeod and Rose, 1993) articular surface specifically is very high. What is missing to fully resolve the question posed by the authors is developmental data indicating whether or not the degree of morphological disparity in the hard tissues of the forelimb change over the course of ontogeny throughout the mammalian tree, and whether changing functional constraints over ontogeny (as is the case in marsupials) affect these patterns.

We thank the reviewer for sharing such an interesting reinterpretation of the results. Combined to the recommendations from the other two reviewers, we substantially changed our discussion, specially modifying the interpretation of results concerning trait integration. We discussed the possible role of the functional variation at the articulations on element integration in lines 263-285.

Author Response

Reviewer #2 (Public Review):

This paper investigates the maintenance and function of memory follicular helper T (Tfh) cell subsets using in vitro approaches, murine immunization models and vaccine-challenged humans. Murine Tfh cell subsets (Tfh1, Tfh2, Tfh17) were generated using in vitro polarization (iTfh1, iTfh2, iTfh17), and then tested for support of humoral response following adoptive transfer or adoptive transfer with resting in vivo for 35 days. iTfh17 cells were statistically better than iTfh1 and iTfh2 cells in promoting GC B cell and plasma cell maturation after resting in vivo, although all 3 populations were capable of B cell help. Tfh17 cells were comparatively enriched among blood borne Tfh central memory cells in humans, and were enriched at the memory phase of vaccination with hepatitis B and influenza vaccines, compared to effector phase, suggesting the possibility they are comparatively superior in Tfh cell memory formation, with greater persistence in aged individuals.

Significance

The enrichment of Tfh17 cells in Tfh cell central memory compartment and the dominance of Tfh17 cell population and the Tfh17 transcriptional signature in circulating Tfh cells at the memory phase are nicely demonstrated, and may well be helpful for understanding the heterogeneity of memory Tfh cells and potentially providing clues for vaccine design. The in vitro differentiation system for mouse Tfh cells also provides a strategy for others to build upon in dissection of Tfh cell development and function.

Points to consider

1) Even though Tfh17 cells are more likely to persist at memory timepoints in mice and in humans, or produce more GC B cells or plasma cells following transfer, all subsets can do this. Is GC output otherwise distinguishable following transfer of the individual subsets, or is their effect (cytokine related perhaps) pre-GC with differential CSR? It is also not clear if the individual subsets populate the GC and assuming they do so, if their respective phenotypes persist when they become GC Tfh cells.

We have conducted new experiments and showed that iTfh17 preferentially generate more GC-Tfh cells in the delay immunization (after 35 day’s resting in vivo). Furthermore, different iTfh subsets maintained polarized cytokine profiles after antigen re-exposure and prompt specific CSR as their Th1 or Th2 counterparts. Please refer to the response (2) to Essential Revisions for details.

2) iTfh17 cells induce more GC B cells and antibodies after resting and antigen challenge (Figures 1, 2). However, it's not clear whether this effect is a consequence of comparatively enhanced iTfh17 survival during resting (as suggested by latter figures), or better expansion or differential skewing to Tfh differentiation during challenge (as suggested by Figure 1 J,K). The total number of remaining adoptively-transferred cells right before challenge and 7 days post challenge will be helpful to understand that.

We have conducted new experiments and our results suggested that the superior immunological memory maintenance of iTfh17 cells was attributed to their better survival capacity and better maintenance of the potential to differentiate into GC-Tfh cells. Please refer to the response (2) to Essential Revisions for details.

3) The authors tried to address whether Tfh17 cells have better ability to survive till memory phase or Tfh17 cells with memory potential are generated at higher frequency at the effector phase of vaccination (Figure 5); however, the experiment is not conclusive. The cTfh population 7 days post vaccination is a mixed population with effector Tph cells and Tfh memory precursors. The increased frequency of Th17 cells at day 28 compared to day 7 could be a consequence of superior survival ability, or Tfh memory precursors with Tfh17 signature are better generated.

As indicated in our gating strategy and the widely accepted definition of cTfh cells - CD4+ CD45RA- CXCR5+ (line 69), we respectively disagree with the reviewer’s comment ‘The cTfh population 7 days post vaccination is a mixed population with effector Tph cells and Tfh memory precursors’. The effector Tph population is defined as PD-1hiCXCR5-CD4+ T cells (Rao DA et al. Pathologically expanded peripheral T helper cell subset drives B cells in rheumatoid arthritis, Nature 2017)

4) Experiments to confirm expansion ability of the human subsets or their B cell helper ability were not performed.

In our new experiments, we demonstrated that iTfh1/2/17 cells showed comparable expansion ability.

Human cTfh1/2/17 cells’ expansion ability and B helper ability were reported previously by Morita et al. (Human blood CXCR5(+)CD4(+) T cells are counterparts of T follicular cells and contain specific subsets that differentially support antibody secretion, Immunity 2011, Figure 4C-D). Human cTfh1/2/17 cells showed comparable expansion ability when co-culturing with SEB-pulsed naive B cells, and cTfh17 cells had superior B cell helper function over cTfh1 but not cTfh2 cells in promoting the B cell expansion and plasma cell formation.

Author Response

Reviewer #1 (Public Review):

In this manuscript, Goering et al. investigate subcellular RNA localization across different cell types focusing on epithelial cells (mouse C2bbe1 and human HCA-7 enterocyte monolayers, canine MDCK epithelial cells) as well as neuronal cultures (mouse CAD cells). They use their recently established Halo-seq method to investigate transcriptome-wide RNA localization biases in C2bbe1 enterocyte monolayers and find that 5'TOP-motif containing mRNAs, which encode ribosomal proteins (RPs), are enriched on the basal side of these cells. These results are supported by smFISH against endogenous RP-encoding mRNAs (RPL7 and RPS28) as well as Firefly luciferase reporter transcripts with and without mutated 5'TOP sequences. Furthermore, they find that 5'TOP-motifs are not only driving localization to the basal side of epithelial cells but also to neuronal processes. To investigate the molecular mechanism behind the observed RNA localization biases, they reduce expression of several Larp proteins and find that RNA localization is consistently Larp1-dependent. Additionally, the localization depends on the placement of the TOP sequence in the 5'UTR and not the 3'UTR. To confirm that similar RNA localization biases can be conserved across cell types for other classes of transcripts, they perform similar experiments with a GA-rich element containing Net1 3'UTR transcript, which has previously been shown to exhibit a strong localization bias in several cell types. In order to determine if motor proteins contribute to these RNA distributions, they use motor protein inhibitors to confirm that the localization of individual members of both classes of transcripts, 5'TOP and GA-rich, is kinesin-dependent and that RNA localization to specific subcellular regions is likely to coincide with RNA localization to microtubule plus ends that concentrate in the basal side of epithelial cells as well as in neuronal processes.

In summary, Goering et al. present an interesting study that contributes to our understanding of RNA localization. While RNA localization has predominantly been studied in a single cell type or experimental system, this work looks for commonalities to explain general principles. I believe that this is an important advance, but there are several points that should be addressed.

Comments:

1) The Mili lab has previously characterized the localization of ribosomal proteins and NET1 to protrusions (Wang et al, 2017, Moissoglu et al 2019, Crisafis et al., 2020) and the role of kinesins in this localization (Pichon et al, 2021). These papers should be cited and their work discussed. I do not believe this reduces the novelty of this study and supports the generality of the RNA localization patterns to additional cellular locations in other cell types.

This was an unintentional oversight on our part, and we apologize. We have added citations for the mentioned publications and discussed our work in the context of theirs.

2) The 5'TOP motif begins with an invariant C nucleotide and mutation of this first nucleotide next to the cap has been shown to reduce translation regulation during mTOR inhibition (Avni et al, 1994 and Biberman et al 1997) and also Lapr1 binding (Lahr et al, 2017). Consequently, it is not clear to me if RPS28 initiates transcription with an A as indicated in Figure 3B. There also seems to be some differences in published CAGE datasets, but this point needs to be clarified. Additionally, it is not clear to me how the 5'TOP Firefly luciferase reporters were generated and if the transcription start site and exact 5'-ends of these constructs were determined. This is again essential to determine if it is a pyrimidine sequence in the 5'UTR that is important for localization or the 5'TOP motif and if Larp1 is directly regulating the localization by binding to the 5'TOP motif or if the effect they observe is indirect (e.g. is Larp1 also basally localized?). It should also be noted that Larp1 has been suggested to bind pyrimidine-rich sequences in the 5'UTR that are not next to the cap, but the details of this interaction are less clear (Al-Ashtal et al, 2021)

We did not fully appreciate the subtleties related to TOP motif location when we submitted this manuscript, so we thank the reviewer for pointing them out.

We also analyzed public CAGE datasets (Andersson et al, 2014 Nat Comm) and found that the start sites for both RPL7 and RPS28 were quite variable within a window of several nucleotides (as is the case for the vast majority of genes), suggesting that a substantial fraction of both do not begin with pyrimidines (Reviewer Figure 1). Yet, by smFISH, endogenous RPL7 and RPS28 are clearly basally/neurite localized (see new figure 3C).

Reviewer Figure 1. Analysis of transcription start sites for RPL7 (A) and RPS28 (B) using CAGE data (Andersson et al, 2014 Nat Comm). Both genes show a window of transcription start sites upstream of current gene models (blue bars at bottom).

A more detailed analysis of our PRRE-containing reporter transcripts led us to find that in these reporters, the pyrimidine-rich element was approximately 90 nucleotides into the body of the 5’ UTR. Yet these reporters are also basally/neurite localized. The organization of the PRRE-containing reporters is now more clearly shown in an updated figure 3D.

From these results, it would seem that the pyrimidine-rich element need not be next to the 5’ cap in order to regulate RNA localization. To generalize this result, we first used previously identified 5’ UTR pyrimidine-rich elements that had been found to regulate translation in an mTOR-dependent manner (Hsieh et al 2012). We found that, as a class, RNAs containing these motifs were similarly basally/neurite localized as RP mRNAs. These results are presented in figures 3A and 3I.

We then asked if the position of the pyrimidine-rich element within the 5’ UTR of these RNAs was related to their localization. We found no relationship between element position and transcript localization as elements within the bodies of 5’ UTRs were seemingly just as able to promote basal/neurite localization as elements immediately next to the 5’ cap. These results are presented in figures 3B and 3J.

To further confirm that pyrimidine-rich elements need not be immediately next to the 5’ cap, we redesigned our RPL7-derived reporter transcripts such that the pyrimidine-rich motif was immediately adjacent to the 5’ cap. This was possible because the reporter uses a CMV promoter that reliably starts transcription at a known nucleotide. We then compared the localization of this reporter (called “RPL7 True TOP”) to our previous reporter in which the pyrimidine-rich element was ~90 nt into the 5’ UTR (called “RPL7 PRRE”) (Reviewer Figure 2). As with the PRRE reporter, the True TOP reporter drove RNA localization in both epithelial and neuronal cells while purine-containing mutant versions of the True TOP reporter did not (Reviewer Figure 2A-D). In the epithelial cells, the True TOP was modestly but significantly better at driving basal RNA localization than the PRRE (Reviewer Figure 2E) while in neuronal cells the True TOPs were modestly but insignificantly better. Again, this suggests that pyrimidine-rich motifs need not be immediately cap-adjacent in order to regulate RNA localization.

Reviewer Figure 2. Experimental confirmation that pyrimidine-rich motif location within 5’ UTRs is not critical for RNA localization. (A) RPL7 True TOP smFISH in epithelial cells. (B) RPL7 True TOP smFISH in neuronal cells. (C) Quantification of epithelial cell smFISH in A. (D) Quantification of neuronal cell smFISH in D. (E) Comparison of the location in epithelial cells of endogenous RPL7 transcripts, RPL7 PRRE reporter transcripts, and PRL7 True TOP reporter transcripts. (F) Comparison of the neurite-enrichment of RPL7 PRRE reporters and RPL7 True TOP reporters. In C-F, the number of cells included in each analysis is shown.

In response to the point about whether the localization results are direct effects of LARP1, we did not assay the binding of LARP1 to our PRRE-containing reporters, so we cannot say for sure. However, given that PRRE-dependent localization required LARP1 and there is much evidence about LARP1 binding pyrimidine-rich elements (including those that are not cap-proximal as the reviewer notes), we believe this to be the most likely explanation.

It should also be noted here that while pyrimidine-rich motif position within the 5’ UTR may not matter, its location within the transcript does. PRREs located within 3’ UTRs were unable to direct RNA localization (Figure 5).

3) In figure 1A, they indicate that mRNA stability can contribute to RNA localization, but this point is never discussed. This may be important to their work since Larp1 has also been found to impact mRNA half-lives (Aoki et al, 2013 and Mattijssen et al 2020, Al-Ashtal et al 2021). Is it possible the effect they see when Larp1 is depleted comes from decreased stability?

We found that PRRE-containing reporter transcripts were generally less abundant than their mutant counterparts in C2bbe1, HCA7, and MDCK cells (figure 3 – figure supplements 5, 6, and 8) although the effect was not consistent in mouse neuronal cells (figure 3 – figure supplement 13).

However, we don’t think it is likely that the changes in localization are due to stability changes. This abundance effect did not seem to be LARP1-dependent as both PRRE-containing and PRRE-mutant reporters were generally more expressed in LARP1-rescue epithelial cells than in LARP1 KO cells (figure 4 – figure supplement 9).

It should be noted here that we are not ever actually measuring transcript stability but rather steady state abundances. It cannot therefore be ruled out that LARP1 is regulating the stability of our PRRE reporters. Given, though, that their localization was dependent on kinesin activity (figures 7F, 7G), we believe the most likely explanation for the localization effects is active transport.

4) Also Moor et al, 2017 saw that feeding cycles changed the localization of 5'TOP mRNAs. Similarly, does mTOR inhibition or activation or simply active translation alter the localization patterns they observe? Further evidence for dynamic regulation of RNA localization would strengthen this paper

We are very interested in this and have begun exploring it. We have data suggesting that PRREs also mediate the feeding cycle-dependent relocalization of RP mRNAs. As the reviewer says, we think this leads to a very attractive model involving mTOR, and we are currently working to test this model. However, we don’t have the room to include those results in this manuscript and would instead prefer to include them in a later manuscript that focuses on nutrient-induced dynamic relocalization.

5) For smFISH quantification, is every mRNA treated as an independent measurement so that the statistics are calculated on hundreds of mRNAs? Large sample sizes can give significant p-values but have very small differences as observe for Firefly vs. OSBPL3 localization. Since determining the biological interpretation of effect size is not always clear, I would suggest plotting RNA position per cell or only treat biological replicates as independent measurements to determine statistical significance. This should also be done for other smFISH comparisons

This is a good suggestion, and we agree that using individual puncta as independent observations will artificially inflate the statistical power in the experiment. To remedy this in the epithelial cell images, we first reanalyzed the smFISH images using each of the following as a unique observation: the mean location of all smFISH puncta in one cell, the mean location of all puncta in a field of view, and the mean location of all puncta in one coverslip. With each metric, the results we observed were very similar (Reviewer Figure 3) while the statistical power of course decreased. We therefore chose to go with the reviewer-suggested metric of mean transcript position per cell.

Reviewer Figure 3. C2bbe1 monolayer smFISH spot position analysis. RNA localization across the apicobasal axis is measured by smFISH spot position in the Z axis. This can be plotted for each spot, where thousands of spots over-power the statistics. Spot position can be averaged per cell as outlined manually within the FISH-quant software. This reduces sample size and allows for more accurate statistical analysis. When spot position is averaged per field of view, sample size further decreases, statistics are less powered but the localization trends are still robust. Finally, we can average spot position per coverslip, which represents biological replicates. We lose almost all statistical power as sample size is limited to 3 coverslips. Despite this, the localization trends are still recognizable.

When we use this metric, all results remain the same with the exception of the smFISH validation of endogenous OSBPL3 localization. That result loses its statistical significance and has now been omitted from the manuscript. All epithelial smFISH panels have been updated to use this new metric, and the number of cells associated with each observation is indicated for each sample.

For the neuronal images, these were already quantified at the per-cell level as we compare soma and neurite transcript counts from the same cell. In lieu of more imaging of these samples, we chose to perform subcellular fractionation into soma and neurite samples followed by RT-qPCR as an orthogonal technique (figure 3K, figure 3 supplement 14). This technique profiles the population average of approximately 3 million cells.

6) F: How was the segmentation of soma vs. neurites performed? It would be good to have a larger image as a supplemental figure so that it is clear the proximal or distal neurites segments are being compared

All neurite vs. soma segmentations were done manually. An example of this segmentation is included as Reviewer Figure 4. This means that often only proximal neurites segments are included in the analysis as it is often difficult to find an entire soma and an entire neurite in one field of view. However, in our experience, inclusion of more distal neurite segments would likely only strengthen the smFISH results as we often observe many molecules of localized transcripts in the distal tips of these neurites.

Reviewer Figure 4. Manual segmentation of differentiated CAD soma and neurite in FISH-quant software. Neurites that do not overlap adjacent neurites are selected for imaging. Often neurites extend beyond the field of view, limiting this assay to RNA localization in proximal neurites.

Also, it should be noted that the neuronal smFISH results are now supplemented by experiments involving subcellular fractionation and RT-qPCR (figure 3 supplement 14). These subcellular fractionation experiments collect the whole neurite, both the proximal and distal portions.

Text has been added to the methods under the header “smFISH computational analysis” to clarify how the segmentation was done.

Author Response

Reviewer #1 (Public Review):

This is timely and foundational work that links cellular neurophysiology with extracellular single-unit recordings used to study LC function during behavior.

The strengths of this paper include:

1. Providing an updated assessment of LC cell morphology and cell types since much of the prior work was completed in the late 1970s and early to mid-1980s.

2. Connecting LC cell morphology with membrane properties and action potential shape.

3. Showing that neurons of the same type have electrical coupling

Collectively, these findings help to link LC neuron morphology and firing properties with recent work using extracellular recordings that identify different types of LC single units by waveform shape.

Another strength of this work is that it addresses recent findings suggesting the LC neurons may release glutamate by showing that, at least within the LC, there is no local glutamatergic excitatory transmission.

Weaknesses:

The authors also propose to test the role of single LC neuron activity in evoking lateral inhibition, as well as proposing that electrical coupling between LC cell pairs is organized into a train pattern. The former point is based on a weak premise and the latter point has weak support in their data given the analyses performed.

Point 1: lateral inhibition in the LC

The authors write in the abstract that "chemical transmission among LC noradrenergic neurons was not detected" and this was a surprising claim given the wealth of prior evidence supporting this in vitro and in vivo (Ennis & Aston-Jones 1986. Brain Res 374, 299-305; Aghajanian, Cedarbaum & Wang 1977. Brain Res 136, 570-577; Cedarbaum & Aghajanian. 1978 Life Sci 23, 1383-1392).

Huang et al. 2007 (Huang et al. 2007. Proc National Acad Sci 104, 1401-1406) showed that local inhibition in the LC is highly dependent on the frequency of action potentials, such that local release requires multiple APs in short succession and then requires some time for the hyperpolarization to appear (even over 1 sec). This work suggests that it is not a "concentration issue" per se, rather it is just that a single AP will not cause local NE release in the LC. Although the authors did try 5APs at 50Hz this may not be enough to generate local NE release according to this prior work. A longer duration may be needed. Additionally, although the authors incubated the slices with a NET inhibitor, that will not increase volume transmission unless there is actually NE release, which may have not happened under the conditions tested. In sum, there is no reason to expect that a single AP from one neuron would cause an immediate (within the 100 msec shown in Fig 3B) hyperpolarization of a nearby neuron. Therefore, the premise of the experiment that driving one neuron to fire one AP (or even 5AP's at 50Hz in some) is not an actual test of lateral inhibition mediated by NE volume neurotransmission in the LC. Strong claims that "chemical transmission...was not detected" require substantial support and testing of a range of AP frequencies and durations. Given the wealth of evidence supporting lateral inhibition of the LC, this claim seems unwarranted.

We thank the reviewers for their constructive comments and interpretations of the data regarding lateral inhibition. In fact, we were fully aware of the prior wealth of data supporting the existence of lateral inhibition and have discussed possible reasons for the absence of lateral inhibition in our dataset. Now both reviewers provided additional potential explanations for this absence. The most plausible explanation appears to be that α2AR-mediated lateral inhibition is a population phenomenon, which would not be readily detected at the single-cell level in in vitro conditions. As reviewers suggested, we may need to vary spike frequency and timing to identify optimal spiking parameters (or stimulating multiple LC neurons at one time) to detect this phenomenon in slices. Alternatively, we could employ other approaches (optogenetic or chemogenetic approach) to activate a group of neurons at one time to evoke this phenomenon, as a recent preprint paper demonstrated (Line 528-535). All these are excellent suggestions, but it may take more than six months to complete these experiments since we need to train another person from scratch for LC recordings (the first author graduated from the program and has left the lab). We thus chose to remove most of the data (about α2AR-mediated lateral inhibition) from the paper in the revision, as the reviewers suggested. We do plan to further explore this interesting topic in our next study.

Point 2: Train-like connection pattern

Demonstrating that connected cell pairs often share a common member is an important demonstration of a connection motif in the LC. However, a "train" connection implies that you can pass from A to B to C to D (and in reverse). However, the authors do not do an analysis to test whether this occurs. Therefore, "train" is not an appropriate term to describe the interesting connection motif that they observed.

In fact, writing A↔B↔C in the paper implies a train without direct support for that form of electrical transmission. For example, in Fig. 6C, it is clear that cell 6 is coupled to cell 1 and that cell 6 is also coupled to cell 8. In both cases, the connection is bilateral. Using the author's formatting of A↔B↔C , would correspond with Cell 6 being B and cells 1 and 8 being A and C (or vice versa). However, writing A↔B↔C implies a train, whereas one can instead draw this connection pattern where B is a common source:

A C

. .

. .

B

An analysis showing that spikes in A can pass through B and later appear in C is necessary to support the use of "train". The example in Fig. 6C argues against train at least for this one example.

Although the analysis is possible to do with the authors' substantial and unique data set, it should be also noted that prior work on putative electrical coupling in extracellular recordings from rat LC showed that trains among 3 single units occurred at an almost negligible rate because out of 12 rats "Only 1 triplet out of 22,100 possible triplet patterns (0.005%) was found in one rat and 4 triplets out of 1,330 possible triplet patterns (0.301%) were found in the other rat." and moreover patterns beyond 3 units were never observed (Totah et al 2018. Neuron 99, 1055-1068.e6). We thank the reviewer for this astute argument and agree that the word “train-like connection” assumes a physiological, functional relationship A→B→C which the data do not show. Therefore, we now term these connections as “chain-like” to indicate the structural nature of the connection, which we believe leaves no room for the interpretation that there is a functional, physiological connection among the three neurons. In fact, we have discussed this issue as a first-order vs second-order coupling issue in our original manuscript (Line 632-639), and concluded that electrical signals hardly pass through the second-order gap junctions in LC, that is, in those two connections sharing the same partner like A↔B↔C (here A and C are not directly connected, but coupled in the second-order), spikes in A hardly pass-through B and later appear in C (Line 632-639).

Reviewer #2 (Public Review):

McKinney et al set out to better understand local circuit organization within the mouse locus coeruleus (LC). To do so, the authors achieved the technical feat of performing multiple, simultaneous whole-cell recordings (up to 8 LC neurons at once). This approach gives the authors a powerful and relatively high throughput means of assessing LC neuronal activity and potentially its rate of interconnectedness. In addition to recording from these cells, many were also filled with biocytin to recover their morphology. Using traditional reconstruction approaches the authors identified two morphological classes of LC neurons, fusiform(FF) and multipolar (MP). Although the selection of these classes was biased from previous literature, the authors used machine classification to rigorously demonstrate that these classes indeed exist. From there, the electrical properties of these distinct LC neurons were compared and a number of distinct action potential properties were identified between the two groups. Although firing in response to injected current showed that the FF class could maintain a higher firing rate, basal firing was not explicitly compared as the cells were prevented from firing upon entering whole-cell. The authors next explored the extent to which local chemical transmission occurs within the LC. Although there is evidence of glutamatergic transmission from LC neurons, the authors did not directly observe any evidence of local glutamate release from these neurons. This effect might be expected given the severing of axons in the slice preparation. Somewhat less expected is the author's claim that they could not find evidence of local NE release via alpha2 adrenergic receptor activation. This lack of evidence might well arise because this phenomenon does not occur, but it also remains possible that we do not have sufficient understanding of volume transmission to properly detect a change, particularly in whole-cell current clamp. The evidence that alpha2-mediated hyperpolarization is intact is somewhat adjacent to the concept as the concentrations of NE and clonidine used to show this robust suppression of firing is well above what is likely physiologically released by these neurons. One thing the authors do not consider is that the slice orientation (horizontal vs. coronal) greatly alters local glutamatergic input to the point that glutamate-mediated phasic bursts often do not occur in horizontal slices.

A major strength of the multi-patch approach used here is the ability to identify electrical connections between LC neurons. While gap junction-coupling has long been established in these neurons, multiple reports suggest that this coupling is decreased as the animal matures into adulthood. Here the authors provide clear evidence for a stable rate of electrical coupling well into adulthood. This approach also gives the authors the relatively unique ability to look for second-order connections between LC neurons and the amount of coupling was elegantly used to model how the LC might wire together more broadly. Although this approach is very powerful and likely at the edge of what is physically possible for whole-cell recordings in this brain structure it still likely undersamples LC local circuitry and biases investigations to be relatively close to one another spatially. While the authors rightfully consider the intersoma distance (ISD), the longest the gets in these studies is still smaller than most anatomical axes of the LC. This is an important limitation because the electrical coupling between FF-FF and FF-MP both appear to increase as ISD increases, suggesting more coupling could be occurring in distal dendrites. Furthermore, if coupling is occurring in distal dendrites it may be harder to detect as shunting in these distal dendrites could prevent signal detection.

This work is timely and important to the LC field which is on the precipice of having a greater understanding of heterogeneity based on a number of different principles, and this work adds local circuit dynamics as one of these principles. It will be important for the field to see how different efferent anatomical modules align with the cell types and circuit properties identified here.

We appreciate the reviewer’s constructive comments and suggestions.

Author Response:

Reviewer #2 (Public Review):

This study addresses the ways in which bacteriophages antagonize or coopt the DNA restriction or recombination functions of the bacterial RecBCD helicase-nuclease.

The strength of the paper lies in the marriage of biochemistry and structural biology.

A cryo-EM structure of the RecBCD•gp5.9 complex establishes that gp5.9 is a DNA-mimetic dimer composed of an acidic parallel coiled coil that occupies the dsDNA binding site on the RecB and RecC subunits. The structure of gp5.9 is different from that of the RecBCD-inhibiting DNA mimetic protein phage λ Gam.

Cryo-EM structures of Abc2 are solved in complex with RecBCD bound to a forked DNA duplex, revealing that Abc2 interacts with the RecC subunit. A companion structure is solved containing PPI that copurifies with RecBCD•Abc2.

Whereas the gp5.9 structure fully rationalizes the effect of gp5.9 on RecBCD activity, the Abc2 structure - while illuminating the docking site on RecBCD, a clear advance - does not clarify how Abc2 impacts RecBCD function.

The authors speculate that Abc2 binding prevents RecA loading on the unwound DNA 3' strand while favoring the loading of the phage recombinase Erf.

Does the structure provide impetus and clues for further experiments to elaborate on that question and, if so, how?

Regarding the first point (Murphy’s results). We have now included more detail about Murphy’s results and a brief comparative discussion of our own (page 13). An important caveat in interpreting small (<5-fold) effects on RecBCD activity is that the complex is known to possess different levels of specific activity between preparations (from 20% to 100% active based on titration of DNA ends). This is especially problematic when assessing the effect of Abc2 on RecBCD because (unlike gp5.9 for instance) the protein cannot be purified in isolation and titrated into free RecBCD to monitor how activity changes. Instead, one is comparing activity between different preparations either including Abc2 or not. Regarding the second point (how much does the structure tells us about the mechanism of Abc2?). We agree with the general sentiment here: the mechanism of RecBCD hijacking by Abc2 is still a “work in progress”. Nevertheless, the structure is suggestive of effects on Chi recognition and/or RecA loading which is both consistent with biochemical results and suggests new avenues for further investigation.

While the RecBCD-gp5.9 structure “nails” the inhibition mechanism as steric exclusion of substrate, the RecBCD-Abc2 structure is less informative. Previously published biochemical and in vivo analyses of Abc2 show that it modulates rather than completely inhibits the enzyme. The hypothesis is that Abc2 modifies the process of Chi recognition and/or RecA loading (which are themselves coupled processes) in order to facilitate loading of the phage recombinase Erf. Given current structural models for the mechanism of RecBCD, it is not entirely obvious from the structure of RecBCD-Abc2 what exactly this small phage protein is doing, because (a) there is no significant change to the structure of RecBCD induced by Abc2 interaction and (b) no known protein interaction site (eg with RecA) is blocked. Indeed, our original manuscript ended with an acknowledgement that understanding how P22 controls recombination in E. coli was ongoing work. As we see it, in addition to simply revealing the binding site of Abc2, our structure has two significant impacts. Firstly, it is consistent with and extends the existing hypothesis. For example, (a) the interaction of Abc2 with RecC is precisely with the domains of the protein that are responsible for Chi recognition and close to a putative site of RecA loading; (b) the recognition that a conserved proline in Abc2 interacts with the active site of PPI implies that Abc2 function is dependent on proline isomerisation; (c) the possible bridging of RecB and RecC by the C-and N-terminal regions of the protein suggest that Abc2 might hinder intersubunit conformational changes. Secondly, the structure facilitates the testing of this hypothesis. For example, (a) does RecA and/or Erf loading depend on interactions with the surfaces destroyed or created by Abc2 at the interface with RecC (b) does P68A mutation inactivate Abc2?; (c) does failure to recognise and respond to Chi require bridging of RecB and RecC that limits conformational transitions? Crucially, as we explain in the discussion, the future study of the P22 recombination system will require the purification and characterisation of additional factors (Abc2, Arf and Erf) beyond just Abc2. This is something we are working on currently in the lab and consider to be beyond the scope of this work.

Author Response

We thank the reviewers for their comments and helpful suggestions. We are currently preparing a revised version of this manuscript. Notable changes we are making include:

• adding a diagram to the introduction to show the overall workflow of the study,
• quantitatively analyzing the fraction of OCT4+ and DDX4+ cells in our immunofluorescence images over time,
• collecting and analyzing additional bulk RNA-seq data on KGN cells and adult human ovarian tissue,
• performing estradiol assays on additional lines of hiPSC-derived granulosa-like cells,
• presenting images from day 70 ovaroids which clearly show follicle formation,
• changing the colors in the figures to be more accessible to colorblind readers,
• clarifying which TFs are present in which of our clonal lines.

These changes will address the weaknesses identified by the reviewers. Along with our revised manuscript, we will also prepare a more comprehensive author response for these reviewer comments.

Author Response:

Reviewer #1 (Public Review):

This paper reports an analysis of the inhibition of the serotonin transporter, SERT, by a novel compound, ECSI#6. The authors perform a comprehensive analysis of SERT transport inhibition for the new agent and compare its properties to those of other well-characterized agents: cocaine and noribogaine, with the data pointing to an unusual noncompetitive mechanism of inhibition, a model also supported by electrophysiological recordings of transport currents. Based on the results of these experiments the authors conclude that ESCI#6 binds essentially exclusively to the inward-facing state of the transporter. The authors further present experiments suggesting that ESCI#6 can stabilize the folded form of an ER-arrested SERT mutant and recover its trafficking to the plasma membrane, with some in-vivo drosophila experiments perhaps also supporting this conclusion. Finally, kinetic simulations using a transport model with rate constants from previous experiments support the basic conclusions of the first sections of the paper.

Strengths:<br /> The transport experiments and simulations here are thorough, carefully performed, and reasonably interpreted. The authors' arguments for noncompetitive inhibition seem well-thought-out and reasonable, as is the conclusion that ESCI#6 binds to the inward-facing state of the transporter. The simulations are also thorough and support the conclusions. In the discussion, the comparison of enzyme noncompetitive inhibition to the process studied here was thoughtful and interesting. Also, the care and analysis of the uptake data are a strength of the paper, with well-presented evidence of reproducibility and statistics. The electrophysiology data is more limited but does communicate the essential conclusion.

Weaknesses:<br /> The most important concern about the work is the interpretation of the in-vivo drosophila data. Though the SERT fluorescence with WT protein is strong, I cannot see any fluorescence in either drug-treated image from the PG mutant. In this context, shouldn't there be additional intracellular staining for ER-resident SERT? If the cell bodies of these cells are elsewhere this should be clearly pointed out.

We have modified Fig. 6 to include, in all instances, images of the posterior brain, where the neurons (FB6K) reside, from which the serotonergic projections originate. These images visualize expression of membrane-anchored GFP (mCD8GFP; in panel B), immunoreactivity of serotonin (panel B’), wild type SERT (panels C’,D’,E’) and mutant SERT-PG601,602AA (panels F’,G’,H’) in the soma. The description of these panels has been added to the pertinent sentences starting on p. 20, line 6 from bottom to the end of end of the first paragraph p. 21, which read:

“These projections (Fig. 6A-A’’) and the FB6K-type neurons, from which they originate in the posterior brain (Fig. 6B-B’’) can be visualized by expressing membrane-anchored GFP (i.e. GFP fused to the C-terminus of murine CD8; [36]) under the control of TRH-T2A-Gal4. Similarly, when placed under the control of TRH-T2A-Gal4, YFP-tagged wild-type human SERT was expressed in the FB6K-type neurons (Fig. 6C’) and delivered to the fan-shaped body (Fig. 6C). In contrast, in flies harboring human SERT-PG601,602AA, the transporter was visualized in the soma of FB6K-type neurons (Fig. 6F’), but the fan-shaped body was devoid of any specific fluorescence (Fig. 6F). However, if three-day old male flies expressing human SERT- PG601,602AA were fed with food pellets containing 100 μM ECSI#6 or 100 μM noribogaine for 48 h, fluorescence accumulated to a level, which allowed for delineating the fan-shaped body (Fig. 6G and H, respectively). This show that ECSI#6 and noribogaine exerted a pharmacochaperoning action in vivo, which partially restored the delivery of the mutant transporter to the presynaptic territory. As expected, in flies harboring wild-type human SERT, feeding of ECSI#6 and noribogaine did not have any appreciable effect on the level of fluorescence in the fan-shaped body (Fig. 6D and E, respectively). “

Similarly, the single Western blot demonstrating enhanced glycosylation in the presence of Noribogaine or ECSI#6 could be strengthened. I can see the increased band at a high molecular weight that the authors attribute to the fully glycosylated form, but this smear, and the band below, look quite different from those in the blot shown in the El-Kasaby et al reference, raising concerns that the band could be aggregated or dimerized protein rather than a glycosylated form. This concern could easily be addressed by control experiments with appropriate glycosidases, as shown in the reference.

We understand that the appearance of the mature glycosylated species is being criticized, at least in part, because it differs from sharper bands, which can be found in our previously published papers. We stress that the resolution very much depends on the electrophoretic conditions. We addressed the reviewers’ criticism by carrying out the recommended deglycosylation experiments: a representative experiment is shown in (the new) panel F of Fig. 5, with lysates prepared from HEK293 cells expressing wild type SERT, from untransfected HEK293 cells and from HEK293 cells, which had been preincubated with 30 μM cocaine, 100 μM ECSI#6 and 30 μM noribogaine. The experiment confirms the band assignment with the upper band(s) M representing the mature glycostylated species (which are resistant to deglycosylation by endoglycosidase H) and the lower band C corresponding to the core- gylcoylated species (which are susceptible to cleavage that (as expected) the mature band show a representative degylcosylation by endoglycosidase H). We also think that the immunoblot in panel F ought to satisfy the aesthetic criticism: the bands are sharper/less smeared.

The description of panel F can be found on p. 18, starting in line 7 from bottom to end of page, and reads: “We confirmed the band assignment by enzymatic deglycosylation (Fig. 5F): the upper bands (labeled M), which appeared in cells incubated in the presence of ECSI#6 and of norbogaine, were resistant to deglycosylation by endoglycosidase H (which cannot cleave mature glycans). In contrast, the core-glycosylated species (labeled C), was susceptible to cleavage by endoglycosidase H resulting in the appearance of the deglycosylated band D.”

The overall interest in the work is reduced given the quite low affinity of ECSI#6 for the transporter.

We agree that it would be preferable to have a compound, which works in the submicromolar/nanomolar range. However, it is worth pointing out that the EC50 is low enough for allowing in vivo rescue of the folding-deficient SERT-PG: feeding flies restores its trafficking to the cell surface and to the presynaptic specialization. Obviously, there is room for improvement, but ECSI#6 provides a starting point.

Reviewer #3 (Public Review):

This is interesting research that uncovers a novel inhibition mechanism for serotonin (SERT) transporters, which is akin to traditional un-competitive inhibitors in enzyme kinetics. These inhibitors are known to preferentially bind to the enzyme-substrate complex, thus stabilizing it, resulting in a decrease of the IC50 with increasing substrate concentrations. In contrast to this classic enzyme inhibition mechanism, the authors show for SERT, through detailed kinetic analysis as well as kinetic modeling, that the inhibitor, ECSI#6, binds preferentially to the inward-facing state of the transporter, which is stabilized by K+. Therefore, inhibition becomes "use-dependent", i.e. increasing substrate concentrations push the transporter to the inward-facing configuration, which then leads to the increased apparent affinity of ECSI#6 binding. Interestingly, this mechanism of action predicts that the inhibitor should be able to rescue SERT misfolding variants. The authors tested this possibility and found that surface expression and function of a misfolding mutant SERT is increased, an important experimental finding. Another strength of the manuscript is the quantitative analysis of the kinetic data, including kinetic modeling, the results of which can reconcile the experimental data very well. Overall, this is important and, in my view, novel work, which may lead to new future approaches in SERT pharmacology.

With that said, some weaknesses of the manuscript should be mentioned. 1) The authors suggest that serotonin and ECSI#6 cannot bind simultaneously to the transporter, however, no direct evidence for this conclusion is provided.

We assessed this point by plotting the data in Fig. 2A,B,C as Dixon plots in (the new) panels D,E,F of Fig. 2. We refer the reader to Segel’s textbook on enzyme kinetics (new ref. 18) on using Dixon plots in the presence of two inhibitors. The pertinent description is on p. 9, lines 12-22 and reads as follows: “We transformed the data summarized in Figs. 2A-C by plotting the reciprocal of bound radioligand as a function of inhibitor concentration to yield Dixon plots (Fig. 2D-F): the x-intercept corresponds to -IC50 of the inhibitor [18]. Thus, Dixon plots allow for differentiating mutually exclusive from mutually non-exclusive binding, if one inhibitor (i.e., cocaine, noribogaine or ECSI#6) is examined at a fixed concentration of the second inhibitor (i.e., serotonin) [18]: if binding of the two inhibitors is mutually non-exclusive, a family of lines of progressively increasing slope, which intersect at -IC50, is to be seen. In contrast, if the two inhibitors bind to the same site, the slope of the inhibition curves is not affected and the x- intercept (i.e, -IC50 of the variable inhibitor) is shifted to more negative values. It is evident from Fig. 2D-E that the presence of 1 and 10 μM serotonin progressively shifted the (expected) x-intercept for cocaine (Fig. 2D), noribogaine (Fig. 2E) and ECSI#6 (Fig. 2D). Thus, binding to SERT of serotonin and of these three ligands was mutually exclusive.” Based on the Dixon plots, we feel that our conclusion is justified, i.e., binding of serotonin and ECSI#6 (and of the other ligands) is mutually exclusive.

2) How does ECSI#6 access the inward-facing binding site? Does it permeate the membrane and bind from the inward-facing conformation, or is it just a very slowly transported low-affinity substrate that stabilizes the inward-facing state with much higher affinity? Including ECSI#6 in the recording electrode may provide further information on this point.

We did the suggested experiments: the data are summarized in panel I of Fig. 4 and described in the first paragraph on p. 15, which also explains why this experiments is possibly inconclusive due to the high diffusivity of ECSI#6:

“Fig. 4I shows representative traces of 5-HT induced currents recorded from SERT expressing cells in the absence (in blue) and presence of 100 μM ECSI#6 (in red) in the electrode solution: when thus applied from the intracellular side, ECSI#6 did not cause an appreciable current block. The right-hand panel summarizes the current amplitude obtained from cells measured in the absence (blue open circles) and presence of intracellular ECSI#6 (open circles in red). These data seem to indicate that ECSI#6 binds to SERT from the extracellular side. Yet this conclusion can be challenged based on the following consideration: in earlier experiments, ibogaine, the parent compound of noribogaine, was found to block HERG channels when applied from the bath solution but failed to do so when added to the electrode solution [27]. However, at a lower intracellular pH (i.e., pH 5.5), ibogaine gained the ability to inhibit HERG from the intracellular side (i.e., via application through the electrode). Conversely, ibogaine was less effective when applied to an acidified bath solution. These observations led to the conclusion that ibogaine blocked HERG from the cytosolic side: because the molecule in its neutral form was so diffusive, a low intracellular pH was required to force its protonation and thus preclude diffusion from the interior of the cell. ECSI#6 is presumed to also be very diffusible given its estimated logP value and polar surface area of 2.48 and 66 Å2, respectively. However, ECSI#6 harbors an amide nitrogen (see Fig. 1A) and thus remains neutral in the experimentally accessible pH range. Hence, it is not possible to verify to which side of SERT it binds.”

Additionally, it is not clear why displacement experiments were not carried out with cocaine. Since cocaine is a competitive inhibitor but does not induce transport (i.e. doesn't induce the formation of the inward-facing conformation), it should act in a competitive mechanism with ECSI#6.

We did not quite understand this comment, because displacement experiments were done with cocaine (Fig. 2A, new Fig 2G/previous Fig. 2D). However, if the reviewer questions why we do not use cocaine rather than 5-HT, in the three-way competition experiment, it is precisely, because we wanted to compare the action/binding mode of ECSI#6 to that of cocaine.

3) Why are dose-response relationships not shown for electrophysiological experiments? These would be a good double-check for the radiotracer flux data.

These experiments were done and are shown in (the new) panels G and H of Fig. 4; the pertinent description is in the second paragraph of p. 14 and reads:

“The protocol depicted in Fig. 4B can also be used to gauge the apparent affinity of ECSI#6 for SERT in the presence of 5-HT. Plotted in Fig. 4G is the block of the serotonin-induced current as a function of the co-applied ECSI#6 concentration. The current was evoked by a saturating concentration of 5-HT (30μM) and inhibited by 3, 10, 30 and 100 μM co-applied ECSI#6, respectively (the inset in Fig. 4G shows representative current traces). A fit of an inhibition curve to the data points yielded an IC50 value of approx. 5 μM. This value was lower but still in reasonable agreement, with the IC50 obtained in the radioligand uptake assay for the condition where the 5-HT concentration had been saturating (cf. dashed line in Fig.1C; 10 μM 5-HT). In the uptake assay the IC50 value of ECSI#6 dropped to about 0.5 mM, in the presence of a low 5-HT concentration (i.e., 0.1 μM). In contrast to uptake experiments, electrophysiological recordings also allow for assessing the apparent affinity of ECSI#6 for SERT in the absence of the substrate. This can be achieved by employing the protocol depicted in Fig. 4H (see representative current traces on the left-hand side): we first applied 30 μM 5- HT to a cell expressing SERT for 0.5 s to elicit a peak current (i.e., a control pulse). We then reapplied 30 μM 5-HT after a superfusing the cell with 100 μM ECSI#6 for 1 s (second upper trace in panel H). We chose this time period because it had been sufficient to allow for full current block in the other protocol (see Fig. 4G): the amplitude of the peak current following pre-application of 100 μM ECSI#6 was essentially identical to the prior control pulse. When we pre-applied 100 μM ECSI#6 for a longer period (i.e., 3 s) the amplitude of the two peak currents also remained the same (cf. lower traces in panel H). The right-hand panel shows the summary of several experiments. Plotted in the graph is the ratio of the second and first pulse, which was always close to one. We previously used this protocol to assess the binding kinetics of cocaine, methylphenidate and desipramine on SERT and DAT. Pre-application of these inhibitors consistently led to a concentration dependent reduction in the peak current amplitude of the second pulse in comparison to the first [23]. The lack of inhibition, thus, indicates that the affinity of ECSI#6 in the absence of 5-HT is low. To obtain estimates for the affinity of ECSI# for SERT in the absence of 5-HT we would need to apply this compound at much higher concentrations. This, however, is not possible, because ECSI#6 is poorly soluble in aqueous solutions (i.e., max. 0.03 mg/ml).”

Author Response

Reviewer #1 (Public Review):

The authors succeeded in fitting their Jansen-Rit model parameters to accurately reproduce individual TEPs. This is a major success already and the first study of this kind to the best of my knowledge. Then the authors make use of this fitted model to introduce virtual lesions in specific time windows after stimulation to analyze which of the response waveforms are local and which come from recurrent circles inside the network. The methodological steps are nicely explained. The authors use a novel parameter fitting method that proves very successful. They use completely openly available data sets and publish their code in a manner that makes reproduction easy. I really enjoyed reading this paper and suspect its methodology to set a new landmark in the field of brain stimulation simulation. The conclusions of the authors are well supported by their results, however, some analysis steps should be clarified, which are specified in the essential revisions.

We are delighted and flattered by the Reviewer’s positive evaluation of our work, and appreciation of our efforts to maximize its reproducibility. We wish also to thank the Reviewer for their compelling and interesting points, which we have addressed in full, and we believe further enhance the quality of the paper. Thanks again!

Reviewer #2 (Public Review):

Here the authors tackle the problem of identifying which parts of a TMS-evoked response are local to the stimulation site versus driven by reverberant activity from other regions. To do this they use a dataset of EEG recorded simultaneously with TMS pulses, and examine virtual lesions of a network of neural masses fitted to the data. The fitting uses a very recent model inversion method developed by the authors, able to fit time series directly rather than just summary statistics thereof. And it apparently works rather well indeed, at least after the first ~50 ms post-stimulus. I expect many readers will be keen to try this fitting method in their own work.

We are delighted by the Reviewer’s appreciation and consideration of our paper. We have addressed the comments and revisions requested following the flow suggested by the Reviewer’s comments. We would take this opportunity to kindly thank the Reviewer for his/her contribution and for helping us to improve the manuscript.

Reviewer #3 (Public Review):

The manuscript is very well written and the graphics are quite iconic. Moreover, the hypothesis is clear and the rationale is very convincing. Overall, the paper has the potential of being of paramount importance for the TMS-EEG community because it provides a valuable tool for a proper interpretation of several previously published TMS-EEG results.

Unfortunately, in my opinion, the dataset used to train and validate the method does not support the implication and interpretation of the results. Indeed, as clearly visible from most of the figures and mentioned by the authors of the database, the data contains residual sensory artifacts (auditory or somatosensory) that can completely bias the authors' interpretation of the re-entrant activity.

We are most grateful to the Reviewer for their positive evaluation of our manuscript. We also sincerely appreciate all the comments and suggestions raised, and for contributing their evident expertise with TMS-EEG data towards the constructive improvement of this research. We hope the Reviewer will appreciate our efforts made to address their excellent points, and are pleased with the resultant strengthening of the paper.

Author Response

Reviewer #2 (Public Review):

Wen et al. developed a useful tool for causal network inference based on scRNA-seq data. The authors comprehensively benchmarked 9 feature selection and 9 causal discovery algorithms using both synthetic data and real scRNA-seq data. Their conclusions regarding the performance of these algorithms on synthetic data are solid and valuable. I believe this tool or platform has the potential to help biologists discover novel cell type-specific signaling pathways or gene regulatory events since there is no prior knowledge (such as known pathway annotations) as inputs. However, several major concerns below need to be addressed to improve the paper.

1) Current validation of the inferred causal networks using real scRNA-seq datasets seems quite simple and is not sufficient to support the accuracy and reliability of results. Annotations from the STRING database do not contain directions of edges among genes or proteins. However, the edge direction in the inferred network is a crucial aspect to explain the causal relationships. Besides using "spike-in" data, a systematic validation of the inferred network, especially the edge directions, should be provided.

We have used the data of the five lung cancer cell lines and alveolar cells and the genes in several pathways (in which causal interactions are better annotated) in the KEGG and WikiPathway databases to validate network inference systematically. Please see the responses to the Essential Revisions (for the authors).

2) In order to illustrate the novel discovery, CausalCell should be further compared to existing gene network construction methods based on scRNA-seq data such as SCENIC (Aibar et al. Nature Methods, 2017).

(a) We have added a "TF=No/Yes" option to feature selection. If this option is ignored, feature selection is as before. If "TF=Yes" is selected, all feature genes are TFs. If "TF=NO" is selected, all feature genes are non-TFs. With this option, normally, two rounds of feature selection are performed. The first round ("TF=Yes" is selected) selects TFs as feature genes of a response variable (RV), and the second round ("TF=No/Yes" is ignored) selects feature genes as before (feature genes contain both TFs and non-TFs). The user selects genes from the results of two rounds to build input to causal discovery.

(b) The networks inferred by SCENIC are TF-centered: each TF and its potential target genes form a regulon, it searches for genes co-expressed with a TF (through GENIE3/GRNBoost), and the union of all or some of the regulons forms a network. Thus, SCENIC helps uncover the "one TF->all targets" relationships. Different from SCENIC, this "TF=No/Yes" option provides a target-centered transcription regulation analysis and helps uncover the "all TF->one target" relationships (the target is the response variable). Thus, the two approaches are complementary. Feature selection based on the "TF=No/Yes" option also differs from SCENIC in that no predefined regulons (defined upon "cisTarget" databases) are needed.

(c) We used SCENIC in our initial analysis of the young and old mouse CD4 T cells (see Figure 5 in Elyahu et al. 2019). In the re-analysis of tumor-infiltrating exhausted CD8 T cells, we find that the "TF=No/Yes" option helps uncover transcription regulation. For example, the transcription factor TOX is reported to regulate PDCD1 critically in mice. When we perform feature selection to identify feature genes of PDCD1, TOX is in the top 50 feature genes in the colorectal cancer dataset but not in the lung and liver cancer datasets (Supplementary file1:Table 1). To re-examine whether TOX critically regulates PDCD1 in the two latter datasets, we perform feature selection with "TF=Yes", and the results are that TOX is a top TF of PDCD1.

3) The authors should also claim what type of the inferred causal network represent from the biological perspective (e.g. signaling networks or gene regulatory networks?).

(a) Although methods have been developed specifically for inferring signaling and regulatory networks, whether a network is a signaling network or a gene regulatory network depends on the input data. Also, many proteins and noncoding RNAs function as complexes instead of individually in both kinds of networks, and RNA-seq and scRNA-seq data contain only transcripts. Thus, researchers must infer signaling and gene regulation in cells upon inferred networks.

(b) The input to causal discovery can be (a) a target gene and its potential TFs, (b) a TF and its potential targets, (c) genes encoding both TFs and non-TFs. Thus, whether an inferred network is signaling or gene regulatory network depends on the input. We have made this clear in the Discussion.

4) Besides edge direction, an important feature of CausalCell is the determination of edge sign (i.e. activation or inhibition). The authors should describe its related procedures.

In the revised section "2.5 Causal discovery", we wrote, ""In all inferred causal networks, edges have a sign that indicates activation or repression and have a thickness that indicates CI test's statistical significance. The sign of the edge from A to B is determined by computing a Pearson correlation coefficient between A and B, which is ‘repression’ if the coefficient is negative or ‘activation’ if the coefficient is positive. In most cases, ‘A activating B’ and ‘A repressing B’ correspond to up-regulated A in the case dataset compared with down-regulated B in the control dataset."

5) The authors did not provide an example of constructing a causal network between cells or cell types, although they mentioned its importance in the Abstract. Such intercellular network examples can distinguish the utility of CausalCell in single-cell data analysis from bulk data analysis.

Constructing causal networks between cells is a quite different work. We delete this sentence in the manuscript because we are still working on it.

6) If the control dataset is available, it is currently not clear whether batch effects of the query and control datasets will be removed in the data preprocessing step. Differentially expressed genes cannot be selected correctly if batch effects exist.

Please see our responses to the second point of Essential Revisions.

Author Response:

Reviewer #1 (Public Review):

This paper investigates waves in embryonic mouse retinas. These stage 1 waves have been studied less than the post-natal (stage 2) waves. The paper uses calcium imaging in whole retinas to determine the properties of the waves and their dependence on cholinergic and electrical synapses. A strength of the work is the ability to monitor waves over the entire retina at high resolution and weaknesses include reliance on pharmacology and some missing details in analysis.

Reliance on pharmacology

The results in the paper depend largely on pharmacological manipulations. Not enough consideration is given to the possible unintended effects of those manipulations. This is particularly true for the gap junction inhibitors. The Discussion brings up the possibility of such effects, but they need to come up much earlier. Is there anything else that can be done to mitigate concerns about the drugs - e.g. does MFA affect waves in Cx36 KO mice?

We have added additional experiments based on whole cells recordings to address some off target effects of MFA but we do make note of the limitations of these new controls since we observed significant variability of voltage-gated conductances across RGCs at this age as well as the limited ability to maintain stable recordings for the requisite time to have within cell controls for MFA. (see Figure 2 Supplemental Figure 1).

Over the years we have done several experiments assessing different Cx knockouts and retinal waves (e.g. F. Caval-Holme, et al, “The Retinal Basis of Light Avoidance in Neonatal Mice”, Journal of Neuroscience 42:2022; Blankenship A.G., et al “The role of neuronal connexins 36 and 45 in shaping spontaneous firing patterns in the developing retina, Journal of Neuroscience, 3, 2011). It appears that there are multiple connexins in RGCs and which regulate stage 1 retina waves beyond Cx 36 and Cx45 and therefore it is difficult to use these mice as controls for general gap junction antagonists.

In the revision, we are more explicit about the caveats of using MFA both in the results (page 5) and discussion (page 10). Notably, we draw attention to past studies where we have done several controls regarding MFA and RGC activity in older retinas in addition to our more limited controls we were able to carry out in E16-E18 retina.

Comparison of ACh receptor block and knockout mice

The ACh receptor knockout mouse provides a useful alternative to the pharmacological block of ACh receptors. But different features are described in Figures 2 and 3, preventing direct comparison of the two.

Our intention was not to use the knockout mice as an alternative to the pharmacological block since we knew that there are compensatory wave mechanisms in the knockout. Rather we are using the β2-nAChR-KO to establish the effectiveness of this KO as a means of testing the role of Stage 1 waves in developmental processes. We do hope the revised manuscript explains this motivation more clearly.

A related point is the apparent increased role of gap junctions in mediating waves in the absence of ACh receptors. On this point, the description of the effect of MFA (page 8, second paragraph, 3rd sentence) was confusing. It looks to me like MFA almost completely eliminates waves in both WT and KO - so the connection to an altered role of gap junctions was not clear.

We clarified our description of the MFA result (page 5):

Application of the gap junction blocker meclofenamic acid (MFA, 50μM) nearly abolished Stage 1 waves, causing a significant reduction in frequency of waves and cell participation during waves (Fig 2A & 2F).

ipRGC densities

The goal of the measurements of ipRGC densities was not entirely clear. Why are ipRGCs a reasonable way to determine the importance of waves for development? For example, the introduction raises the issue that changes in RGC proliferation depend on RGC type. Is there reason to think ipRGCs are "special" or, alternatively, that they are following the same developmental trajectory as other RGCs? Is it possible to track another RGC type (e.g. using SMI32 staining)? Related to this general point, page 9 (top) sets up the need to identify the mechanism of RGC cell death but then jumps to waves without a clear connection between the two. It would also be good to mention early that the measurements include multiple ipRGC types, so that issue does not come up only as an explanation for why the ipRGCs are not organized spatially (page 10 top).

We have revised text extensively to better motivate our selection of ipRGCs (page 6). Our goal was to use an identified differentiated RGC subtype for which we had genetic access to assess the impact of reduced retinal waves on cell number. We settled on ipRGCs because: 1) ipRGCs undergo a significant amount of cell death during the same period there are retinal waves (Chen et al, Elife 2013) and 2) we show ipRGCs participate in retinal waves.

Analysis

Quantitative analysis of the calcium measurements relies on the discretization of the signals measured in small ROIs. It was not clear how closely the discretized signals represented the original recordings. The traces illustrated in Figures 1 and 2, for example, appear to be measured in larger ROIs. Two things would be helpful here: (1) an illustration of several original recorded traces in the small ROIs plotted with the discretized versions of those traces; (2) a determination of how sensitive the results are to specifics of the discretization process.

We have modified Figure 1 to include example traces of the fractional change in fluorescence computed across the small ROIs used for the analysis of waves on the macroscope. They are at the top of Figure 1B. As can be seen by these traces, the signal-to-noise is fantastic.

Reviewer #2 (Public Review):

The overall goal of this study is to determine the mechanism of early retinal wave initiation and propagation. Despite a number of earlier studies, the precise mechanism of Stage1 waves and how they differ from Stage 2 waves remained controversial. To address this, the authors describe the timing and character of Stage 1 retinal waves using a custom build imaging system allowing for wide-field monitoring of neuronal activity while preserving high spatial resolution. In a set of elegantly designed experiments, they reveal that the initiation and propagation of Stage 1 waves are driven by distinct mechanisms involving complex circuitry of nAChRs and gap junctions. Interestingly, the data also demonstrate that Stage 1 and Stage 2 waves rely on different subtypes of AChRs. The signaling via beta2AChRs appears to be the driver of Stage 2 waves. However, the precise identity of nAChRs and GJs contributing to Stage 1 waves remains a mystery. Next, to determine the impact of early retinal waves on retinal circuit formation, the authors evaluate their impact on the survival of ipRGC. They show that ipRGC cell survival and their distribution mosaics are not influenced by spontaneous activity. While the observation of ipRGC data and their mosaic are interesting, the rationale for these experiments in the context of this study is not well presented.

We thank the reviewer for positive comments. We do hope the revised rationale for ipRGC measurements addresses these comments. It is included here for convenience (page 7)

RGCs undergo a period of dramatic cell death during the first two postnatal weeks of development, the majority occurring during the first postnatal week (Abed et al., 2022; Braunger et al., 2014). Whether this cell death process is regulated by retinal waves is unknown. We looked specifically at intrinsically photosensitive ganglion cells (ipRGCs) for several reasons. First, ipRGCs have completed proliferation (Lucas and Schmidt, 2019; McNeill et al., 2011) and appear to be fully differentiated by E16 (Shekhar et al., 2022; Whitney et al., 2022), the onset of Stage 1 waves. ipRGCs undergo a period of dramatic cell death during the first two postnatal weeks of development, the majority occurring during the first postnatal week, prevention of which profoundly disrupts several important developmental processes in the retina – including spacing of ipRGC somas as well as rod and cone mediated circadian entrainment through the activation of ipRGCs (Chen et al., 2013). However, the exact mechanism regulating ipRGC cell death is unknown. Here we assessed the impact of disrupting Stage 1 and Stage 2 waves on the number and distribution of ipRGCs.

Reviewer #3 (Public Review):

The manuscript by Voufo et al. aims to advance our understanding of the mechanisms responsible for the earliest pattern of spontaneous activity in the mouse retina, stage I retinal waves. These waves occur during embryonic development (E16-18) and are the least known form of activity in the immature retina.

The authors show that stage I waves have broad spatiotemporal features and are mediated by circuitry involving subtypes of nicotinic acetylcholine receptors (nAChRs) and gap junctions. The authors also found that the developmental decrease of intrinsic photoreceptor retinal ganglion cells (ipRGCs) density is preserved between control and ß2-nAChR-KO mice, indicating that processes regulating ipRGC distribution are not influenced by early spontaneous activity.

The quality of the data is excellent, and the conclusions of this paper are mostly well supported by data, but the presentation of the data and the analysis need to be clarified and extended.

Strengths:

The earliest patterns of spontaneous activity are crucial for the correct development of sensory circuits. In the visual system, most studies focus on postnatal activity (stage 2 and 3 retinal waves) overlooking embryonic stages, likely due to challenges related to methods and animal handling. Therefore, in this manuscript, the authors from a laboratory pioneer in studying retinal waves in the mouse, tackle a very relevant subject that has not been explored in detail. The bibliography that encompasses most of the current knowledge about stage 1 retinal waves in mammals is compressed into three fairly dated publications: Galli and Maffei 1988, Bansal et al 2000, and Syed et al 2004. These publications were pioneering attempts to describe early spontaneous activity; however, much work remained to be done regarding the molecular and cellular mechanisms involved. Here, Voufo and colleagues provide additional fundamental details about the properties and components of stage 1 waves. The dataset has excellent quality and plenty of information could be extracted from it. The authors used a macroscope that allows the acquisition of images from the entire retina while preserving a good spatial resolution.

Weakness:

The authors distinguish different subtypes of activity during embryonic stages in the retina of mice. However, they do not provide a detailed characterization that allows a clear definition of these subtypes (and specifically stage 1 waves). Moreover, throughout the manuscript, there are many technical details of the analysis that are missing and preclude a complete understanding of the robustness of the data. The authors have an excellent dataset that needs more analysis and an improvement in the presentation of the results.

We do hope the extensive revisions satisfy reviewer.

Author Response

Reviewer #1 (Public Review):

Ciliary length control is a basic question in cell biology and is fascinating. Regulation of IFT via calcium is a simple model that can explain length control. In this model, ciliary elongation associates with an increase in intraciliary calcium level that leads to calcium increase at the ciliary base. Calcium increase acts to reduce IFT injection and thus ciliary assembly rate. The longer the cilia, the more increase of calcium level and the more reduction of IFT injection and thus the ciliary assembly rate. When the cilia approach the genetic defined length, the gradual reducing assembly rate eventually balances the constitutive disassembly activity. Cilia then stop elongation and a final length is achieved. This work tested this model by manipulating the calcium level in cilia by using an ion channel mutant and treatment of the cells with EGTA. In addition, IFT injection was measured before and after calcium ciliary influx. Based on the outcome of these and other experiments, it was concluded that there is no correlation between changes in calcium level and IFT injection, thus challenging the previous model. This work is well written and the experiments appear to be properly executed. It nicely showed an increase of intraciliary calcium during cilia elongation, and beautifully showed that ciliary calcium influx depends on extracellular calcium. However, I felt the current data are inadequate to support the author's conclusion.

We thank the reviewer for the positive assessment of the interest in our work, and we have performed additional experiments to address the reviewers concerns as discussed below.

The authors showed that ciliary calcium increases along with ciliary elongation, which correlates with reduction of IFT injection. Thus, this result would support that calcium increase reduces IFT injection. To test whether reducing calcium influx would alter the IFT injection, the authors used an ion channel mutant cav2. Indeed, ciliary calcium level in the mutant cilia appears to be lower compared to the control in average. After measuring ciliary calcium level and IFT injection during ciliary elongation with mathematical analysis, it was concluded that reducing ciliary calcium level did not lead to increased IFT injection, which is distinct from the control cells. Thus, the authors concluded that calcium does not act as a negative regulator of IFT injection. However, if one examines the calcium flux in Figure 3B and IFT injection in Figure 4B of cilia less than 6 micron, one may draw a different conclusion. For the mutant cilia, the calcium influx is higher than that in control cilia and IFT injection is reduced compared to the control. Thus, this analysis is the opposite of the authors' conclusion, and is supporting the previous model. There is a rapid change in ciliary assembly rate at the early stages of ciliary assembly (see Figure 1C), thus, the changes in calcium influx and IFT injection in the earlier assembly stage would be more appropriate to assess the relationship between intraciliary calcium level and IFT injection.

We thank the reviewer for raising this issue, which led us to examine the data more carefully. In looking at the numbers of cells with flagella in each length range, we became concerned that the apparently low calcium influx in shorter flagella in control cells compared to ppr2 or EGTA treatment might actually due to bias from technical issues: it is relatively difficult to image shorter flagella in our TIRF imaging setup, because shorter flagella have less flagellar surface area to attach the coverslip. The more motile the flagella are, the more likely are the cells to detach when their flagella are short, because the bending force of the flagella is strong enough to pull them away from their small area of adhesion. This effect is much stronger in control cells than in either the ppr2 mutants or EGTA treated cells, whose flagella are less motile. This led to a reduced number of cells examined with flagella shorter than 6 um (17 versus 34 for control and ppr2 cells, respectively). To overcome the difficulties and biased result, we observed more flagella in control cells. The new data has now been integrated with our previous data and shown in Figure 3. The new result shows that calcium influx in control cells is in fact higher than in the ppr2 mutant cells. So, our result is remains consistent with our conclusion, and we believe that it is not useful to analyze the shorter flagella separately.

The authors used EGTA treatment to support their conclusion. However, EGTA treatment may induce a global calcium change of the cell, the outcome may not reflect actual regulation of IFT injection by ciliary calcium influx. For example, as reported elsewhere, the change of cAMP level in the cell body and cilia has a different impact on ciliary length and hedgehog regulation. The slower assembly of cilia in EGTA treated cells may be caused by many other factors instead of sole regulation by IFT.

It is certainly possible that EGTA is affecting some process inside the cell that then indirectly affects IFT. Our experiments cannot rule this out. The fact that similar effects are seen with the ppr2 mutant argues against this idea, but again cannot rule it out. We have added the following caveat to the discussion:

"Other calcium dependent processes in the cytoplasm might also potentially address IFT, and our results cannot rule out this possibility. However, we note that the ppr2 mutant also fails to show the effect on IFT or regeneration predicted by the ion current model."

The authors only examined the impact of reducing ciliary calcium influx. To further support the authors' conclusion, it is recommended that the authors should examine IFT injection in a condition where ciliary calcium level is increased. Using calcium ionophore may not be a good choice as it may change the global calcium level. One approach to consider is using mutants of a calcium pump present in cilia.

We thank the reviewers for this suggestion. The calcium current model would predict that if a calcium pump mutant failed to export calcium, the increased calcium building up inside the flagellum should lead to decreased IFT entry and a shorter flagellar length. We found at least two calcium pumps in the published Chlamydomonas flagella proteome (Pazour et al., 2005) and ordered several mutant strains from Chlamydomonas Library Project (CLiP) which are annotated as affecting these pumps. We measured the flagellar length of these potential calcium pump mutant strains, but none showed a statistically significant difference in length relative to control cells. We have now included this data as Figure S4. Because no length change was observed, we did not perform the extremely time consuming process of constructing strains that contain these mutations along with DRC4-GCaMP and KAP-GFP.

As an alternative strategy to get at this reviewer's suggestion, we measured DRC4-GCaMP and KAP-GFP intensity in 1 mM CaCl2 treated flagella and found that CaCl2 treatment increases both the flagellar calcium level (Figure 3, see below) and IFT injection (Figure 4). This increase in IFT injection is the opposite of what the calcium current model predicts.

Based on these results, we think the calcium pump experiment is not necessary because of the following reasons. 1. These calcium pump mutants might not increase the flagellar calcium level. 2. Even if the flagellar calcium was increased in these mutants, it does not affect the flagellar length and thus our conclusions would still hold. 3. These mutant strains might still have functional calcium pumps since the existing data on calcium pumps in flagella is likely to be incomplete. 4. The CaCl2 experiment clearly increased the flagellar calcium level inside flagella, directly addressing the point that the reviewer is getting at.

The conclusion on line 272-273 may need more evidence. The authors showed that addition of 1 mM CaCl2 does not change ciliary assembly, and used this as one of the evidences to argue against the ion-current model. The addition of calcium extracellularly may not alter intracellular/intraciliary calcium level given that cells have robust systems to control calcium homeostasis. To support the authors' conclusion, one should measure the changes of calcium level in the cell/cilia or revise their conclusion.

We have now performed these measurements and have included the data in Figure 3D.

The authors showed nicely the changes in IFT properties before, during and after ciliary calcium influx and found that the intensity and frequency of IFT do not have a correlation with calcium influx though calcium influx restarts paused IFT trains for retrograde transport as previously reported (Collingride 2013). The authors again concluded that this is supporting their conclusions in that there is no correlation between IFT injection and calcium influx. However, I am not sure whether the short pulses of calcium influx at one time point would change the calcium level in the whole cilia in a significant way that would alter IFT injection at the ciliary base.

We agree that individual pulses might not have an effect on the average level of IFT injection. We were specifically trying to see if, having previously ruled out the predicted correlation at the level of average rates, there might still be a trace of the correlation for individual events.

Reviewer #2 (Public Review):

The authors use a genetically encoded calcium indicator to measure Ca in flagella to establish that Ca influx correlates with flagellar length. (Despite this correlation, there is so much noise that it is dubious that Ca level can regulate the flagella's length.) Then, they show that reduced Ca decreases the rate of IFT trains entering flagella, which ruins the ion-current model of regulating flagella's length. (Ca can still be one of the factors that sets the target length.) Ca does not seem to change the disassembly rate either. There are also no correlations between Ca influx spikes and IFT injection events. Curiously, these spikes broke pauses of retrograde IFT trains, but that still did not affect IFTs entering dynamics.

Some other possibilities like Ca regulating unloading rates are discussed and convincingly rejected.

The study ends with an interesting Discussion, which talks about other possible models, and concludes that the only model not easily rejected so far is the mechanism relying on diffusion time for kinesins from flagella to the cell body being greater in longer flagella.

The paper is well written, very thorough, contains significant results.

We thank the reviewer for this strong positive assessment.

Reviewer #3 (Public Review):

This work by Ishikawa et. al is focused on testing the hypothesis first proposed by Rosenbaum that Ca2+ levels in the primary cilia act as an internal regulator of cilia length by negatively regulating intraflagellar transport (IFT) injection and/or microtubule assembly. The authors first built a mathematical model for Ca2+ based regulation of cilia length through the activity of a Ca2+ dependent kinase. They then tested this model in the growing cilia of Chlamydomonas cells expressing an axonemal localized GCaMP. Ca2+ levels were manipulated genetically with a calcium channel deficient mutant line and with the addition of EGTA. While increases in Ca2+ levels do correlate with cilia length as expected by the model they found that IFT injection was positively correlated with IFT injection and increased axonemal stability which contradicts its potential as a mechanism for the cell to internally regulate cilia length.

Overall the conclusions of the paper are supported by their data. They greatly benefit from first establishing their model in a clear form and then experimentally interrogating the model from multiple angles in order to test its viability. The importance of cilia length to our understanding of human health has only become greater in recent history and the authors are making a significant contribution to our understanding of ciliary length regulation.

We thank the reviewer for this positive assessment, including of the relevance of the model. We have attempted to address all suggestions.

Author Response

Evaluation summary

This important study advances our understanding of respiratory complex I. The authors present convincing structural data for the enzyme from Drosophila melanogaster although the interpretation of conformational states is still not conclusively settled. This work will be of interest to researchers studying respiratory enzymes, the evolution of respiration, and mitochondrial diseases.

Thank you for this positive evaluation of our work. Although we have presented a robust and coherent interpretation of the conformational states we observe, we accept that different opinions on this topic still exist in the field.

Reviewer #1 (Public Review):

Agip et al. have resolved the first cryoEM structure of the mitochondrial Complex I from Drosophila melanogaster, an important model organism in biology. The structure revealed a 43-subunit enzyme complex that closely resembles the mammalian Complex I. The authors resolved Complex I in three different conformational states at 3.3-4.0 Å global resolution, with an overall resemblance to the active form of the mammalian Complex I, but also with some characteristic conformational changes near the quinone substrate pocket and surrounding subunits that resemble, at least in part, the deactive form of the mammalian enzyme. The third resolved class was considered 'damaged/broken', and a possible artifact arising from the sample preparation. Biochemical assays showed that the Drosophila Complex I does not undergo an active/deactive transition (as characterized by the N-ethylmaleimide sensitivity), although the structures revealed an exposed ND3 loop that has been linked to transition. The authors could also show that conformational change between an alpha and pi form of transmembrane helix (TM3-ND6) is likely to be involved in catalysis, and distinct from the deactivation mechanism of the mammalian isoform. Due to the 3.3 Å global resolution, water molecules could not be experimentally resolved, and how the observed conformational changes link to the proton pumping activity therefore remains an open question and basis for future studies. Overall I find that this work provides an important basis for understanding mechanistic principles of the mitochondrial Complex I and more specifically a starting point for detailed genetic studies on the fruit fly Complex I.

We thank the reviewer for their positive evaluation of our manuscript.

We would like to note that in all three conformational states of Drosophila complex I observed in our study, we do not observe an exposed ND3 loop (Cys39 in particular), as outlined in Figures 3 & 6 and Figure 6 – Figure Supplement 1 (as well as in Figures 5 and 7). This observation is fully consistent with the lack of N-ethylmaleimide (NEM) sensitivity observed in our Drosophila preparation.

We discuss the relevance of the π-bulge/α-helical nature of ND6-TMH3 to catalysis in the Discussion section in the context of an intercalated phospholipid molecule in the Dm1 structure: “Indeed, if ND6-TMH3 converts between its -bulge and -helical structures during catalysis (Agip et al., 2018; Kampjut and Sazanov, 2020; Kravchuk et al., 2022; Parey et al., 2021; Röpke et al., 2021), then the intercalating phospholipid is very unlikely to be present in the -helical state, moving repeatedly in and out.” While our structures are consistent with this helical change being involved in catalysis, they are resting-state structures and therefore do not provide further evidence in this regard.

Finally, the reviewer is correct in that the resolutions of the structures resolved here are insufficient to model water molecules, and that how the conformational changes observed here contribute to our currently limited knowledge of the coupling mechanism remains to be answered.

Reviewer #2 (Public Review):

• Aim of the study:

Agip et al. studied the structure of respiratory complex I from Drosophila melanogaster, an important model organism with well-developed genetic toolkit and sufficiently close phylogenetic relationship to mammals. They isolated the complex and analyzed its structure by single-particle electron cryo-microscopy (cryo-EM). They also used mass spectrometry to characterize new subunits. So far, the structures of complex I have been reported for several organisms, including mammals, plants, ciliates, fungi and bacteria, but ones from insects have been missing. This study aims to fill this gap and shed light on some of the key questions pertaining complex I biology, such as 1) the conservacy of supernumerary subunits, 2) the mechanisms and physiological relevance of active/deactive transition and 3) the correspondence between the structurally defined closed/open conformations and the biochemically defined active/deactive states.

We thank the reviewer for clearly summarising the key aims of the study relative to the current status of the complex I field.

• Strengths:

The study provides the first structure of complex I from insects, the organisms at an important phylogenetic branch that has diverged from mammals more recently than other eukaryotic species such as plants and fungi. Using purification methods they developed for mammalian enzymes previously, the authors successfully purified the insect enzyme with high quality - a monodisperse peak in gel filtration, the NADH oxidation activity comparable to mammalian enzymes, and the homogenous subunit composition as confirmed by single-particle analyses. It is noteworthy that the authors used state-of-the art tools in model building and validation, such as ISOLDE and MapQ, which makes this model of high standard. In my opinion such careful validation is particularly important for modelling such a gigantic complex, since without cares one can easily misinterpret the density and draw wrong conclusions.

The resolution is 3.3 Angstrom for the best class (Dm1), which allowed modelling side chains and comparing between the observed 3D classes and to the known structures. The model confirms the presence of 43 subunits, akin to mammalinan enzymes, composed of 14 conserved core subunits, 28 supernumerary subunits that have close homologs in mammals, and one supernumerary subunit CG9034 that has not been predicted. They are also structurally similar to mammalian enzymes except for minor local differences. The two supernumerary subunits (NDUFC1 and NDUFA2) that are present in mammals are missing. The authors discuss evidence that NDUFC1 is absent from the Drosophila genome and NDUFA2 is genomically present but its expression is restricted to the male germline. Together, the overall similarity to the mammalian enzyme underlines the use of Drosophila complex I as a model system.

One of the remarkable findings is that common biochemical treatments that are used to deactivate mammalian complex I - heat treatment or NEM treatment - did not reveal deactive state of Drosophila complex I. This is in agreement with their observation that most structural elements are in the active state. The major Dm1 conformation shows all structural features in the active conformation, whereas Dm2 state shows two features in the deactive conformations. Here the author raises an interesting point that the structural elements formerly believed to behave in a consorted manner are actually not coupled, providing new perspective in interpreting complex I structures presented so far and in future. Notably, the authors adopted the same purification procedure for bovine and murine samples. This is a particular strength that they applied a similar procedure for but still observed different behaviors for Drosophila (the absence of the deactive state).

We thank the reviewer for their positive evaluation of the strengths of the paper.

• Weaknesses:

As the authors point out in Discussion, the biochemical statuses of the two described conformations, Dm1 and Dm2, are uncertain. If we assume that Dm1 is a ready-to-go active state, Dm2 could represent several of the possible states; a partially broken state due to delipidation by detergent, a meta-stable state during enzyme turnover, an intermediate towards "full deactiving" structural transition (which the authors argue is unlikely to occur), or a fully reversible state that is in equilibrium to Dm1. Despite these uncertainties, the structure will serve as an excellent starting point to address many open questions in the complex I field in future.

We agree that the biochemical status of Dm2 is uncertain and as the reviewer notes, we made an attempt to address this question in the Discussion section.

In the final 3D classification the number of classes was set to 3 (K = 3). This is an arbitrary human decision and implicitly forces particles to separate into 3 descrete classes. It would have been great to mention if the authors had tried different classification parameters and, if so, whether that had led to similar classification results. There are different methods available to dissect conformational heterogeneity other than simple 3D classification. For example, focused classification can differentiate local structural features. MultiBody refinement and 3D variabitlity can analyze continuous conformational changes. The simple 3D classification with local angular sampling employed here may lead to over-simplification of the more complex structural heterogeneity.

First, the number of classes was set to 5 (K = 5) as written in the Materials and methods section (page 20), which is greater than the number of complex I conformations observed in this study. We apologise if this was not clear and we have amended Figure 1 – Figure Supplement 2 to clarify it.

Second, as the reviewer correctly points out, there are many different methods to dissect conformational heterogeneity, and for this reason we purposefully performed several classification strategies before validating that the Global 3D classification approach used here (with local angular search extending to 0.2º sampling) yielded comparable (or even better) results. These additional classification strategies include:

(i) Focus-revert-classify (a strategy often used for complex I (Kampjut and Sazanov, 2020; Klusch et al., 2021; Kravchuk et al., 2022; Letts et al., 2019)) in RELION, where the membrane arm of complex I is first subtracted to focus-refine on the hydrophilic arm, the subtraction reverted, and then focus-classification performed without alignment on the membrane arm. Here, we used a regularisation parameter, t = 8, and K = 5, and the process yielded three complex I classes matching Dm1, Dm2, and Dm3 with comparable population distribution to the aforementioned Global 3D classification method, plus two junk classes.

(ii) A 3D classification without alignment approach (a strategy also used for complex I (Gu et al., 2022)) in RELION. We used t = 20 and up to K = 12 classes, which resulted in two < 4 Å resolution complex I classes, with the major class matching Dm1 and the minor class a likely mixture of Dm2 and Dm3.

Based on these three classification strategies, we chose to work with the Global 3D classification approach that has previously proven robust for separating complex I heterogeneity in our hands (Agip, 2018; Chung et al., 2022b; Zhu et al., 2016). However, we agree with the reviewer that it would be valuable to provide this extra information. Therefore, we have amended the Materials and methods section on page 20: “The ‘Focus-Revert-Classify’ classification strategy (Letts et al., 2019), applied using the regularisation parameter t = 8 and K = 5, yielded comparable population distributions (three complex I classes matching Dm1, Dm2, and Dm3, plus two junk classes) whilst 3D classification without alignment using t = 20 and K ≤ 12 yielded two < 4 Å complex I classes, with the major class matching Dm1 and the minor class an apparent mixture of Dm2 and Dm3. The 3D classification approach with local angular sampling was therefore employed to give the final set of Dm1, Dm2 and Dm3 particles as described above.” Furthermore, clear cryo-EM densities for Dm2-specific local features, including the ‘flipped’ ND1-TMH4-Tyr149 and the ND6-TMH3 π-bulge, revealed no evidence for Dm1 contamination in the Dm2 population. This is also now noted on page 20.

Although 37 degrees heat treatment and NEM treatment did not reveal any sign of deactivation in Drosophila complex I, it does not rule out the possibility that insect complex I has different ways to deactivate the enzyme, to prevent ROS production. It is probably the limitation of applying existing assays that are originally for mammalian and fungal enzymes to the study of insect enzymes.

The reviewer is correct that Drosophila complex I may have a different way to ‘deactivate’ that does not involve an exposure of ND3-Cys39, and it is also possible that the conditions used for deactivation of mammalian mitochondrial membranes (i.e. 37 ºC heat treatment for 30 min) may not be sufficient to deactivate the Drosophila enzyme. Our approach here was to evaluate if Drosophila complex I undergoes the same active/deactive transition as the mammalian enzyme both structurally and biochemically (and our results suggest that it doesn’t). Moving forward, deactivation mechanisms in different phylogenetic lineages will be an important question to address in the complex I field. We have addressed this question in the first paragraph of the Discussion.

• Whether they achieved the aims and whether the conclusions are supported by the results:

Overall, they successfully isolated the active enzyme and determined its structure at 3.3 A resolution, which meets the current state-of-the-art for single-particle cryo-EM and provided an atomic picture of the enzyme composition. The study confirms that the Drosophila complex I is structurally similar to mammalian complex I, but biochemically different in that it does not show the deactive state. It still does not exclude the possibility that Drosophila complex I can transition into a currently unknown state that prevents reverse electron transfer. This question however can be tackled in future by mutagenesis analyses as Drosophila is a genetically tractable organism.

We agree with the reviewer on his evaluation of the study, and the genetic tractability of the Drosophila enzyme will serve as a crucial tool for future studies.

• Impact to the field and utility of the data to the community:

Complex I is important not only for human health but also for understanding universal principles of biological respiration, because of its universal presence in most organisms on Earth. This study provides a basis for relating mammalian complex I with those from other branches of organisms. The current structures will allow Drosophila researchers to interpret and design any mutations that affect complex I functions, and relate them to behavioral, developmental and metabolical changes at tissues, organs and individuals levels.

We agree with the reviewer on his evaluation of the impact of the study, and thank the reviewer for their comments on the manuscript.

Reviewer #3 (Public Review):

The mitochondrial NADH dehydrogenase complex (complex I) is of prime importance for cellular respiration. It has been biochemically and structurally characterized for several groups of organisms, including mammals, fungi, algae, seed plants and protozoa. Furthermore, different complex I conformation have been reported, which are considered to possibly represent distinct physiological states of the enzyme complex. E.g. in mammalian mitochondria, two resting states can be distinguished, designated 'ready-to-go' resting state, and 'deactive' resting state. To better understand the physiological relevance of these states, complex I is here investigated from the fruit fly Drosophila melanogaster, which represents a model for insects but beyond for metazoan in general and which can be easily genetically modified.

Complex I from Drosophila is presented at up to 3.3 Angstrom resolution. It includes 43 of the 45 complex I subunits defined for mammalian complex I. Subunit NDUFA3 has been found in Drosophila complex I for the first time. Overall, Drosophila complex I is remarkably similar in its composition and structure to the mammalian enzyme. Only minor topological differences were found in some subunits. Furthermore, three different complex I states are described, termed Dm1, Dm2 and Dm3. The three states are extensively discussed and compared to the states found in mammalian complex I. Dm1, which is the dominating class, likely represents the active resting state. In Dm2, the two complex I arms are slightly twisted with respect to Dm1. In Dm3, the membrane arm appears to be 'cracked' at the interface between ND2 and ND4. It possibly represents an artefact resulting from detergent-induced loss of stability in the distal membrane domain of the Dm2 state. Both, Dm2 and Dm3 most closely correspond to the mammalian active state. A state resembling the mammalian deactive state could not be found. This result is further supported by biochemical experiments. It is concluded that Drosophila complex I, despite its remarkable similarity to the mammalian enzyme, does not undergo the mammalian-type active/deactive transition.

In conclusion, complex I structure from Drosophila is of limited value for the better understanding of the states of mammalian complex I (which could be stated more clearly). However, insights into complex I structure and function of an insect is highly interesting. The conclusions are justified by the presented data. The manuscript is well written and the figures are thoroughly prepared. The discussion very much focusses on the interpretation of the three complex I states. The deactivate state, which is interpreted to protect mammalian mitochondria from ROS production during reverse electron transfer, might be dispensable in species characterized by a comparatively short life cycle like Drosophila, which is in the range of weeks.

We thank the reviewer for clearly summarising the key findings of the study. We agree that Drosophila complex I may have limitations for studying the full active/deactive transition so far observed exclusively in mammalian enzymes, but we argue that the lack of a fully deactivated state also provides a good system to study which local elements in complex I may offer protection against RET. Despite these limitations, Drosophila remains a powerful model system to study complex I mechanism, assembly, and regulation in physiological contexts.

Author Response

Reviewer #1 (Public Review):

Neuronal tissues are very complex and are composed of a large number of neuronal types. With the advent of single-cell sequencing, many researchers have used this technology to generate atlases of neuronal structures that would describe in detail the transcriptome profiles of the different cell types. Along these lines, in this manuscript, the authors present single-cell transcriptomic data of the fruitless-expressing neurons in the Drosophila male and female central nervous systems. The authors initially compare cell cluster composition between male and female flies. They then use the expression of known markers (such as Hox genes and KC neuronal markers) to annotate several of their clusters. Then, they look in detail at the expression of different terminal neuronal genes in their transcriptomic data: first, they look into neurotransmitter-related genes and how they are expressed in the fruitless-expressing neurons; they describe in detail these populations that they then verify the expression patterns by looking into genetic intersections of Fru with different neurotransmitter-related genes. Then, they look at Fru-neurons that express circadian clock genes, different neuropeptides and neuropeptide receptors, and different subunits of acetylcholine receptors. Finally, they look into genes that are differentially expressed between male and female neurons that belong to the same clusters. They find a large number of genes; through GO term enrichment analysis, they conclude that many IgSF proteins are differentially expressed, so they look into their expression in Fru-neurons in more detail. Finally, they compare transcription factor expression between male and female neurons of the same cluster and they identify 69 TFs with cluster-specific sex-differential expression.

In general, the authors achieved their goal of generating and presenting a large and very useful dataset that will definitely open a large number of research avenues and has already raised a number of interesting hypotheses. The data seem to be of good quality and the authors present a different aspect of their atlas.

The main drawback is that many of the analyses are very superficial, resulting in the manuscript being handwavy and unsupported. The manuscript would benefit by reducing the number of "analyses" to the ones that are also in vivo validated and by discussing some of the drawbacks that are inherent to their experimental procedure.

scRNA-seq studies generate atlases that are descriptive, by their nature. Therefore, we decided to keep interesting gene-expression analyses in the paper that are based on the scRNA-seq results, especially for the discoveries that point to exciting avenues for future pursuit. We reduced the text as suggested.

1) The authors treat their male, female, and full datasets as three different samples. At the end of the day, these are, for the most part, equivalent neuronal types. The authors should decide to a) either only use the full dataset and present all analyses in this, or b) give a clear correspondence of male and female clusters onto the full ones.

In this paper, all the analyses presented are on the full data set, with some links to the male or female data sets included. We now make clear that the full data set is the focus of the paper (lines 137-141). We provide the male and female data sets for our reader, with the individual Seurat objects uploaded to GEO, to make it easy for the reader to do follow-up analyses using the same criteria we used. We think this is helpful for our research community. We also compare the male and female clusters to the full data set using ClustifyR and report which clusters in the male or female data set analyses correspond to those in the full data set (Source data 2), as suggested by the reviewer, though ClustifyR has some limitations based on our evaluation of this tool for other annotations (see below).

2) Most of their sections are heavily reliant on marker genes. In fact, in almost every section they mention how many of their genes of interest are marker genes. This depends heavily on specific cutoffs, making the conclusions fragile. Similarly, GO terms are used selectively and are, in many cases, vague (such as “signaling”, “neurogenesis”, “translation”).

We evaluated marker genes, as those provide molecular identities to the clusters, given by definition they are significantly more highly expressed in a specific cluster, compared to all clusters. We used a Wilcox rank sum test with the following parameters in Seurat: (min.pct=0.25, logfc.threshold=0.25), which resulted in all called marker genes having p values < 0.05. We did not use a more stringent criteria given that most of the marker gene analyses are descriptive, and it is important to capture a broad range of genes. Our criteria are similar to Ma et al. 2021 (PMID: 33438579) and Corrales at al. 2022 (PMID: 36289550). In the text, we refer to the top 5 marker genes in several analyses, though these marker genes have a much more significant enrichment. We agree with the reviewers’ criticisms regarding the cluster-specific GO-term analyses in the text and those have been removed from the manuscript.

3) A few of the results are not confirmed in vivo. The authors should add a Discussion section where they discuss the inherent issues of their analyses. Are there clusters of low quality? Are there many doublets?

We have added discussion around these topics to the conclusions section of the manuscript and the results, when appropriate.

On the same note, their clusters are obviously non-homogeneous (i.e. they house more than one cell types. This could obviously affect the authors' cluster-specific sex-differential expression, as differences could also be attributed to the differential composition of the male and female subclusters.

We discuss this potential limitation in the discussion of sex differences in gene expression (Lines 959-961).

4) Immunostainings are often unannotated and, in some cases especially in the Supplement, they are blurry. The authors should annotate their images and provide better images whenever possible.

We appreciate this being pointed out and have provided higher resolution figures. The issue was we exceeded the manuscript submission file size on initial submission.

5) I believe that the manuscript would benefit significantly by being heavily reduced in size and being focused on in vivo rigorously confirmed observations.

We have addressed this comment by removing some of the analyses.

Reviewer #3 (Public Review):

This paper uses single-cell transcriptome sequencing to identify and characterize some of the neuronal populations responsible for sex-specific behaviour and physiology. This question is of interest to many biologists, and the approach taken by the authors is productive and will lead to new insights into the molecular programs that underpin sexually dimorphic development in the CNS. The dataset produced by the authors is of high quality, the analyses are detailed and well described, and the authors have made substantial progress toward the identification and characterization of some of the neuron populations. At the same time, many other cell types whose existence is suggested by this dataset remain to be identified and matched to specific neuron populations or circuits. We expect the value of this dataset to increase as other groups begin to follow up on the data and analyses reported in this paper. In general, the value of this paper to the field of Drosophila neurobiology will be high even if it is published in close to its present form. On the other hand, the current manuscript does not succeed in presenting the key take-home messages to a broader audience. A modest effort in this direction, especially re-writing the Conclusions section, will greatly enhance the accessibility and broader impact of this paper.

While the biological conclusions reached by the authors are generally robust and of high interest, we believe that some conclusions are not sufficiently supported by the analyses that have been performed so far and need to be reexamined and confirmed. A major question concerns the authors' ability to distinguish a shared cell type with sex-biased gene expression from a pair of closely related, sex-limited cell types. There appear to be many cases that fall into this grey area, and the current analysis does not provide an objective criterion for distinguishing between sex-specific and sexually dimorphic clusters. Below we suggest some technical approaches that could be used to examine this issue. A second problem, which we do not believe to be fatal but that needs to be discussed, concerns potential differences in developmental timing and cell cycle phase between males and females, and how these differences might impact the inferences of sexual dimorphism in cell numbers and gene expression. Finally, we identify several areas, including the expression of transcription factors in different neuronal populations, that we believe could be described in more biologically insightful ways.

For our review, we focus on three levels of evaluation:

1). Is the dataset of high quality, useful to a large number of people, well annotated, and clearly described?

The data appear to be high quality. The authors use reasonable neuronal markers to infer that 99% of their cells are neuronal in origin, suggesting extremely low levels of contamination from non-neuronal cells. Moreover, the gene/UMIs detected per cell are high relative to what has been reported in previous Drosophila scRNA-seq neuron papers (e.g. Allen et al., 2020). The cluster annotations are incomplete - which is not surprising, given the complexity of the cell population the authors are working with. 46 of the 113 clusters in the full dataset are named based on published expression data, gene ontology enrichments of cluster marker genes, and overlap with other CNS single cell datasets. This leaves rather a lot outstanding. It is probably unrealistic to aim for a 100% complete annotation of this dataset. But if we're thinking about how this dataset might be used by other researchers, in most cases the validation that a given cluster corresponds to a real, distinct neuron subpopulation will be left to the user.

A major comment we have about the quality of the dataset relates to how doublets are identified and dealt with. The presence of doublets, an unavoidable byproduct of droplet-based scRNAseq protocols (like the 10x protocol used by the authors), could affect the clustering or at least bias the detection of marker genes. In large clusters, one might expect the influence of doublets on marker gene detection to be diluted, but in smaller clusters it could cause more significant problems. In extreme cases, a high proportion of doublets can produce artifactual clusters. The potential for problems is particularly high in cases where the authors identify cells with hybrid properties, such as clusters 86 and 92, which the authors describe as being serotonergic, glutamatergic, and peptidergic. Currently, the authors filter out cells with high UMI/gene counts, but it's unclear how many are removed based on these criteria, and cells can naturally vary in these values so it is not clear to us whether this approach will reliably remove doublets. That said, we acknowledge that by limiting their 'FindMarkers' analysis to genes detected in >25% of cells in a cluster the authors are likely excluding genes derived from doublets that contaminate clusters in low (but not high) numbers. We think it would be useful for the authors to report the number of cells that are filtered out because they met their doublet criteria and compare this value to the number of expected doublets for the number of cells they recovered (10x provides these figures). We would also recommend that the authors trial a doublet detection algorithm (e.g. DoubletFinder) on the unfiltered datasets (that is, unfiltered at the top end of the UMI/gene distribution). Does this identify the same cells as doublets as those the authors were filtering out?

We appreciate this suggestion and have now added results from the doublet detection algorithm, DoubletFinder to our manuscript. Please see above response in editorial comments. We provide a table in Figure 1 – supplement 1 that indicates the number of cells removed by our filtering criteria: We acknowledge that there may be additional doublets in our data set that were not removed in our filtering criteria in the discussion (Lines 1098-1102) and have also provided a new table in Source data 2 indicating the number of potential doublets identified by DoubletFinder that are present in each cluster.

2). What is the value of this study to its immediate field, Drosophila neurobiology? Are the annotation and analysis of specific cell clusters as precise and insightful as they could be? Has all the most important and novel information been extracted from this dataset?

This is the part that we are least qualified to assess, since we, unlike the authors, are not neurobiologists. We hope some of the other referees will have sufficient expertise to evaluate the paper at this level.

One thing we noticed (more on that in Part 3) is that the authors rely on JackStraw plots and clustree plots to identify the optimal combination of PCs and resolution to guide their clustering. This represents a relatively objective way of settling on clustering parameters. However, in a number of the UMAPs it looks like there are sub clusters that go undiscussed. E.g. in Fig. 2E clusters 1 and 3 are associated with smaller, distinct clusters and the same is true of clusters 2 and 6 in Fig 4b. Given that the authors are attempting to assemble a comprehensive atlas of fru+ neurons, it seems important for them to assess (at least transcriptomically) whether these are likely to represent distinct subpopulations.

We appreciate these comments and address this above in the editorial comments section.

3). How interesting, and how accessible is this paper to people outside of the authors' immediate field? What does it contribute to the "big picture" science?

Here, we think the authors missed an important opportunity by under-utilizing the Conclusions section. The manuscript has a combined "Results and Discussion" section, where the authors talk about their identification and analysis of specific cell clusters / cell types. Frankly, to a non-specialist this often reads like a laundry list, and the key conclusions are swamped by a flood of details. This is not to criticize that section - given the complexity and potential value of this dataset, we think it is entirely appropriate to describe all these details in the Results and Discussion. However, the Conclusions section does not, in its present form, pull it all back together. We recommend using that section to summarize the 5-8 most important high-level conclusions that the authors see emerging from their work. What are the most important take-home messages they want to convey to a developmental biologist who does not work on brains, or to a neurobiologist who does not work on Drosophila? The authors can enhance the value of this paper by making it more interesting and more accessible to a broader audience.

We appreciate this suggestion and made changes to the conclusions section to address this comment.

Author Response

eLife assessment

The author customises an alpha-fold multimer neural network to predict TCR-pMHC and applies this to the problem of identifying peptides from a limited library, that might engage TCR with a known sequence from a limited list of potential peptides. This is an important structural problem and a useful step that can be further improved through better metrics, comparison to existing approaches, and consideration of the sensitivity of the recognition processes to small changes in structure.

I appreciate the time taken by the editor and reviewers to assess this manuscript. In response to their comments, I've made significant changes and additions to the manuscript, most importantly adding (1) comparisons to TCRpMHCmodels and sequence-similarity based template selection, (2) analysis of peptide modeling accuracy in structure prediction and epitope prediction, (3) analysis and discussion of bias in the ternary structure database, (4) identification of key factors driving structure prediction accuracy, (5) binding predictions for three experimental systems with altered peptide ligand data, and (6) additional discussion of the generalizability of the epitope specificity prediction results to systems without structural characterization.

One minor correction to the wording of the above assessment: the alphafold network used as the basis of our protocol is the original "monomer" network, not the multimer network. We chose to start from the monomer network because it was not trained on complexes, allowing for a more accurate assessment of the expected performance when modeling unseen TCR:pMHC complexes. On the other hand, performance comparisons such as in Fig. 2 are made to the AlphaFold multimer pipeline, since that pipeline can directly build models of complexes.

Reviewer #1 (Public Review):

The author has generated a specific version of alpha-fold deep neural network-based protein folding prediction programme for TCR-pMHC docking. The alpha-fold multimer programme doesn't perform well for TCR-pMHC docking as the TCR uses random amino acids in the CDRs and the docking geometry is flexible. A version of the alpha-fold was developed that provides templates for TCR alpha-beta pairing and docking with class I pMHC. This enables structural predictions that can be used to rank TCR for docking with a set of peptides to identify the best peptide based on the quality of the structural prediction - with the best binders having the smallest residuals. This approach provides a step toward more general prediction and may immediately solve a class of practical problems in which one wants to determine what pMHC a given TCR recognizes from a limited set of possible peptides.

Very minor point: the structure prediction pipeline (Fig. 2) handles both MHC class I and class II complexes. For epitope binding specificity prediction (Figs. 3-6), I only tested MHC class I targets due to limitations in data availability (very few class II epitopes have had their TCR repertoires mapped and also ternary complexes solved).

Reviewer #2 (Public Review):

The application of AlphaFold to the prediction of the peptide TCR recognition process is not without challenge; at heart, this is a multi-protein recognition event. While Alphafold does very well at modelling single protein chains its handling of multi-chain interactions such as those of antibody-antigens pairs have performed substantially lower than for other targets (Ghani et al. 2021). This has led to the development of specialised pipelines that tweak the prediction process to improve the prediction of such key biological interactions. Prediction of individual TCR:pMHC complexes shares many of the challenges apparent within antibody-antigen prediction but also has its own unique possibilities for error.

One of the current limitations of AlphaFold Multimer is that it doesn't support multi-chain templating. As with antibodies, this is a major issue for the prediction of TCR:pMHC complexes as the nearest model for a given pMHC, TRAV, or TRBV sequence may be in entirely different files. Bradley's pipeline creates a diverse set of 12-hybrid AlphaFold templates to circumvent this limitation, this approach constrains inter-chain docking and therefore speeds predictions by removing the time-consuming MSA step of the AlphaFold pipeline. This adapted pipeline produces higher-quality models when benchmarked on 20 targets without a close homolog within the training data.

The challenge to the work is of course not generating predictions but establishing a functional scoring system for the docked poses of the pMHC:TCR and most importantly clearly understanding/communicating when modelling has failed. Thus, importantly Bradley's pipeline shows a strong correlation between its predicted and observed model accuracy. To this end, Bradley uses a receiver operating characteristic curve to discriminate between a TCR's actual antigen and 9 test decoys. This is an interesting testing regime, which appears to function well for the 8 case studies reported. It certainly leaves me wanting to better understand the failure mode for the two outliers - have these correctly modelled the pMHC but failed to dock the TCRs for example or visa versa?

From the analysis in Figure 5 and Figure 5, supplement 2, it looks to me like the pMHC is pretty well modeled in all cases, and the main difference between the working and non-working targets is in the docking of TCR to pMHC. But as the reviewer rightly points out below, binding specificity is likely sensitive to small details of the structure that may not be well captured by these RMSD metrics. With an N of 8, it's hard to make definitive conclusions. As additional systems with ternary structures and TCR repertoires become available, we should be able to provide better answers.

The real test of the current work, or its future iteration, will be the ability to make predictions from large tetramer-sorted datasets which then couple with experimental testing. The pipeline's current iteration may have some utility here but future improvements will make for exciting changes to current experimental methods. Overall the work is a step towards applying structural understanding to the vast amount of next-generation TCR sequence data currently being produced and improves upon current AlphaFold capability.

I completely agree. I am also excited about using this pipeline for design of TCR sequences with altered specificity and/or enhanced affinity. Even an imperfect in silico specificity prediction method can be a useful filter for designed TCRs (in other words, we want TCR designs that are predicted to have specificity for their intended targets). This has been amply demonstrated for protein fold design, where re-prediction of the structure from the designed sequence provides one of the most powerful quality metrics.

Reviewer #3 (Public Review):

This manuscript is well organized, and the author has generally shown good rigor in generating and presenting results. For instance, the author utilized TCRdist and structure-based metrics to remove redundancies and cluster complex structures. Additionally, the consideration of only recent structures (Fig. 2B) and structures that do not overlap with the finetuning dataset (Fig. 2D) is highly warranted.

In some cases, it seems possible that there may be train/test overlap, including the binding specificity prediction section and results, where native complexes being studied in that section may be closely related to or matching with structures that were previously used by the author to fine-tune the AlphaFold model. This could possibly bias the structure prediction accuracy and should be addressed by the author.

Other areas of the results and methods require some clarification, including the generation and composition of the hybrid templates, and the benchmark sets shown in some panels of Figure 2. Overall this is a very good manuscript with interesting results, and the author is encouraged to address the specific comments below related to the above concerns.

1) In the Results section, the statement "visual inspection revealed that many of the predicted models had displaced peptides and/or TCR:pMHC docking modes that were outside the range observed in native proteins" only references Fig. S1. However, with the UMAP representation in that figure, it is difficult for readers to readily see the displaced peptides noted by the author; only two example models are shown in that figure, and neither seems to have displaced peptides. The author should provide more details to support this statement, specifically structures of example models/complexes where the peptide was displaced, and/or summary statistics noting (out of the 130 tested) how many exhibited displaced peptides and aberrant TCR binding modes.

This is a good point, especially since what constitutes a "displaced peptide" is open to interpretation. I've added an analysis of peptide backbone RMSD (Fig. 2, supplement 2) that should make it possible for readers to assess this more quantitatively using an RMSD threshold (e.g. 10 Å) that makes sense to them.

2) The template selection protocol described in Figure 1 and in the Results and Methods should be clarified further. It seems that the use of 12 docking geometries in addition to four individual templates for each TCR alpha, TCR beta, and peptide-MHC would lead to a large combinatorial amount of hybrid templates, yet only 12 hybrid templates are described in the text and depicted in Figure 1. It's not clear whether the individual chain templates are randomly assigned within the 12 docking geometries, as an exhaustive combination of individual chains and docking geometries does not seem possible within the 12 hybrid models.

This was poorly explained; I hope I've clarified it now in the methods. The same four templates for each of the individual chains are used in each of the three AlphaFold runs, only the docking geometries vary between the runs. In other words, not all combinations of chain template and docking geometry are provided to AlphaFold.

3) Neither the docking RMSD nor the CDR RMSD metrics used in Figure 2 will show whether the peptide is modeled in the MHC groove and in the correct register. This would be an important element to gauge whether the TCR-pMHC interface is correctly modeled, particularly in light of the author's note regarding peptide displacement out of the groove with AlphaFold-Multimer. The author should provide an assessment of the models for peptide RMSD (after MHC superposition), possibly as a scatterplot along with docking RMSD or CDR RMSD to view both the TCR and peptide modeling fidelity of individual models. Otherwise, or in addition, another metric of interface quality that would account for the peptide, such as interface RMSD or CAPRI docking accuracy, could be included.

This is an excellent suggestion. The new Figure 2, supplement 2, addresses this.

4) It is not clear what benchmark set is being considered in Fig. 2E and 2F; that should be noted in the figure legend and the Results text. If needed, the author should discuss possible overlap in training and test sets for those results, particularly if the analysis in Fig. 2E and 2F includes the fine-tuned model noted in Fig. 2D and the test set in Fig. 2E and 2F is not the set of murine TCR-pMHC complexes shown in Fig. 2D. Likewise, the set being considered in Fig. 2C (which may possibly be the same set as Fig. 2E and 2F) is not clear based on the figure legend and text.

This has been fixed. More details below.

5) The docking accuracy results reported in Fig. 2 do not seem to have a comparison with an existing TCR-pMHC modeling method, even though several of them are currently available. At least for the set of new cases shown in Fig. 2B, it would be helpful for readers to see RMSD results with an existing template-based method as a baseline, for instance, either ImmuneScape (https://sysimm.org/immune-scape/) or TCRpMHCmodels (https://services.healthtech.dtu.dk/service.php?TCRpMHCmodels-1.0; this only appears to model Class I complexes, so Class I-only cases could be considered here).

This is a great suggestion. We've now added a comparison to TCRpMHCmodels (Fig. 2, supplement 3), which shows that the AlphaFold-based TCR pipeline significantly improves over that baseline method on MHC Class I complexes. Unfortunately, ImmuneScape is not available as a stand-alone software package, and the web interface doesn't allow customization of the template selection process to exclude closely-related templates, which is necessary for benchmarking. Given that ImmuneScape selects a single docking template based on sequence similarity, I compared the AF_TCR dock RMSDs to the dock RMSDs of the closest sequence template (excluding related complexes). This analysis (Fig. 2, supplement 3) shows that AlphaFold modeling produces significantly better docking geometries than simply taking the closest template by sequence similarity.

6) As noted in the text, the epitopes noted in Table 1 for the specificity prediction are present in existing structures, and most of those are human epitopes that may have been represented in the AF_TCR finetuning dataset. Were there any controls put in place to prevent the finetuning set from including complexes that are redundant with the TCRs and epitopes being used in the docking-based and specificity predictions if the AF_TCR finetuned model was used in those predictions? For instance, the GILGFVFTL epitope has many known TCR-pMHC structures and the TCRs and TCR-pMHC interfaces are known to have common structural and sequence motifs in those structures. Is it possible that the finetuning dataset included such a complex in its training, which could have influenced the success in Figure 3? The docking RMSD accuracy results in Fig. 5A, where certain epitopes seem to have very accuracy docking RMSDs and may have representative complex structures in the AF_TCR finetuning set, may be impacted by this train/test overlap. If so, the author should consider using an altered finetuned model with no train/test overlap for the binding specificity prediction section and results, or else remove the epitopes and TCRs that would be redundant with the complex structures present in the finetuning set.

This is an excellent point. It wasn't at all clear in the original submission, but the AlphaFold model that was fine-tuned on TCR complexes was only used for the mouse comparison in Fig. 2D (now Fig. 2F), and for exactly the reasons you mention. There is too much overlap between the epitopes with well-characterized repertoires and the epitopes with solved structures. This is also the reason we used the original AlphaFold monomer network, which was only trained on individual protein chains, rather than the AlphaFold multimer network, as the basis of the AF_TCR pipeline. As noted in the discussion, there is still the possibility that individual TCR chain structures in the benchmark or specificity prediction sets were part of the AlphaFold monomer training set, which could make the docking and specificity prediction results look better than they should (though not in Fig. 2B).

7) The alanine scanning results (Figure 6) do not seem to be validated against any experimental data, so it's not possible to gauge their accuracy. For peptide-MHC targets where there is a clear signal of disruption, it seems to correspond to prominently exposed side chains on the peptide which could likely be detected by a more simplistic structural analysis of the peptide-MHC itself. Thus the utility of the described approach in real-world scenarios (e.g. to detect viral escape mutants) is not clear. It would be helpful if the author can show results for a viral epitope variant (e.g. from one of the influenza epitopes, or the HCV epitope, in Table 1) that is known to disrupt binding for single or multiple TCRs, if such an example is available from the literature.

This is another great point. For me, the main motivation for the alanine scanning results was to further "stress test" the pipeline to see if it produced plausible results. A particular worry was that the use of pMHC:TCR confidence scores might allow the results to be skewed by peptide-MHC binding strength, rather than the intended TCR - pMHC interaction strength. We've seen in other work that the AlphaFold confidence scores for the peptide are correlated with peptide-MHC affinity. In the AF_TCR specificity predictions, we use the mean binding scores for the "irrelevant" background TCRs to subtract out peptide-intrinsic effects. The fact that we don't see strong signal in Figure 6 at the peptide anchor positions suggests that this is working, at least to some extent. It is also encouraging that the native peptide-MHC has stronger predicted binding than the majority of the alanine variants (excepting the two epitopes with poor performance).

I agree that comparing the repertoire-level mutation sensitivity predictions to real-world experimental data is challenging, given uncertainty about which TCR clones drive selection for escape, and other viral fitness pressures that influence the escape process. The fact that some of the positions predicted to be most sensitive are also the sites of escape mutations (examples now given in the text) is encouraging. But the new peptide-variant results (Fig. 6, supplement 1) highlight the challenges that remain in discriminating between very similar peptides (especially in the single-TCR setting).

Author Response

Reviewer #1 (Public Review):

In this study, Menjivar et al. examine the specific role of the enzyme arginase 1 (Arg1), which is expressed in immunosuppressive macrophages and catabolizes arginine to ornithine, in pancreatic cancer. They use an elegant genetic approach that leverages a dual recombinase-based genetically engineered mouse model of pancreatic cancer, which efficiently deletes Arg1 and recovers extracellular arginine in cultured macrophages. Within the pancreas, macrophage Arg1 deletion increased T cell infiltration and fewer mice developed invasive pancreatic cancer. Interestingly, when tumors did develop, the authors observed that compensatory mechanisms of arginine depletion were induced, including Arg1 overexpression in epithelial cells identified as tuft cells or Arg2 overexpression in macrophages. To overcome these compensatory mechanisms, pharmacological targeting of arginase was tested and found to increase T cell infiltration and sensitize to immune checkpoint blockade, suggesting this is a promising approach for pancreatic cancer.

Strengths:

This is a very rigorous, well-designed study and the findings are broadly interesting for the metabolism, immunometabolism, and pancreatic cancer communities. The methods are comprehensive and the experimental details in the legends are complete.

Weaknesses:

The claim that Arg1 deletion in macrophages delayed the formation of invasive disease is not completely justified by the data presented. Only a small number of mice are analyzed, and no statistics are included.