Reviewer #2 (Public Review):

Summary:<br /> Proteins that bind to double-stranded RNA regulate various cellular processes, including gene expression and viral recognition. Such proteins often contain multiple double-stranded RNA-binding domains (dsRBDs) that play an important role in target search and recognition. In this work, Chug and colleagues have characterized the backbone dynamics of one of the dsRBDs of a protein called TRBP2, which carries two tandem dsRBDs. Using solution NMR spectroscopy, the authors characterize the backbone motions of dsRBD2 in the absence and presence of dsRNA and compare these with their previously published results on dsRBD1. The authors show that dsRBD2 is comparatively more rigid than dsRBD1 and claim that these differences in backbone motions are important for target recognition.

Strengths:<br /> The strengths of this study are multiple solution NMR measurements to characterize the backbone motions of dsRBD2. These include 15N-R1, R2, and HetNOE experiments in the absence and presence of RNA and the analysis of these data using an extended-model-free approach; HARD-15N-experiments and their analysis to characterize the kex. The authors also report differences in binding affinities of dsRBD1 and dsRBD2 using ITC and have performed MD simulations to probe the differential flexibility of these two domains.

Weaknesses:<br /> While it may be true that dsRBD2 is more rigid than dsRBD1, the manuscript lacks conclusive and decisive proof that such changes in backbone dynamics are responsible for target search and recognition and the diffusion of TRBP2 along the RNA molecule. To conclusively prove the central claim of this manuscript, the authors could have considered a larger construct that carries both RBDs. With such a construct, authors can probe the characteristics of these two tandem domains (e.g., semi-independent tumbling) and their interactions with the RNA. Additionally, mutational experiments may be carried out where specific residues are altered to change the conformational dynamics of these two domains. The corresponding changes in interactions with RNA will provide additional evidence for the model presented in Figure 8 of the manuscript. Finally, there are inconsistencies in the reported data between different figures and tables.

eLife assessment

This study presents a useful comparison of the dynamic properties of two RNA-binding domains. The data collection and analysis are solid, making excellent use of a suite of NMR methods. However, evidence to support the proposed model linking dynamic behavior to RNA recognition and binding by the tandem domains remains incomplete. The work will be of interest to biophysicists working on RNA-binding proteins.

Reviewer #1 (Public Review):

Summary:<br /> In the manuscript entitled "Differential conformational dynamics in two type-A RNA-binding domains drive the double-stranded RNA recognition and binding," Chugh and co-workers utilize a suite of NMR relaxation methods to probe the dynamic landscape of the TAR RNA binding protein (TRBP) double-stranded RNA-binding domain 2 (dsRBD2) and compare these to their previously published results on TRBP dsRBD1. The authors show that, unlike dsRBD1, dsRBD2 is a rigid protein with minimal ps-ns or us-ms time scale dynamics in the absence of RNA. They then show that dsRBD2 binds to canonical A-form dsRNA with a higher affinity compared to dsRBD1 and does so without much alteration in protein dynamics. Using their previously published data, the authors propose a model whereby dsRBD2 recognizes dsRNA first and brings dsRBD1 into proximity to search for RNA bulge and internal loop structures.

Strengths:<br /> The authors expertly use a variety of NMR techniques to probe protein motions over six orders of magnitude in time. Other NMR titration experiments and ITC data support the RNA-binding model.

Weaknesses:<br /> The data collection and analysis are sound. The only weakness in the manuscript is the lack of context with the much broader field of RNA-binding proteins. For example, many studies have shown that RNA recognition motif (RRM) domains have similar dynamic characteristics when binding diverse RNA substrates. Furthermore, there was no discussion about the entropy of binding derived from ITC. It might be interesting to compare with dynamics from NMR.

eLife assessment

This study provides a fundamental advance in palaeontology by reporting the fossils of a new invertrebrate, Beretella spinosa, and inferring its relationship with already described species. The analysis placed the newly described species in the earliest branch of moulting invertebrates. The study, supported by convincing fossil observation, hypothesizes that early moulting invertebrate animals were not vermiform.

Reviewer #1 (Public Review):

Summary:<br /> Wang and co-workers characterise the fossil of Beretella spinosa from the early Cambrian, Yanjiahe Formation, South China. Combining morphological analyses with phylogenetic reconstructions, the authors conclude that B. spinosa is closely related to Saccorhytus, an enigmatic fossil recently ascribed to Ecdysozoa, or moulting animals, as an extinct "basal" lineage. Finding additional representatives of the clade Saccorhytida strengthens the idea that there existed a diversity of body plans previously underappreciated in Ecdysozoa, which may have implications for our understanding of the earliest steps in the evolution of this major animal group.

Strengths:<br /> I'm not a paleobiologist; therefore, I cannot give an expert opinion on the descriptions of the fossils. However, the similarities with Saccorhytus seem evident, and the phylogenetic reconstructions are adequate. Evolutionary interpretations are generally justified, and the consolidation of Saccorhytida as the extinct sister lineage to extant Ecdysozoans will have significant implications for our understanding of this major animal clade.

Weaknesses:<br /> While I generally agree with the author's interpretations, the idea of Saccorhytida as a divergent, simplified off-shot is slightly contradictory with a probably non-vermiform ecdysozoan ancestor. The author's analyses do not discard the possibility of a vermiform ecdysozoan ancestor (importantly, Supplementary Table 4 does not reconstruct that character), and outgroup comparison with Spiralia (and even Deuterostomia for Protostomia as a whole) indicates that a more or less anteroposteriorly elongated (i.e., vermiform) body is likely common and ancestral to all major bilaterian groups, including Ecdysozoa. Indeed, Figure 4b depicts the potential ancestor as a "worm". The authors argue that the simplification of Saccorhytida from a vermiform ancestor is unlikely "because it would involve considerable anatomical transformations such as the loss of vermiform organization, introvert, and pharynx in addition to that of the digestive system". However, their data support the introvert as a specialisation of Scalidophora (Figure 4a and Supplementary Table 4), and a pharyngeal structure cannot be ruled out in Saccorhytida. Likewise, loss of an anus is not uncommon in Bilateria. Moreover, this can easily become a semantics discussion (to what extent can an animal be defined as "vermiform"? Where is the limit?). Therefore, I suggest to leave the evolutionary scenario more open. Supporting Saccorhytida as a true group at the early steps of Ecdysozoa evolution is important and demonstrates that animal body plans are more plastic than previously appreciated. However, with the current data, it is unlikely that Saccorhytida represents the ancestral state for Ecdysozoa (as the authors admit), and a vermiform nature is not ruled out (and even likely) in this animal group. Suggesting that the ancestral Ecdysozoan might have been small and meiobenthic is perhaps more interesting and supported by the current data (phylogeny and outgroup comparison with Spiralia).

Reviewer #2 (Public Review):

Summary:<br /> This work provides important anatomical features of a new species from the Lower Cambrian, which helps advance our understanding of the evolutionary origins of animal body plans. The authors interpreted that the new species possessed a bilateral body covered with cuticular polygonal reticulation and a ventral mouth. Based on cladistic analyses using maximum likelihood, Bayesian, and parsimony, the new species was placed, along with Saccorhytus, in a sister group ("Saccorhytida") of the Ecdysozoa. The phylogenetic position of Saccorhytida suggests a new scenario of the evolutionary origin of the crown ecdysozoan body plan.

Strengths:<br /> Although the new species reported in this paper show strange morphologies, the interpretation of anatomical features was based on detailed observations of multiple fossil specimens, thereby convincing at the moment. Morphological data about fossil taxa in the Ediacaran and Early Cambrian are quite important for our understanding of the evolution of body plans (and origins of phyla) in paleontology and evolutionary developmental biology, and this paper represents a valuable contribution to such research fields.

Weaknesses:<br /> The preservations of the specimens, in particular on the putative ventral side, are not good, and the interpretation of the anatomical features needs to be tested with additional specimens in the future. The monophyly of Cycloneuralia (Nematoida + Scalidophora) was not necessarily well-supported by cladistic analyses, and the evolutionary scenario (Figure 4) also needs to be tested in future works.

Reviewer #3 (Public Review):

Summary:<br /> The authors of this manuscript identified the fossils of the newly designated species Beretella spinosa and analyzed its phylogenetic position in relation to the extinct described species and extant species. Their analysis placed the newly described species Beretella spinosa and Saccorhytus as an independent clade from the rest of the ecdysozoans. Remarkably, these species are non-vermiform, and the resulting evolutionary scenario assumes non-vermiform as early ecdysozoans.

Strengths:<br /> The study presents outstanding, novel data and provides new insights into the evolution of animal forms especially regarding their morphological diversity after the Cambrian explosion.

Weaknesses:<br /> I, as a paleontology non-expert, experienced several difficulties in reading the manuscript. This should be taken into consideration when assuming a wide range of readers including non-experts.

eLife assessment

The work by Lewis and co-workers presents important findings on the role of myosin structure/energetics on the molecular mechanisms of hibernation by comparing muscle samples from small and large hibernating mammals. The solid methodological approaches have revealed insights into the mechanisms of non-shivering thermogenesis and energy expenditure.

Reviewer #1 (Public Review):

Summary:<br /> The evolution of non-shivering thermogenesis is of fundamental importance to understand. Here, in small mammals, the contractile apparatus of the muscle is shown to increase energy expenditure upon a drop in ambient temperature. Additionally, in the state of torpor, small hibernators did not show an increase in energy expenditure under the same challenge.

Strengths:<br /> The authors have conducted a very well-planned study that has sampled the muscles of large and small hibernators from two continents. Multiple approaches were then used to identify the state of the contractile apparatus, and its energy expenditure under torpor or otherwise.

Weaknesses:<br /> There was only one site of biopsy from the animals used (leg). It would be interesting to know if non-shivering thermogenesis is something that is regionally different in the animal, given the core body and distal limbs have different temperatures.

Reviewer #2 (Public Review):

Summary:<br /> The authors utilized (permeabilized) fibers from muscle samples obtained from brown and black bears, squirrels, and Garden dormice, to provide interesting and valuable data regarding changes in myosin conformational states and energetics during hibernation and different types of activity in summer and winter. Assuming that myosin structure is similar between species then its role as a regulator of metabolism would be similar and not different, yet the data reveal some interesting and perplexing differences between the selected hibernating species.

Strengths:<br /> The experiments on the permeabilized fibers are complementary, sophisticated, and well-performed, providing new information regarding the characteristics of skeletal muscle fibers between selected hibernating mammalian species under different conditions (summer, interarousal, and winter).

The studies involve complementary assessments of muscle fiber biochemistry, sarcomeric structure using X-ray diffraction, and proteomic analyses of posttranslational modifications.

Weaknesses:<br /> It would be helpful to put these findings on permeabilized fibers into context with the other anatomical/metabolic differences between the species to determine the relative contribution of myosin energetics (with these other contributors) to overall metabolism in these different species, including factors such as fat volume/distribution.

Reviewer #3 (Public Review):

Summary and strengths:<br /> The manuscript, "Remodelling of skeletal muscle myosin metabolic states in hibernating mammals", by Lewis et al, investigates whether myosin ATP activity may differ between states of hibernation and activity in both large and small mammals. The study interrogates (primarily) permeabilized muscle strips or myofibrils using several state-of-the-art assays, including the mant-ATP assay to investigate ATP utilization of myosin, X-ray diffraction of muscles, proteomics studies, metabolic tests, and computational simulations. The overall data suggests that ATP utilization of myosin during hibernation is different than in active conditions.

A clear strength of this study is the use of multiple animals that utilize two different states of hibernation or torpor. Two large animal hibernators (Eurasian Brown Bear, American Black Bear) represent large animal hibernators that typically undergo prolonged hibernation. Two small animal hibernators (Garden Dormouse, 13 Lined Ground Squirrel) undergo torpor with more substantial reductions in heart rate and body temperature, but whose torpor bouts are interrupted by short arousals that bring the animals back to near-summer-like metabolic conditions.

Especially interesting, the investigators analyze the impact that body temperature may have on myosin ATP utilization by performing assays at two different temperatures (8 and 20 degrees C, in 13 Lined Ground Squirrels).

The multiple assays utilized provide a more comprehensive set of methods with which to test their hypothesis that muscle myosins change their metabolic efficiency during hibernation.

Suggestions and potential weaknesses:<br /> While the samples and assays provide a robust and comprehensive coverage of metabolic needs and testing, the data is less categorical. Some of these may be dependent on sample size or statistical analysis while others may be dependent on interpretation.

(1) Statistical Analysis<br /> (1.a) The results of this study often cannot be assessed properly due to a lack of clarity in the statistical tests.<br /> For example, the results related to the large animal hibernators (Figure 1) do not describe the statistical test (in the text of the results, methods, or figure legends). (Similarly for figure 6 and Supplemental Figure 1). Further, it is not clear whether or when the analysis was performed with paired samples. As the methods described, it appears that the Eurasian Brown Bear data should be paired per animal.

(1.b) The statistical methods state that non-parametric testing was utilized "where data was unevenly distributed". Please clarify when this was used.

(1.c) While there are two different myosin isoforms, the isoform may be considered a factor. It is unclear why a one-way ANOVA is generally used for most of the mant-ATP chase data.

(1.d) While the technical replicates on studies such as the mant-ATP chase assay are well done, the total biological replicates are small. A consideration of the sample power should be included.

(1.e) An analysis of the biological vs statistical significance should be considered, especially for the mant-ATP chase data from the American Black Bear, where there appear to be shifts between the summer and winter data.

(2) Consistency of DRX/SRX data.<br /> (2.a) The investigators performed both mant-ATP chase and x-ray diffraction studies to investigate whether myosin heads are in an "on" or "off" state. The results of these two studies do not appear to be fully consistent with each other, which should not be a surprise. The recent work of Mohran et al (PMID 38103642) suggests that the mant-ATP-predicted SRX:DRX proportions are inconsistent with the position of the myosin heads. The discussion appears to lack a detailed assessment of this prior work and lack a substantive assessment contrasting the differing results of the two assays in the current study. i.e. why the current study's mant-ATP chase and x-ray diffraction results differ.

(2.b) The discussion of the current study's x-ray diffraction data relating to the I_1,1/I_1,0 ratio and how substantially different this is to the M6 results merits discussion. i.e. how can myosin both be more primed to contract during IBA versus torpor (according to intensity ratio), but also have less mass near the thick filament (M6).

(3) Possible interactions with Heat Shock Proteins<br /> Heat Shock Proteins (HSPs), such as HSP70, have been shown to be differential during torpor vs active states. A brief search of HSP and myosin reveals HPSs related to thick filament assembly and Heat Shock Cognate 70 interacting with myosin binding protein C. Especially given the author's discussion of protein stability and the potential interaction with myosin binding protein C and the SRX state, the limitation of not assessing HSPs should be discussed. (While HSP's relation to thick filament assembly might conceivably modify the interpretation of the M3 x-ray diffraction results, this reviewer acknowledges that possibility as a leap.)

Despite the above substantial concerns/weaknesses, this reviewer believes that this manuscript represents a valuable data set.

Other comments related to interpretation:<br /> (4) The authors briefly mention the study by Toepher et al [Ref 25] and that it utilizes cardiac muscles. There would benefit from increased discussion regarding the possible differences in energetics between cardiac and skeletal muscle in these states.

(5) The author's analysis of temperature is somewhat limited.<br /> (5.a) First, the authors use 20 degrees C (room temperature), not 37 degrees C, a more physiologic body temperature for large mammals. While it is true that limbs are likely at a lower temperature, 20 degrees C seems substantially outside of a normal range. Thus, temperature differences may have been minimized by the author's protocol.

(5.b) Second, the authors discuss the possibility of myosin contributing to non-shivering thermogenesis. The magnitude of this impact should be discussed. The suggestion of myosin ATP utilization also implies that there is some basal muscle tone (contraction), as the myosin ATPase utilizes ATP to release from actin, before binding and hydrolyzing again. Evidence of this tone should be discussed.

eLife assessment

This paper presents valuable findings that shed light on the mental organisation of knowledge about real-world objects. It provides diverse, if incomplete and tentative, evidence from behaviour, brain, and large language models that this knowledge is divided categorically between relatively small objects (closer to the relevant scale for direct manipulation) and larger objects (further from the typical scope of human affordances for action).

Reviewer #1 (Public Review):

Ps observed 24 objects and were asked which afforded particular actions (14 action types). Affordances for each object were represented by a 14-item vector, values reflecting the percentage of Ps who agreed on a particular action being afforded by the object. An affordance similarity matrix was generated which reflected similarity in affordances between pairs of objects. Two clusters emerged, reflecting correlations between affordance ratings in objects smaller than body size and larger than body size. These clusters did not correlate themselves. There was a trough in similarity ratings between objects ~105 cm and ~130 cm, arguably reflecting the body size boundary. The authors subsequently provide some evidence that this clear demarcation is not simply an incidental reflection of body size, but likely causally related. This evidence comes in the flavour of requiring Ps to imagine themselves as small as a cat or as large as an elephant and showing a predicted shift in the affordance boundary. The manuscript further demonstrates that ChatGPT (theoretically interesting because it's trained on language alone without sensorimotor information; trained now on words rather than images) showed a similar boundary.

The authors also conducted a small MRI study task where Ps decide whether a probe action was affordable (graspable?) and created a congruency factor according to the answer (yes/no). There was an effect of congruency in posterior fusiform and superior parietal lobule for objects within body size range, but not outside. No effects in LOC or M1.

The major strength of this manuscript in my opinion is the methodological novelty. I felt the correlation matrices were a clever method for demonstrating these demarcations, the imagination manipulation was also exciting, and the ChatGPT analysis provided excellent food for thought. These findings are important for our understanding of the interactions between action and perception, and hence for researchers from a range of domains of cognitive neuroscience.

The major element that limits conclusions is that an MRI study with 12 P in this context can really only provide pilot data. Certainly the effects are not strong enough for 12 P to generate much confidence. The others of my concerns have been addressed in the revision.

Reviewer #2 (Public Review):

Summary

In this work, the authors seek to test a version of an old idea, which is that our perception of the world and our understanding of the objects in it are deeply influenced by the nature of our bodies and the kinds of behaviours and actions that those objects afford. The studies presented here muster three kinds of evidence for a discontinuity in the encoding of objects, with a mental "border" between objects roughly of human body scale or smaller, which tend to relate to similar kinds of actions that are yet distinct from the kinds of actions implied by human-or-larger scale objects. This is demonstrated through observers' judgments of the kinds of actions different objects afford; through similar questioning of AI large-language models (LLMs); and through a neuroimaging study examining how brain regions implicated in object understanding make distinctions between kinds of objects at human and larger-than-human scales.

Strengths 

The authors address questions of longstanding interest in the cognitive neurosciences -- namely how we encode and interact with the many diverse kinds of objects we see and use in daily life. A key strength of the work lies in the application of multiple approaches. Examining the correlations among kinds of objects, with respect to their suitability for different action kinds, is novel, as are the complementary tests of judgments made by LLMs. The authors include a clever manipulation in which participants are asked to judge action-object pairs, having first adopted the imagined size of either a cat or an elephant, showing that the discontinuity in similarity judgments effectively moved to a new boundary closer to the imagined scale than the veridical human scale. The dynamic nature of the discontinuity hints that action affordances may be computed dynamically, "on the fly", during actual action behaviours with objects in the real world.

Weaknesses 

A limitation of the tests of LLMs may be that it is not always known what kinds of training material was used to build these models, leading to a possible "black box" problem. Further, presuming that those models are largely trained on previous human-written material, it may not necessarily be theoretically telling that the "judgments" of these models about action-object pairs shows human-like discontinuities. Indeed, verbal descriptions of actions are very likely to mainly refer to typical human behaviour, and so the finding that these models demonstrate an affordance discontinuity may simply reflect those statistics, rather than providing independent evidence for affordance boundaries.

The relatively small sample size of the brain imaging experiment, and some design features (such as the task participants performed, and the relatively narrow range of objects tested) provide some limits on the extent to which it can be taken as support for the authors' claims.

Reviewer #3 (Public Review):

Summary:<br /> Feng et al. test the hypothesis that human body size constrains the perception of object affordances, whereby only objects that are smaller than the body size will be perceived as useful and manipulable parts of the environment, whereas larger objects will be perceived as "less interesting components."

To test this idea, the study employs a multi-method approach consisting of three parts:

In the first part, human observers classify a set of 24 objects that vary systematically in size (e.g., ball, piano, airplane) based on 14 different affordances (e.g., sit, throw, grasp). Based on the average agreement of ratings across participants, the authors compute the similarity of affordance profiles between all object pairs. They report evidence for two homogenous object clusters that are separated based on their size with the boundary between clusters roughly coinciding with the average human body size. In follow-up experiments, the authors show that this boundary is larger/smaller in separate groups of participants who are instructed to imagine themselves as an elephant/cat.

In the second part, the authors ask different large language models (LLMs) to provide ratings for the same set of objects and affordances and conduct equivalent analyses on the obtained data. Some, but not all, of the models produce patterns of ratings that appear to show similar boundary effects, though less pronounced and at a different boundary size than in humans.

In the third part, the authors conduct an fMRI experiment. Human observers are presented with four different objects of different sizes and asked if these objects afford a small set of specific actions. Affordances are either congruent or incongruent with objects. Contrasting brain activity on incongruent trials against brain activity on congruent trials yields significant effects in regions within the ventral and dorsal visual stream, but only for small objects and not for large objects.

The authors interpret their findings as support for their hypothesis that human body size constrains object perception. They further conclude that this effect is cognitively penetrable, and only partly relies on sensorimotor interaction with the environment (and partly on linguistic abilities).

Strengths:<br /> The authors examine an interesting and relevant question and articulate a plausible (though somewhat underspecified) hypothesis that certainly seems worth testing. Providing more detailed insights into how object affordances shape perception would be highly desirable. Their method of analyzing similarity ratings between sets of objects seems useful and the multi-method approach is original and interesting.

Weaknesses:<br /> The study presents several shortcomings that clearly weaken the link between the obtained evidence and the drawn conclusions. Below I outline my concerns in no particular order:

(1) It is not entirely clear to me what the authors are proposing and to what extent the conducted work actually speaks to this. For example, in the introduction, the authors write that they seek to test if body size serves not merely as a reference for object manipulation but also "plays a pivotal role in shaping the representation of objects." This motivation seems rather vague motivation and it is not clear to me how it could be falsified.

Overall, the lack of theoretical precision makes it difficult to judge the appropriateness of the approaches and the persuasiveness of the obtained results. I would strongly suggest clarifying the theoretical rationale and explaining in more detail how the chosen experiments allow them to test falsifiable predictions.

(2) The authors used only a very small set of objects and affordances in their study and they do not describe in sufficient detail how these stimuli were selected. This renders the results rather exploratory and clearly limits their potential to discover general principles of human perception. Much larger sets of objects and affordances and explicit data-driven approaches for their selection would provide a more convincing approach and allow the authors to rule out that their results are just a consequence of the selected set of objects and actions.

(3) Relatedly, the authors could be more thorough in ruling out potential alternative explanations. Object size likely correlates with other variables that could shape human similarity judgments and the estimated boundary is quite broad (depending on the method, either between 80 and 150 cm or between 105 to 130 cm). More precise estimates of the boundary and more rigorous tests of alternative explanations would add a lot to strengthen the authors' interpretation.

(4) While I appreciate the manipulation of imagined body size, as a clever way to solidify the link between body size and affordance perception, I find it unfortunate that it is implemented in a between-subjects design, as this clearly leaves open the possibility of pre-existing differences between groups. I certainly disagree with the authors' statement that their findings suggest "a causal link between body size and affordance perception."

(5) The use of LLMs in the current study is not clearly motivated and I find it hard to understand what exactly the authors are trying to test through their inclusion. As it currently stands, I find it hard to discern how the presence of perceptual boundaries in LLMs could constitute evidence for affordance-based perception.

(6) Along the same lines, the fMRI study also provides little evidence to support the authors' claims. The use of congruency effects as a way of probing affordance perception is not well motivated. Importantly (and related to comment 2 above), the very small set of objects and affordances in this experiment heavily complicates any conclusions about object size being the crucial variable determining the occurrence of congruency effects.

Overall, I consider the main conclusions of the paper to be far beyond the reported data. Articulating a clearer theoretical framework with more specific hypotheses as well as conducting more principled analyses on more comprehensive data sets could help the authors obtain stronger tests of their ideas.

eLife assessment

This important study explores the potential influence of physiologically relevant mechanical forces on the extrusion of vesicles from C. elegans neurons. The authors provide compelling evidence to support the idea that uterine distension can induce vesicular extrusion from adjacent neurons. The work would be strengthened by using an additional construct (preferably single-copy) to demonstrate that the observed phenotypes are not unique to a single transgenic reporter. Overall, this work will be of interest to neuroscientists and investigators in the extracellular vesicle and proteostasis fields.

Reviewer #1 (Public Review):

Summary:<br /> The authors sought to understand the stage-dependent regulation of exophergenesis, a process thought to contribute to promoting neuronal proteostasis in C. elegans. Focusing on the ALMR neuron, they show that the frequency of exopher production correlates with the timing of reproduction. Using many genetic tools, they dissect the requirements of this pathway to eventually find that occupancy of the uterus acts as a signal to induce exophergenesis. Interestingly, the physical proximity of neurons to the egg zone correlates with exophergenesis frequency. The authors conclude that communication between the uterus and proximal neurons occurs through the sensing of mechanic forces of expansion normally provided by egg occupancy to coordinate exophergenesis with reproduction.

Strengths:<br /> The genetic data presented is thorough and solid, and the observation is novel.

Weaknesses:<br /> The main weakness of the study is that the detection of exophers is based on the overexpression of a fluorescent protein in touch neurons, and it is not clear whether this process is actually stimulated in wild-type animals, or if neurons have accumulated damaged proteins in relatively young day 2 animals.

Reviewer #2 (Public Review):

Summary:<br /> This paper reports that mechanical stress from egg accumulation is a biological stimulus that drives the formation of extruded vesicles from the neurons of C. elegans ALMR touch neurons. Using powerful genetic experiments only readily available in the C. elegans system, the authors manipulate oocyte production, fertilization, embryo accumulation, and egg-laying behavior, providing convincing evidence that exopher production is driven by stretch-dependent feedback of fertilized, intact eggs in the adult uterus. Shifting the timing of egg production and egg laying alters the onset of observed exophers. Pharmacological manipulation of egg laying has the predicted effects, with animals retaining fewer eggs having fewer exophers and animals with increased egg accumulation having more. The authors show that egg production and accumulation have dramatic consequences for the viscera, and moving the ALMR process away from eggs prevents the formation of exophers. This effect is not unique to ALMR but is also observed in other touch neurons, with a clear bias toward neurons whose cell bodies are adjacent to the filled uterus. Embryos lacking an intact eggshell with reduced rigidity have impaired exopher production. Acute injection into the uterus to mimic the stretch that accompanies egg production causes a similar induction of exopher release. Together these results are consistent with a model where stretch caused by fertilized embryo accumulation, and not chemical signals from the eggs themselves or egg release, underlies ALMR exopher production seen in adult animals.

Strengths:<br /> Overall, the experiments are very convincing, using a battery of RNAi and mutant approaches to distinguish direct from indirect effects. Indeed, these experiments provide a model generally for how one would methodically test different models for exopher production. The paper is well-written and easy to understand. I had been skeptical of the origin and purpose of exophers, concerned they were an artefact of imaging conditions, caused by deranged calcium activity under stressful conditions, or as evidence for impaired animal health overall. As this study addresses how and when they form in the animal using otherwise physiologically meaningful manipulations, the stage is now set to address at a cellular level how exophers like these are made and what their functions are.

Weaknesses:<br /> Not many. The experiments are about as good as could be done. Some of the n's on the more difficult-to-work strains or experiments are comparatively low, but this is not a significant concern because of the number of different, complementary approaches used. The microinjection experiment in Figure 7 is very interesting, there are missing details that would confirm whether this is a sound experiment.

Reviewer #3 (Public Review):

Summary:<br /> In this paper, the authors use the C. elegans system to explore how already-stressed neurons respond to additional mechanical stress. Exophers are large extracellular vesicles secreted by cells, which can contain protein aggregates and organelles. These can be a way of getting rid of cellular debris, but as they are endocytosed by other cells can also pass protein, lipid, and RNA to recipient cells. The authors find that when the uterus fills with eggs or otherwise expands, a nearby neuron (ALMR) is far more likely to secrete exophers. This paper highlights the importance of the mechanical environment in the behavior of neurons and may be relevant to the response of neurons exposed to traumatic injury.

Strengths:<br /> The paper has a logical flow and a compelling narrative supported by crisp and clear figures.

The evidence that egg accumulation leads to exopher production is strong. The authors use a variety of genetic and pharmacological methods to show that increasing pressure leads to more exopher production, and reducing pressure leads to lower exopher production. For example, egg-laying defective animals, which retain eggs in the uterus, produce many more exophers, and hyperactive egg-laying is accompanied by low exopher production. The authors even inject fluid into the uterus and observe the production of exophers.

Weaknesses:<br /> The main weakness of the paper is that it does not explore the molecular mechanism by which the mechanical signals are received or responded to by the neuron, but this could easily be the subject of a follow-up study.

I was intrigued by this paper, and have many questions. I list a few below, which could be addressed in this paper or which could be the subject of follow-up studies.

- Why do such a low percentage of ALMR neurons produce exophers (5-20%)? Does it have to do with the variability of the proteostress?<br /> - Why does the production of exophers lag the peak in progeny production by 24-48 hours? Especially when the injection method produces exophers right away?<br /> - As mentioned in the discussion, it would be interesting to know if PEZO-1/PIEZO is required for uterine stretching to activate exophergenesis. pezo-1 animals accumulate crushed oocytes in the uterus.

eLife assessment

This is a saturation mutagenesis screening of the CDKN2A gene, successfully assessing the functionality of the missense variants. The results seem robust, but currently, the manuscript is incomplete with a number of weaknesses. The work has the potential to serve as a valuable resource for diagnostic labs as well as cancer geneticists.

Reviewer #1 (Public Review):

Summary:<br /> Kimura et al performed a saturation mutagenesis study of CDKN2A to assess the functionality of all possible missense variants and compare them to previously identified pathogenic variants. They also compared their assay result with those from in silico predictors.

Strengths:<br /> CDKN2A is an important gene that modulates cell cycle and apoptosis, therefore it is critical to accurately assess the functionality of missense variants. Overall, the paper reads well and touches upon major discoveries in a logical manner.

Weaknesses:<br /> The paper lacks proper details for experiments and basic data, leaving the results less convincing. Analyses are superficial and do not provide variant-level resolution.

Reviewer #2 (Public Review):

This study describes a deep mutational scan across CDKN2A using suppression of cell proliferation in pancreatic adenocarcinoma cells as a readout for CDKN2A function. The results are also compared to in silico variant predictors currently utilized by the current diagnostic frameworks to gauge these predictors' performance. The authors also functionally classify CDKN2A somatic mutations in cancers across different tissues.

This study is a potentially important contribution to the field of cancer variant interpretation for CDKN2A, but is almost impossible to review because of the severe lack of details regarding the methods and incompleteness of the data provided with the paper. We do believe that the cell proliferation suppression assay is robust and works, but when it comes to the screening of the library of CDKN2A variants the lack of primary data and experimental detail prevents assessment of the scientific merit and experimental rigor.

eLife assessment

This valuable study combines multidisciplinary approaches to examine the role of insulin-like growth factor 2 mRNA-binding protein 2 (IGF2BP2) as a potential novel host dependency factor for Zika virus. The main claims are partially supported by the data, but remain incomplete. The evidence would be strengthened by improving the immunofluorescence analyses, addressing the role of IGF2BP2 in "milder" infections, and elucidating the role of IGF2BP2 in the biogenesis of the viral replication organelle. With the experimental evidence strengthened, this work will be of interest to virologists working on flaviviruses.

Reviewer #1 (Public Review):

Summary:<br /> This study investigated the co-option of IGF2BP2, an RNA-binding protein by ZIKV proteins. Designed experiments evaluated if IFG2BP2 co-localized to sites of viral RNA replication, interacted with ZIKV proteins, and how ZIKV infection changed the IGF2BP2 interactome.

Strengths:<br /> The authors have used multiple interdisciplinary techniques to address several questions regarding the interaction of ZIKV proteins and IGF2BP2.<br /> The findings could be exciting, specifically regarding how ZIKV infection alters the interactome of IGF2BP2.

Weaknesses:<br /> Significant concerns regarding the current state of the figures, descriptions in the figure legends, and the quality of the immunofluorescence and electron microscopy exist.

Reviewer #2 (Public Review):

Clément Mazeaud et al. identified the insulin-like growth factor 2 mRNA-binding protein 2 (IGF2BP2) as a proviral cellular protein that regulates Zika virus RNA replication by modulating the biogenesis of virus-induced replication organelles.

The absence of IGF2BP2 specifically dampens ZIKV replication without having a major impact on DENV replication. The authors show that ZIKV infection changes IGF2BP2 cellular distribution, which relocates to the perinuclear viral replication compartment. These assays were conducted by infecting cells with an MOI of 10 for 48 hours. Considering the ZIKV life cycle, it is noteworthy that at this time there may be a cytopathic effect. One point of concern arises regarding how the authors can ascertain that the observed change in localization is a consequence of the infection rather than of the cytopathic effect. To address this concern, shorter infection periods (e.g., 24 hours post-infection) or additional controls, such as assessing cellular proteins that do not change their localization or infecting with another flavivirus lacking the IGF2BP2 effect, could be incorporated into their experiments.

By performing co-immunoprecipitation assays on mock and infected cells that express HA-tagged IGF2BP2, the authors propose that the observed change in IGF2BP2 localization results from its recruitment to the replication compartment by the viral NS5 polymerase and associated with the viral RNA. Given that both IGF2BP2 and NS5 are RNA-binding proteins, it is plausible that their interaction is mediated indirectly through the RNA molecule. Notably, the authors do not address the treatment of lysates with RNAse before the IP assay, leaving open the possibility of this indirect interaction between IGF2BP2 and NS5.

In in vitro binding assays, the authors demonstrate that the RNA-recognition motifs of the IGF2BP2 protein specifically bind to the 3' nontranslated region (NTR) of the ZIKV genome, excluding binding to the 5' NTR. However, they cannot rule out the possibility of this host protein associating with other regions of the viral genome. Using a reporter ZIKV subgenomic replicon system in IGF2BP2 knock-down cells, they additionally demonstrate that IGF2BP2 enhances viral genome replication. Despite its proviral function, the authors note that the "overexpression of IGF2BP2 had no impact on total vRNA levels." However, the authors do not delve into a discussion of this latter statement.

In this study, the authors extend their findings by illustrating that ZIKV infection triggers a remodeling of IGF2BP2 ribonucleoprotein complex. They initially evaluate the impact of ZIKV infection on IGF2BP2's interaction with its endogenous mRNA ligands. Their results reveal that viral infection alters the binding of specific mRNA ligands, yet the physiological consequences of this loss of binding in the cell remain unexplored. Additionally, the authors demonstrate that ZIKV infection modifies the IGF2BP2 interactome. Through proteomic assays, they identified 62 altered partners of IGF2BP2 following ZIKV infection, with proteins associated with mRNA splicing and ribosome biogenesis being the most represented. In particular, the authors focused their research on the heightened interaction between IGF2BP2 and Atlastin 2, an ER-shaping protein reported to be involved in flavivirus vesicle packet formation. The validation of this interaction by Western blot assays prompted an analysis of the effect of ZIKV on organelle biogenesis using a newly described replication-independent vesicle packet induction system. Consequently, the authors demonstrate that IGF2BP2 plays a regulatory role in the biogenesis of ZIKV replication organelles.

Based on these findings and previously published data, the authors propose a model outlining the role of IGF2BP2 in ZIKV infectious cycle, detailing the changes in IGF2BP2 interactions with both cellular and viral proteins and RNAs that occur during viral infection.

The conclusions drawn in this paper are generally well substantiated by the data. However, it is worth noting that the majority of infections were conducted at a high MOI for 48 hours, spanning more than one infectious cycle. To enhance the robustness of their findings and mitigate potential cell stress, it would be valuable to observe these effects at shorter time intervals, such as 24 hours post-infection.<br /> Furthermore, the assertion regarding the association of IGF2BP2 with NS5 could be strengthened through additional immunoprecipitation (IP) assays. These assays, performed in the presence of RNAse treatment, would help exclude the possibility of an indirect interaction between IGF2BP2 and NS5 (both RNA-binding proteins) through viral RNA, thus providing more confidence in the observed association.

Reviewer #3 (Public Review):

Summary:<br /> The manuscript by Mazeaud and colleagues pursued a small-scale screen of a targeted RNAi library to identify novel players involved in Zika (ZIKV) and dengue (DENV) virus replication. Loss-of-function of IGF2BP2 resulted in reduced titers for ZIKV of the Asian and African lineages in hepatic Huh7.5 cells, but not for either of the four DENV serotypes nor West Nile virus (WNV). The phenotype was further confirmed in two additional cell lines and using a ZIKV reporter virus. In addition, using immunoprecipitation assays the interaction between IGF2BP2 and ZIKV NS5 protein and RNA genome was detected. The work addressed the role of IGF2BP2 in the infected cell combining confocal microscopy imaging, and proteomic analysis. The approach indicated an altered distribution of IGF2BP2 in infected cells and changes in the protein interactome including disrupted association with partner mRNAs and modulation of the abundance of a specific set of protein partners in IGF2BP2 immunoprecipitated ribonucleoprotein (RNP) complexes. Finally, based on the changes in IGF2BP2 interactome and specifically the increment in the abundance of Atlastin 2, the biogenesis of ZIKV replication organelles (vRO) is investigated using a genetic system that allows virus replication-independent assembly of vRO. Electron microscopy showed that knockdown of IGF2BP2 expression reduced the number of cells with vRO.

Strengths:<br /> The role of IGF2BP2 as a proviral factor for ZIKV replication is novel.

The study follows a logical flow of experiments that altogether support the assembly of a specialized RNP complex containing IGF2BP2 and ZIKV NS5 and RNA genome.

Weaknesses:<br /> The statistical analysis should clearly indicate the number of biological replicates of experiments to support statistical significance.

The claim that IGF2BP2 knockdown impairs de novo viral organelle biogenesis and viral RNA synthesis is built upon data that show a reduction in RNA synthesis <0.5-fold as assessed using a reporter replicon, thus suggesting a limited impact of the knockdown on RNA replication.

Validation of IGF2BP2 partners that are modulated upon ZIKV infection (i.e. virus yield in knocked down cells) can be relevant especially for partners such as Atlastin 2, as the hypothesis of a role for IGF2BP2 RNP in vRO biogenesis is based on the observed increase in the abundance of Atlastin 2 in the RNP complex preciìtated from infected cells.

eLife assessment

The manuscript explores the ways in which the genetic code evolves, specifically how stop codons are reassigned to become sense codons. The authors present phylogenetic data showing that mutations at position 67 of the termination factor are present in organisms that nevertheless use the UGA codon as a stop codon, thereby questioning the importance of this position in the reassignment of stop codons. Alternative models on the role of eRF1 would reflect a more balanced view of the data. Overall, the data are solid and these findings will be valuable to the genomic/evolution fields.

Reviewer #1 (Public Review):

The issue:<br /> The ciliates are a zoo of genetic codes, where there have been many reassignments of stop codons, sometimes with conditional meanings which include retention of termination function, and thus > 1 meaning. Thus ciliate coding provides a hotspot for the study of genetic code reassignments.

The particular issue here is the suggestion that translation of a stop (UGA) in Blastocritihidia has been attributed to a joint change in the protein release factor that reads UGA's and also breaking a base pair at the top of the anticodon stem of tRNATrp (Nature 613, 751, 2023).

The work:<br /> However, Swart, et al have looked into this suggestion, and find that the recently suggested mechanism is overly complicated.

The broken pairing at the top of the anticodon stem of tRNATrp indeed accompanies the reading of UGA as Trp as previously suggested. It changes the codon translated even though the anticodon remains CCA, complementary to UGG. A compelling point is that this misreading matches previous mutational studies of E coli tRNA's, in which breaking the same base pair in a mutant tRNATrp suppressor tRNA stimulated the same kind of miscoding.

But the amino acid change in release factor eRF1, the protein that catalyzes termination of protein biosynthesis at UGA is broadly distributed. There are about 9 organisms where this mutation can be compared with the meaning of UGA, and the changes are not highly correlated with a change in the meaning of the codon. Therefore, because UGA can be translated as Trp with or without the eRF1 mutation, Swart et al suggest that the tRNA anticodon stem change is the principal cause of the coding change.

The review:<br /> Swart et al have a good argument. I would only add that eRF1 participation is not ruled out, because finding that UGA encodes Trp does not distinguish between encoding Trp 90% of the time and encoding it 99% of the time. The release factor could still play a measurable quantitative role, but the major inference here seems convincing.

Reviewer #2 (Public Review):

The manuscript raises interesting observations about the potential evolution of release factors and tRNA to readdress the meaning of stop codons. The manuscript is divided into two parts: The first consists of revealing that the presence of a trp tRNA with an AS of 5bp in Condylostoma magnum is probably linked to contamination in the databases by sequences from bacteria. This is an interesting point which seems to be well supported by the data provided. It highlights the difficulty of identifying active tRNA genes from poorly annotated or incompletely assembled genomes. The second part criticises the fact that a mutation at position S67 of eRF1 is required to allow the UGA codon to be reassigned as a sense codon. As supporting evidence, they provide a phylogenetic study of the eRF1 factor showing that there are numerous ciliates in which this position is mutated, whereas the organism shows no trace of the reassignment of the UGA codon into a sense codon. While this criticism seems valid at first glance, it suffers from the lack of information on the level of translation of UGA codons in the organisms considered. It has been clearly shown that S67G or S67A mutations allow a strong increase in the reading of UGA codons by tRNAs, so this point is not in doubt. However, this has been demonstrated in model organisms, and we now need to determine whether other changes in the translational apparatus could accompany this mutation by modifying its impact on the UGA codon. This is a point partly raised at the end of the manuscript. Indeed, it is quite possible that in these organisms the UGA codon is both used to complete translation and is subject to a high level of readthrough. Actually, in the presence of a mutation at position 67 (or elsewhere), the reading of the UGA can be tolerated under specific stress conditions (nutrient deficiency, oxidative stress, etc.), so the presence of this mutation could allow translational control of the expression of certain genes. On the other hand, it seems obvious to me that there are other ways of reading through a stop codon without mutating eRF1 at position S67. So the absence of a mutation at this position is not really indicative of a level of reading of the UGA codon. Before writing such a strong assertion as that found on page 3, experiments should be carried out. The authors should therefore moderate their assertion.

To make a definitive conclusion, we would need to be able to measure the level of termination and readthrough in these organisms. So, from my point of view, all the arguments seem rather weak. Moreover, the authors themselves indicate that the conjunction between a Trp tRNA that is efficient at reading the UGA codon and an eRF1 factor that is not efficient at recognising this stop codon could be the key to reassignment.

eLife assessment

This study presents a valuable finding on the process of brown to white adipogenic transdifferentiation within the perirenal adipose depot. The evidence supporting the claims is convincing, although limited sequencing depth of single nuclei and lack of regulatory insights somewhat lessens the impact of these findings. The work will be of interested to adipose tissue biologists.

Reviewer #1 (Public Review):

Summary: In this manuscript, the authors performed single nucleus RNA-seq for perirenal adipose tissue (PRAT) at different ages. They concluded a distinct subpopulation of adipocytes arises through beige-to-white conversion and can convert to a thermogenic phenotype upon cold exposure.

Strengths: PRAT adipose tissue has been reported as an adipose tissue that undergoes browning. This study confirms that beige-to-white and white-to-beige conversions also exist in PRAT, as previously reported in the subcutaneous adipose tissue.

Weaknesses:<br /> (1) There is overall a disconnection between single nucleus RNA-seq data and the lineage chasing data. No specific markers of this population have been validated by staining.<br /> (2) It would be nice to provide more evidence to support the conclusion shown in lines 243 to 245 "These results indicated that new BAs induced by cold exposure were mainly derived from UCP1- adipocytes rather than de novo ASPC differentiation in puPRAT". Pdgfra-negative progenitor cells may also contribute to these new beige adipocytes.<br /> (3) The UCP1Cre-ERT2; Ai14 system should be validated by showing Tomato and UCP1 co-staining right after the Tamoxifen treatment.

Reviewer #2 (Public Review):

Summary:<br /> In the present manuscript, Zhang et al utilize single-nuclei RNA-Seq to investigate the heterogeneity of perirenal adipose tissue. The perirenal depot is interesting because it contains both brown and white adipocytes, a subset of which undergo functional "whitening" during early development. While adipocyte thermogenic transdifferentiation has been previously reported, there remains many unanswered questions regarding this phenomenon and the mechanisms by which it is regulated.

Strengths:<br /> The combination of UCP1-lineage tracing with the single nuclei analysis allowed the authors to identify four populations of adipocytes with differing thermogenic potential, including an "whitened" adipocyte (mPRAT-ad2) that retains the capacity to rapidly revert to a brown phenotype upon cold exposure. They also identify two populations of white adipocytes that do not undergo browning with acute cold exposure.

Anatomically distinct adipose depots display interesting functional differences, and this work contributes to our understanding of one of the few brown depots present in humans.

Weaknesses:<br /> The most interesting aspect of this work is the identification of a highly plastic mature adipocyte population with the capacity to switch between a white and brown phenotype. The authors attempt to identify the transcriptional signature of this ad2 subpopulation, however the limited sequencing depth of single nuclei somewhat lessens the impact of these findings. Furthermore, the lack of any form of mechanistic investigation into the regulation of mPRAT whitening limits the utility of this manuscript. However, the combination of well-executed lineage tracing with comprehensive cross-depot single-nuclei presented in this manuscript could still serve as a useful reference for the field.

eLife assessment

This study presents valuable insights into the evolution of the gasdermin family, making a strong case that a GSDMA-like gasdermin activated by caspase-1 cleavage was already present in early land vertebrates. Convincing biochemical evidence is provided that extant avian, reptilian, and amphibian GSDMA proteins can still be activated by caspase-1 and upon cleavage induce pyroptosis-like cell death -- at least that they do so in the context of human cell lines. The caspase-1 cleavage site has only been lost in mammals, which use the more recently evolved GSDMD as a caspase-1 cleavable pyroptosis inducer. The presented work will be of considerable interest to scientists working on the evolution of cell death pathways, or on cell death regulation in non-mammalian vertebrates.

Reviewer #1 (Public Review):

Summary:<br /> In their revised manuscript, the authors analyze the evolution of the gasdermin family and observe that the GSDMA proteins from birds, reptiles and amphibians does not form a clade with the mammalian GSDMAs. Moreover, the non-mammalian GSDMA proteins share a conserved caspase-1 cleavage motif at the predicted activation site. The authors provide several series of experiments showing that the non-mammalian GSDMA proteins can indeed be activated by caspase-1 and that this activation leads to cell death (in human cells). They also investigate the role of the caspase-1 recognition tetrapeptide for cleavage by caspase-1 and for the pathogen-derived protease SpeB.

Strengths:<br /> The evolutionary analysis performed in this manuscript appears to use a broader data basis than what has been used in other published work. An interesting result of this analysis is the suggestion that GSDMA is evolutionary older than the main mammalian pyroptotic GSDMD, and that birds, reptiles and amphibians lack GSDMD but use GSDMA for the same purpose. The consequence that bird GSDMA should be activated by an inflammatory caspase (=caspase1) is convincingly supported by the experiments provided in the manuscript.

The changes made by the authors in response to the previous reviewer comments are (in my opinion) sufficient.

Reviewer #2 (Public Review):

Summary:

The authors investigated the molecular evolution of members of the gasdermin (GSDM) family. By adding the evolutionary time axis of animals, they created a new molecular phylogenetic tree different from previous ones. The analyzed result verified that non-mammalian GSDMAs and mammalian GSDMAs have diverged into completely different and separate clades. Furthermore, by biochemical analyses, the authors demonstrated non-mammalian GSDMA proteins are cleaved by the host-encoded caspase-1. They also showed mammalian GSDMAs have lost the cleavage site recognized by caspase-1. Instead, the authors proposed that the newly appeared GSDMD is now cleaved by caspase-1.

Through this study, we have been able to understand the changes in the molecular evolution of GSDMs, and by presenting the cleavage of GSDMAs through biochemical experiments, we have become able to grasp the comprehensive picture of this family molecules. However, there are some parts where explanations are insufficient, so supplementary explanations and experiments seem to be necessary.

Strengths:

It has a strong impact in advancing ideas into the study of pyroptotic cell death and even inflammatory responses involving caspase-1.

Weaknesses:

Based on the position of mammalian GSDMA shown in the molecular phylogenetic tree (Figure 1), it may be difficult to completely agree with the authors' explanation of the evolution of GSDMA.

(1) Focusing on mammalian GSDMA, this group and mammalian GSDMD diverged into two clades, and before that, GSDMA/D groups and mammalian GSDMC separated into two, more before that, GSDMB, and further before that, non-mammalian GSDMA, when we checked Figure 1. In the molecular phylogenetic tree, it is impossible that GSDMA appears during evolution again. Mammalian GSDMAs are clearly paralogous molecules to non-mammalian GSDMAs in the figure. If they are bona fide orthologous, the mammalian GSDMA group should show a sub-clade in the non-mammalian GSDMA clade. It is better to describe the plausibility of the divergence in the molecular evolution of mammalian GSDMA in the Discussion section.

(2) Regarding (1), it is recommended that the authors reconsider the validity of estimates of divergence dates by focusing on mammalian species divergence. Because the validity of this estimation requires recheck of the molecular phylogenetic tree, including alignment.

(3) If GSDMB and/or GSDMC between non-mammalian GSDMA and mammalian GSDMD as shown in the molecular phylogenetic tree would be cleaved by caspase-1, the story of this study becomes clearer. The authors should try that possibility.

eLife assessment

Bladder dysfunction following spinal cord injury (SCI) represents a severe and disabling complication without effective therapies. Following evidence that AMPA receptors play a key role in bladder function the authors show convincingly that AMPA allosteric activators can ameliorate many of the subacute defects in bladder and sphincter function following SCI, including prolonged voiding intervals and high bladder pressure thresholds for voiding. These valuable results in rodents may help in the development of these agents as therapeutics for humans with SCI-induced bladder dysfunction.

Reviewer #1 (Public Review):

Summary:

Spinal cord injury (SCI) causes immediate and prolonged bladder dysfunction, for which there are poor treatments. Following up on evidence that AMPA glutamatergic receptors play a key role in bladder function, the authors induced spinal cord injury and its attendant bladder dysfunction and examined the effects of graded doses of allosteric AMPA receptor activators (ampakines). They show that ampakines ameliorate several prominent derangements in bladder function resulting from SCI, improving voiding intervals and pressure thresholds for voiding and sphincter function.

Strengths:

Well performed studies on a relevant model system. The authors induced SCI reproducibly and showed that they had achieved their model. The drugs revealed clear and striking effects. Notably, in some mice which had such bad SCI that they could not void, the drug appeared to restore voiding function.

Weaknesses:

The studies are well conducted, but it would be helpful to include information on the kinetics of the drugs used, their half-life and how long they are present in rats after administration. What blood levels of the drugs are achieved after infusion? How do these compare with blood levels achieved when these drugs are used in humans?

Reviewer #2 (Public Review):

Summary:

In this study, Rana and colleagues present interesting findings demonstrating potential beneficial effects of AMPA receptor modulator with ampakines in the context of neurogenic bladder following acute spinal cord injury. Neurogenic bladder dysfunction is characterized by urinary retention and/or incontinence, with limited treatments available. Based on recent observations showing that ampakines improved respiratory function in rats with SCI, the authors explored the use of ampakine CX1739 on bladder and external urethral sphincter (EUS) function and coordination early after mid-thoracic contusion injury. Using continuous flow cystometry and EUS myography the authors showed that ampakine treatment led to decreased peak pressures, threshold pressure, intercontraction interval and voided volume in SCI rats versus vehicle-treated controls. Although CX1739 did not alter EUS EMG burst duration, treatment did lead to EUS EMG bursting at lower bladder pressure compared to baseline. In a subset of rats that did not show regular cystometric voiding, CX1739 treatment diminished non-voiding contractions and improved coordinated EUS EMG bursting. Based on these findings the authors conclude that ampakines may have utility in recovery of bladder function following SCI.

Strengths and Weaknesses:

The experimental design is thoughtful and rigorous, providing evaluation of both the bladder and external urethral sphincter function in the absence and presence of ampakine treatment. The data in support of a role for CX1789 treatment in the context of neurogenic bladder are presented clearly, and the conclusions are adequately supported by the findings. The authors have addressed essentially all of the weaknesses related to translational significance, CX1789 half-life, and the use of female animals only in this study.

Reviewer #3 (Public Review):

Summary:

In this manuscript, Rana and colleagues examined the effect of a "low impact" ampakine, an AMPA receptor allosteric modulator, on the voiding function of rats subjected to midline T9 spinal cord contusion injury. Previous studies have shown that the micturition reflex fully depends on AMPA glutaminergic signaling, and, that the glutaminergic circuits are reorganized after spinal cord injury. In chronic paraplegic rats, other circuits (no glutaminergic) become engage in the spinal reflex mechanism controlling micturition. The authors employed continuous flow cystometry and external urethral sphincter electromyography to assess bladder function and bladder-urethral sphincter coordination in naïve rats (control) and rats subjected to spinal cord injury (SCI). In the acute phase after SCI, rats exhibit larger voids with lower frequency than naïve rats. This study shows that CX1739 improves, in a dose-dependent manner, bladder function in rats with SCI. The interval between voids and the voided volume were reduced in rat with SCI when compared to controls. In summary, this is an interesting study that describes a potential treatment for patients with SCI.

Strengths:

The findings described in this manuscript are significant because neurogenic bladder predisposes patients with SCI to urinary tract infections, hydronephrosis and kidney failure. The manuscript is clearly written. The study is technically outstanding, and the conclusions are well justified by the data.

Weaknesses:

The study was conducted 5 days after spinal cord contusion when the bladder is underactive. In rats with chronic SCI, the bladder is overactive. Therefore, the therapeutic approach described here is expected to be effective only in the underactive bladder phase of SCI. The mechanism and site of action of CX1739 is not defined.

eLife assessment

This important study identifies the TNXB-AKT pathway as a potential mechanism underlying hemophilia-associated cartilage degeneration. The evidence supporting the conclusions is convincing, with murine and human patient evidence as well as genome-wide DNA methylation analysis. This paper would be of interest to cell biologists and biochemists working on musculoskeletal disorders.

Reviewer #1 (Public Review):

Summary:<br /> Chen and colleagues first compared the cartilage tissues collected from OA and HA patients using histology and immunostaining. Then, a genome-wide DNA methylation analysis was performed, which informed the changes of a novel gene, TNXB. IHC confirmed that TNXB has a lower expression level in HA cartilage than OA. Next, the authors demonstrated that TNXB levels were reduced in the HA animal model, and intraarticular injection of AAV carrying TNXB siRNA induced cartilage degradation and promoted chondrocyte apoptosis. Based on KEGG enrichment, histopathological analysis, and western blot, the authors also showed the relationship between TNXB and AKT phosphorylation. Lastly, AKT agonist, specifically SC79 in this study, was shown to partially rescue the changes of in vitro-cultured chondrocytes induced by Tnxb knock-down. Overall, this is an interesting study and provided sufficient data to support their conclusion.

Strengths:<br /> (1) Both human and mouse samples were examined.<br /> (2) The HA model was used.<br /> (3) Genome-wide DNA methylation analysis was performed.

Weaknesses:<br /> (1) In some experiments, the selection of the control groups was not ideal.<br /> (2) More details on analyzing methods and information on replicates need to be included.<br /> (3) Discussion can be improved by comparing findings to other relevant studies.<br /> (4) The use of transgenic mice with conditional Tnxb depletion can further define the physiological roles of Tnxb.

Reviewer #2 (Public Review):

Summary:<br /> This manuscript mainly studied the biological effect of tenascin XB (TNXB) on hemophilic arthropathy (HA) progression. Using bioinformatic and histopathological approaches, the authors identified the novel candidate gene TNXB for HA. Next, the authors showed that TNXB knockdown leads to chondrocyte apoptosis, matrix degeneration, and subchondral bone loss in vivo/vitro. Furthermore, AKT agonists promoted extracellular matrix synthesis and prevented apoptosis in TNXB knockdown chondrocytes.

Strengths:<br /> In general, this study significantly advances our understanding of HA pathogenesis. The authors utilize comprehensive experimental strategies to demonstrate the role of TNXB in cartilage degeneration associated with HA. The results are clearly presented, and the conclusions appear appropriate.

Weaknesses:<br /> Additional clarification is required regarding the gender of the F8-/- mouse in the study. Is the mouse male or female?

Reviewer #3 (Public Review):

Summary:<br /> The manuscript by Dr. Chen et al. investigates the genes that are differentially methylated and associated with cartilage degeneration in hemophilia patients. The study demonstrates the functional mechanisms of the TNXB gene in chondrocytes and F8-/- mice. The authors first showed significant DNA methylation differences between hemophilic arthritis (HA) and osteoarthritis through genome-wide DNA methylation analysis. Subsequently, they showed a decreased expression of the differentially methylated TNXB gene in cartilage from HA patients and mice. By knocking down TNXB in vivo and in vitro, the results indicated that TNXB regulates extracellular matrix homeostasis and apoptosis by modulating p-AKT. The findings are novel and interesting, and the study presents valuable information in blood-induced arthritis research.

Strengths:<br /> The authors adopted a comprehensive approach by combining genome-wide DNA methylation analysis, in vivo and in vitro experiments using human and mouse samples to illustrate the molecular mechanisms involved in HA progression, which is crucial for developing targeted therapeutic strategies. The study identifies Tenascin XB (TNXB) as a central mediator in cartilage matrix degradation. It provides mechanistic insights into how TNXB influences cartilage matrix degradation by regulating the activation of AKT. It opens avenues for future research and potential therapeutic interventions using AKT agonists for cartilage protection in hemophilic arthropathy. The conclusions drawn from the study are clear and directly tied to the findings.

Weaknesses:<br /> (1) The study utilizes a small sample size (N=5 for both osteoarthritis and hemophilic arthropathy). A larger sample size would enhance the generalizability and statistical power of the findings.<br /> (2) The use of an animal model (F8-/- mouse) to investigate the role of TNXB may not fully capture the complexity of human hemophilic arthropathy. Differences in the biology between species may affect the translatability of the findings to human patients.<br /> (3) The study primarily focuses on TNXB as a central mediator, but it might overlook other potentially relevant factors contributing to cartilage degradation in hemophilic arthropathy. A more holistic exploration of genetic and molecular factors could provide a broader understanding of the condition.

eLife assessment

This important study identifies the role of Caveolin1 and Cavin1 as regulators of TransEndothelial Macroaperture (TEM). The methodology used is rigorous and compelling, and further research can point to more mechanistic understanding of the process.

Reviewer #2 (Public Review):

Summary:<br /> The manuscript by Morel et al. aims at identifying some potential mechano-regulators of transendothelial cell macro-aperture (TEM). Guided by the recognized role of caveolar invaginations in buffering the membrane tension of cells, the authors focused on caveolin-1 and associated regulator PTRF. They report a comprehensive in vitro work based on siRNA knockdown and optical imaging approach complemented with an in vivo work on mice, a biophysical assay allowing to measure the mechanical properties of membranes and a theoretical analysis inspired from soft matter physics.

The authors should be complimented for this multi-facetted and rigorous work. The accumulation of pieces of evidence collected from each type of approach makes very convincing the conclusion drawn by the authors on the new role of cavolin-1 as an individual protein instead of the main molecular component of caveolae. On a personal note, I was very impressed by the quality of STORM images (Fig. 2) which are very illuminating and useful, in particular for validating some hypotheses of the theoretical analysis.

While this work pins down the key role of caveolin-1, its mechanism remains to be further investigated. The hypotheses proposed by the authors in the discussions about the link between caveolin and lipids/cholesterol are very plausible though challenging. Even though we may feel slightly frustrated by the absence of data in this direction, the quality and merit of this paper remain.

The analogy with dewetting processes drawn to derive the theoretical model is very attractive. However, and although part of the model has already been published several times by the same group of authors, the validity of the Helfrich formalism is a key assumption that has to be explained clearly. Here, for the first time, thanks to these STORM analysis, the authors show that HUVECs intoxicated by ExoC3 exhibit a loose and defective cortex with a significantly increase mesh size, which supports this hypothesis.

eLife assessment

This valuable study introduces an innovative method for measuring interocular suppression depth, which implicates mechanisms underlying subconscious visual processing. The evidence is solid in suggesting a limitation of measuring conventional bCFS threshold alone that could be remediated by the new method. It will be of interest not only to cognitive psychologists and neuroscientists who study sensation and perception but also to philosophers who work on theories of consciousness.

Reviewer #1 (Public Review):

Summary<br /> A new method, tCFS, is introduced to offer richer and more efficient measurement of interocular suppression. It generates a new index, the suppression depth, based on the contrast difference between the up-ramped contrast for the target to breakthrough suppression and the down-ramped contrast for the target to disappear into suppression. A uniform suppression depth regardless of image types (e.g., faces, gratings and scrambles) was discovered in the paper, favoring an early-stage mechanism involving in CFS. Discussions about claims of unconscious processing and the related mechanisms.

Strength<br /> The tCFS method adds to the existing bCFS paradigms by providing the (re-)suppression threshold and thereafter the depression depth. Benefiting from adaptive procedures with continuous trials, the tCFS is able to give fast and efficient measurements. It also provides a new opportunity to test theories and models about how information is processed outside visual awareness.

Weakness:<br /> This paper reports the surprising finding of uniform suppression depth over a variety of stimuli. This is novel and interesting. But given the limited samples being tested, the claim of uniformity suppression depth needs to be further examined, with respect to different complexities and semantic meanings.<br /> From an intuitive aspect, the results challenged previous views about "preferential processing" for certain categories, though it invites further research to explore what exactly could suppression depth tell us about unconscious visual processing. The authors discussed about the possibility of gaining awareness according to different CRF functions in V1 and V4 neurons. But it confuses me about how the logic goes, especially from Line 713 to Line 718.

Reviewer #2 (Public Review):

The following review for a revised manuscript is updated where appropriate and otherwise unchanged for completeness.

Summary<br /> The paper concerns the phenomenon of continuous flash suppression (CFS), relevant to questions about the extent and nature of subconscious visual processing. Whereas standard CFS studies only measure the breakthrough threshold-the contrast at which an initially suppressed target stimulus with steadily increasing contrast becomes visible-the authors also measure the re-suppression threshold, the contrast at which a visible target with decreasing contrast becomes suppressed. Thus, the authors could calculate suppression depth, the ratio between the breakthrough and re-suppression thresholds. To measure both thresholds, the authors introduce the tracking-CFS method, a continuous-trial design that results in faster, better controlled, and lower-variance threshold estimates compared to the discrete trials standard in the literature. The study finds that suppression depths are similar for different image categories, providing an interesting contrast to previous results that breakthrough thresholds differ for different image categories. The new finding calls for a reassessment of interpretations based solely on the breakthrough threshold that subconscious visual processing is category-specific.

Strengths<br /> (1) The tCFS method quickly estimates breakthrough and re-suppression thresholds using continuous trials, which also better control for slowly varying factors such as adaptation and attention. Indeed, tCFS produces estimates with lower across-subject variance than the standard discrete-trial method (Fig. 2). The tCFS method is straightforward to adopt in future research on CFS and binocular rivalry.

(2) The CFS literature has lacked re-suppression threshold measurements. By measuring both breakthrough and re-suppression thresholds, this work calculated suppression depth (i.e., the difference between the two thresholds), which warrants different interpretations from the breakthrough threshold alone.

(3) The work found that different image categories show similar suppression depths, suggesting some aspects of CFS are not category-specific. This result enriches previous findings that breakthrough thresholds vary with image categories. Re-suppression thresholds vary symmetrically, such that their differences are constant.

Weakness<br /> The following concern remains from my initial review. Reviewer #3 raised a similar point in the last revision round, and I believe the authors do not fully address either comment. Thus, here I paraphrase my initial concern with reference to the authors' reply and discuss why it needs further elaboration.

I do not follow the authors' reasoning as to why the suppression depth is a better (or fuller, superior, more informative) indication of subconscious visual processing than the breakthrough threshold alone. To my previous round of comments, the authors replied that 'breakthrough provides only half of the needed information.' I do not understand this. One cannot infer the suppression depth from the breakthrough threshold alone, but *one cannot obtain the breakthrough threshold from the suppression depth alone*, either. The two measures are complementary. (To be sure, given *both* the suppression depth and the re-suppression threshold, one can trivially recover the breakthrough threshold. The discussion concerns the suppression depth *alone* and the breakthrough threshold *alone*.) I am fully open to being convinced that there is a good reason why the suppression depth may be more informative than the breakthrough threshold about a specific topic, e.g., inter-ocular suppression or subconscious visual processing. I only request that the authors make such an argument explicit. Preferably, this argument will precede claims that require it. For example, in the significance statement, the authors write, 'all images show equal suppression when both thresholds are measured. We *thus* find no evidence of differential unconscious processing and *conclude* reliance on breakthrough thresholds is misleading' (emphasis added). Just what supports the 'thus' and the 'conclude'? Similarly, at the end of the introduction, the authors write, '[...] suppression depth was constant for faces, objects, gratings and visual noise. *In other words*, we find no evidence to support differential unconscious processing among these particular, diverse categories of suppressed images' (emphasis added). I believe the statements before and after the period have not been shown to be equivalent. In the abstract, the authors revised, 'variations in bCFS thresholds alone are insufficient for inferring whether the barrier to achieving awareness exerted by interocular suppression is weaker for some categories of visual stimuli compared to others.' While I appreciate the added specificity, this claim still needs more support because the authors have not established that suppression depth is a better index than the breakthrough threshold of 'the barrier to achieving awareness exerted by interocular suppression.'

The authors' reply included a discussion of neural CRFs, which may explain why the bCFS thresholds differ across image categories. However, CRFs do not explain why the bCFS threshold does not implicate some component of subconscious processing. For example, the bCFS threshold may reflect the aspect of subconscious visual processing that corresponds to V1/V4 neural responses.

Reviewer #3 (Public Review):

Summary:<br /> In the 'bCFS' paradigm, a monocular target gradually increases in contrast until it breaks interocular suppression by a rich monocular suppressor in the other eye. The present authors extend the bCFS paradigm by allowing the target to reduce back down in contrast until it becomes suppressed again. The main variable of interest is the contrast difference between breaking suppression and (re) entering suppression. The authors find this difference to be constant across a range of target types, even ones that differ substantially in the contrast at which they break interocular suppression (the variable conventionally measured in bCFS). They also measure how the difference changes as a function of other manipulations. Interpretation is in terms of the processing of unconscious visual content, as well as in terms of the mechanism of interocular suppression.

Strengths:<br /> Interpretation of bCFS findings is mired in controversy, and this is an ingenuous effort to move beyond the paradigm's exclusive focus on breaking suppression. The notion of using the contrast difference between breaking and entering suppression as an index of suppression depth is interesting.

eLife assessment

The manuscript from Richter et al. is a very thorough anatomical description of the external sensory organs in Drosophila larvae. It represents a fundamental step forward for sensory physiology, and provides a tool for investigating the relationship between the structure and function of sensory organs. Using improved electron microscopy analysis and digital modelling, the authors provide compelling evidence that form the basis for further molecular and functional studies to decipher the sensory strategies used by larvae to navigate through their environment.

Author Response

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

eLife assessment

The manuscript from Richter et al. is a very thorough anatomical description of the external sensory organs in Drosophila larvae. It represents an important tool for investigating the relationship between the structure and function of sensory organs. Using improved electron microscopy analysis and digital modeling, the authors provide compelling evidence offering the basis for molecular and functional studies to decipher the sensory strategies of larvae to navigate through their environment.

Public Reviews:

Summary

This is a very meticulous and precise anatomical description of the external sensory organs (sensillia) in Drosophila larvae. Extending on their previous study (Rist and Thum 2017) that analyzed the anatomy of the terminal organ, a major external taste organ of fruit fly larva, the authors examined the anatomy of the remaining head sensory organs - the dorsal organ, the ventral organ, and the labial organ-also described the sensory organs of the thoracic and abdominal segments. Improved serial electron microscopy and digital modeling are used to the fullest to provide a definitive and clear picture of the sensory organs, the sensillia, and adjacent ganglia, providing an integral and accurate map, which is dearly needed in the field. The authors revise all the data for the abdominal and thoracic segments and describe in detail, for the first time, the head and tail segments and construct a complete structural and neuronal map of the external larval sensilla.

Strengths

It is a very thorough anatomical description of the external sensory organs of the genetically amenable fruitfly. This study represents a very useful tool for the research community that will definitely use it as a reference paper. In addition to the classification and nomenclature of the different types of sensilla throughout the larval body, the wealth of data presented here will be valuable to the scientific community. It will allow for investigating sensory processing in depth. Serial electron microscopy and digital modeling are used to the fullest to provide a comprehensive, definitive, and clear picture of the sensory organs. The discussion places the anatomical data into a functional and developmental frame. The study offers fundamental anatomical insights, which will be helpful for future functional studies and to understand the sensory strategies of Drosophila larvae in response to the external environment. By analyzing different larval stages (L1 and L3), this work offers some insights into the developmental aspects of the larval sense organs and their corresponding sensory cells.

Weaknesses

There are no apparent weaknesses, although it is not a complete novel anatomical study. It revisits many data that already existed, adding new information. However, the repetitiveness of some data and prior studies may be avoided for easy readability.

We would like to thank the reviewers for their respective reviews. The detailed comments and efforts have helped us to improve our manuscript. In the following, we have listed the comments one by one and provide the respective information on how we addressed the concerns.

Recommendations for the authors:

We have tried to address every single comment as far as possible. In order to structure our response a little better, we have listed the relevant page number and the original comments once again. Directly following this you will find our response and a description of what we have changed in the manuscript.

REVIEWER #1 (Recommendations For The Authors):

I have a few comments that will help the reader navigate this long and detailed paper.

REVIEWER 1.1. page 4

The final section of "the Structural organization of Drosophila larvae" needs some reorganization.

Specifically:

"The DO and the TO are prominently located on the tip of the head lobes" Can the authors rewrite the sentence in a way that it is clear that there is one DO and one VO on each side of the head? Check at the beginning of each section, please. There is a mention about hemi-segments but it is still confusing.

Done – replaced with “The largest sense organs of Drosophila larvae are arranged in pairs on the right and left side of the head.”

REVIEWER 1.2. page 5

"The sequence of sensilla is always similar for and different between T1, T2-T3, and A1-A7" This sentence is not clear, please break it into two sentences.

Done – replaced with: “We noticed varying arrangements for T1, T2-T3, and A1-A7, with a consistent sequence of sensilla in each configuration.”

REVIEWER 1.3. figures page 4

Double hair can't be found in Figure 1B or C (is it h3, h4?) - please clarify.

Done - changed to double hair organ in page 11, included double hair sketch in legend in figure 1B. We changed the name of the structure to double hair organ, to clarify that this is a compound sensillum consisting of two individual sensilla.

REVIEWER 1.4. page 5

The authors go back and forth in their descriptions of the different sensory organs. Knob sensilla and then papilla sensilla are discussed and then a few lines later a further description is done. Please unify the description of each separately.

Done – we restructured the whole section.

REVIEWER 1.5. figures page 6

"We found three hair sensilla on T1-T3, and "two" on A1-A7" - in the figure there seem to be "four" on A1-A7.

Done – we included the two hair sensilla of the double hair organ

REVIEWER 1.6. figures page 6

DORSAL ORGAN:

Can the authors explain the colour map meaning in Figure 2A? It is explained in 2C but the image already has colours. Add your sentence "Color code in A applies to all micrographs in this Figure".

Done – we added a sentence to explain that the color code in A applies to the whole figure.

REVIEWER 1.7. page 6

Page 10: which comprises seven olfactory sensilla "composing" three dendrites each: replace this with"with". At the end, we want to think 7 X 3= 21 ORNs.

Done – replaced.

REVIEWER 1.8. page 9

CHORDOTONAL ORGANS:

"We find these these DO associated ChO (doChO).. .". Please remove one "these"

Done – removed.

REVIEWER 1.9. page 8

Is the DO associated ChO part of the dorsal ganglion???? It does not look like it. Could you clarify?

Done – we added a sentence that clarifies that the ChO neuron is not iside the DOG.

REVIEWER 1.10. page 9 VENTRAL ORGAN: A figures page 12

Please add to the Figure 8 legend the description of 8c' and 8c'?

Done – added description in figure legend.

B page 9

8H, what are the *, arrows? Please clarify - it is hard to interpret the figure.

Done – we added parentheses in the figure legend that state which structures the asterisks and arrows indicate.

C page 9

"Three of them are innervated by a single neuron () and one by two neurons () (Figure 8F-I). Please add which are innervated by 1 (VO1, VO2-VO4) and which by 2 (VO3).

Done – we added parentheses that clarify which sensilla are innervated by 1 or 2 neurons.

REVIEWER 1.11. page 9

Can you add something (or speculate) about the difference in sensory processing of the different types of sensilla?

Done – new sentence in discussion:

‘Their different size and microtubule organization likely correlate with processing of different stimulus intesities applied to the mechanotransduction apparatus (Bechstedt et al. 2010).’

REVIEWER 1.12. figures page 16

PAPILLA AND HAIR SENSILLA:

FIGURE 10a, please add the name of each sensillum from p1, p2, px py, etc... (if not we have to go back to figure 1 when you describe specific ps.)

Thanks for the comment, it really makes it a lot easier for the reader.

REVIEWER 1.13. figures page 18 Figure 11, can you add the name of each hair, please?

Done – updated figure.

REVIEWER 1.14. figures pages 16, 18, 20

In Figures 10, 11, and 12 you clearly draw an area on the internal side that I assume is what you call the "electron-dense sheath". It is wider in papilla sensilla than in hair sensilla, most likely due to the difference in stimuli sensed that you explain in detail in the discussion. Can you say in the figure what this "internal" thing is? Can you add this difference to your list "Apart from the difference in outer appearance and structure of the tubular body"?

This is the basal septum, but it is not certain that it is wider in the papillae sensillae, at least we could not observe this in our data sets. The impression could have been created by different scales in the 3D reconstructions and a perspective view. Therefore, we do not want to list this as a difference here, as we are not sure.

However, we have now specified the socket septum in the figure legends and in Figures 10A, 11A and 12A.

REVIEWER 1.15. page 11

KNOB SENSILLA:

Page 25;" Knob sensilla have been described under "vaious" names such as": add various.

Done

REVIEWER 1.16. page 12

"reveals that the three hair and the two papilla sensilla are associated with a single dendrite." Can you write that "reveals THAT EACH OF the three hair and the two papilla sensilla" if not it seems that there is only one dendrite.

Done

REVIEWER 1.17. figures page 25 TERMINAL SENSORY CONES:

Please name the t1-t7 cones in Figure 15A.

Done – we updated the figure.

REVIEWER 1.18. page 13

The spiracle sense organ deserves a new paragraph. As does the papilla sensillum of the anal plate.

Done – we added subtitles before the prargraphs.

Discussion:

REVIEWER 1.19. page 15

Page 38: "v'entral" correct typo

PAGE 15

Done – we have updated the nomenclature  ventral 1 (v), ventral 2 (v’) and ventral 3 (v’’)

REVIEWER #2 (Recommendations For The Authors):

I have only a few comments:

REVIEWER 2.1. page 5

p.5, right column, middle: the use of trichoid, campaniform, and basiconical (sensilla) in previous works were based on even older papers and reviews that attempted to link EM architecture to function (e.g., KEIL, T. A. & STEINBRECHT, R. A. (1986). Mechanosensitive and olfactory sensilla of insects. In Insect Ultrastructure, vol. 2. (ed. R. C. King & H. Akai), pp. 477-516. New York/London: Plenum Press). Trichoid sensilla can be mechano-sensitive, olfactory, or gustatory; trichoid simply refers to the shape (hair). The same applies to basiconical sensilla. The use of "campaniform", which Ghysen et al called "papilla sensilla", was the only really problematic case, because these (Drosophila larval) sensilla did not really resemble closely the classical campaniform sensilla (e.g., adult haltere). The only reason we called them campaniform is because they were not more similar to any other type of (previously named) sensillum.

Thank you for the explanation. The nomenclature of structures is generally always a complex topic with often different approaches and principles. We are aware of this and have therefore tried to be as careful as possible. We were not sure from this comment whether you were suggesting to change the text or whether you wanted to explain how these names were assigned to the sensilla in the past. However, we hope that the current version is in line with your understanding, but could of course make changes if necessary (see also comments of reviewer 1).

REVIEWER 2.2. page 9

p.21, Labial Organ: the ventral lip is the labium; the dorsal one is the labrum.

Done – replaced labrum with labium.

REVIEWER 2.3. page 9

p.20/21, Ventral organ and labial organ: here, the projection of the axons could be mentioned as an ordering principle. In the previous literature, for larva and embryo, a labial organ (lbo) was described that most likely corresponds to the labial organ presented here. This (previously mentioned) lbo characteristically projects along the labial nerve to the labial segment (hence the name). It fasciculates with axons of another sensory complex, also generated by the labial segment, namely the ventral pharyngeal sensory organ (VPS). Does the labial organ described here share this axonal path?

Yes, it has the same axonal pathway and is the same organ as the lbo. We have tried to standardise the nomenclature for all important external head organs (DO, TO, VO, LO) and have therefore used abbreviations with two letters. However, to avoid confusion, we have now added that the LO was also called lbo in the past.

For the ventral organ, the segmental origin (to my knowledge) was never clarified. The axons of the ventral organ project along the maxillary nerve (which carries axons of the terminal=maxillary organ). This nerve, closely before entering the VNC, splits into a main branch to the maxillary segment (TO axons) and a thinner branch that appears to target the mandibular segment. This branch could contain the axons of the ventral organ (as described previously and in this paper). Could the authors confirm this axonal projection of the VO?

In this work, we did not focus on the axonal projections into the SEZ. This is also not a simple and fast process, as in the entire larval dataset, the large head nerves unfortunately exhibit a highly variable quality of representation. Therefore, the reconstruction of nerves and individual neurons within it is often challenging and very time-consuming. The research question is, of course, very intriguing, and one could also attempt to match each sensory neuron of the periphery with the existing map of the brain connectome. However, this is a project in itself, exceeding the scope of this work, and is therefore more feasible as a subsequent project.

REVIEWER #3 (Recommendations For The Authors):

Minor suggestions that the authors might consider:

REVIEWER 3.1. figures all

Recheck the scale bar in figures and figure legends. Missing in a few places.

Done – we replaced or added some (missing) scale bars in figures and figure legends (see annotated figure document).

REVIEWER 3.2. figures page 4

The color schematic in Figure 1 can be improved for readability.

Done – we changed the color schematic, especially for the head region to improve readability.

Reviewer #1 (Public Review):

Summary: This is a very meticulous and precise anatomical description of the external sensory organs in Drosophila larvae. It generates an integral and accurate map. The authors revise all the data for the abdominal and thoracic segments and describe in detail, for the first time, the head and tail segments.

Strengths: It is a very thorough anatomical description of the external sensory organs of the genetically amenable fruitfly. This study represents a very useful tool for the research community that will definitely be used it as a reference paper. It will allow us to investigate sensory processing in depth. The discussion places the anatomical data into a functional and developmental frame.

Reviewer #2 (Public Review):

Summary: This study is a superbly written and illustrated documentation of the external sensilla of the Drosophila larva. Serial electron microscopy and digital modeling is used to the fullest to provide a definitive and clear picture of the sensory organs, which is dearly needed in the field.

Strengths: Serial electron microscopy and digital modeling is used to the fullest to provide a comprehensive, definitive and clear picture of the sensory organs, which is dearly needed in the field.

Weaknesses: none detected.

Reviewer #3 (Public Review):

Summary: Richter et al. present a comprehensive anatomical analysis of the external sensory organs of the D. melanogaster larva. Extending on their previous study (Rist and Thum 2017) that analyzed the anatomy of the terminal organ, a major external taste organ of fruit fly larva, the authors examined the anatomy of the remaining head sensory organs - the dorsal organ, the ventral organ, and the labial organ-also described the sensory organs of the thoracic and abdominal segments. Using improved electron microscopy, the authors performed a three-dimensional anatomical analysis of the sensilla and adjacent ganglia to construct a complete structural and neuronal map of the external larval sensilla.

Strengths: Though the manuscript is lengthy, it is written clearly, and the presented data supports the conclusion. In addition to the classification and nomenclature of the different types of sensilla throughout the larval body, the wealth of data presented here will be valuable to the scientific community. The study offers fundamental anatomical insights, which will be helpful for future functional studies and to understand the sensory strategies of Drosophila larvae in response to the external environment. By analyzing different larval stages (L1 and L3), this work offers some insights into the developmental aspects of the larval sense organs and their corresponding sensory cells.

Weaknesses: There are no apparent weaknesses. The repetitiveness of some data and prior studies may be avoided for easy readability.

eLife assessment

This important paper uses a multifaceted approach to implicate the locus coeruleus-noradrenaline system in the stress-induced transcriptional changes of dorsal and ventral hippocampus. It provides an inventory of dorsal and ventral hippocampal gene expression upregulated by activation of LC-NA system, which can be used as starting point for more functional studies related to the effects of stress-induced physiological and pathological changes. The results convincingly support the conclusions. This paper will be of interest to those interested in stress neurobiology, hippocampal, and/or noradrenaline function.

Author Response

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

eLife assessment

This manuscript provides novel and important findings regarding the impact of noradrenergic signaling from the locus coeruleus on hippocampal gene expression. The locus coeruleus is the sole source of noradrenaline to the hippocampus and many rapid molecular changes induced by stress are regulated by noradrenaline. This manuscript provides a rigorous investigation into hippocampal genes uniquely regulated by noradrenaline in the presence or absence of stress. Data were collected and analyses were performed using solid methodology, and the results mostly convincingly support the conclusion made with few weaknesses. The study would benefit from a more comprehensive analyses of sex differences.

Response: We thank the reviewers and the editors for the positive evaluation of our work and for the constructive feedback. To address some of the key criticisms, we have performed several new experiments and analyses. Importantly, we now provide a much more rigorous comparison of males and females, which strongly suggests that there are no major sex differences in the transcriptomic response to stress and noradrenaline in the hippocampus. We think that these - and other additions discussed below - significantly strengthen the manuscript. We provide detailed responses to all the reviewers comments. We have added numbers to the reviewers’ comments for easier referencing.

Reviewer #1 (Public Review):

Comment 1: Privitera et al., provide a comprehensive and rigorous assessment of how noradrenaline (NA) inputs from the locus coeruleus (LC) to the hippocampus regulate stress-induced acute changes in gene expression. They utilize RNA-sequencing with selective activation/inhibition of LC-NA activity using pharmacological, chemogenetic and optogenetic manipulations to identify a great number of reproducible sets of genes impacted by LC activation. It is noteworthy that this study compares transcriptomic changes in the hippocampus induced by stress alone, as compared with selective circuit activation/inhibition. This reveals a small set of genes that were found to be highly reproducible. Further, the publicly available data will be highly useful to the scientific community.

Response: We are very grateful for this positive evaluation.

Comment 2: A major strength of the study is the inclusion of both males and females. However, with this aspect of the study also lies the biggest weakness. While the experiments tested males and females, they were not powered for identifying sex differences. There are vast amounts of literature documenting the inherent sex differences, both under resting and stress-evoked conditions, in the LC-NA system and this is a major missed opportunity to better understand if there is an impact of these sex-specific differences at the genetic level in a major LC projection region. There are many instances whereby sex effects are apparent, but do not pass multiple testing correction due to low n's. The authors highlight one of them (Ctla2b) in supplemental figure 6. This gene is only upregulated by stress in females. It is appreciated that the manuscript provides an incredible amount of novel data, making the investigation of sex differences ambitious. Data are publicly available for others to conduct follow up work, and therefore it may be useful if a list of those genes that were different based on targeted interrogation of the dataset be provided with a clear statement that multiple testing corrections failed. This will aid further investigations that are powered to evaluate sex effects.

Response: The assessment of the reviewers and the editorial feedback encouraged us to look more thoroughly into potential sex differences, because we believe it would indeed be a major additional strength if our manuscript could make a firm statement on this important issue. To this end, we have expanded the manuscript in two major ways:

(1) To expand the analysis of sex effects also to the dorsal hippocampus, and to increase robustness of the data, we have performed RNA-seq in 32 additional samples of male and female mice exposed to stress (or control) and propranolol (or saline) injection. Figure 1fh and Supplementary Figure 1d-f have been updated to reflect this new addition, and the results are presented in a new section on Pages 3-4 (pasted below for ease of reviewing). In summary, the strongly support our initial observation that the effects of stress on gene expression, as well as the effects of propranolol on blocking stress-induced effects, are highly similar in both sexes.

(2) To further increase the power for detection of sex-effects, we have performed a small meta-analysis. For this, we combined several RNAseq datasets from the current manuscript and published datasets from our previous work (Floriou-Servou et al., 2018; von Ziegler et al., 2022), which also investigated transcriptomic sex-differences in the hippocampus 45 min after cold swim stress exposure in the same setup as used for the current manuscript. This approach increased our sample size to 51 males and 20 females. In summary, this well-powered approach shows no evidence for sex differences in the transcriptional response to stress, even when more lenient analyses were applied. These results are described in a new section on page 4, and summarized in Supplementary Figures 1f+g. This section is pasted below for ease of reviewing.

"While blocking β-adrenergic receptors was able to block stress-induced gene expression, we did not test whether propranolol might decrease gene expression already at baseline, independent of stress. Additionally, all tests had thus far been conducted in male mice, raising the question about potential sex differences in NA-mediated transcriptomic responses. To address these two issues, we repeated the experiment in both sexes and included a group that received a propranolol injection but was not exposed to stress (Fig. 1f). Combining the data from both experiments, we repeated the analysis for each region, to identify genes whose response to stress was inhibited by propranolol (Figure 1g). As in the previous experiment, we found that many of the stress-induced gene expression changes were blocked by propranolol injection in both dHC (Figure 1g, left panel) and vHC (Figure 1g, right panel). Importantly, propranolol did not change the expression level of these genes in the absence of stress. We then directly compared the genes sensitive to stress and propranolol treatment in both dHC and vHC. To this end, we plotted the union of genes showing a significant stress:propranolol interaction in either region in one heatmap across both dHC and vHC (Supplementary Figure 1d). This showed again that the stress-induced changes were very similar in dHC and vHC, and that propranolol similarly blocked many of them. Finally, we asked whether the response differs between males and females. Despite clear sex differences in gene expression at baseline (data not shown), we found no significant sex differences in response to stress or propranolol between male and female mice (FDR<0.05; Fig. 1g). To more directly visualize this, we compared females and males by plotting the log2-fold changes of the stress:propranolol interaction across all stress-induced genes that were blocked by propranolol. We find very similar regulation patterns in both sexes (Figure 1h). Although none of these sex differences are significant, some genes seem to show quantitative differences, so we plotted the expression patterns of the 5 genes showing the largest difference in interaction term as box-plots, which suggest that these spurious differences are likely due to noisy coefficient estimates (Supplementary Fig. 1e). To address concerns that our analysis of sex differences might not have been sufficiently powered, we performed a meta-analysis of the experiments shown here along with previously published datasets from our lab (Floriou-Servou et al. 2018; von Ziegler et al. 2022). In all these experiments, the vHC of male and female mice was profiled 45 min after exposure to an acute swim stress challenge. This resulted in a sample size of 51 males and 20 females. Despite this high number of independent samples, we could not identify any statistically significant interaction between sex and the stress response. To identify candidates that might not reach significance while discounting differences due to noise in fold-change estimates, we reproduced the same analysis using DESeq2 with Approximate Posterior Estimation for generalized linear model (apeglm) logFC shrinkage (A. Zhu, Ibrahim, and Love 2018). This analysis also did not reveal any sex differences in the stress response (Supplementary Fig. 1f). We then tailored the meta-analysis specifically to the set of stress-responsive genes that were blocked by propranolol, and also for these genes the response to stress was strikingly similar in both sexes (Supplementary Fig. 1g). Altogether, we conclude that there are no major sex differences in the rapid transcriptomic stress response in the hippocampus, and that blocking beta-receptors prevents a large set of stress-induced genes in both females and males."

To put these findings in context with existing literature, we agree with the reviewer that there are many studies that have reported sex differences in the LC-circuitry as summarized by Bangasser and colleagues (Bangasser et al., 2016, 2019). However, these studies primarily focus on the LC itself, suggesting that female rats have more LC neurons, denser LC-dendrites in the peri-LC region, and that LC neurons are more readily activated by stress in females because of heightened sensitivity to CRF-signaling. A recent study in mice reports, in contrast, that females have fewer TH-positive neurons in the LC, but they also find enhanced excitability of LC neurons in females (Mariscal et al., 2023). Similarly, one study has suggested molecular differences in the makeup of the LC (Mulvey et al., 2018). Our experiments, however, focus on the impact of NA release in a projection region (hippocampus). Further, we use a strong stress induction protocol (swim stress) and various potent modes of direct LC activation, so differences in "LC-excitability" are likely less relevant in this context. We added evidence showing that we trigger powerful NA release in both sexes (Supplementary Figure 2c-h; see response to Reviewer #2, Comment #3 for more details). In addition, we show that the intensity or pattern of LC stimulation does not appear to alter the molecular response (Figure 3a-b), and that various stressors (mild or intense) all trigger the same NA-dependent molecular changes (Figure 4a-b). Therefore, our results suggest that once NA is released (in the hippocampus), the molecular downstream effects on gene expression are very similar - independent of stimulation intensity, sex, or hippocampal subregion (dorsal/ventral). This does not mean that there are no sex differences for activation of LC, but rather that the transcriptional response to NA release in the hippocampus is robust across sexes, and that propranolol seems to block NA-dependent effects similarly in both sexes. This does not rule out quantitative differences between sexes that only emerge with targeted analyses of individual genes, or once fluctuations in ovarian hormones are taken into account. We have updated the section in the discussion to summarize these considerations in light of the new results (see pages 20-21, section: "A uniform molecular response to stress and noradrenaline release in both sexes").

Comment 3: A major finding of the present study is the involvement of noradrenergic transcriptomic changes occurring in astrocytic genes in the hippocampus. Given the stated importance of this finding within the discussion, it seems that some additional dialogue integrating this with current literature about the role of astrocytes in the hippocampus during stress or fear memory would be important.

Response: We thank the reviewer for giving us an opportunity to add a more detailed discussion about the role of astrocytes and thyroid hormones in the hippocampus during learning and memory formation. We have added these statements to the discussion:

“Within the hippocampus, astrocytic pathways are emerging as important players for learning and memory processes (Gibbs, Hutchinson, and Hertz 2008; Bohmbach et al. 2022). In fact, it is well-known that NA enhances memory consolidation (Schwabe et al. 2022; McGaugh and Roozendaal 2002), and recent work suggests that these effects are mediated by astrocytic β-adrenergic receptors (Gao et al. 2016; Iqbal et al. 2023). Our transcriptomic screens revealed Dio2 as the most prominent target influenced by LC activity. Dio2 is selectively expressed in astrocytes and encodes for the intracellular type II iodothyronine deiodinase, which converts thyroxine (T4) to the bioactive thyroid hormone 3,3',5-triiodothyronine (T3) and therefore regulates the local availability of T3 in the brain (Bianco et al. 2019). Enzymatic activity of DIO2 has further been shown to be increased by prolonged noradrenergic transmission through desipramine treatment in LC projection areas (Campos-Barros et al. 1994). This suggests that the LC-NA system and its widespread projections could act as a major regulator of brain-derived T3. Notably, T3-signaling plays a role in hippocampal memory formation (Rivas and Naranjo 2007; Sui et al. 2006), raising the possibility that NA-induced Dio2 activity in astrocytes might mediate some of these effects.”

Comment 4: The comparison of the candidate genes activated by the LC in the present study (swim) with datasets published by Floriou-Servou et al., 2018 (Novelty, swim, restraint, and footshock) is an interesting and important comparison. Were there other stressors identified in this paper or other publications that do not regulate these candidate genes? Further, can references be added to clarify to the reader, that prior studies have identified that novelty, restraint and footshock all activate LC-NA neurons.

ponse: Thank you for the positive feedback. We have only tested the stressors reported in Figure 4a-b (novelty, swim, restraint, and footshock). It is known that all these stressors trigger noradrenaline release, in fact we are not aware of stressors that do not trigger NA release. This reproducible finding supports the notion that the identified set of genes is indeed highly NAresponsive. As suggested, we have now included references that show increased NA release in response to all these stressors:

“Therefore, we assessed their expression in a dataset comparing the effect of various stressors on the hippocampal transcriptome (Floriou-Servou et al., 2018). The stressors included restraint, novelty and footshock stress, which have all previously been shown to increase hippocampal NA release (HajósKorcsok et al., 2003; Lima et al., 2019; Masatoshi Tanaka et al., 1982).”

Comment 5: Comparisons are made between chemogenetic studies and yohimbine, stating that fewer genes were activated by chemogenetic activation of LC neurons. There is clear justification for why this may occur, but a caveat may need to be mentioned, that evidence of neuronal activation in the LC by each of these methods were conducted at 90 (yohimbine) versus 45 (hM3Dq) minutes, and therefore it cannot be ruled out that differences in LC-NA activity levels might also contribute.

Response: The reviewer raises an important point about some inconsistencies between the time points chosen in our study, an aspect that was also pointed out by Reviewer #2. We have chosen the 45 and 90 min time points for two different reasons. On the one hand, cFos changes on the protein level are known to peak 90 min after neuronal activation, and we wanted to capture the strongest possible cFos signal in the LC. On the other hand, we wanted to measure gene expression changes triggered by NA release, which already occur 45 min after noradrenergic activation (Roszkowski et al., 2016). Thus, when the experimental design allowed separate experiments (e.g. systemic yohimbine injection), we chose to measure gene expression after 45 min, but to validate cFos activation in the LC separately after 90min. In response to DREADD activation, however, we wanted to confirm within the same animal that LC activation was successful, and thus we collected LC and hippocampus simultaneously (Figure 2c,d). While the cFos increase is already very pronounced at the 45min time point (Figure 2g), the quality of IHC is slightly lower because the tissue cannot be perfused in this experimental design. Therefore, we do not think that the time point for cFos sampling matters in this context. However, we agree with the reviewer that it remains unclear whether yohimbine and DREADDs activate the LC with similar potency. To directly compare NA release would require a set of photometry-based experiments to measure NA release using genetically-encoded NA-sensors. While we have added such experiments for LC activation with DREADDs and optogenetics to show rapid NA release indeed occurs in the hippocampus (see Reviewer #2, Comment 3; Supplementary Figure 2c-h), yohimbine interferes with the NA-sensors as explained in detail in response to Reviewer 2, Comment 3. Thus, it was too challenging for us to directly compare the release dynamics in response to DREADDs and yohimbine, which was also not the main focus of our work. To explicitly address this caveat, we have extended the corresponding section in the discussion:

"Finally, our observation that systemic administration of the α2-adrenergic receptor antagonist yohimbine very closely recapitulates the transcriptional response to stress stands in contrast to the much more selective transcriptional changes observed after chemogenetic or optogenetic LC-NA activation. This difference could be due to various factors. First, it remains unclear how strong the LC gets activated by yohimbine versus hM3Dq-DREADDs. However, given the potent LC activation observed after DREADD activation, it seems unlikely that yohimbine would lead to a more pronounced LC activation, thus explaining the stronger transcriptional effects. Second, contrary to LC-specific DREADD-activation, systemic yohimbine injection will also antagonize postsynaptic α2-adrenergic receptors throughout the brain (and periphery). More research is needed to determine whether this could have a more widespread impact on the hippocampus (and other brain regions) than isolated LC-NA activation, further enhancing excitability by preventing α2-mediated inhibition of cAMP production. Finally, systemic yohimbine administration and noradrenergic activity have been shown to induce corticosterone release into the blood (Johnston, Baldwin, and File 1988; Leibowitz et al. 1988; Fink 2016). Thus, yohimbine injection could have broader transcriptional consequences, including corticosteroid-mediated effects on gene expression."

Comment 6: Please add information about how virus or cannula placement was confirmed in these studies. Were missed placements also analyzed separately?

Response: Pupillometry recordings were performed with all animals involving optogenetic or chemogenetic manipulations of the LC, before subjecting them to stress experiments. These assessments account for both correct optic fiber placement and virus expression (Privitera et al., 2020). If an animal did not show a clear pupil response, it was not included any further in the study. To demonstrate correct cannula placement for drug infusion of isoprotenerol in the dorsal hippocampus, we added a representative image of cannula placement in Supplementary Figure 1h.

Comment 7: Time of day for tissue collection used in genetic analysis should be reported for all studies conducted or reanalyzed.

Response: Thank you for pointing out this omission. Tissue collection for RNA-seq analysis was always performed between 11am and 5pm during the dark phase of the reversed light-dark cycle. We have added this information to the corresponding method section (“Tissue collection”).

Reviewer #1 (Recommendations For The Authors):

Comment 8: This is a well written, comprehensive and rigorous manuscript that will be of great interest to those in the scientific community.

Response: Thank you for the positive evaluation of our work and for the constructive feedback.

Reviewer #2 (Public Review):

Comment 1: The present manuscript investigates the implication of locus coeruleus-noradrenaline system in the stress-induced transcriptional changes of dorsal and ventral hippocampus, combining pharmacological, chemogenetic, and optogenetic techniques. Authors have revealed that stress-induced release of noradrenaline from locus coeruleus plays a modulatory role in the expression of a large scale of genes in both ventral and dorsal hippocampus through activation of β-adrenoreceptors. Similar transcriptional responses were observed after optogenetic and chemogenetic stimulation of locus coeruleus. Among all the genes analysed, authors identified the most affected ones in response to locus coeruleus-noradrenaline stimulation as being Dio2, Ppp1r3c, Ppp1r3g, Sik1, and Nr4a1. By comparing their transcriptomic data with publicly available datasets, authors revealed that these genes were upregulated upon exposure to different stressors. Additionally, authors found that upregulation of Ppp1r3c, Ppp1r3g, and Dio2 genes following swim stress was sustained from 90 min up to 2-4 hours after stress and that it was predominantly restricted to hippocampal astrocytes, while Sik1 and Nr4a1 genes showed a broader cellular expression and a sharp rise and fall in expression, within 90 min of stress onset.

Overall, the paper is well written and provides a useful inventory of dorsal and ventral hippocampal gene expression upregulated by activation of LC-NA system, which can be used as starting point for more functional studies related to the effects of stress-induced physiological and pathological changes.

Response: We thank the reviewer for the careful assessment of our work.

Comment 2: However, I believe that the study would have benefited of a more comprehensive analyses of sex differences. Experiments in females were conducted only in one experiment and analyses restricted to the ventral hippocampus.

Response: In response to the comments by the reviewer, as well as Reviewer #1 and the editors, we have sequenced an additional 32 brain samples to expand the comparison of sex effects in females and males across dorsal and ventral hippocampus, and we included a new meta-analysis of 3 experimental datasets (51 male and 20 female) samples, to thoroughly assess sex differences in the transcriptomic response to stress. We refer the reviewer to our detailed response provided above to Reviewer #1, comment #2, and the updated results section on pages 3-4.

Comment 3: Although, the experiments were overall sound and the results broadly support the conclusion made, I think some methodological choices should be better explained and rationalized. For instance, the study focuses on identifying transcriptional changes in the hippocampus induced by stress-mediated activation of the LC-NA system, however NA release following stress exposure and pharmacological or optogenetic manipulation was mostly measured in the cortex.

Response: Because the hippocampus was used for RNA-sequencing, we could not assess NA release in the hippocampus (as this would require fiber implants that would interfere with molecular measures, or different tissue processing for HPLC). Nonetheless, we wanted to assess the transcriptional changes in the hippocampus, while simultaneously measuring successful stimulation of the LC-NA system in the same animals. To achieve this, we pursued 3 routes: 1) we used pupillometry to confirm functional LC activation; 2) we measured cFOS in the LC to directly demonstrate LC activation; 3) we assessed NA release using uHPLC (which requires larger tissue samples) and we chose the cortex because both cortex and hippocampus receive NA predominantly from the LC (Samuels & Szabadi, 2008). Importantly, we had previously shown that chemogenetic LC activation leads to a similar NA turnover in both the cortex and hippocampus, as measured by uHPLC (Zerbi et al., 2019). The relevant figure from that paper is inserted below to quickly show the striking similarity between hippocampus and cortex.

Author response image 1.

Levels of noradrenaline (NE) turnover (MHPG/NE ratio) in the cortex (CTX) and hippocampus (HC), measured in whole tissue with uHPLC 90min after hM3Dq-DREADD activation of the LC (copied and cropped from Zerbi et al, 2019, Neuron).

In response to the reviewers comment, we performed additional experiments to directly demonstrate that LC-activation with DREADDs as well as optogenetics causes an increase in hippocampal NA-release. We recorded NA release in the hippocampus (using fiber photometry combined with genetically encoded NA sensors). For DREADD activation, we observed a strong increase in hippocampal noradrenaline that started a few minutes after clozapine administration, and this increase was sustained throughout the duration of the 21 minute recording (see Supplementary Figure2c-e). For optogenetic LC activation, we find a rapid and immediate sharp increase in NA levels in the hippocampus (Supplementary Figure 2f-h). These experiments were performed in females and males and triggered similar responses. An adapted and cropped version of Supplementary Figure 2 is pasted below for ease of reading.

Please note that we could not perform a similar experiment using yohimbine, because the GRABNE sensors are based on the alpha-2 adrenergic receptor, thus yohimbine administration interferes with the photometry recording. However, we believe that it is clear from this response that strong activation of the LC leads to uniform release of NA in the hippocampus and cortex.

Author response image 2.

c, Schematic of fiber photometry recording of hippocampal NA during chemogenetic activation of the LC. After 5 min baseline recording in the homecage animals were injected with clozapine (0.03mg/kg, i.p.) and placed in the OFT for 21min. d, Average ΔF/F traces of GRABNE2m photometry recordings in response to chemogenetic activation of the LC (mean±SEM for hM3DGq+ and hM3DGq- split into females and males, n=3/group/sex). e, Peak ΔF/F response of fiber photometry trace. f, Schematic of fiber photometry recording of hippocampal NA during optogenetic activation of the LC. Animals were lightly anesthetized (1.5% isoflurane) and recorded in a stereotaxic frame. After 1 min baseline recording, animals were stimulated three times with 5Hz for 10s (10ms pulse width, ~8mW laser power) and recorded for 2 min post-stimulation. g, Average ΔF/F traces of the NA sensors GRABNE1m and nLightG in response to optogenetic activation of the LC (mean±SEM for females and males, n(females)= 10, n(males)=5. h, Peak ΔF/F response of fiber photometry trace.

Comment 4: Furthermore, behavioral changes following systemic pharmacologic or chemogenetic manipulation were observed in the open field task immediately after peripheral injections of yohimbine or CNO, respectively. Is this timing sufficient for both drugs to cross the blood brain barrier and to exert behavioral effects?

Response: We have previously shown that chemogenetic activation of the LC through clozapine elicits pupil responses within 1-2 minutes after injection (Privitera et al., 2020; Zerbi et al., 2019). This indicates that clozapine rapidly crosses the blood brain barrier and affects LC activity within a few minutes after injection. Our additional experiments using genetically encoded sensors in the hippocampus show this even more directly (Supplementary Figure 2d), see also the response to Comment 3 above.

Similarly, yohimbine also rapidly crosses the blood brain barrier within the same time frame (Hubbard et al., 1988). These observations are consistent with the rapid behavioral effects that can be detected within a few minutes after injection of clozapine for LC-DREADD activation (Zerbi et al., 2019), and for yohimbine as well (von Ziegler et al., 2023). In response to another comment of this reviewer, we have also re-analyzed the behavior presented in the current manuscript in time-bins of 3 minutes, which also shows the rapid onset of effects in response to yohimbine (within the first 3 min) and DREADDs (within 6 min), see Supplementary Fig. 3.

Comment 5: Finally, the study shows that activation of noradrenergic hippocampus-projecting LC neurons is sufficient to regulate the expression of several hippocampal genes, although the necessity of these projection to induce the observed transcriptional effects has been tested to some extent through systemic blockade of beta-adrenoceptor, I believe the study would have benefited of more selective (optogenetic or chemogenetic) necessity experiments.

Response: We understand the reviewer's point that blocking the LC during stress exposure would be an interesting experiment. However, it is very hard to completely silence the LC during intense stressors. In fact, despite intense efforts, we have not been able to silence the LC during swim stress exposure using DREADDs or other chemogenetic approaches (PSAM/PSEM). We were in fact able to silence the LC with the optogenetic inhibitor JAWS (and others have reported successful LC silencing with GtACR2), but there is a major issue involving the "rebound effect", where more NA is released once the inhibition is stopped. We would thus have had to optogenetically silence the LC for 45-90 min, which would create heat artifacts, and require challenging control experiments to draw firm conclusions. Given all these issues, we reasoned that blocking adrenergic receptors is a simple and elegant solution, which provides clear evidence for the necessity of beta-adrenergic signaling.

Reviewer #2 (Recommendations For The Authors):

Major concerns:

Comment 6: The study focuses on the identification of transcriptional changes in the hippocampus induced by stress-mediated activation of the LC-NA system, however, noradrenaline release following stress exposure or yohimbine injection was measured in the cortex. Authors should consider measuring NA concentrations in the hippocampus after exposure to swim stress or administration of yohimbine, or at least explain their choice to analyse to cortex in the manuscript.

Response: We have addressed this issue in detail in Response to "Reviewer 2, Comment #3", where we provided an overview of the additional data that support our approach. As mentioned before, measuring NA release after yohimbine is not compatible with our GRABNE-photometry approach, as the GRAB-sensor is based on alpha2-adrenoceptor. Here, we would like to add that measuring NA release using photometry during swim stress is also challenging. The challenge is the vigorous movement (swimming, typically in one direction), which creates pressure on the cables/implants. We felt that overcoming these experimental challenges (setup, troubleshooting and controls) would be beyond the scope of the paper, given that it is already known that this stressor leads to strong NA release in the hippocampus. We have now included references that demonstrate that all the stressors used in our work trigger NA increase in the hippocampus (see response to Reviewer 1, Comment 3): “Therefore, we assessed their expression in a dataset comparing the effect of various stressors on the hippocampal transcriptome (Floriou-Servou et al., 2018). The stressors included restraint, novelty and footshock stress, which have all previously been shown to increase hippocampal NA release (Hajós-Korcsok et al., 2003; Lima et al., 2019; Masatoshi Tanaka et al., 1982).”

Comment 7: Concerning the experiment aimed at investigating sex differences in gene expression, it is not clear the reason why authors decided to restrict their analyses in females to the ventral hippocampal only. The explanation that in males they did not detect major differences between the dorsal and ventral hippocampus is not sufficient, because there could have been different effects in females. Therefore, the conclusion made by the authors that their "results suggest that the transcriptomic response is independent of sex" is not entirely correct, since sex differences were only evaluated in the ventral hippocampus.

Response: We appreciate the reviewer's critique. As described above, we have now also sequenced the dorsal hippocampal tissue from the propranolol experiment (males and females, 32 samples) and additionally added an extensive meta-analysis of three large datasets (n=71) to compare transcriptional sex differences in response to stress. A detailed description of these experiments and how they have extended/supported our conclusions have been provided in response to Reviewer #1, Comment #2.

Comment 8: Besides the effects on females, the same experiment examined whether propranolol by itself (in the absence of stress) would have been able to alter gene expression: such effects were not examined in the dorsal hippocampus. In contrast, in a different experiment, the effects of isoproterenol on genes expression were restricted to the dorsal hippocampus only. Furthermore, related to this latter experiment, intra-dorsal hippocampal injection of isoproterenol should presumably mimic the rise in NA observed after stress exposure, why was gene expression measured 90 min after isoproterenol central injections while in the other experiments gene expression was determined 45 min after stress, that is when authors observe the peak NA concentration?

Response: We have addressed the reviewer's critique of dorsal vs ventral hippocampus by reanalyzing 32 additional samples from dorsal hippocampus of male and female mice after propranolol (or saline) injection. Please see response to Reviewer #1, comment #2.

Regarding the time points: We have chosen the 45 and 90 min time points mainly for two reasons. First, cFos protein changes are known to be strongest 90 min after neuronal activation. Second, because we wanted to capture gene expression changes triggered by NA release, we reasoned that these effects must be fast and should thus be measured at an early transcriptional time-point (45min). However, after performing the time-course experiment after swim stress exposure (Figure 4d,c), we observed that the LC-NA-sensitive genes (e.g. Dio2 and several PP1-subunits) show the strongest changes 90 min after stress exposure. Therefore, in some of our experiments we opted to analyze gene expression changes at 90min, converging with the time-point we typically use for cFos staining. Contrary to the reviewer's statement, peak NA concentrations are not observed 45 min after the various interventions, but rather the peak in the main metabolite (MHPG) is observed then, due to the temporal dynamics of NA release and breakdown. NA release occurs immediately upon stress exposure (or direct LC activation), which we also show in the new photometry data described above. Thus, rapid NA release triggers intracellular cascades that lead to downstream transcriptional changes, which peak presumably between 4590 min later.

Comment 9: Behavioral changes following systemic pharmacologic or chemogenetic manipulation were observed in the open field task immediately after peripheral injections of yohimbine or CNO, respectively. Is this timing sufficient for both drugs to cross the blood brain barrier and to exert behavioral effects? It is also not immediately clear the reason why the open field tasks have different durations depending on the experiments, which can also impact the results. Authors might also consider to split the open field data analyses in 2 or 3 min time-bins, to allow for a better comparison across the different results.

Response: We thank the reviewer for the suggestion to plot the behavior data as time-bins. We have implemented this change for the yohimbine and DREADD experiments, and updated the corresponding figure accordingly (Supplementary Figure 3, pasted below for ease of reading). The new visualization clearly shows that yohimbine injection triggers rapid behavioral effects already in the first three minutes, whereas the LC-DREADD activation triggers behavioral changes within 3-6 minutes after injection. Thus, clear drug effects are visible in the first 10 minutes, which is comparable to the standard OFT test (10min testing) shown in response to swim stress exposure (Suppl. Figure 3a). The choice to expose mice to the OFT for 21 minutes in total was due to the fact that we based our experimental approach on the optogenetic LC-stimulation protocol first published by McCall and colleagues (McCall et al, Neuron, 2015), in which the LC is stimulated for 3 min followed by 3 min pauses (see Suppl. Figure 3d). Because of this on-off design, we decided to keep the optogenetic analysis simple and show the overall effect (Supplementary Figure 3d), particularly as we know that NA dynamics do not recover rapidly enough after 3 min continuous stimulation to justify a bin-analysis (unpublished data).

Author response image 3.

Effects of acute stress and noradrenergic stimulation on anxiety-like behaviour in the open field test. a, Stress-induced changes in the open field test 45 min after stress onset. Stressed animals show overall reductions in distance traveled (unpaired t-test; t=3.55, df=22, p=0.0018), time in center (welch unpaired t-test; t=3.50, df=13.61, p=0.0036), supported rears (unpaired t-test; t=3.39, df=22, p=0.0026) and unsupported rears (unpaired t-test; t=5.53, df=22, p = 1.47e-05) compared to controls (Control n = 12; Stress n = 12). This data have been previously published (von Ziegler et al., 2022). b, Yohimbine (3 mg/kg, i.p.) injected animals show reduced distance traveled (unpaired t-test; t=2.39, df=10, p=0.03772), reduced supported rears (unpaired t-test; t=6.56, df=10, p=0.00006) and reduced unsupported rears (welch unpaired t-test; t=3.69, df=4.4, p = 0.01785) compared to vehicle injected animals (Vehicle n = 6; Yohimbine n = 7). c, Chemogenetic LC activation induced changes in the open field test immediately after clozapine (0.03 mg/kg, i.p.) injection. hM3Dq+ animals show reduced distance traveled (unpaired t-test; t=6.28, df=13, p=0.00003), reduced supported rears (unpaired t-test; t=4.28, df=13, p=0.0009), as well as reduced unsupported rears (welch unpaired t-test; t=4.28, df=13, p = 0.00437) compared to hM3D- animals (hM3Dq- n = 7; hM3Dq+ n = 8). d, Optogenetic 5 Hz LC activation induced changes during the open field test. ChR2+ animals show reduced supported rears (unpaired t-test; t=2.42, df=64, p=0.0185) and reduced unsupported rears (unpaired ttest; t=2.91, df=64, p = 0.00499) compared to ChR2- animals (ChR2- n = 32; ChR2+ n = 36). Data expressed as mean ± SEM. p < 0.05, p < 0.01, p < 0.001, **p < 0.0001.

Comment 9: The study shows that activation of noradrenergic hippocampus-projecting LC neurons is sufficient to regulate the expression of several hippocampal genes. I believe the study would have benefited of more selective necessity experiments. Authors might consider adding optogenetic (or chemogenetic) experiments aimed at inhibiting LC-NA hippocampal projections during stress exposure (or, alternatively, perform intrahippocampal pharmacological blockade of β-adrenoreceptors during stress exposure), and determine the effects on gene expression.

Response: We kindly refer the reviewer to our previous response to Comment #2 above.

Minor concerns:

There is a typo in the abstract. Please correct "LN-NA" with "LC-NA"

Response: Thank you, we have corrected it.

References

Bangasser, D. A., Eck, S. R., & Ordoñes Sanchez, E. (1/2019). Sex differences in stress reactivity in arousal and attention systems. Neuropsychopharmacology: Official Publication of the American College of Neuropsychopharmacology, 44(1), 129–139.

Bangasser, D. A., Wiersielis, K. R., & Khantsis, S. (06/2016). Sex differences in the locus coeruleusnorepinephrine system and its regulation by stress. Brain Research, 1641, 177–188.

Reviewer #2 (Public Review):

The present manuscript investigates the implication of locus coeruleus-noradrenaline system in the stress-induced transcriptional changes of dorsal and ventral hippocampus, combining pharmacological, chemogenetic, and optogenetic techniques. Authors have revealed that stress-induced release of noradrenaline from locus coeruleus plays a modulatory role in the expression of a large scale of genes in both ventral and dorsal hippocampus through activation of β-adrenoreceptors. Similar transcriptional responses were observed after optogenetic and chemogenetic stimulation of locus coeruleus. Among all the genes analysed, authors identified the most affected ones in response to locus coeruleus-noradrenaline stimulation as being Dio2, Ppp1r3c, Ppp1r3g, Sik1, and Nr4a1. By comparing their transcriptomic data with publicly available datasets, authors revealed that these genes were upregulated upon exposure to different stressors. Additionally, authors found that upregulation of Ppp1r3c, Ppp1r3g, and Dio2 genes following swim stress was sustained from 90 min up to 2-4 hours after stress and that it was predominantly restricted to hippocampal astrocytes, while Sik1 and Nr4a1 genes showed a broader cellular expression and a sharp rise and fall in expression, within 90 min of stress onset.

The paper is well written and provides a useful inventory of dorsal and ventral hippocampal gene expression upregulated by activation of LC-NA system, which can be used as starting point for more functional studies related to the effects of stress-induced physiological and pathological changes. Sex-differences were also explored which represents a strength of the study.

Author Response

The authors' responses to the public reviews can be found here

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The following is the authors’ response to the most recent recommendations.

Recommendations for the authors:

Reviewer #2 (Recommendations For The Authors):

I appreciate the effort that the authors have put into this revised version of the manuscript. Before going into details, I would suggest that, in the future, the authors include enough information in their response to allow reviewers to follow the changes made. Not simply "Fixed", but instead "we have modified the description of these results and now state on lines XXX to XXX (revised text)".

We greatly apologize, we certainly did not wish to cause more work for the reviewer to find the necessary changes. We will list the line number and our changes in the following response.

The authors' response to my comments was confined to the minor points, with no attention to more important questions regarding speculations about mechanism which were (and still are) presented as factual conclusions. I do not consider the responses adequate.

We responded to each of your comments and where we disagree, we have explained in detail.

With respect to the meaning of "above" and "below" in the context of an intracellular organelle, I think that referring to up and down in a figure is fine, provided that the cytoplasmic and luminal sides are indicated in that figure. I think that labeling to that effect in each figure would be immensely helpful for the reader.

We agree with this point and have updated all the figures to include these labels.

The statement on lines 333-335 about non-competitive inhibition is a bit naïve. The only thing ruled out by this type of inhibition is that substrate and TBZ binding do not share the same binding process, in which case they would compete. It doesn't show that TBZ gets to its binding site from the lumen or from the bilayer, or by any other process that isn't shared with substrate. It also doesn't rule out kinetic effects, such as slow inhibitor dissociation, that result in non-competitive kinetics. Please rewrite this sentence to indicate that one explanation of the non-competitive nature of TBZ inhibition would be that TBZ diffuses into the vesicle and binds from the lumen. It's not the only explanation.

We have changed this sentence lines 334-336 to be more speculative and not include any statement about non-competitive inhibition. Please see, “Studies have proposed that TBZ first enters VMAT2 from the lumenal side, binding to a lumenal-open conformation.”

The revised version integrates the MD simulations into a plausible mechanism for luminal release of substrate. A key element in this mechanism is the protonation of D33, E312 and D399, which allows substrate to leave following water entry into the binding site. The acidic interior of synaptic vesicles should facilitate such protonation, but the fate of those protons needs to be considered. Are any of them predicted to dissociate prior to the return to a cytoplasm-facing conformation? If so, are all 3 released in that conformation? Postulating protonation events at one point in the reaction cycle requires some accounting for those protons - or at least recognition of the problem of reconciling their binding with the known stoichiometry of VMAT.

We completely agree with this point and while we cannot account for all protons with a single structure and simulation of neurotransmitter release, some discussion of the fate of the protons is warranted. We have included a highly speculative statement in the discussion on this point, see lines 462-465, “Given the known transport stoichiometry of two protons per neurotransmitter, we speculate that two protons may dissociate back into the lumen, perhaps driven by the formation of salt bridges between D33 and K138 or R189 and E312 for example in an cytosol-facing state.”

Reviewer #3 (Recommendations For The Authors):

On page 13, line 238, the statement "The protonation states of titratable residues D33, E312, D399, D426, K138 and R189, which are in close proximity to TBZ, also impact its binding stability (Table 4)" is misleading. Table 4 only shows that D426 is charged and what the pKa values are. This should be rephrased to separate out which residues are in close proximity from what is known about how their protonation states affect TBZ stability.

We agree with this statement and have rephrased this on line 290-294 on page 13 to read, “Several titratable residues, including D33, E312, D399, D426, K138, and R189, line the central cavity of VMAT2 and impact TBZ binding stability (Table 4). We found that maintaining an overall neutral charge within the TBZ binding pocket, as observed in system TBZ_1, most effectively preserves the TBZ-bound occluded state of VMAT2. Residues R189 and E312 in particular are within close proximity of TBZ and participate directly in binding.” We note that given the acidic pH of the vesicle lumen (5.5), it is likely all four residues may be protonated to a significant degree in this state.

Typos:

• luminal is another name for the drug generically known as phenobarbital, lumenal means in the lumen. (This typo seems to have crept into the published literature now too).

Thank you for pointing this out. Indeed, we had considered carefully whether to use ‘lumenal’ or ‘luminal’ in our revised text. In fact, both are used interchangeably throughout the scientific literature and luminal is the more commonly used term. Please also see: https://www.merriam-webster.com/medical/luminal we do agree that there may be confusion because ‘Luminal’ is a trademark of phenobarbital. Therefore, we have changed the text to read ‘lumenal’ throughout.

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

Reviewer #1 (Recommendations For The Authors):

I congratulate the authors on this study, which I enjoyed reading. Overall, the study reports a novel and exciting new structure for a member of the SLC18 family of vesicular monoamine transporters. Associated MD, binding and transport assays provide support for the hypothesis and firm up the modelled pose for the TBZ drug. The main strengths of the study largely sit with the structure, which, as the authors say, provides additional and essential insights above those available from AF2. The structures also reveal several potentially interesting observations concerning the mechanism of gating and proton-driven transport. The main weakness lies in the limited mutational data and studies into the role of pH in regulating ligand binding. As detailed below, my main comment would be to spend a little extra time expanding the mutational data (perhaps already done during the review?) to enable more evidence-based conclusions to be drawn.

We thank reviewer #1 for their helpful comments and suggestions. We agree that mutational analysis specifically of neurotransmitter transport would strengthen the mechanistic conclusions of the work. We also agree with reviewer #1 and #3 that the role of pH and the protonation state of charged residues was a weakness in the first version of the manuscript. Therefore, we have expanded our mutational and computational data as detailed below and we believe that this has further solidified our findings.

Specific comments & suggestions:

It is an interesting strategy to fuse the mVenus and anti-GFP nanobody to the N-/C-termini. The authors should also include in SI Fig. 1 a full model for the features observed in these maps and deposit this in the PDB.

Great point, we have made a main text panel describing the construct. Figure S1 includes a full description of the construct. The reviewer will note that the PDB entry contains the entire amino acid sequence of the construct and while the GFP and GFP-Nb cannot be well modeled into the density, we have included all of the relevant information for the reader.

Difficult to make out the ligand in Fig. 2b, I would suggest changing the color of the carbon atoms.

Fixed.

It is difficult to make out the side chains in ED Fig. 5d.

This is now its own supplemental figure and is presented larger.

ED Figures are called out of order in the manuscript. For example, in line 143 ED Fig.6 is called before ED Fig. 5d (line 152), and then ED 5d is called before ED 5a. This makes it rather confusing to follow the description, analysis, and data when reading the paper. Although there are other examples. I would suggest trying to order the figure callouts to flow with the narrative of the study.

Agreed. Fixed.

It wasn't clear to me what the result was produced by just imaging the ligand-free chimaera protein. It would be useful to say whether this resulted in low-resolution maps and whether the presence of the TBZ compound was essential for high-resolution structure determination.

The ligand is likely required for structure determination. We have not, however, made such a statement largely because we have yet to determine an apo reconstruction.

The role of E127 and W318 on EL1 in gating the luminal side of the transporter is very intriguing. As the authors suggest, this may represent an atypical gating mechanism for the MFS (line 182). I did wonder if the authors had considered providing more insight into this potentially novel mechanism. Additional experiments would be further mutations of W318 to F, Y, V, and I to see if they can identify a non-dead variant that could be analysed kinetically. They may have more luck with variants of E127, as they suggest this stabilises W318. If these side chains are important for gating and transport regulation, one might expect to see interesting effects on the transport kinetics.

This is a fantastic suggestion. We have done this, and we think that the reviewer will find the results to be quite interesting. Some VMAT2 sequences have an R or an H at position 318 while VPAT has an F at the equivalent position. We have made these mutants including the E127A mutant and analyzed them using TBZ binding and transport experiments. Interestingly the W318R, H, and F mutants preserve activity in varying degrees with the R mutant closely resembling wild type. W318A has no transport activity. Only the W318F mutant retains some TBZ binding. The E127A mutant also has little transport activity but nearly wild type like TBZ binding which we believe suggests a role for this residue also in stabilizing W318.

The authors identify an interesting polar network, which is described in detail and shown in Fig. 2d. However, the authors present no experimental data to shed further mechanistic insight into how these side chains contribute to monoamine transport or ligand binding. Additional experiments that would be helpful here might include repeating the binding and competition assays shown in Fig. 1c under different pH conditions for the WT and different mutations of this polar network. At present, this section of the manuscript is very descriptive without providing much novel insight into the mechanism of VMAT transport. I did wonder whether a similar analysis of pH effects on DTBZ binding might also provide insight into the role of E312 and the role of protons in the mechanism.

Thank you, we have addressed this point in several different ways. The first is that many of these residues have already been characterized in several earlier studies, see refs 31, 32, and 42 and we have incorporated this into our discussion where appropriate. With respect to E312, the reviewers’ comments are again very appropriate. We have addressed this using computational experiments exploring the protonation status of E312 and other residues as well as TBZ. Our simulations and Propka calculations clearly show that E312 must be protonated and TBZ must be deprotonated to maintain TBZ binding. We have also extended these computational studies toward understanding the protonation status of residues which orchestrate dopamine binding and release.

The authors then describe the binding pose for TBZ. This section also provides some biochemical characterisation of the binding site, in the form of the binding assay introduced in Fig. 1. However, the insights are again somewhat reduced as the mutants were chosen to show reduced binding. Could the authors return to this assay and try more conservative mutations of the key side chains to illuminate more detail? For example, does an R189K mutant still show binding but not transport? Similarly, what properties does an E312D have? The authors speculate that K138 might play a role in coupling ligand binding/transport to the protonation, possibly through an interaction with D426 and D33 (line 236). Given the presence of D33 in the polar network described previously, I was left wondering how this might occur. I feel that some of the experiments with pH and conservative mutants might shed some light on this important aspect. Please label the data points in Fig. 3d.

Indeed, alanine mutants at these positions while valuable do not provide the level of detailed insight into mechanism that we also would have liked to obtain. Thus, we have made more conservative and targeted mutants like the R189K mutant and various mutants at N34 for example and tested them in both transport and binding assays. We have also made a mutant at K138 and found that it is not transport competent or able to bind TBZ to a significant degree. With respect to labels and color codes, we have made the color codes consistent between the bar graphs and the curves. We have also labeled the data points in the figure legends.

The manuscript currently doesn't present a hypothesis for how TBZ induces the 'dead-end' complex compared to physiological ligands. Does the MD shed any light on this aspect of the study? If the authors place the physiological ligand in the same location as the TBZ and run the simulation for 500ns, what do they observe? 100ns is also a very short time window. I appreciate the comment about N34 in line 303, but is this really the answer? It would be very interesting to provide more evidence on this important aspect of VMAT pharmacology.

MD with a natural ligand (dopamine) provides substantial insight into why TBZ is a dead-end complex. Since water cannot penetrate into the binding site in the TBZ bound complex, this does not allow for substantial luminal release. In contrast, simulations conducted in the presence of DA bound to the occluded VMAT2 show the propensity of that structure to accommodate an influx of water molecules that promote the release of DA to the lumen. The new results are illustrated in Figure 5 (main text) as well as supplemental figure 8 panels d-h. The new simulations further emphasized the importance of the protonation state of acidic residues near the substrate-binding pocket.

Reviewer #2 (Recommendations For The Authors):

Line 68, "both sides of the membrane" -> "alternately to either side of the membrane".

Fixed. Thanks.

Transmembrane proteins in intracellular organelles present unique issues of nomenclature. I suggest the authors refer to cytoplasmic and luminal faces of the protein (not intracellular or extracellular (line 124)) and adhere to these names to avoid confusion. This creates problems for loops called IL and EL, but they could be defined on first use.

We agree with this point and had initially gone with the conventional definitions used in the literature. We have now changed this throughout the text to be luminal and cytosolic.

Lines 135-6, are these residue numbers correct? The pdb file lists 126 as Asp and 333 as Ala.

Thank you. This is fixed.

ED Fig. 6 is not clear. A higher-resolution figure is needed.

We have updated this figure and hope that the reviewer will find it to be much clearer.

Lines 158-9, Is there any data to support effects on dynamics or folding? If not, please indicate that this is speculation.

Fixed.

Line 174, Should "I315" be "L315"?

Fixed.

Line 179, Please indicate what is meant by "inner" and "below" (also lines 183 and 258).

We have added Figure calls here where needed.

Line 192, S197 is listed as part of polar network 1, but not discussed further. Is it actually involved, or just in the neighborhood?

It is part of the network, but we did not discuss in further detail because we do not have data indicating its precise function and thus have left this as a description.

Line 199, E312, and N388 are fairly distant from each other. Do you want to clarify why they represent a network?

While they are not within hydrogen bonding distance, we nevertheless include them as part of the same network because they may come into closer proximity in a different conformational state.

Line 206, Protonation of all 3? VMAT2 doesn't transport 3 protons per cycle. Please clarify.

We believe that these residues may be protonated, but they may not necessarily all be involved in proton transport.

Line 219, Do you mean the aspartate unique to DAT, NET, and SERT? This is Gly in all the amino acid transporters in the NSS family. Please be specific.

Fixed. Thank you.

Line 224, "mutation of E312 to Q" or "mutation of Glu312 to Gln".

Fixed. Thank you.

Fig. 3d, Normally, one would expect full saturation curves for each mutant. How can a reader distinguish between low affinity or a decrease in the number of binding sites? Would full binding curves be prohibitive for the mutants because of the cost or availability of the ligand? These points should be addressed. A couple of the curves are not visible. Would an expanded scale inset show them more clearly? Also, would it be possible to include chemical structures for all ligands discussed?

Many if not most of these mutants bind TBZ with such low affinity that it is not possible to measure a full saturation curve either because of ligand availability (radioactive ligand concentration is only in µM) or due to technical issues with being able to measure such low affinity binding. We have changed the presentation of the curves and have split the gating and binding site mutants into their own figures. We feel this improves the readability of these curves. We have also included a table with the respective Kd values determined for each of the mutants where possible.

Line 235, The distances are long for a direct interaction between K138 and the TBZ methoxy groups. The unusual distances should be mentioned if an interaction is being proposed.

We do not think that K138 is directly involved in TBZ binding, however this was written in a confusing way and has been now changed.

Line 243, Please give a quantitative estimate of the affinity difference. "modestly" is vague.

It is an approximately 2-fold difference. Fixed in the text.

Line 248, 150 nM is, at best, a Kd, not an affinity.

Agreed, this is changed.

Reviewer #3 (Recommendations For The Authors):

The (3 x ~100ns-long) molecular dynamics simulations provided suggest some instability of the pose identified by cryo-EM. While it is not unreasonable that ligands shift around and adopt multiple conformations within a single binding site (in a reversible manner), the present results do raise questions about the assumptions made when starting the simulations, in particular (1) the protonation states of charged residues in the TBZ binding sites; (2) the parameters used for tetrabenazine; (3) the conformations of acidic side chains that are notoriously difficult to resolve in cryoEM maps; and (4) any contributions of the truncated regions truncated in the simulated structure, namely the cysteine cross-linked loop and the terminal domains. The authors should examine and/or discuss these contributions before attributing mechanistic insights into the newly observed binding orientation.

In order to estimate the effects of protonation states on TBZ binding, we now added three new systems with altered protonation on TBZ and binding pocket lining residues (see Table 3 in the revised vision); and for each system, we performed multiple MD runs to address the question and concerns raised by reviewer.

Regarding the protonation states: Propka3.0 was used to determine the protonation states, finding that E312 and D399 should be protonated. If I am not mistaken, this version of ProPka cannot account for non-protein ligands (https://github.com/jensengroup/propka). Given their proximity to the binding site, these protonation states will be critical factors for the stability of the simulations. The authors could test their assumption by repeating the calculations with Propka 3.1 or higher, to establish sensitivity to the ligand. Beyond this, showing the resultant hydrogen bond networks will help to reassure the reader that the dynamics in the lumenal gates do not arise from an artifact.

We thank the reviewer for suggestion of using higher version of Propka. We used the most recent Propka3.5 and carried out protonation calculations in the presence and absence of TBZ. The new calculations are presented in Table 4 and SI Figure 8c of the revised version.

It should be possible to assess whether waters penetrate the ligand binding site during the simulations if that is of concern.

We now added the number of waters within the ligand binding pockets for all MD simulations we performed, which are presented in Table 3 and Table 5 of the revised version.

Finally, I didn't fully understand the conclusion based on the simulations and the "binding affinity" calculations: do they imply that the pose identified in the EM map is not stable? What is the value of the binding affinity histogram?

We apologize for this confusion. For each MD snapshot, we calculated TBZ binding affinity using PRODIGY-LIG (Vangone et al., Bioinformatics 2019), which is a contact-based tool for computing ligand binding affinity. The binding affinity histogram shown in the original submission was the histogram of those binding affinities calculated for MD snapshots. In the revision, we replaced binding affinity histogram by time evolution of binding affinity changes (SI Fig 6c in the revision). The simulations confirmed that the pose identified in the EM map is stable, with a flattened binding affinity of -9.4 ± 0.3 kcal/mol in all three runs.

Recommendations regarding writing/presentation:

The authors use active tense terminology in attributing forces to elements of structure (cinching, packing tightly, locking). While appealing and commonplace in structural biology, this style frequently overstates the understanding obtained from a static structure and can give a rather misleading picture, so I encourage rephrasing.

We appreciate this point; the use of these words is not meant to overstate or provide a misleading picture but rather to aid the reader in mechanistic understanding of the proposed processes.

I would also recommend replacing the terms "above" and "below" for identifying aspects of the structure; the protein's location in the vesicular membrane makes these terms particularly difficult to follow.

These terms refer specifically to the Figures themselves which we have always oriented with the luminal side at the top of the page and the cytosolic on the bottom. We have indicated in Figure 1 the orientation of VMAT2. The Figures are the point of reference which we refer to, and the ‘above’ and ‘below’ terms have been used to assist the reader to make the manuscript easier for a more casual or non-expert reader to follow.

Minor corrections:

• the legend in Figure 2 lacks details, e.g. how many simulation frames are shown, how were the electrostatic maps calculated?

We revised Figure 2 and moved simulation frames to SI figure 6e. A total of 503 simulation frames are shown.

• how were the TBZ RMSDs calculated? using all atoms or just the non-hydrogen atoms?

For TBZ RMSDs, we used non-hydrogen atoms. This information is presented in the Methods section.

MD simulation snapshots and input files can be provided via zenodo or another website.

We will upload snapshots and input files to Zenodo upon acceptance of the manuscript.

Reviewing editor specific points:

Specific points

L.97: Remove "readily available"

Fixed.

L.99: The authors are not measuring competition binding. It is well known that reserpine and substrates inhibit TBZ binding only at concentrations 100 times higher than their respective KD and KM values. It is, therefore, surprising that the authors use this isotherm and refrain from commenting on the significance of the finding. Moreover, the presentation of results as "Normalized Counts" does not provide any information about the fraction of VMAT molecules binding the ligand. At least, the authors should provide the specific activity of the ligand, and the number of moles bound per mole of protein should be calculated.

The point was not to infer any details about the conformations that TBZ and reserpine bind but merely to point out that both constructs have a similar behavior with respect to their Ki for reserpine. We have added a sentence to say that reserpine binding stabilizes cytoplasmic-open so the reader is aware of the significance of this competition experiment.

L.102: The characterization of serotonin transport activity needs to be more satisfactory. The Km in rVMAT2 is 100-200 nM, so why are the experiments done at 1 and 10 micromolar? Is the Km of this construct very different? The results provided (counts per minute at the steady state) need to give more information.

The Km of human VMAT2 varies somewhat according to the source but has generally been reported to be between 0.6 to 1.4 µM for serotonin according to these references.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3019297/ https://www.cell.com/cell/pdf/0092-8674(92)90425-C.pdf https://www.pnas.org/doi/abs/10.1073/pnas.93.10.5166

Fig 1B could be more informative. I suggest adding a cartoon model with TMs labeled, similar to ED Fig6a.

This panel is to aid the reader in accessing the overall map quality and thus we do not wish to add additional labels/fits which would distract from that point. Instead, we have added overall views of the model in Figs 2,3.

L.179: The authors claim that the inner gate is located "below" (whatever this could mean) the TBZ ligand. In L.214, they claim that TBZ adopts a pose.....just "below" the location of the luminal gating residues. Please clarify and use appropriate terminology.

This refers to the position of these residues in the Figures themselves. We have added figure calls where appropriate here.

Fig. 4: The cartoon could be more informative.

We have added more information to the mechanism cartoon which is now Figure 6. This incorporates some of our new data and we believe it will be more informative.

L. 213: The paragraph describes residues involved in TBZ binding. Mutagenesis is used to validate the structural information. However, the results (ED fig. 5B) must be corrected for protein expression levels. In the Methods section, the authors state (L.444), "Mutants were evaluated similarly from cell lysates of transfected cells." Without normalization of protein expression levels, the results are meaningless even if they agree with predictions.

In fact, we have normalized the concentrations of protein in our binding experiments. This was noted in the methods section. And to account for these differences, experiments were conducted using 2.5 nM of VMAT2 protein as assessed by FSEC.

L.220: The referral to ED Fig.7 is not appropriate here. The figure shows docking-predicted poses of dopamine and serotonin.

Figure call has been changed.

L.226: The referral to Fig. 3b needs to be corrected. The figure shows TBZ and not the neurotransmitter.

This has been corrected.

L. 337: "The neurotransmitter substrate is bound at the central site." What do the authors mean in this cartoon? Do they have evidence for this? Tetrabenazine is not a substrate.

This cartoon drawing is meant to illustrate the elements of structure. Similar drawings are presented throughout the literature such as here: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5940252/ Figure 3 and here: https://pubs.acs.org/doi/10.1021/acs.chemrev.0c00983 Figure 2.

The same compound is mentioned with different names: 3H-dihydrotetrabenazine and 3H-labeled DTBZ.

Fixed.

ED fig 1d is illegible.

The high-resolution figure is completely legible. We will provide this to the journal upon publication.

Figure 2d: A side view would be more visual.

We have updated this figure and believe that it is much easier to understand now.

L. 179: The inner gate is located 'below' the TBZ ligand

Please see above response, this refers to the figures themselves. The figures are our point of reference.

L. 213-215: Tetrabenazine binding site just 'below' the location of the luminal gating residues.

See above.

Throughout the paper, results are given as cpm or counts. The reader can only estimate the magnitude of the binding/transport by knowing the specific activity of the radiolabel. I recommend switching to nano/picomoles or supplying enough information to understand what the given cpm values could mean.

Binding experiments were done using scintillation proximity assays and therefore converting the CPMs to values in pmol of bound ligand is simply not possible. For the transport experiments (now Fig 1d) the point was to show that the wild type was similar in activity to the chimera. In our new transport experiments we have presented for the mutants, many experiments were combined together and therefore, we have normalized the counts to the relative activity of wild type VMAT2.

Reviewer #2 (Public Review):

As a report of the first structure of VMAT2, indeed the first structure of any vesicular monoamine transporter, this manuscript represents an important milestone in the field of neurotransmitter transport. VMAT2 belongs to a large family (the major facilitator superfamily, MFS) containing transporters from all living species. There is a wealth of information relating to the way that MFS transporters bind substrates, undergo conformational changes to transport them across the membrane and couple these events to the transmembrane movement of ions. VMAT2 couples the movement of protons out of synaptic vesicles to the vesicular uptake of biogenic amines (serotonin, dopamine and norepinephrine) from the cytoplasm. The new structure presented in this manuscript can be expected to contribute to an understanding of this proton/amine antiport process.

The structure contains a molecule of the inhibitor TBZ bound in a central cavity, with no access to either luminal or cytoplasmic compartments. The authors carefully analyze which residues interact with bound TBZ and measure TBZ binding to VMAT2 mutated at some of those residues. These measurements allow well-reasoned conclusions about the differences in inhibitor selectivity between VMAT1 and VMAT2 and differences in affinity between TBZ derivatives.

The structure also reveals polar networks within the protein and hydrophobic residues in positions that may allow them to open and close pathways between the central binding site and the cytoplasm or the vesicle lumen. The authors propose involvement of these networks and hydrophobic residues in coupling of transport to proton translocation and conformational changes. However, these proposals are quite speculative in the absence of supporting structures and experimentation that would test specific mechanistic details.

eLife assessment

The authors report the cryo-EM structure of human vesicular monoamine transporter 2 (VMAT2) bound to the noncompetitive inhibitor tetrabenazine (in an occluded state). This important achievement captures the structure of a major facilitator superfamily (MFS) transporter critical for human neurotransmission. The evidence for the structure is solid, but the molecular dynamics aspect of the study is incomplete.

Reviewer #3 (Public Review):

Summary:

The vesicular monoamine transporter is a key component in neuronal signaling and is implicated in diseases such as Parkinson's. Understanding of monoamine processing and our ability to target that process therapeutically has been to date provided by structural modeling and extensive biochemical studies. However, structural data is required to establish these findings more firmly.

Strengths:

Dalton et al resolved a structure of VMAT2 in the presence of an important inhibitor, tetrabenazine, with the protein in detergent micelles, using cryo-EM and with the aid of protein domains fused to its N- and C-terminal ends, including one fluorescent protein that facilitated protein screening and purification. The resolution of the maps allows clear assignment of the amino acids in the core of the protein. The structure is in good agreement with a wealth of experimental and structural prediction data, and provides important insights into the binding site for tetrabenazine and selectivity relative to analogous compounds. The authors provide additional biochemical analyses that further support their findings. The comparison with AlphaFold models is enlightening.

Weaknesses:

The authors follow up their structures with molecular dynamics simulations of the tetrabenazine-bound state, and test several protonation states of acidic residues in the binding pocket, but not all possible combinations; thus, it is not clear the extent to which tetrabenazine rearrangements observed in these simulations are meaningful. Additional simulations of the substrate dopamine docked into this structure were also carried out, although it is unclear whether this "dead-end" occluded state is a relevant state for dopamine binding. The authors report release of dopamine during these simulations, but it is notable that this only occurs when all four acidic binding site residues were protonated and when an enhanced sampling approach was applied.

Reviewer #1 (Public Review):

Summary:

This study presents fundamental new insights into vesicular monoamine transport and the binding pose of the clinical drug tetrabenazine (TBZ) to the mammalian VMAT2 transporter. Specifically, this study reports the first structure for the mammalian VMAT (SLC18) family of vesicular monoamine transporters. It provides insights into the mechanism by which this inhibitor traps VMAT2 into a 'dead-end' conformation. The structure also provides some evidence for a novel gating mechanism within VMAT2, which may have wider implications for understanding the mechanism of transport in the wider SLC18 family.

Strengths:

The structure is high quality, and the method used to determine the structure via fusing mVenus and the anti-GFP nanobody to the amino and carboxyl termini is novel. The binding and transport data are convincing and provide new insights into the role of conserved side chains within the SLC18 members. The binding position of TBZ is of high value, given its role in treating Huntington's chorea and for being a 'dead-end' inhibitor for VMAT2.

Author Response

Reviewer #1 (Public Review):

Summary:

This manuscript introduces an exciting way to measure SARS-CoV-2 aerosolized shedding using a disposable exhaled breath condensate collection device (EBCD). The paper draws the conclusion that the contagious shedding of the virus via aerosol route persists at a high level 8 days after symptoms.

Strengths:

The methodology is potentially of high importance and the paper is clearly written. The study design is clever. If aerosolized viral load kinetics truly differed from those of nasal swabs, then this would be a very important finding.

Thank you for your encouraging remarks. We agree that a comparison between aerosolized viral load and nasal swabs would strengthen our findings, and we have collected new specimens which will enable this comparison: In each session we collected both nasal swabs and exhaled breath samples, and we are in the process of analyzing these data. These data will be included in our revised manuscript.

Weaknesses:

The study conclusions are not entirely supported by the data for several reasons:

(1) Most data points in the study are relatively late during infection when viral loads from other compartments (nasal and oral swabs) are typically much lower than peak viral loads which often occur in the pre-symptomatic or early symptomatic phase of infection. Moreover, the generation time for SARS-CoV-2 has been estimated to be 3-4 days on average meaning that most infections occur before or very early during symptoms. Therefore, the available epidemiologic data does not support 12 days of infection (day 8 symptoms) as important for most transmissions. Therefore, many of the measurement timepoints in this study may not be relevant for transmission.

Thank you for your comment. Notably, our new data set includes a small number of specimens that were collected prior to the start of symptoms, and so we may be able to partially address this concern with those data. That said, we agree that a limitation of our study is that we were unable to collect specimens prior to symptom onset, and that this pre-symptomatic period represents a fruitful area for future work. However, significant questions do remain open regarding transmission dynamics of SARS-CoV-2, including the extent of transmission after symptom onset, and therefore, despite this limitation of our data, we feel that our method may contribute to further understanding of those dynamics. However, we will include a more prominent discussion of this limitation in the revised manuscript.

(2) Fig 1A would be more powerful as a correlation plot between viral load from nasal samples (x-axis) and aerosol (y-axis). One would expect at least a rough correlation (as has been seen between viral loads in oral and nasal samples) and deviations from this correlation would provide crucial information about how and when aerosol shedding is discordant from nasal samples (ie early vs late time points, low versus high viral loads< etc...). It is too strong to state correspondence is 100% when viral load is only measured in one compartment and nasal swabs are reduced to the oversimplified "positive or negative".

Thank you for this suggestion, we agree that the figure would be more powerful as a correlation plot between viral load from nasal samples and aerosol. Unfortunately, at the time these samples were collected, the ER at Northwestern Hospital was diagnosing SARS-CoV-2 patients using the Abbott ID NOW rapid diagnostic platform, which, despite being a PCR-based system, does not provide quantitative information about viral loads, and instead provides a binary positive/negative result. Since we were looking for a direct comparison between the clinical diagnostic test and our test, we considered the binary aspect of our data (detected/undetected), and found 100% correspondence, meaning that when the clinical test detected SARS-CoV-2, our test did too. We have collected additional data which includes quantitative PCR values from nasal swabs collected at the same time as breath samples and we will include these data in the format you suggest, once analyzed, in our revised manuscript.

(3) Results are reported in RNA copies which is fine but particle-forming units (pfu, or quantitative culture) are likely a more accurate surrogate of infectivity. It is quite possible that all of these samples would have been negative for pfu given that the ratio of RNA: pfu is often >1000 (though also dynamic over time during infection). This could be another indicator that most samples in the study were collected too late during infection to represent contagious time points.

We agree that culturing exhaled breath samples would be an important addition to our understanding of the transmission dynamics of SARS-CoV-2 and we consider this to be an important next step for our method. Because we did not perform culturing of our breath samples in this study, we avoided making claims about infectivity of our samples in this manuscript, and instead speculate about the future utility of our method in understanding transmission dynamics, once an appropriate surrogate of infectivity is performed. We will make sure this is clearer in the revised manuscript. That said, other groups have successfully cultured breath samples with corresponding CT values in a range that are well within the range we found in our study, and sufficient for transmission (for example, Alsved et al, 2023, CT range ~33-38). These studies support the idea that a significant portion of the viral RNA measured in our samples may come from viable virus. Therefore, quantifying the ratio of viable to nonviable virus in our samples is an important next step. We appreciate this comment, and we will add a clearer discussion of this point to the revised manuscript.

(4) Individual kinetic curves should be shown for participants with more than three time points to demonstrate whether there are clear kinetic trends within individuals that would help further validate this approach. The inclusion of single samples from individuals is less informative.

We will add individual kinetic curves to the revised manuscript.

(5) The S-shaped model in 2A is somewhat misleading as it is fit to means but there is tremendous variability within the data. Therefore the 8-day threshold should be listed clearly as a mean but not a rule for all individuals. The statement that viral RNA copies do not decrease until 8 days from symptom onset is unlikely to be true for all infected people and can't be made based on the available data in this study given that many people contributed only one datapoint.

We will clarify the language in the manuscript and make limitations of the 8-day interpretation clearer.

(6) The incubation period for SARS-CoV-2 is highly variable. Therefore duration of symptoms is a rather poor correlate of the duration of infection. This further diminishes the interpretive value of positive samples from individuals who were only sampled once.

We will add a discussion of this point to the revised manuscript.

Reviewer #2 (Public Review):

Summary:

In this manuscript, Lane and colleagues measured the abundance of SARS-CoV-2 on breath in 60 outpatients after the development of COVID-19 symptoms using a novel breath collection apparatus. They found that, overall, viral abundance remains high for approximately eight days following the development of symptoms, after which viral abundance on breath drops to a low level that may persist for approximately 20 days or more. They did not identify significant differences in viral shedding on breath by vaccination status or viral variant. They also noted substantial variation in the degree and duration of shedding across individuals.

Strengths:

The primary strengths of this study are (1) the focus on breath, rather than the more traditional nasal/oropharyngeal swabs, and (2) the fact that the data were collected at multiple time points for each infection. This allows the authors to characterize not only mean viral abundance across individuals but also how that abundance changes over time, allowing for a better understanding of the potential duration of infectiousness of SARS-CoV-2.

Weaknesses:

The sample size is moderate (60) and focuses only on outpatients. While these are minor weaknesses (as the authors note, the majority of SARS-CoV-2 transmission likely occurs among those with symptoms below the threshold of hospitalization), it would nevertheless be useful to have a fuller understanding of variation in viral shedding across clinical groups.

We agree this would be very interesting and feel our method, which is straightforward to perform in clinical settings, lends itself to future studies across clinical groups. We have added discussion of this to the discussion section of the manuscript.

Furthermore, the study lacks information on viral shedding prior to the development of symptoms, which may be a critical period for transmission. Since the samples were collected at home by study participants using a novel apparatus, it is difficult to assess the degree to which actual variation in viral abundance, user variability, and/or measurement variation is inherent to the apparatus.

This is a great point, which we will discuss in our revised manuscript.

eLife assessment

This valuable manuscript by Lane introduces an exciting way to measure SARS-CoV-2 aerosolized shedding using a disposable exhaled breath condensate collection device (EBCD). The paper draws the conclusion that the contagious shedding of the virus via the aerosol route persists at a high level until 8 days after symptoms. While the methodology is potentially of high importance and the paper is clearly written, the conclusions are incomplete and only partially supported by the data.

Reviewer #1 (Public Review):

Summary:

This manuscript introduces an exciting way to measure SARS-CoV-2 aerosolized shedding using a disposable exhaled breath condensate collection device (EBCD). The paper draws the conclusion that the contagious shedding of the virus via aerosol route persists at a high level 8 days after symptoms.

Strengths:

The methodology is potentially of high importance and the paper is clearly written. The study design is clever. If aerosolized viral load kinetics truly differed from those of nasal swabs, then this would be a very important finding.

Weaknesses:

The study conclusions are not entirely supported by the data for several reasons:

(1) Most data points in the study are relatively late during infection when viral loads from other compartments (nasal and oral swabs) are typically much lower than peak viral loads which often occur in the pre-symptomatic or early symptomatic phase of infection. Moreover, the generation time for SARS-CoV-2 has been estimated to be 3-4 days on average meaning that most infections occur before or very early during symptoms. Therefore, the available epidemiologic data does not support 12 days of infection (day 8 symptoms) as important for most transmissions. Therefore, many of the measurement timepoints in this study may not be relevant for transmission.

(2) Fig 1A would be more powerful as a correlation plot between viral load from nasal samples (x-axis) and aerosol (y-axis). One would expect at least a rough correlation (as has been seen between viral loads in oral and nasal samples) and deviations from this correlation would provide crucial information about how and when aerosol shedding is discordant from nasal samples (ie early vs late time points, low versus high viral loads< etc...). It is too strong to state correspondence is 100% when viral load is only measured in one compartment and nasal swabs are reduced to the oversimplified "positive or negative".

(3) Results are reported in RNA copies which is fine but particle-forming units (pfu, or quantitative culture) are likely a more accurate surrogate of infectivity. It is quite possible that all of these samples would have been negative for pfu given that the ratio of RNA: pfu is often >1000 (though also dynamic over time during infection). This could be another indicator that most samples in the study were collected too late during infection to represent contagious time points.

(4) Individual kinetic curves should be shown for participants with more than three time points to demonstrate whether there are clear kinetic trends within individuals that would help further validate this approach. The inclusion of single samples from individuals is less informative.

(5) The S-shaped model in 2A is somewhat misleading as it is fit to means but there is tremendous variability within the data. Therefore the 8-day threshold should be listed clearly as a mean but not a rule for all individuals. The statement that viral RNA copies do not decrease until 8 days from symptom onset is unlikely to be true for all infected people and can't be made based on the available data in this study given that many people contributed only one datapoint.

(6) The incubation period for SARS-CoV-2 is highly variable. Therefore duration of symptoms is a rather poor correlate of the duration of infection. This further diminishes the interpretive value of positive samples from individuals who were only sampled once.

Reviewer #2 (Public Review):

Summary:

In this manuscript, Lane and colleagues measured the abundance of SARS-CoV-2 on breath in 60 outpatients after the development of COVID-19 symptoms using a novel breath collection apparatus. They found that, overall, viral abundance remains high for approximately eight days following the development of symptoms, after which viral abundance on breath drops to a low level that may persist for approximately 20 days or more. They did not identify significant differences in viral shedding on breath by vaccination status or viral variant. They also noted substantial variation in the degree and duration of shedding across individuals.

Strengths:

The primary strengths of this study are (1) the focus on breath, rather than the more traditional nasal/oropharyngeal swabs, and (2) the fact that the data were collected at multiple time points for each infection. This allows the authors to characterize not only mean viral abundance across individuals but also how that abundance changes over time, allowing for a better understanding of the potential duration of infectiousness of SARS-CoV-2.

Weaknesses:

The sample size is moderate (60) and focuses only on outpatients. While these are minor weaknesses (as the authors note, the majority of SARS-CoV-2 transmission likely occurs among those with symptoms below the threshold of hospitalization), it would nevertheless be useful to have a fuller understanding of variation in viral shedding across clinical groups. Furthermore, the study lacks information on viral shedding prior to the development of symptoms, which may be a critical period for transmission. Since the samples were collected at home by study participants using a novel apparatus, it is difficult to assess the degree to which actual variation in viral abundance, user variability, and/or measurement variation is inherent to the apparatus.

Author Response

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

Reviewer #1

Reviewer #1’s main concerns revolved around the evidential strength of the study’s conclusion that age-specific effects of birth weight on brain structure are more localized and less consistent across cohorts than age-uniform, stable effects. Specifically, the reviewer points out the evidence (or lack of such) for age-specific effects. We have rearticulated as a “bullet-point summarization” the reviewer’s concerns for a better response (please, see the original reviewer’s response in the annexed document). We thank the reviewer for his/her comment.

Concern #1: No direct statistical comparisons are conducted between samples (beyond the spin-tests).

In the initial version of the manuscript, the spin-tests represented a key test since they compared the spatial distribution of birth weight effects across samples. In the revised manuscript, we additionally perform a replicability analysis across samples both for birth weight effects on brain characteristics and on brain change in a similar fashion as described for the within-sample analysis. The results of these analyses provide complementary evidence of robust associations of birth weight effects on cortical characteristics (for area and volume, less so for thickness) and of unreliable associations of birth weight on cortical change. These analyses are briefly mentioned in the main document and fully described as supplementary information. Briefly, the effects of birth weight on cortical area and cortical volume showed high (exploratory and confirmatory) replicability while replicability was almost nonexistent for the effects of birth weight on cortical change. See below, under Reviewer #1, concern #2, for a description of the changes in the revised manuscript.

Concern #2: The differential composition of samples in terms of age distribution leads to the possibility that lack of results is explained by methodological differences.

The revised version of the manuscript provides now a within-sample replicability analysis of the birth weight effects on cortical change. This analysis addresses the reviewer’s concern as the lack of replicability in this analysis cannot be attributed to sample or methodological differences. We thank the reviewer for suggesting this analysis which provides further quantification of the (lack of) robustness of the birth weight effects on cortical change. See below for changes in the revised version of the manuscript concerning additional replicability analyses which were carried out as a response to reviewer #1 concerns #1 and #2.

pp. 12-3. “Additionally, we performed replicability analyses both across and within samples to further investigate the robustness of the effects of birth weight on cortical characteristics and cortical change. Split-half analyses within datasets were performed, to investigate the replicability of significant effects 36,37 of BW on cortical characteristics within samples (refer to Figure 1). These analyses further confirmed that the significant effects were largely replicable for volume and area, but not for thickness (see Supplementary Figure 11). Split-half analyses of BW on cortical change (refer to Figure 2) showed, in general, a very low degree of replicability on the three different cortical measures. See Supplementary Table 3. Replicability across datasets showed a similar pattern, that is, replicability was high for the effect of brain weight on cortical characteristics but very low for the effects of cortical change. See Supplementary Table 4 for stats. See Supplementary statistical methods for a full description of the analyses. These analyses provide complementary evidence of robust associations of BW with cortical area and volume – but not cortical change - across and within samples.”

p. 41. “For each dataset and cortical measure, we assessed the effects of birth weight on cortical structure and cortical change (…)”

p. 42. “Across samples replicability was performed as described in the within-sample replicability analysis (i.e., we assessed the exploratory and confirmatory replicability) except that split-half was not performed - the three datasets were compared with each other - and the analyses were performed in the original fsaverage space.”

pp. 54-55. “The exploratory replicability of birth weight on cortical change was negligible across datasets and measures [.00 (.00), .00 (.00), .00 (.00) for area, .02 (.09), .00 (.02), .01 (.03) for volume, and .01 (.05), .01 (.14), .00 (.01) for thickness] while confirmatory replicability was generally poor, except for the ABCD dataset [.02 (.05), .68 (.35), .00 (.00) for area, .08 (.14), .56 (.25), .00 (.02) for volume, and .37 (.26), .60 (.27), .01 (.03) for thickness] (see Supplementary Table 3).

These results are not fully comparable to other studies assessing the replicability of brain phenotype associations due to analytical differences (e.g. sample size, multiple-comparison correction method)20,36, yet clearly show that the rate of replicability of BW associations with cortical area and volume are comparable to benchmark brain-phenotype associations such as body-mass index and age68. Lower levels of replicability in the LCBC subsample are likely attributable to higher sample variability (e.g. increased age span). Kinship may lead to inflated patterns of replicability within the ABCD cohort. Confirmatory replicability is, also, to some degree, affected by sample size, and thus the estimates of confirmatory replicability may be somewhat inflated in the ABCD dataset.

Finally, the degree of across-sample replicability was high for the effects of birth weight on cortical area and volume (average confirmatory replicability = .96 and .93), low for thickness (.27), and negligible for the effects of birth weight on cortical change (.03, .06, and .06). See further information in Supplementary Table 4.”

Concern #3: Some datasets have a narrow age range precluding the detection of age-related effects.

We do not believe concern #3 is a major problem since timebirth weight refers to a within subject contrast, e.g., longitudinal-only-based contrast. Birth weight, even when self reported, is a highly reliable measure and the sample sizes are relatively large (n = 635, 1759, and 3324 unique individuals). Note that the smaller dataset does have longer follow-up times and more observations per participant, increasing the reliability of estimations in individual change. Structural MRI measures have very high reliability. Clearly, longitudinal brain change is less reliable, yet the present sample size and the high reliability of birth weight should provide enough statistical power to capture even small time-varying effects of birth weight on brain structure. Note as well that in each model age is treated as a covariate. Rather, the consistency of timebirth weight (that is, the effects of birth weight on cortical change) is assessed with split-half replications within and across samples. In this methodological pipeline, a narrow age range for a given dataset, if anything, may constitute an advantage. We have clarified the statistical model (see changes in the revised manuscript, referred to in response to reviewer #1, concern #5).

Concern #4 The modeling strategy does not allow for non-linear interaction between age and BW suggesting the use of spline models instead in a mega-analytical fashion.

Indeed, we agree that some - if not most - brain structures follow non-linear trajectories throughout life. In the present study, age regressors are used only for accounting for variance in the data rather than capturing any effect of interest. Rather, it is the time*birth weight regressor that captures age-varying changes in brain structure. Time reflects within-subject follow-up time. We believe non-linear modeling of age will only account for additional variance (compared to linear models) in the LCBC dataset given the dataset’s wider age range, while it will not have any consequential effect in the ABCD and UKB datasets (as predicted in the provisional response). In any case, we recognize it as a valid concern. Consequently, we have rerun the main models in an ROI-based fashion using or not using spline models to fit age. Specifically, we have fitted the models in each of Desikan-Killiany’s ROIs using generalized additive mixed models (GAMM with age as a smooth term) or linear mixed models (LME with age as a linear regressor). The results are shown in Supplementary Figures 13 and 14. The Beta regressors are nearly identical. As expected, the differences are noticeable in the LCBC dataset while the effect of using - or not using- splines to fit age is almost null in the other two datasets. See also FDR-corrected maps below for both birth weight effects on brain structure and brain change (we opted to show Beta-maps as supplementary material as the multiple-comparisons correction in the ROI-based analysis is not fully comparable with the one used in the vertex-wise approach).

p. 9: “Both birth weight effects on cortical characteristics and cortical change were rerun (ROIwise) using spline models that accounted for possible non-linear effects of age on cortical structure. The results were comparable to those reported above in Figures 1 and 2. See Supplementary Figures 13 and 14 for birth weight effects on cortical characteristics and cortical change, respectively.”

Caption to Supplementary Figure 13. “Comparison between spline (GAMM) and linear (LME) models on the effect of birth weight on cortical characteristics. Age was fitted either as a smoothing spline using generalized additive mixed models (GAMM, mgcv r-package) or a linear regressor with a linear mixed models (LME, lmer r-package) framework. The analyses were performed ROI-wise using the Desikan-Killiany atlas. Significance was considered at a FDR corrected threshold of p < 0.04. All the remaining parameters were comparable to the main analyses shown in Figure 1. The viridis-yellow scale represents the lower-higher Beta regressors. Red contour displays regions showing significant effects of birth weight. Note the high correspondence with both fitting models. Differences are only noticeable in the LCBC sample due to the datasets’ wider age range (i.e., lifespan dataset).” Caption to Supplementary Figure 14. “Comparison between spline (GAMM) and linear (LME) models on the effect of birth weight on cortical change. Age was fitted either as a smoothing spline using generalized additive mixed models (GAMM, mgcv r-package) or a linear regressor with a linear mixed models (LME, lmer r-package) framework. The analyses were performed on ROI-based using the Desikan-Killiany atlas. Significance was considered at a FDR corrected threshold of p < 0.04. All the remaining parameters were comparable to the main analyses shown in Figure 1. The viridis-yellow scale represents the lower-higher Beta regressors. Red contour displays regions showing significant effects of birth weight. Note the high correspondence with both fitting models. Differences are only noticeable in the LCBC sample due to the datasets’ wider age range (i.e., lifespan dataset).” The figures below show the birth weight effects on brain characteristics (above) and change (below) using a GAMM or an LME approach; that is, using age as a smooth term or as a regressor. FDR-corrected p < 0.05 values are shown in a signed logarithmic scale. Red-yellow values represent positive associations between birth weight and brain while blue-lightblue values represent negative associations. The results are qualitatively comparable and quantitative differences exist only in the LCBC dataset. Please see Supplementary Figures 13 and 14 in the revised manuscript.

Author response image 1.

Concern #5: Greater clarity regarding the statistical models and the provision of effect-size maps.

The revised manuscript provides additional information regarding the statistical model, especially in the results section, to avoid misunderstanding (see below examples of clarifications in the revised manuscript). We now provide Beta-maps, F-maps, unthresholded p-values maps, and degrees of freedom for the main univariate analyses. That is, we provide this information for both the whole sample and the twin analyses which correspond to Figures 1, 2, 4, and 5. We opted not to compute effect-size estimates (e.g. partial eta-squared, cohen’s d) due to the ambiguous interpretation of these maps in the context of linear mixed models.

p.8. “To test the effect of birth weight on cortical change we rerun the analyses with BW x time and age x time interactions. Note BW x time (i.e., within-subject follow-up time) represents the contrasts of interest while age – and age interactions – are used to account for differences in age across individuals.”

p.11. “In contrast, the spatial correlation of the maps capturing BW-associated cortical change (i.e., BW x time contrast) …”

p. 12. “Additionally, we performed replicability analysis both across and within samples to further investigate the robustness of the effects of birth weight on cortical characteristics and cortical change.”

p. 14: “BW discordance analyses on twins specifically were run as described for the main analyses above, with the exception that twin scans were reconstructed using FS v6.0.1. for ABCD and the addition of the twin’s mean birth weight as a covariate.”

p .31. “Group-level unthresholded p-maps, F-maps, Beta-maps, and degrees of freedom for the univariate analyses accompany this manuscript as additional material.”

eLife assessment

This valuable study uses multiple large neuroimaging data sets acquired at different points through the lifespan to provide solid evidence that birthweight (BW) is associated with robust and persistent variations in cortical anatomy, but less-substantial influences on cortical change over time. These findings, supported by robust statistical methods, illustrate the long temporal reach of early developmental influences and carry relevance for how we conceptualize, study, and potentially modify such influences more generally. The paper will be of interest to people interested in brain development and aging.

Reviewer #1 (Public Review):

This manuscript uses 3 large neuroimaging datasets - which together span childhood to late adulthood - to model the relationship between birthweight (BW) and cortical anatomy over time. The authors separately consider BW associations with the "height" of cortical anatomy trajectories (intercept effects) vs. BW associations with trajectory shape. They authors also distinguish between BW associations with cortical surface area (SA) and cortical thickness (CT), which together determine cortical volume (CV). Prior studies have firmly established robust positive associations between BW and cortical SA, but this study adds evidence for the protracted lifespan persistence of these associations, and the degree to which BW associations with cortical change over time are much weaker.

The study has several strengths including: clearly motivation of this work in the Introduction and contextualization of the results in Discussion; use of three large neuroimaging datasets; inclusion of sensible sensitivity analyses; disambiguation of SA and CT findings; and use of formal spatial analysis to quantify the reproducibility of effects across cohorts.

The primary way in which this work seeks to extend beyond established findings is to determine if BW is associated with differences in cortical change over time. The results presented clearly establish that such BW-change associations are much more localized and less consistent across cohorts that BW-intercept associations. The authors use multiple complementary approaches to verify the robustness of this inference to dataset subsampling and variation in statistical methods.

Overall, this work provides a valuable new data point in our understanding of the profound and protracted influences that prenatal developmental features can have on postnatal outcomes.

Reviewer #2 (Public Review):

This study focuses on the association between weight at birth and area, volume and thickness of the cerebral cortex measured at timepoints throughout the lifespan. Overall, the study is well designed, supported by evidence from a large sample drawn from three geographically distinct cohorts with robust analytical and statistical methods.

The authors test the hypotheses: that higher birth weight is associated with greater cortical area in later life; that associations are robust across samples and age; and that associations are stable across the lifespan. Analyses are performed separately in three cohorts: ABCD, UKBB and LCBC and the pattern of associations compared by means of spatial correlations. They find that BW is positively associated with cortical area (and, as a consequence, cortical volume) across most of the cortex, with effect sizes greatest in frontal and temporal regions. These associations remain largely unchanged when accounting for age, sex, length of gestation and (in one cohort) ethnicity. Variations due to MRI scanner and site are accounted for statistically. Measures are taken to determine within sample replicability through split-half analyses.

The authors conclude that BW, as a marker of early development, is associated with brain characteristics throughout the lifespan.

Author Response

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

Reviewer #1 (Recommendations For the Authors):

(1) While not absolutely necessary - it would be nice to see at least at the in-situ level what happens to the handful of other HC-important transcription factors in the Rbm24 KO (IKZF2, Barlh1, RFX) as the authors did look at Insm1.

Reply: Thanks for your suggested experiments. We agree that knowing whether the genes that are known to be involved in cell survival regulation are changed will provide insights into the mechanisms underlying cell death of Rbm24-/- HCs. Our data showed that Ikzf2 seemed to be upregulated when in the Rbm24-/- HCs, relative to Rbm24+/+ HCs at P5. We also tested Barlh1 and RFX, but we did not obtain confident data to present. Nonetheless, following the reviewer’s logic, we further tested Gata3, another gene involved in HC survival, and found that Gata3 was down-regulated in Rbm24 -/- HCs, compared to Rbm24+/+ HCs. Please refer to the text on lines 12-22 on page 12 and lines 1-10 on page 13, and Figure 3-figure supplement 1.

(2) Major comments: The nomenclature for mouse gene vs. mouse protein needs to be addressed throughout the manuscript. The nomenclature when referring to a mouse gene: gene symbols are italicized, with only the first letter in upper-case (e.g. Rbm24).

The nomenclature when referring to a mouse protein: Protein symbols are not italicized, and all letters are in upper-case (e.g. RBM24).

Reply: Thanks for pointing it out. In the entire manuscript, we have followed the reviewer’s comments to list gene and protein.

(3) Supplemental Figure 2D: Individual data points should be displayed on the bar graph via dots. SEM is not appropriate for this graph as SEM precision with only 3 samples is low. Furthermore, readers are more interested in knowing the variability within samples and not proximity of mean to the population mean, therefore standard deviation (SD) should be used instead.

Reply: We have edited the Figure 1-figure supplement 2D, as suggested. The Figure 1figure supplement 2 legend was updated, too. Please refer to line 21-22 on page 32.

(4) Red/Green should be avoided, especially when both are on the same image (merged immunofluorescence images that are found throughout the manuscript). I highly recommend changing to a color-blind friendly color scheme (such as cyan/green/magenta, cyan/magenta/yellow, etc.) for inclusivity.

Reply: Thanks for pointing it out. We have changed the red to magenta in all our Figures and figure supplements.

(5) Minor comments: As CRISPR-stop is a major method used throughout the paper, a brief explanation is needed for readers to understand what this methodology entails and why it was used. Something along the lines of," The CRISPR-stop technique allows for the introduction of early stop codons without the induction of DNA damage via Cas9 which can cause deleterious effects".

Reply: We have further elaborated how CRISPR-stop works and its advantages. Please refer to lines 8-13 on page 5.

(6) Page 5; line 5 - "Phenotypes occur earlier..." Grammar

Reply: The grammar error was corrected. Please refer to line 4, page 5.

(7) Page 5; line 5 - "Given Pou4f3 is the upstream regulator..." Not proven, rephrase

Reply: We have rephrased this sentence. Please refer to lines 5-6 on page 5.

(8) Supplemental 1A: Fine, Proof of knockout, I wouldn't mention INSM1 being "irregular"

Reply: We have rephrased this sentence. Please refer to lines 2-3 on page 6.

(9) Page 5; line21 - "Alignment of Insm1+ OHCs was not as regular..." Not a good description

Reply: We have rephrased this sentence. Please refer to lines 2-3 on page 6.

(10) Page 6; line11 - "Rbm24 was completely absent.." Redundancy with line 9

Reply: Thanks for pointing it out, and we have removed the redundant sentence.

(11) Page 7 - HA tag should be indicated originally as: Hemaglutinin (HA)

Reply: We have switched “HA” to “Hemaglutinin (HA)”. Please refer to line 15, page 7.

(12) Page 9, line 11- "Determine if autonomous/noncell autonomous." Disagree, cells still clustered in supplemental fig 4.

Reply: We have removed this sentence.

Reviewer #2 (Recommendations For The Authors):

The writing of the manuscript is adequate, but it would certainly be improved by professional editing.

Reply: Thanks for the reviewer’s encouraging comments. The revised version of our manuscript has been edited by an English native speaker.

eLife assessment

In this valuable study, the authors explore regulatory cascades governing mammalian cochlear hair cell development and survival. They confirm previous studies that the transcription factors Pou4f3 and Gfi1 are necessary for hair cell survival, and use compelling evidence to demonstrate that the RNA binding protein gene RBM24 is regulated by Pou4f3, but not Gfi1. These findings will be of interest to those working on hearing loss, and hold significance for viral gene delivery methods aiming to manipulate gene expression.

Reviewer #1 (Public Review):

Wang and colleagues recently demonstrated the essential role of RBM24 (RNA-binding motif protein 24a) in the development of mouse hair cells (source: https://doi.org/10.1002/jcp.31003). In this study, they further expand on their findings by revealing that Rbm24 expression is absent in Pou4f3 mutant mice but not in Gfi1 mutant mice. This observation suggests that POU4F3 acts as an upstream regulator of Rbm24. The researchers effectively demonstrate that POU4F3 can bind to and regulate Rbm24 through three distant enhancers, which are located in open chromatin regions and are bound by POU4F3. Lastly, Wang and colleagues discovered that ectopic expression of Rbm24 was unable to prevent the degeneration of POU4F3 null hair cells.

The findings in this manuscript hold great significance as they provide additional insights into the transcriptional cascades crucial for hair cell development. The discovery of enhancers capable of driving transgene expression specifically in hair cells holds promising therapeutic implications. The figures presented in the study are of excellent quality, the employed techniques are state-of-the-art, the data are accurately represented without exaggeration, and the study demonstrates a high level of rigor.

Reviewer #2 (Public Review):

Previous studies have shown that two hair cell transcription factors, Pou4f3 and Gfi1 are both necessary for the survival of cochlear hair cells, and that Gfi1 is regulated by Pou4f3. The authors have previously also shown that mosaic inactivation of the RNA-binding protein RBM24 leads to outer hair cell death.

In the present study, the authors show that hair cells dies in Pou4f3 and Gfi1 mutant mice. They show that Gfi1 is regulated by Pou4f3. Both these observations have been published before. They then show that RBM24 is absent in Pou4f3 knockouts, but not Gfi1 knockouts. They ectopically activate RMB24 in the hair cells of Poui4f3 knockouts, but this does not rescue the hair cell death. Finally the authors validate three RMB24 enhancers that are active in young hair cells and which have been previously shown to bind Pou4f3.

The experiments are well-executed and the data are clear. The results support the conclusions of the paper. The authors have revised the paper slightly, mostly to modify the red/green staining in the figures, and to perform additional analyses of the RBM24 and Ikzf2 mutants, now shown in Supplementary Figure 3.

Much of the work in the paper has been reported before. The result that hair cell transcription factors operate in a network, with some transcription factors activating only a subset of hair cell genes, is an expected result. Since RBM24 is only one of many genes regulated directly by Pou4f3, it is not surprising that it cannot rescue the Pou4f3 knockout hair cell degeneration, and indeed the rationale for attempting such a rescue experiment is not provided by the authors.

The identification of new hair cell enhancers may be of use to investigators wishing to express genes in hair cells.

In sum, this work, although carefully performed, does not shed significant new light on our understanding of hair cell development or survival.

Author Response

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The manuscript titled "Coevolution due to physical interactions is not a major driving force behind evolutionary rate covariation" by Little et al., explores the potential contribution of physical interaction between correlated evolutionary rates among gene pairs. The authors find that physical interaction is not the main driving of evolutionary rate covariation (ECR). This finding is similar to a previous report by Clark et al. (2012), Genome Research, wherein the authors stated that "direct physical interaction is not required to produce ERC." The previous study used 18 Saccharomycotina yeast species, whereas the present study used 332 Saccharomycotina yeast species and 11 outgroup taxa. As a result, the present study is better positioned to evaluate the interplay between physical interaction and ECR more robustly.

Strengths & Weaknesses:

Various analyses nicely support the authors' claims. Accordingly, I have only one significant comment and several minor comments that focus on wordsmithing - e.g., clarifying the interpretation of statistical results and requesting additional citations to support claims in the introduction.

We are pleased the reviewer found the analyses to support the claims. We have addressed comments related to clarifying interpretations as suggested in the Recommendations to the Authors. For example, we have added discussion and clarification on the other parameters that could affect the strength of ERC correlations.

Reviewer #2 (Public Review):

Summary:

The authors address an important outstanding question: what forces are the primary drivers of evolutionary rate covariation? Exploration of this topic is important because it is currently difficult to interpret the functional/mechanistic implications of evolutionary covariation. These analyses also speak to the predictive power (and limits) of evolutionary rate covariation. This study reinforces the existing paradigm that covariation is driven by a varied/mixed set of interaction types that all fall under the umbrella explanation of 'co-functional interactions'.

Strengths:

Very smart experimental design that leverages individual protein domains for increased resolution.

Weaknesses:

Nuanced and sometimes inconclusive results that are difficult to capture in a short title/abstract statement.

We appreciate the reviewer’s acknowledgement of the experimental design. We have addressed the nuance of the results by changing the title and clarifying other statements throughout the manuscript as suggested in the reviewer’s recommendations. We have also addressed reviewer comments asking for further explanation on using Fisher transformations when normalizing the Pearson correlations for branch counts.

Reviewer #3 (Public Review):

Summary:

The paper makes a convincing argument that physical interactions of proteins do not cause substantial evolutionary co-variation.

Strengths:

The presented analyses are reasonable and look correct and the conclusions make sense.

Weaknesses:

The overall problem of the analysis is that nobody who has followed the literature on evolutionary rate variation over the last 20 years would think that physical interactions are a major cause of evolutionary rate variation. First, there have been probably hundreds of studies showing that gene expression level is the primary driver of evolutionary rate variation (see, for example, [1]). The present study doesn't mention this once. People can argue the causes or the strength of the effect, but entirely ignoring this body of literature is a serious lack of scholarship. Second, interacting proteins will likely be co-expressed, so the obvious null hypothesis would be to ask whether their observed rates are higher or lower than expected given their respective gene expression levels. Third, protein-protein interfaces exert a relatively weak selection pressure so I wouldn't expect them to play much role in the overall evolutionary rate of a protein.

We thank the reviewer for their comments and suggestions. A point to immediately clarify is that the methods studied in this manuscript deal with rate variation of individual proteins over time, and if that variation correlates with that of another protein.. The numerous studies the reviewer refers to deal with explaining the differences in average rate between proteins. These are different sources of variation. It has not, to our knowledge, been shown that variation in the expression level of a single protein over time is responsible for its variation in evolutionary rate over time, let alone to a degree that allows its variation to correlate with that of a functionally related protein. That question interests us, but it is not the focus of this study.

In our study, we sought to test for a contribution of physical interaction to the correlation of evolutionary rate changes as they vary over time, i.e. between branches. We made many changes to clarify this distinction in our revisions.

We agree that the manuscript would be more clear to define the forces proposed to lead to difference in rate in general, which includes expression levels. We had generally considered expression level as one of the many potential non-physical forces, but failed to make that explicit and instead focused on selection pressure. In our revision we describe expression level as another potential driver of evolutionary rate variation over time. References to previous literature have been made in the introduction. We also added a more explicit explanation of the rate covariation over time that we are measuring in contrast with the association between expression level and rate differences between proteins that was studied in previous literature.

On point 3, the authors seem confused though, as they claim a co-evolving interface would evolve faster than the rest of the protein (Figure 1, caption). Instead, the observation is they evolve slower (see, for example, [2]). This makes sense: A binding interface adds additional constraint that reduces the rate at which mutations accumulate. However, the effect is rather weak.

The values in Fig 1B are a measure of correlation, specifically a Fisher transformed correlation coefficient. They are not evolutionary rates, so they are not reflecting faster or slower evolution, rather more or less covariation of evolutionary rates over time. We are not predicting that physically interacting interfaces evolve faster than the rest of the protein, but rather that if physical interaction drives covariation in evolutionary rates over time, their correlation would be stronger between pairs of physically interacting domains. In response, we have used clearer language in the figure caption and reorganized labels in Figure 1B to clearly show that the values are correlations. Revised Figure 1 Legend:

“Overview of experimental schema and hypotheses. Proteins that share functional/physical relationships have similar relative rates of evolution across the phylogeny, as shown in (A) with SMC5 and SMC6. The color scale along the bottom indicates the relative evolutionary rate (RER) of the specific protein for that species compared to the genome-wide average. A higher (red) RER indicates that the protein is evolving at a faster rate than the genome average for that branch. Conversely, a lower (blue) RER indicates that protein is evolving at a slower rate than the genome average. The ERC (right) is a Pearson correlation of the RERs for each shared branch of the gene pair. (B) Suppose the correlation in relative evolutionary rates between two proteins is due to compensatory coevolution and physical interactions. In that case, the correlation of their rates (ie. ERC value) would be higher for just the amino acids in the physically interacting domain. (C) Outline of experimental design. Created with Biorender.com”

All in all, I'm fine with the analysis the authors perform, and I think the conclusions make sense, but the authors have to put some serious effort into reading the relevant literature and then reassess whether they are actually asking a meaningful question and, if so, whether they're doing the best analysis they could do or whether alternative hypotheses or analyses would make more sense.

[1] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4523088/

[2] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4854464/

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Major comments

(1) Numerous parameters influence ECR calculation. The authors note that their use of a large dataset of budding yeast provides sufficient statistical power to calculate ECR. I agree with that. However, a discussion of other parameters needs to be improved, especially when comparing the present study to others like Kann et al., Hakes et al., and Jothi et al.. For example, what is the evolutionary breadth and depth used in the Kann, Hakes, Jothi and other studies? How does that compare to the present study? Budding yeast evolve rapidly with gene presence/absence polymorphisms observed in genes otherwise considered universally conserved. Is there any reason to expect different results in a younger, slower-evolving clade such as mammals? There is potential to acknowledge and discuss other parameters that may influence ECR, such as codon optimization and gene/complex "essentiality," among others.

More discussion of these parameters is a good idea. We have added the number and phylogeny of species used in the previous studies in the discussion paragraph starting with “Previous studies attributed varying degrees of evolutionary rate covariation signal to physical interactions between proteins.” We also like the idea of studying the effect of younger and more slowly evolving clades as opposed to the contrary, but currently we lack the required number of datasets to do this.

We have also added more discussion and clarification of potential non-physical forces leading to ERC correlations in the introduction.

Minor comments

(1) It would be good to add a citation to the second sentence of the first paragraph, which reads, "It has been observed that some genes have rates that covary with those of other genes and that they tend to be functionally related."

Added citation to Clark et al. 2012

(2) In the last sentence of the first paragraph of the introduction, ERC is discussed in the context of only amino acid divergence, however, there is no reason that DNA sequences can't be used, especially if ERC is being calculated among species that are less ancient than, for example, Saccharomycotina yeasts. Thus, it may be more accurate to suggest that ERC measures how correlated branch-specific rates of sequence divergence are with those of another gene.

Nice suggestion to generalize. We have made this change.

(3) ERC was not calculated in reference #2. For the sentence "Protein pairs that have high ERC values (i.e., high rate covariation) are often found to participate in shared cellular functions, such as in a metabolic pathway2 or meiosis3 or being in a protein complex together," I think more appropriate citations (including inspiring work by the corresponding author) would be

a) Coevolution of Interacting Fertilization Proteins (https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1000570)

b) Evolutionary rate covariation analysis of E-cadherin identifies Raskol as a regulator of cell adhesion and actin dynamics in Drosophila (https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1007720)

c) An orthologous gene coevolution network provides insight into eukaryotic cellular and genomic structure and function (https://www.science.org/doi/10.1126/sciadv.abn0105)

d) PhyKIT: a broadly applicable UNIX shell toolkit for processing and analyzing phylogenomic data (https://academic.oup.com/bioinformatics/article/37/16/2325/6131675)

Thank you for pointing out these works. We agree that there are more appropriate citations and we have referenced your suggested b-d.

(4) The dataset of 343 yeast species also includes outgroup taxa. Therefore, indicating that 332 species are Saccharomycotina yeast and 11 are closely related outgroup taxa may be more accurate.

Thank you for the suggestion, the following sentence has been added, citing the Shen et. al 2018 paper that the dataset was derived from:

“To investigate the discrepancy between contributions to ERC signal from co-function and physical interaction, we used a dataset of 343 evolutionarily distant yeast species. 332 of the species are Saccharomycotina with 11 closely related outgroup species providing as much evolutionary divergence as humans to roundworms3”

(5) Are there statistics/figures to support the claim that "Almost all complexes and pathways had mean ERC values significantly greater than a null distribution consisting of random protein pairs"?

This is shown in supplementary figure 1. A reference to this figure was added as well as quantification within the text.

(6)Similar to the previous comment, can quantitative values be added to the statement "While protein complexes appear to have higher mean ERC scores than the pathways..."?

The median of the mean ERC scores for protein complexes is 5.366 while the median for the mean ERC score in pathways is 4.597. This quantification has been included in the text: “While protein complexes have higher mean ERC scores (median 5.366) than the pathways (median 4.597), the members of a given complex are also co-functional, making interpretation of the relative contribution of physical interactions to the average ERC score difficult”

These quantifications are were also added to the figure caption for figure 2A

(7) A semantic point: In the sentence "The lack of significance in the global permutation test shows that the...", I recommend saying that the analysis suggests, not shows, because there is potential for a type II error.

Good suggestion, we have made this change.

(8) The authors suggest that shared evolutionary pressures, "and hence shared levels of constraint," drive signatures of coevolution. The manuscript does not delve into selection measures (e.g., dN/dS). Perhaps it would be more representative to remove any implication of selection.

We have added better language to clarify that discussion of selection is purely a hypothesis and that selection is not probed in our analyses.

“Previous work finds evidence that relaxation of selective constraint can lead to drastic rate variation and hence covariation6. Rather, the greater and consistent contribution comes from non-physical interaction drivers that could include variation in essentiality, expression level, codon adaptation, and network connectivity. These non-physical forces would be under shared selective pressures and hence shared levels of constraint, the result of which was elevated ERC between non-interacting proteins, as visible in our study of genetic pathways that do not physically interact (Figure 2).”

Reviewer #2 (Recommendations For The Authors):

Major comments:

-Title: In my opinion, the title of the manuscript is a somewhat misleading summary of the results of this paper. In the majority of the analyses in this paper, physical interactions do account for a significantly outsized portion of the ERC signature. The current title downplays the consistent (although sometimes small effect-sized) result that physically interacting domains do show higher ERC than non-physically interacting domains by every statistical measure employed in this paper to compare physical vs non-physical interactions. The authors' interpretation of their results within the manuscript body is that the effect of physical interactions is an inconsistent, weak, and non-generalizable driver of ERC. I generally agree with the authors' interpretations, but the nuance of these interpretations is lost in the title of the paper. I would suggest rewording the title to try to capture the nuance or at least be subjectively accurate. For example, stating that "...physical interactions are not the sole driving force.." is inarguably accurate based on these results.

As an alternative title, I would suggest focusing on an important takeaway from the paper: ERC is a reliable predictor of co-functional interactions but not necessarily physical interactions. I agree with the statement that "there is not a strong enough signal to confidently call an interaction physical or not and would be of little value to an experimentalist wanting to infer interacting domains" and I think that a title that emphasizes this idea would be more accurate and impactful.

Great suggestion. We agree that the current title is downplaying the minutiae of the method and the signal we capture with it, we have used your suggested title.

There are an outsized number of complexes that had ROC-AUCs greater than 0.5 which is why we performed the permutation tests to determine how significant each of the individual ROC-AUCs were given the differing number of protein/domain pairs in each complex. Between the statistical methods used only 3 of the 17 complexes ranked physical interactions significantly higher than non-physically interacting domains in every analysis. Even among the 3 that were statistically significant some of the physically interacting domains still fell among the bottom portion of the ERC scores for that complex (Figure 5: MCM and CUL8 complexes) This is why we concluded that physical interactions are not the sole driving force of the signal captured by ERC.

-Abstract: related to my preceding comment, the word "negligible" in the abstract is misleading. If physical interactions were truly entirely negligible, the comparisons of physically interacting vs non-physically interacting domains would yield 0.5. Instead, these comparisons always yielded results greater than 0.5. Consider rewording.

Thank you for the suggestion this phrasing has been changed to “Therefore, we conclude that coevolution due to physical interaction is weak, but present in the signal captured by ERC”

We agree that “negligible” may be too strong of a word, however, the comparisons do not always yield results greater than 0.5.

5 of the 17 complexes do not reach the 0.5 threshold for the initial ROC analysis and even among those that do, only 4 had significantly high ROC-AUCs. You are correct that the signal is not completely negligible which is why we continued by determining if the physical interaction was driving high ERC only within proteins (Figure 5)

-Figure 3: I think there may be an error in the domain labeling in Figure 3. The comparison between OKP1_2 and AME1_3 is the highest ERC value in the matrix. From the complex structure, it appears that OKP1_2 and AME1_3 are two helix domains that appear to physically interact. However, in the ERC matrix, they are not shaded to indicate they are a physical interaction pair. Please double-check that the interacting domains are properly annotated, since mis-annotation would have a large impact on the interpretation of this figure with respect to the overall question the paper addresses.

Thank you for catching this - fixed.

Minor comments:

-Methods: "The full ERC pipeline can be found at (Github)." Provide github URL here? Thanks for the catch, fixed

-Discussion: "Evidence for physical coevolution however was tempered by a global permutation test, which did not reach significance, indicating that this inference is sensitive to approach and further underlines the relatively weak contribution of physical coevolution." The word "relatively" may not be a good choice of words. In comparison to what? As is, the phrasing could be interpreted as implying "in comparison to non-physical interactions". This would not be accurate, because the results show that in general, physical interactions are a stronger contributor to ERC (consistent trend but varied significance, depending on methodology) than non-physical interactions.

Thank you for your help with clarification. The word relatively was removed.

However, we do not agree that in general physical interactions are a stronger contributor to ERC than non-physical interactions (such as gene expression, codon adaptation, etc.). In all of our statistical tests a maximum of 5 of the 17 complexes ranked physical interactions significantly higher than non-physical interactions. While the ROC-AUC is greater than 0.5 for 12 of the 17 complexes only 4 of those were significant.

-I have not seen Fisher-transformed correlation coefficients used in the context of ERC. I understand that it's helpful in normalizing the results so that they are comparable between ERC comparisons with differing numbers of overlapping branches (i.e. points on a linear correlation plot). A reference of where the authors got this idea or a little more verbiage to describe the rationale would be helpful. On a related note, I would expect that using linear correlation p-value instead of R-squared would account for differences in overlapping branches, eliminating the need to apply fisher-transformation. It would be helpful for the authors to outline their rationale for using a correlation coefficient rather than a P-value.

We agree that this method could be made clearer. We made a methodological choice to use Fisher transformation over linear correlation p-value. Both methods should achieve the same end result by taking the number of branches into consideration. We have added additional explanation to the results section “Both protein pathways and complexes have elevated ERC”:

“ERC was calculated for all pairs of the 12,552 genes. For each pair the correlation is Fisher transformed to normalize for the number of shared branches that contribute to the correlation. This normalization is necessary to reduce false positives that have high correlation solely due to a small number of data points. This normalization also allows for direct comparison of ERC between gene pairs that have differing numbers of branches contributing to the score.”

We also added additional explanation in the methods section including the formula used to calculate the Fisher transformation

-Did the authors use Pearson or Spearman correlation coefficient?

Pearson. We clarified this in the methods section, “Calculating evolutionary rate covariation” : “Evolutionary rate covariation is calculated by correlating relative evolutionary rates (RERs) between two gene trees using a Pearson correlation.”

-Did the authors explore ERC between domains within a single protein? Do domains within a protein exhibit ERC? I would expect that they do. If they do, this could likely be attributed to linkage/genetic hitchhiking, representing a new angle/factor beyond physical interaction that could lead to ERC. This is just an idea for a future analysis, not necessarily a request within the scope of the present paper.

We did calculate the ERC between domains of a single protein but did not include them in the analysis since they didn’t address the specific question we posed. As expected they are highly correlated, and past unpublished studies in the lab do find a very weak, but detectable genome-wide, signature of rate covariation between neighboring colinear genes on a chromosome. That signal was however so weak as to be eclipsed by true functional relationships, when present.

Reviewer #3 (Recommendations For The Authors):

Please read the literature and revise accordingly.

We understand the confusion surrounding previous literature on the relationship between expression levels and evolutionary rates when comparing between different proteins. Those studies clearly showed how expression level is highly predictive of a given protein’s average evolutionary rate. However, we are studying the change in evolutionary rate over branches for single proteins. This is inherently different because we’re following rate fluctuations in the same protein over time. To our knowledge it has not yet been shown that expression level commonly varies enough over time to produce large rate variations over time in the same protein, and if it is responsible for the correlations of rate we observe between co-functional proteins. It is however reasonable to expect that what governs between-protein differences in rate could also contribute to between-branch differences (over time for a single protein). In fact, our earlier study approached this (Clark et al. Genome Research 2012). We expect expression level could influence rate over time and lump its effect together with general non-physical forces, such as selection pressures. We recognize we could do better in defining more of the non-physical forces and the past literature. We added the following section to the introduction and many other clarifying statements throughout the manuscript:

“For the purposes of this study, the forces that contribute to correlated evolutionary rates are grouped into two bins, physical and non-physical. The physical force is coevolution occurring at physical interaction interfaces. Non-physical forces include gene co-expression, codon adaptation, selective pressures, and gene essentiality. There is a well accepted negative relationship between gene expression and rate of protein evolution where genes that are highly expressed generally have slower rates of evolution14,15. However, Cope et al.16 found that there is a weak relationship between both gene expression and the number of interactions a protein has with the coevolution of expression level. Conversely, they found a strong relationship between proteins that physically interact and the coevolution of gene expression. These findings illuminate the difference between the strong relationship of gene expression level on the average evolutionary rate of a protein and the weak contribution of gene expression level to correlated evolutionary rates of proteins across branches. The finding that physically interacting proteins have strong expression level coevolution brings to question how much coevolution of physically interacting proteins contributes to overall covariation in protein evolutionary rates.”

eLife assessment

This useful study seeks to address the importance of physical interaction between proteins in higher-order complexes for covariation of evolutionary rates at different sites in these interacting proteins. Following up on a previous analysis with a smaller dataset, the authors provide compelling evidence that the exact contribution of physical interactions, if any, remains difficult to quantify. The work will be of relevance to anyone interested in protein evolution.

Reviewer #1 (Public Review):

Summary:<br /> In the manuscript titled "Coevolution due to physical interactions is not a major driving force behind evolutionary rate covariation" by Little et al., explores the potential contribution of physical interaction between correlated evolutionary rates among gene pairs. They find that physical interaction is not the main driving of evolutionary rate covariation (ECR). This finding is similar to a previous report by Clark et al. (2012), Genome Research, wherein the authors stated that "direct physical interaction is not required to produce ERC." The previous study used 18 Saccharomycotina yeast species, whereas the present study used 332 Saccharomycotina yeast species and 11 outgroup taxa. As a result, the present study is better positioned to evaluate the interplay between physical interaction and ECR more robustly.

Strengths & Weaknesses:<br /> Various analyses nicely support the authors' claims.

Reviewer #2 (Public Review):

Summary:<br /> The authors address an important outstanding question: what forces are the primary drivers of evolutionary rate covariation? Exploration of this topic is important because it is currently difficult to interpret the functional/mechanistic implications of evolutionary covariation. These analyses also speak to the predictive power (and limits) of evolutionary rate covariation. This study reinforces the existing paradigm that covariation is driven by a varied/mixed set of interaction-types that all fall under the umbrella explanation of 'co-functional interactions'.

Strengths:<br /> Very smart experimental design that leverages individual protein domains for increased resolution.

Weaknesses:<br /> Nuanced and sometimes inconclusive results that are difficult to capture in a short title/abstract statement.

EDIT: The authors have done a satisfactory job of honing their language to get the nuanced ideas across clearly. The added scholarship and theoretical discussion they added strengthen the impact of the manuscript. The revised edition addresses my concerns.

Author Response

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

Reviewer #1 (Recommendations For The Authors):

All comments made in the public section.

We would like to thank the reviewer for their assessment of our study and for suggestions for additional experiments to follow up our studies.

Reviewer #2 (Recommendations For The Authors):

‐ Preparation of spike proteins and VLPs. Although Triton‐X114 extraction was done to remove endotoxin from the recombinant spike protein preparations, its removal efficiency depends on the levels of endotoxin in the samples. Therefore, the residual endotoxin levels in each of the test samples and batches should be measured. Even very low but varying levels of residual endotoxin would substantially impact the reported results, as they create inconsistent data that are not interpretable.

Certainly, endotoxin contamination in instilled materials is always an issue. Established protocols for inducing acute inflammatory responses using endotoxin outline specific ranges of endotoxin levels in the instillation materials. To induce acute lung inflammation in mice at least 2 µg of endotoxin must be instilled. We have endeavored to reduce the possibility of endotoxin contamination in our recombinant proteins by using a mammalian expression system; careful aseptic culture and protein purification techniques; and a final Triton-X114 partitioning protocol. We assessed the possibility of endotoxin contamination using the Pierce™ Chromogenic Endotoxin Quant Kit, which is based on the amebocyte lysate assay. Our analysis revealed that the endotoxin level in the purified recombinant protein preparation is below 1.0 EU/ml, which closely aligns with the levels specified for recombinant proteins. An endotoxin concentration of 1.0 EU/ml is equivalent to approximately 0.1 ng/ml. Throughout all mouse nasal instillation experiments, the total volume of recombinant protein administered did not exceed 6 µl. The amount of contaminant endotoxin instilled did not exceed 1 pg (50 µl of 0.02 ng/ml of endotoxin). Consequently, we can confirm that the extent of endotoxin contamination is at trace levels. Moreover, our study reveals multiple results indicating that the level of endotoxin contamination in the recombinant protein was inadequate to independently induce neutrophil recruitment in the cremaster muscle, lymph nodes, and liver. For further insights, refer to Figure 5.

‐ Doses of spike and VLPs: The amount of spike protein incorporated into HIV Gag‐based VLPs should be determined and compared to that found in the native SARS‐CoV‐2 virus particles. This should provide more physiologic doses (or dose ranges/titration) of spike than the arbitrary doses (3 ug or 5 ug) used in the mouse experiments.

To visualize the acquisition of spike protein and track cells that have acquired the spike protein, we conducted a series of tests and optimizations using different concentrations of Alexa 488 labeled spike protein, ranging from 0.5 to 5 µg. During the processing of lung tissue for microscopic imaging, it was of utmost importance to preserve the integrity of the labeled spike protein in the tissue samples. We determined that instillation of 3 µg of Alexa 488 labeled spike protein yielded the optimal signal strength across the lung sections. Notably, in many mouse models employing intra-nasal instillation protocols for SARS-CoV2 spike protein or RBD domain-only recombinant proteins, a dosage of approximately 3 µg or higher were commonly used. Regarding the titer of spike-incorporated VLPs, it is important to highlight that we did not directly compare the quantity of spike protein present in NL4.3 VLPs to that of the naïve SARS-CoV-2 virus. HIV-1 and SARS-CoV-2 viruses typically carry around 70 gp120 spikes and 30 spikes, respectively. We estimated that SARS-CoV-2 spike-incorporated NL4.3 VLPs may display twice the number of spikes compared to naïve SARS-CoV-2. Notably, our measurements of SARS-CoV-2 spike on NL4.3 VLPs demonstrated similar behavior to SARS-CoV-2 in terms of specific binding to ACE2-expressing 293T cells, indicating their functional similarity in this context.

Author response image 1.

Spike protein-incorporated NL4.3 VLPs test with human ACE2-transfected HEK293 cells. The wild-type spike protein-incorporated VLPs and delta envelope NL4.3 VLPs were analyzed using human ACE2-transfected HEK293 cells. The first plot shows ACE2 expression levels in HEK293 cells. The second plot displays the binding pattern of Delta Env NL4.3 VLPs on ACE2-expressing HEK293 cells. The third plot illustrates the binding pattern of wild-type spike protein-incorporated NL4.3 VLPs on ACE2expressing HEK293 cells. The histogram provides a comparison of VLP binding strength to ACE2expressing HEK293 cells.

‐ The PNGase F‐treated protein was not studied in Fig 1. In Fig 2, glycan‐removal by PNGaseF has little effects on cell uptake and cell recruitment in the lung. If binding to one of the Siglec lectins is a critical initial step, experiments should be designed to evaluate this aspect of the spike‐cell interaction in a greater depth.

As the reviewer states results with the PNGase F-treated protein were not shown in Fig. 1 although we showed results in Figs. 2 & 3. See discussion below about our preparation of the PNGase F-treated protein. Perhaps because we elected to use a purified fraction that retained ACE2 binding, the protein we used likely retained some complex glycans. As the reviewer notes the PNGase F treated protein had similar overall cellular recruitment and uptake profiles compared to the untreated spike protein. The PNGase Ftreated fraction we used no longer bound Siglec-F in the flow-based assay, shown in Fig. 7. This argues that the initial uptake and cellular recruitment following intranasal instillation of the Spike protein did not depend upon the engagement of Siglec-F. While Siglec-F on the murine alveolar macrophage can likely efficiently capture the spike proteins other cellular receptors contribute and the overall impact of the spike protein on alveolar macrophages likely reflects its engagement of multiple receptors.

• Enzymatic removal of sialic acids from spike may be one parameter to explore. The efficiency of enzymatic removal should also be verified prior to experiments. Finally, the authors need to assess whether the proteins remained functional, folded properly, and did not aggregate.

To obtain the de-glycosylated form of the SARS-CoV-2 spike protein, we employed PNGase F enzymatic digestion to remove glycans. Subsequently, the spike protein was purified using a size exclusion column. During this purification process, the PNGase F-treated spike protein segregated into two distinct fractions, specifically fraction 6 to 8 and fraction 9 to 11 (see revised Figure 1- figure supplement 1).

Author response image 2.

Size exclusion chromatography. The peak lines represent the absorbance at 280 nm. PNGase F-treated spike proteins were loaded onto a Superdex 26/60 column, resolved at a flow rate of 1.0 ml/min, and collected in 1 ml fractions.

The Coomassie blue staining of an SDS-PAGE gel revealed that fractions 6 to 8 likely underwent a more pronounced de-glycosylation by PNGase F compared to fractions 9 to 11. Additionally, during the size column purification, we noticed that fraction 6 to 8 exhibited a faster mobility than the untreated spike protein, implying a potentially substantial modification of the protein's conformation. To probe the functional characteristics of the de-glycosylated spike protein in fraction 6 to 8, we conducted binding tests with human ACE2. Strikingly, the spike protein in fraction 6 to 8 completely lost its binding affinity to ACE2, indicating a loss of its ACE2-binding capability. Conversely, the protein in fraction 9 to 11 showed partial de-glycosylation but still retained its original functionality to bind to ACE2 and its antibody.

Author response image 3.

FACS analysis of various spike protein-bound beads. Protein bound beads were detected with labeled spike antibody, recombinant human ACE2, and recombinant mouse Siglec-F.

Based on these results, we concluded that fraction 9 to 11 would be the most suitable choice for further studies as the de-glycosylated spike protein, considering its retained functional properties relevant for ligating ACE2 and antibody motifs yet had lost Siglec-F binding. In the revised manuscript we have describe in more detail the purification of the PNGase F treated Trimer and its functional assessment.

‐ Increases in macrophages and alveolar macrophages by Kifunensine Tx spike in Fig 2A suggest effects that are not related to Siglec lectins. These effects are not seen with the wild type or D614 spike trimers, so the relevance of high‐ mannose spike is unclear. On the other hand, there were clear differences between Wuhan and D614 trimers seen in Fig 2A and 2B, but there was no verification to ascertain whether these differences were indeed due to strain differences and not due to batch‐to‐batch variability of the recombinant protein production. The overall glycan contents of the Wuhan and D614 spike protein samples should be measured. If Siglec interaction is the main interest in this study, the terminal sialic acid contents should be determined and compared to those in the corresponding strains in the context of native SARS‐CoV‐2 virions.

Our initial observation that Siglec-F positive alveolar macrophages (AMs) avidly acquired spike proteins followed by a rapid leukocyte recruitment provided the rational for us to examine the impact of modifying the glycosylation pattern on the spike protein (de-glycosylated and spike variants) on their binding tropism and their cellular recruitment profiles in the lung. In this context, we examined the influence of several glycan modification on spike proteins, hypothesizing that these modifications would alter the acquisition of the spike protein by mouse AMs compared to the wild-type trimer. While we did not conduct an indepth analysis of the glycan composition and terminal sialic acid contents of the SARS-CoV-2 spike proteins we used we did verify that the different proteins behaved as expected. Most of the biochemical studies were performed in Jim Arthos’ laboratory, which has a long interest in the glycosylation of the HIV envelope protein. On SDS-PAGE the SARS-CoV-2 spike protein purified from the Kifunesine treated CHO cells exhibited a 12 kDa reduction. It bound much better to L-Sign, DC-Sign, and maltose binding lectin, and poorly to Siglec-F. In the cellular studies it bound less well to most of the cellular subsets examined including murine alveolar macrophages. In studies with human blood leukocytes, it relied on cations for binding. However, it retained its toxicity directed at mouse and human neutrophils and it elicited a similar cytokine profile when added to human macrophages. The D614G mutation increased the spike protein binding to P-Selectin, CD163, and snowdrop lectin (mannose binding) suggesting that the mutation had altered the glycan content of the protein. We used the D614G spike protein in a limited number of experiments as it behaved like the wild-type protein except for a slightly altered cellular retention pattern 18 hrs after intranasal instillation. In the revised manuscript we have included its binding to peripheral blood leukocytes. The D614G mutation conferred stronger binding to human monocytes than the original Spike protein. As discussed above, we recovered two fractions following the PNGase F treatment, one with a 40 kDa reduction on SDS-PAGE and the other a 60 kDa decrease and we chose to evaluate the fraction with a 40 kDa reduction in subsequent experiments. Consistent with a loss of N-linked glycans the PNGase F treatment reduced the binding to the lectin PHA, which recognizes complex carbohydrates, and it resulted in a sharp reduction in Siglec-F binding. The lower molecular weight fraction recovered after PNGase F treatment no longer bound ACE2. While our studies showed that alveolar macrophages likely employ Siglec-F as a capturing receptor they possess other receptors that also can capture the spike protein. The downstream consequences of engaging SiglecF and other Siglecs by the SARS-CoV-2 spike protein will require additional studies.

While acknowledging the possibility of some batch-batch variation in recombinant protein preparation, we don’t think this was a major issue. We have noted some batch-batch variations in yield- efficiency, however the purified proteins consistently gave similar results in the various experiments.

‐ Fig 3: The same concern described above applies to the hCoV‐HKU1 spike protein. In Panel D, the PNGase and Kifunensine treatment did not appear to abrogate the neutrophil recruitment. Panel A did not include PNGase and Kif Tx spike proteins. Quantification of images in panel D is missing and should be done on many randomly selected areas.

We analyzed the neutrophil count of images in panel D and the results are presented. (Figure 3-figure supplement 1C). The Kifunensine treatment reduced the neutrophil recruitment at 3 hours, while the PNGase F treated Spike protein recruited as well or slightly more neutrophils. The hCoV-HKU1 S1 domain did not differ much from the saline control.

‐ Fig 4: Kifunensine Tx spike caused more increase in neutrophil damage after intrascrotal injections. PNGase Tx spike was not tested. Connection between Siglec‐spike binding and neutrophil recruitment/damage is lacking.

Exteriorized cremaster muscle imaging functions as a model system for monitoring neutrophil behavior recruited by spike proteins within the local tissue, distinct from Siglec F-positive alveolar macrophages residing in lung tissue. Hence, our primary focus was not on investigating the Siglec/Spike protein interaction. Consequently, we did not utilize PNGase F-treated spike protein in these experiments. To clarify this issue, we added a sentence in main text ‘Although this model lacks Siglec F-positive macrophages, it is worth monitoring the effect of the SARS-CoV-2 Spike protein on neutrophils recruited in the inflammatory local tissue.’

‐ Fig 5. Neutrophil injury was also seen after inhalation (intranasal) of spike protein in mice and in vitro with human neutrophils. Panel B shows no titrating effects of spike (from 0.1 to 2) on Netosis of murine neutrophils. Panel C: Netosis was seen with human neutrophils at 1 but not 0.1. Is this species difference important?

Given the observation of neutrophil NETosis in the mouse imaging experiment, our objective was to characterize the direct impact of the spike protein on human and murine neutrophils. The origins of the neutrophils are different as the murine neutrophils were purified from mouse bone marrow while the human neutrophils were purified from human blood. Both purification protocols led to greater than 98% neutrophils. However, the murine neutrophils contain many more immature cells (50-60%) because the bone marrow served as their source. Furthermore, the murine neutrophils are from 6–8-week-old mice while the human neutrophils are from 30-50 year-old humans. More work would be needed to sort out whether there is any difference between human and mouse neutrophils in their propensity to undergo netosis in response to Spike protein.

‐ Kifunensine Tx again did not cause any reduction, indicating the lack of involvement of sialic acid. How was this related to Siglec participation directly or indirectly? There was no quantification for Panel D.

We do not think that Siglecs play a role in the induction of neutrophil netosis as the Spike proteins lacking Siglec interactions induced similar levels of netosis. Likely other neutrophil receptors are important. As noted in the text,

"human neutrophils express several C-type lectin receptors including CLEC5A, which has been implicated in SARS-CoV-2 triggered neutrophil NETosis." Our goal with the data in Panel D was to visualize human neutrophil NETosis on trimer-bearing A549 cells we relied on the flow cytometry assays for quantification.

‐ The rationale for testing cation dependence is unclear and should be described. What is the significance of "cations enhanced leukocyte binding particularly so with the high mannose protein"? Are there cationdependent receptors for spike independent of glycans and huACE‐2? If so, how is this relevant to the main topic of this paper?

It is well known that many glycan bindings by C-type lectins are calcium-dependent, involving specific amino acid residues that coordinate with calcium ions and bind to the hydroxyl groups of sugars. As discussed in our previous draft, the C-type lectin receptor L-SIGN has been suggested as a calciumdependent receptor for SARS-CoV-2, specifically interacting with high-mannose-type N-glycans on the SARS-CoV-2 spike protein. Therefore, it was worthwhile to investigate the calcium-dependent manner of spike protein binding to various types of immune cells. We added some data to this figure. It now includes the binding profile of the D614G protein. In addition, we corrected the binding data by subtracting the fluorescent signal from the unstained control cells.

‐ Fig 7: human Siglec 5 and 8 were studied in comparison with mouse Siglec F. Recombinant protein data are not congruent with transfected 293 cell data. Panel A, the best binding to hSiglec 5 and 8 are the PNGase F Tx spike protein; how to interpret these data? Panel B: only the WT and D614G spike proteins binding to Siglec 5 and 8 on transfected cells. It made sense that kif Tx (high‐mannose) and PNGaseF Tx (no glycan) spike would not bind to the Siglecs, but they did not bind to ACE2 either, indicative of nonfunctional spike proteins.

We discussed this as follows: ‘The closest human paralog of mouse Siglec-F is hSiglec-8 (reference 40). While expressed on human eosinophils and mast cells, human AMs apparently lack it. In contrast, human AMs do express Siglec-5 (reference 37). Along with its paired receptor, hSiglec-14, Siglec-5 can modulate innate immune responses (reference 41). When tested in a bead binding assay, in contrast to Siglec-F, neither hSiglec-5 or -8 bound the recombinant spike protein, yet their expression in a cellular context allowed binding. The in vitro bead binding assay we established demonstrated the specific binding of the bait molecule to target molecules. However, it does have limitations in replicating the complexities of the actual cellular environment. As discussed previously the PNGase Tx fraction we used in these experiments retained ACE2 binding, but loss binding to Siglec-F in the bead assay. In a biacore assay, not shown, the PNGase Tx fraction bound L-Sign and DC-Sign better than the untreated trimer, and it retained human ACE2 binding although it bound less well than wild type-trimer. Why the PNGase Tx fractions bound poorly to the human ACE2 transfected HEK293 cells is unclear. A higher density of recombinant ACE2 on the beads compared to that expressed on the surface of HEK293 may explain the difference. Alternatively in the bead assay we used a recombinant human ACE2-Fc fragment fusion protein purified from HEK293 cells, while in the transfection assay, we expressed human full length ACE2. The biacore, the bead binding, and the functional assays we performed all suggest that we had used intact recombinant proteins.

‐ Fig 8: This last set of experiment was to measure cytokine release by different types of macrophage cultures treated with spike from different cells with vs without Kifunensine Tx. The connection of these experiments to the rest is tenuous and is not explained. This is one of the examples where bits of data are presented without tying them together.

Dysregulated cytokine production significantly contributes to the pathogenesis of severe COVID-19 infection. Since we had observed strong binding of the spike protein to human monocytes and murine alveolar macrophages, we tested whether the spike protein altered cytokine production by human monocyte-derived macrophages. Depending on the culture conditions human monocytes can be differentiated M0, M1, or M2 phenotypes. Each type of macrophage responds differently to stimulants, often leading to distinct patterns of cytokine secretion. These patterns offer valuable insights into the immune response. The cytokine profiling conducted in this study enhances our understanding of how distinct macrophage types react to the spike protein.

‐ Discussion section did not describe how the various experiments and data are tied together. The authors explained the interactions of spike with different cell types in each paragraph separately, leaving this reviewer really confused as to what the authors want to convey as the main message of the paper.

We have modified discussion to address this issue.

Reviewer #3 (Recommendations For The Authors):

‐ The authors may want to refer to "intranasal instillation" to distinguish it from inhalation of an aerosolised liquid. How was the dose of the spike protein selected? There is some dose information in different settings, but usually between 0.1‐1 µg/ml or 0.1 µg‐5 µg range for in vivo injection, but the rationale for these ranges should be discussed. Is this mimicking a real situation during infections or a condition that might be used for vaccines?

While inhalation of aerosolized liquid closely mimics the natural route of human exposure to respiratory infectious materials, intranasal instillation with a liquid inoculum remains a widely accepted standard approach for virus or vaccine inoculation across various laboratory species. To clearly define our mouse model, we are changing the term 'inhalation' to 'instillation'. We previously answered to Reviewer #2 as following: To visualize the acquisition of spike protein and track cells that have acquired the spike protein, we conducted a series of tests and optimizations using different concentrations of Alexa Fluor 488 labeled spike protein, ranging from 0.5 to 5 µg. During the processing of lung tissue for microscopic imaging, it was of utmost importance to preserve the integrity of the labeled spike protein on the tissue samples. Through our investigations, we determined that an instillation of 3 µg of Alexa Fluor 488 labeled spike protein yielded the most optimal signal strength across the lung sections. Notably, in many mouse models employing intra-nasal instillation protocols for SARS-CoV-2 spike protein or RBD domain-only recombinant proteins, a dosage of approximately 3 µg or higher was commonly used. Hence, based on these references and our preliminary studies, we selected 3 µg as the optimal concentration of instilled spike protein per mouse.

‐ Controls are not evenly applied. In some cases, the control for the large and complex SARS‐CoV2 spiker trimer is PBS. This seems insufficient to control against effects of injecting such complex proteins that can undergo significant conformational changes after uptake by a cell. In some cases, human coronavirus spike proteins from different viruses are used, but not much is said about these proteins and the different glycoforms are not explored. Are these prepared in the same way and do they have similar glycoforms. For example, if the Siglecs bind sialic acid on N‐linked glycans, then why do the purified Siglecs or Siglecs expressed in cells not bind the HKU‐1 spike, which would have such sialic acids if expressed in the same way as the CoV2 spike?

We have taken careful consideration to select an appropriate control material for these experiments. Initially, we opted to employ Saline or PBS for intranasal instillation as a vehicle control, a choice aligned with the approach taken in numerous previous studies involving lung inflammation mouse models. However, as the reviewer pointed out, we share the concern for achieving more meaningful and comparable control materials, particularly considering the size and complexity of the recombinant protein. In accordance with this perspective, we introduced glycan-modified spike proteins and the HCoV-HKU1 S1 subunit. Figure 3 illustrates our comprehensive evaluation of various spike proteins in terms of their impact on neutrophil recruitment. The diversity of sialic acid structures observed on recombinant proteins expressed within the same cell emerges from the intricate interplay of multiple factors within the cellular glycosylation machinery. This complex enzymatic process empowers cells to finely modulate glycan structures and sialic acid patterns, tailoring them to suit the diverse biological functions of distinct proteins. Despite structural similarities between the HCoV-HKU1 and SARS-CoV-2 spike proteins, their glycan modifications vary, thereby leading to distinct binding properties with various Siglec subtypes. All recombinant proteins used in this study except for the S1 subunits were generated within our laboratory. These include the wild-type spike protein, the D614G Spike protein, the Kifunensine-treated high mannose spike proteins, and the PNGase F-treated deglycosylated spike proteins. All the proteins were produced using the same protocol using CHO cells or on occasion HEK293F cells. We have indicated in the manuscript where we used HEK293F cells for the protein production otherwise they were produced in CHO cells.

‐ Figure 1 F‐I, there should be a control for VLP without SARS‐CoV2 spike as the VLP will contain other components that may be active in the system.

We tested the delta Env VLP for alveolar macrophage acquisition and neutrophil recruitment. We found a similar alveolar macrophage acquisition of the VLPs, but significantly less neutrophil recruitment compared to the free Spike protein. Since the uptake pattern with the VLPs matched that of the spike protein we did not consider adding a non-spike bearing VLP as a control. The rapid VLPs clearance into the lymphatics shortly after instillation may account for the reduced neutrophil recruitment following their instillation (Figure 1 figure supplement 2B, C).

‐ In Figure 1H, that do they mean by autofluorescence? Is this the cyan signal?

Is the green signal also autofluorescence as this is identified as the VLP?

We appreciate reviewer pointing out the typo regarding autofluorescence in the figure image. To provide clarity regarding the background in all lung section images, we have included additional supplemental data. During the fixation process of lung tissue, various endogenous elements in the tissue sample contribute to autofluorescence when exposed to lasers in the confocal microscope. Specifically, collagen and elastin present in the lung vasculature, including airways and blood vessels, are dominant structures that generate autofluorescence. To address this issue, we have implemented optimizations to distinguish between real signals and the noise caused by autofluorescence. We inadvertently failed to indicate the source of the strong cyan signal. The signal is due to Evans Blue dye delineating lung airway structures, which contain collagen and elastin—known binding materials for Evans Blue dye. This explains the strong fluorescence signals observed in the airways. We conjugated the recombinant spike protein with Alexa Fluor 488, and viral-like particles (VLPs) were visualized with gag-GFP. (Figure 1 figure supplement 2A, D)

‐ The control for SARS‐CoV2 spike trimer is PBS, but how can the authors distinguish patterns specific to the spike trimer from any other protein delivered by intranasal instillation. Could they use another channel with a control glycoprotein to determine if there is anything unique about the pattern for spike trimer?

Alveolar macrophages employ numerous receptors to capture glycoproteins that have mannose, Nacetylglucosamine, or glucose exposed. Galactose-terminal glycoproteins are typically not bound. We do not think that the Spike protein is unique in its propensity to target alveolar macrophages.

‐ What is the parameter measured in Figure S2B?

The percentage of the different cell types that have retained the instilled Spike protein at the three-hour time point. .

‐ The Spike trimer with high mannose oligosaccharides may gain binding to the mannose receptor. It may be helpful to state the distribution of this receptor and comment is it could be responsible for this having the largest effect size for some cell types.

We agree that the spike trimer with high mannose should target cells bearing the mannose receptor. We have modified the discussion to address this point and have mentioned some of the cell types likely to bind the high mannose bearing spike protein.

‐ A key experiment is the Evans Blue measure of lung injury in Figure 3A. A control with the HKU‐1 spike is also performed, but more details on the matching of this proteins production to the SARS‐CoV2 spike trimer and the quantification of these comparative result should be provided. To show that the SARSCoV2 spike trimer can cause tissue injury on its own seems like a very important result, but the impact is currently reduced by the inconsistent application of controls and quantification of key results. Furthermore, if these results can be repeated in the B6 and B6 K18‐hACE2 mouse model it might further increase the impact by demonstrating whether or not hACE2 contributes to this effect.

We repeated the lung permeability assay using the S1 subunit from the original SARS-CoV-2 and the S1 subunit from HCoV-HKU1. Both proteins were made by the same company using a similar expression system and purification protocol. Consistent with our original data, the instillation of the SARS-CoV-2 S1 subunit led to an increase in lung vasculature permeability, whereas the HCoV-HKU-1 S1 subunit had a minimal impact. (Figure 3 figure supplement 1A). This experiment suggests that it the S1 subunit that leads to the increase in vascular permeability. To address the contribution of hACE2 in this phenomenon, we conducted a lung permeability assay using K18-hACE2 transgenic mice. The K18-hACE2 transgenic mice exhibited a slight increase in lung vasculature permeability upon SARS-CoV-2 trimer instillation compared to the non-transgenic mice. This suggests that the hACE2-Spike protein interaction may contribute to an increase in lung vascular permeability during SARS-CoV-2 lung infection (Figure 3 figure supplement 1B).

‐ For Figure 4A, could they provide quantification. The neutrophil extravasation with Trimer appears quite robust, but the authors seem to down‐play this and it's not clear without quantification.

To address this issue, we analyzed and graphed the neutrophil numbers in each image. Injection of the trimer along with IL-1β significantly increased neutrophil infiltration. (Figure 4 figure supplement 1)

‐ In Figure 4B, there are no neutrophils at all in the BSA condition. Is this correct? Intravascular neutrophils were detected with PBS injection in Figure 4A.

We demonstrated that the neutrophil behaviors occur within the infiltrated tissue rather than within the blood vessels. Even when examining the blood vessels in all other images, it is challenging to identify neutrophils adhering to the endothelium of the blood vessels. Neutrophils observed in the PBS 3-hour control group are likely acute responders to the local injection, as a smaller number of neutrophils were observed in the 6-hour image.

‐ In Figure 5A the observation of neutrophil response in lung slices seems to be presented an anecdotal account. The neutrophil appears to polarize, but is this a consistent observation? How many such observations were made?

We have consistent observations across three different experiments. In addition, highly polarized and fragmented neutrophils were consistently observed in the fixed lung section images.

‐ The statement: "human Siglec‐5 and Siglec‐8 bound poorly despite being the structural and functional equivalents of Siglec F, respectively (37)". How can one Siglec be the structural and the other the functional equivalent of Siglec‐F? It might help to provide a little more detail as to how these should be seen.

Mouse Siglec-F has two distinct counterparts in the human Siglec system, both in terms of structure and function. In the context of domain structure, human Siglec-5 serves as the counterpart to mouse Siglec-F. However, it's important to note that while human Siglec-8 is not a genetic ortholog of mouse Siglec-F, it is expressed on similar cellular populations and functions as a functional paralog.

‐ The assay using purified proteins and proteins expressed in cells don't fully agree. For example, it's very surprising that recombinant Siglec 5 and 8 bind better to the non‐glycosylated form than to the glycosylated trimer. It appears from Figure S1 that the PNGaseF treated Spike contains at least partly glycosylated monomers and it also appears that the Kifunesine effect may be partial. PNGaseF may have a hard time removing some glycans from a native protein.

We were also surprised by the results using the PNGase F treated Spike protein in that it lost binding to Siglec-F and retained binding to human Siglec-5 and 8 in the bead assay, shown in Figure 7A. As explained above we used a purified fraction of the PNGase F treated protein that retained some functional activity as assessed in the ACE2 binding assay and in biacore assays not shown. The persistent binding of Siglec-5 and Siglec-8 suggests that removal of some of the complex glycans had revealed sites capable of binding Siglec-5 and 8. We would agree with the reviewer that the PNGase treatment we used only removed some of the glycans from the native protein. In data not shown the high mannose spike protein behaved as predicted in biacore assays, binding better to DC-SIGN and maltose binding lectin, but less well to PHA and less well to ACE2. The high mannose trimer also bound less to the HEK293 cells expressing ACE2, Siglec-5, or Siglec-8 as well as peripheral blood leukocytes.

eLife assessment

This paper investigates the impact of intranasal instillation of SARS CoV2 spike protein in mouse models of lung inflammation. The authors conclude that the spike protein can interact with macrophages through carbohydrate recognition and can induce recruitment and NETosis of neutrophils, contributing to lung inflammation. They also use the cremaster muscle model to investigate effect of the spike proteins on neutrophil dynamics and death using intravital microscopy. Given that mucosal vaccines using SARS CoV2 spike variants could be envisioned as desirable, the observation that spike can induce lung/mucosal inflammation even without an adjuvant is important. Despite limitations of some loose terminology and some weak controls, the key observations are solid and demand further attention given the importance of the antigen.

Reviewer #1 (Public Review):

The manuscript by Park et. al. examines the interaction of macrophages with SARS-CoV-2 spike protein and subsequent inflammatory reactions. The authors demonstrate that following intranasal delivery of spike it rapidly accumulates in alveolar macrophages. Inflammation associated with internalized spike recruits neutrophils to the lung, where they undergo a cell death process consistent with NETosis. The authors demonstrate that modifications spike to contain high mannose reduces uptake of spike protein and limits the inflammation induced. This finding could have implications for vaccine development, as vaccines containing modified spike could be safer and better tolerated.

The authors use a number of different techniques, including in vivo modeling, imaging, human and murine systems to interrogate their hypotheses. These systems provide robust supporting information for their conclusions. There are two key aspects from the current manuscript which would add key evidence. The authors suggest that neutrophils exposed to spike protein undergo a process of NETosis. To confirm this hypothesis inhibitors of NETosis should be used to demonstrate that the cell death is prevented. Additionally, vaccination of a murine model with the modified spike protein would add additional support to the conclusion that modified spike protein would be less inflammatory while maintaining its utility as a vaccine antigen.

Reviewer #3 (Public Review):

The study focuses on in vivo and in vitro cellular responses intranasal instillation of glycoforms and mutants of SARS-CoV2 spike trimer or spike bearing VLP in mice. Collectively, the experiments suggest that SARS-CoV2 spike has pro-inflammatory roles through increase M1 macrophage associated cytokines and induction of neutrophil netosis/necrosis, a proinflammatory cell death pathway. These effects seem largely independent of hACE2 interaction and partly depend upon interactions with SIGLECs on macrophages and neutrophils. A strength of the study is that a number sophisticated methods are used, including intravital microscopy in the cramaster and liver as well as acute lung slice models, to look at uptake of the spike proteins and immune cell dynamics. The weakness is that some of the reagents maybe contaminated with uncharacterized glycoforms and some important controls, such as control spike protein and control VLP are unevenly applied or not included. The authors have revised the manuscript through some improvements in the writing, but the survey nature and suggestive level of evidence is still a weakness. The study calls attention to sources of proinflammatory activity in the SARS CoV2 spike that may involve some carbohydrate interactions.

Author Response

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

eLife assessment

This study presents valuable findings that examine both how Down syndrome (DS)-related physiological, behavioral, and phenotypic traits track across time, as well as how chronic treatment with green tea extracts 25 enriched in epigallocatechin-3-gallate (GTE-EGCG), administered in drinking water spanning prenatal through 5 months of age, impacts these measures in wild-type and Ts65Dn mice. However, the strength of the evidence is incomplete, due to high variability across measures, perhaps attributable to a failure to include sex as a factor for measures known to be sexually dimorphic. This study is of interest to scientists interested in Down Syndrome and its' treatment, as well as scientists who study disorders that impact multiple organ systems.

Public Reviews:

Using Ts65Dn - the most commonly used mouse model of Down syndrome (DS) - the goal of this study is two-pronged: 1) to conduct a thorough assessment of DS-related genotypic, physiological, behavioral, and phenotypic measures in a longitudinal manner; and 2) to measure the effects of chronic GTE-EGCG on these measures in the Ts65Dn mouse model. Corroborating results from several previous studies on Ts65Dn mice, findings of this study show confirm the Ts65Dn mouse model exhibits the suite of traits associated with DS. The findings also suggest that the mouse model might have experienced drift, given the milder phenotypes than those reported by earlier studies. Results of the GTE-EGCG treatment do not support its therapeutic use and instead show that the treatment exacerbated certain DS-related phenotypes.

Strengths:

The authors performed a rigorous assessment of treatment and examined treatment and genotypic alterations at multiple time points during growth and aging. Detailed analysis shows differences in genotype during aging as well as genotype with treatment. This study is solid in the overarching methodological approach (with the exception of RNAseq, described below). The biggest strength of the study is its approach and dataset, which corroborate results from a multitude of past studies on Ts65Dn mice, albeit on adult specimens. It would be beneficial for the dataset to be made available to other researchers using a public data repository.

We deeply appreciate the reviewers' positive feedback. Their acknowledgment of the solid methodological approach and the rigorous assessment of genotypic and treatment effects over various developmental stages resonates with our motivation. Their suggestion to make the dataset available in a public data repository for other researchers is well-taken. We are committed to data sharing and we are creating a dedicated platform to facilitate the accessibility of our research data to the scientific community. Given its size and complexity, we currently hold the dataset available upon reasonable request to the corresponding authors.

Weaknesses:

There are several primary weaknesses, described below:

Sex was not considered in the analyses.

The number of experimental animals of each sex are not clearly represented in the paper, but are buried in supplemental tables, and the Ns for the RNAseq are unclear. No analyses were done to examine sex differences in male/female DS or WT animals with or without treatment. Body measurements will greatly vary by sex, but this was not taken into consideration during assessments. As such, there is a high amount of variability within each cohort measured for body assessments (tibia, body weight, skeletal development etc.). Supplemental table 14 had the list of each animal, but not collated by sex, genotype or treatment, making it difficult to assess the strength of each measurement.

Our study primarily concentrated on providing a holistic understanding of the impact of trisomy and GTE-EGCG treatment on Down syndrome, and was not explicitly designed to investigate sexual dimorphism. However, instead of reporting on only one sex and thereby obviating sex as a source of variation, as in previously published studies, we decided to include both male and female mice within the study design to represent a more realistic portrayal of the nature of Down syndrome in a heterogeneous population. By encompassing both sexes, we aim to better capture the variability in Down syndrome.

As we do acknowledge the significance of sex bias in scientific research, we considered performing post-hoc analyses to test the effect of sexual dimorphism, but found that our dataset was underpowered to obtain reliable results, since our experiments were not a priori designed to investigate this question and sample sizes for each sex by separate were not large enough. Nevertheless, considering the reviewer’s comment, we have taken specific steps to improve the representation of sex-related information and to enhance the clarity of our manuscript.

First, we have redesigned all figures using empty and full symbols to distinguish male from female mice within each analysis, providing readers with an immediate sense of the sex distribution in each experimental group. Moreover, we have modified Supplementary Table 1 to offer a comprehensive breakdown of the number of male and female mice for each test, along with their respective genotypes and treatment groups. This table aims to make the sample size and sex distribution within our study as transparent as possible for our readers. While we acknowledge that our study lacked the statistical power to perform a detailed sex-based analysis, the visual representation of sex in our data shows which systems are mainly affected by sexual dysmorphism. This evidence can guide future investigations directly designed to investigate sexual effects in certain systems or structures.

Key results are not clearly depicted in the main figures

Rigorous assessment of each figure and the clarity of the figure to convey the results of the analysis needs to be performed. Many of the figures do not clearly represent the findings, with authors heavily relying on supplemental figures to present details to explain results. Figure legends do not adequately describe figures; rather, they are limited to describing how the analysis is performed. For example, LDA plots in Figure 4 do not clearly convey the results of metabolite analysis.

Overall, the amount of data presented here is overwhelming, making it difficult to interpret the findings. Some assessments that do not add to the overall paper need to be removed. Clarifying the text, figures and trimming the supplement to represent the data in a manner that is easily understood will improve the readability of the paper. For example, perhaps measures which are not strongly impacted by genotype could be moved to the supplement, because they are not directly relevant to the question of whether GTE-EGCG reverses the impact of trisomy on the measures.

As rightly pointed out by the reviewers, the vast amount of data generated by our holistic and longitudinal approach is one of the primary strengths, but also an important challenge in our study. Our dataset encompasses a comprehensive assessment of the effects of treatment and genotypic alterations at multiple time points during growth and aging. This multi-dimensional evaluation is pivotal to our research, and relegating data to supplementary material would restrict access to this holistic understanding. Our aim is to provide readers with a complete view of the complex interactions we have explored, and retaining this data in the main text is essential to uphold the integrity of our work.

Indeed, we specifically chose to submit or manuscript to eLife because this journal allows to access supplemental information directly from the text and figures in the main manuscript and best aligned with our approach to data presentation. The eLife format permits us to offer readers a quick and informative overview of all the data within the main figures employing multivariate techniques such as Linear Discriminant Analysis or Principal Component Analysis. Subsequently, we supply more detailed analyses in the supplementary figures for readers who wish to delve deeper into specific aspects. Furthermore, while certain figures may be categorized as supplementary, for us it is crucial, and we would like to emphasize, that every result is comprehensively described in the main text.

Acknowledging the concerns raised about the density of our paper and the potential challenges in interpreting the findings, we have conducted a thorough review of the text and figure legends. We have made revisions with the goal to enhance clarity and readability. We have made dedicated efforts to ensure that readers can readily grasp the significance of our results and appreciate the intricacies of our findings. We firmly believe that with these revisions, our chosen approach is the most effective means of presenting the richness of our data and maintaining the integrity of our findings.

Lack of clarity in the behavioral analyses

Behavioral assessments are not clearly written in the methods. For example, for the novel object recognition task, it isn't clear how preference was calculated. Is this simply the percent of time spent with the novel object, or is this a relative measure (novel:familiar ratio)? This matters because if it is simply the percent of time, the relevant measure is to compare each group to 50% (the absence of a preference). The key measures for each test need to be readily distinguished from the control measures.

There are also many dependent behavioral measures. For example, speed and distance are directly related to each other, but these are typically reported as control measures to help interpret the key measure, which is the anxiety-like behavior. Similarly, some behavioral tests were used to represent multiple behavioral dimensions, such as anxiety and arousal. In general, the measurements of arousal seem atypical (speed and distance are typically reported as control measures, not measures of arousal). Similarly, measures of latency during training would not typically be used as a measure of long-term memory but instead reported as a control measure to show learning occurred. LDA analysis requires independence of the measures, as well as normality. It does not appear that all of the measures fed into this analysis would have met these assumptions, but the methods also do not clearly describe which measures were actually used in the LDA.

We agree with the reviewers’ concerns about the clarity of our behavioral analyses and we have thus added information to the methods section to clarify the procedures. Specifically, for SPSN, social approach was recorded as time spent close to STR1, and a preference ratio was calculated as Pref= 100 Time close to STR1/(Time close to STR1 + Time close to Empty). Social recognition memory was scored as preference towards STR2 and calculated as Pref =100 (time close to STR2) / (Time close to STR1 + Time close to STR2). For NOR, preference for novel object was calculated as Pref=100* Time novel object / (Time familiar object + novel object).

With regards to the different variables reported for the behavioral protocols, we agree that some measures, such as path length and speed can be used as control measures. For example, in an open field test, path length is an important control measure to assess whether an animal is engaged in the task. However, if an animal is actively moving, the amount of distance covered can but does not have to correlate with the amount of time that a mouse spends in the center of the open field. Using the measure of distance covered as a measure for general arousal and time spent in the center as a measure for anxiety related behavior allows a more nuanced evaluation of animal behavior. For instance, two animals spending similar amounts of time in the center may exhibit differences in the distance they cover. In this scenario, we would argue that anxiety related behavior (defined as exploring the center of an open field) would not reflect well a behavioral difference between the two animals, while the aspect of arousal clearly is a differencing factor.

Regarding the PA task and the use of latency during training, we agree that typically latency during training can be used as control measure to show that learning occurred. However, our study involved testing animals at two distinct time points. Contextual fear conditioning creates very robust memory traces that can persist for weeks or even months, and therefore the starting premise is very different when repeating the test. Initially, the animals were experimentally naïve and had not yet experienced a foot shock, leading to a rapid entry into the dark box. However, after experiencing the first CS-US presentation, a robust and persistent contextual fear memory trace is formed. Therefore, the latency observed in the second training phase of the PA reflects in essence long-term contextual fear memory, that is robustly displayed in WT animals but less in treated WT and TS animals. We have included this clarification in the methods and results sections.

Finally, we want to thank the reviewer for noticing the error in the LDAs, as the analysis was indeed performed including dependent variables for some systems. We have re-evaluated the LDAs for the behavioral tests and tibia microarchitecture tests, excluding dependent variables. As a result, the text and significance levels have been adjusted accordingly. To enhance transparency and clarity, we have included Supplementary Table S21, which precisely outlines the variables included in each LDA.

Unclear value of RNAseq

RNAseq was performed in cerebellum, a relatively spared region in DS pathology at an early time point in disease. Further, the expression of 125 genes triplicated in DS was shown in a PCA plot to highly overlap with WT, indicating that there are minimal differences in gene expression in these genes. If these genes are not critical for cerebellar function, perhaps this could account for the lack of differences between WT and Ts65Dn mice. If the authors are interested in performing RNAseq, it would have made more sense to perform this in hippocampus (to compare with metabolites) and to perform more stringent bioinformatic analysis than assessment by PCA of a limited subset of genes. Supplementary Table S14, which shows the differentially expressed genes, appears to be missing from the manuscript and cannot be evaluated. Additionally, the methods of the RNAseq are not sufficiently described and lack critical details. For example, what was the normalization performed, and which groups were compared to identify differentially expressed genes? It would also be worthwhile to describe how animals were identified for RNAseq-were those animals representative of their groups across other measures?

We acknowledge the reviewers' comments on the RNAseq analysis and would like to provide additional insights into our rationale and choices for this analysis. The primary aim of our RNAseq analysis was to offer supplementary evidence in support of the broader context of our paper. Rather than focusing on specific genes, our aim was to assess potential alterations in transcription within genes triplicated in the mouse model and explore differentially expressed genes across the entire genome. Therefore, we conducted a global analysis of the triplicated genes using a PCA and analyzed the differentially expressed genes across the entire genome as shown in Supplementary Table S14. The table was originally included as a separate Excel file but apparently it was not received by the reviewers. We have contacted the eLife editorial to ensure its inclusion in the current version. Furthermore, we have modified the text to clarify that both the triplicated genes and the entire genome were analyzed.

Regarding the use of cerebellum instead of hippocampus, we agree with the reviewers that the hippocampus is a major tissue of interest in the study of Down Syndrome since it mostly relates to cognition. Trisomic patients, however, also display other typical features such as for example a delay in the acquisition of motor skills. Here we decided to focus on the cerebellum as it is primarily associated to the locomotor system but also plays a role in other cognitive functions such as language processing and memory. Furthermore, at the time of the RNAseq analysis, the mice were 8 months old, equivalent to the adult human stage, and previous studies have shown transcriptomic alterations in this tissue and mouse model (Olmos-Serrano et al., 2016; Saran et al., 2003).

The lack of observable differences between WT and Ts65Dn mice in our PCA analysis may be attributed to several factors as discussed in our article. First, the high variability within each group, inherent to the complexity of DS, may obscure inter-group differences. Additionally, the subtlety of gene expression differences between WT and trisomic mice in the set of triplicated genes, as suggested by other transcriptomics studies on DS (Aït Yahya-Graison et al., 2007; Lyle et al., 2004; Olmos-Serrano et al., 2016; Saran et al., 2003), may contribute to the limited distinctions observed. Furthermore, regarding treatment effects, the timing of the RNAseq analysis should be considered since it was conducted at the endpoint, three months after treatment cessation. This temporal aspect could imply that the effects of the drug are not persistent, and a molecular memory might not be formed and maintained.

Nevertheless, we appreciate the reviewers' constructive comments and acknowledge the potential for more stringent bioinformatic analyses. While our intention was to provide an initial, global perspective, we are eager to support further investigations that delve deeper into the complexities of DS-related molecular mechanisms. Consequently, the dataset is available for other researchers to explore more specific questions upon request.

Finally, we have updated the methods section of the article to offer more detailed information on RNAseq processing and analysis. We have also clarified that all the surviving mice were included in the analysis.

Recommendations for the authors:

(1) Please add power calculations for each of the assessments.

We would like to clarify that we had already conducted power calculations as part of the initial planning and design phase of our study. After data acquisition and analysis, we have utilized appropriate statistical methods to interpret the results based on the data we have collected. Given that we had conducted a priori power calculations prior to data collection and that our analysis is based on the acquired data, we do not see the added value in including post hoc power calculations. Our primary focus has been on performing the correct statistical analyses to accurately interpret the results and draw meaningful conclusions.

(2) Introduction has some excessive references for each statement, which are not necessary. For instance: lines 67-73 are only references for 1 statement and lines 74-76 are references for a 2nd statement in the same sentence.

We have removed redundant references.

(3) Introduction: Lines 136-146 Gene names need to be spelled out, not just the IDs. Were these studies done in human or mouse models of DS?

We have spelled out the names of the genes.

(4) Why was brain volume and brain structure size normalized to body weight, not clearly explained?

The choice to normalize brain volume and brain structure size to body weight was a deliberate decision made to address potential confounding factors in our study. In the case of trisomic (TS) mice, they are generally smaller in size compared to their wild-type (WT) counterparts. The same may hold true for sex-related size differences. Without normalization, assessing brain volume and structure size could be misleading, as it might reflect the differences in overall body size rather than providing insights into the specific aspects of brain structure that we aimed to investigate. We have clarified this in the methods section.

(5) In cognitive tests, some of the WT data represented in Figure 3 does not match supplemental findings. Again power calculations may indicate a higher number of WT mice are needed to clarify this discrepancy.

We appreciate the reviewers' observation regarding the disparities between the data presented in Figure 3 and the supplemental figures. We would like to clarify that these variations are a result of the distinct analytical approaches employed in the two sets of data.

In Figure 3 and all main figures, the data were analyzed using multivariate tests, which consider multiple variables simultaneously and are particularly suited for investigating the collective impact of multiple factors. Conversely, the results shown in the supplementary figures were derived from univariate tests, which focus on individual variables and are well-suited for addressing specific questions related to each variable in isolation. The discrepancies between the data in the main figures and the supplementary figures can be attributed to the differences in the analytical methods chosen.

As for the suggestion of conducting power calculations to address the observed differences, we believe that the differences in data are inherent to the distinct analytical strategies and the specific research questions each analysis intended to answer. Power calculations may not be the most suitable approach in this context, as they pertain to sample size planning for hypothesis testing and may not reconcile the inherent dissimilarity between multivariate and univariate analyses.

Aït Yahya-Graison, E., Aubert, J., Dauphinot, L., Rivals, I., Prieur, M., Golfier, G., . . . Potier, M. C. (2007). Classification of human chromosome 21 gene-expression variations in Down syndrome: impact on disease phenotypes. Am J Hum Genet, 81(3), 475-491. https://doi.org/10.1086/520000

Lyle, R., Gehrig, C., Neergaard-Henrichsen, C., Deutsch, S., & Antonarakis, S. E. (2004). Gene expression from the aneuploid chromosome in a trisomy mouse model of down syndrome. Genome Res, 14(7), 1268-1274. https://doi.org/10.1101/gr.2090904

Olmos-Serrano, J. L., Kang, H. J., Tyler, W. A., Silbereis, J. C., Cheng, F., Zhu, Y., . . . Sestan, N. (2016). Down Syndrome Developmental Brain Transcriptome Reveals Defective Oligodendrocyte Differentiation and Myelination. Neuron, 89(6), 1208-1222. https://doi.org/10.1016/j.neuron.2016.01.042

Saran, N. G., Pletcher, M. T., Natale, J. E., Cheng, Y., & Reeves, R. H. (2003). Global disruption of the cerebellar transcriptome in a Down syndrome mouse model. Hum Mol Genet, 12(16), 2013-2019. https://doi.org/10.1093/hmg/ddg217

Joint Public Review:

Using Ts65Dn - the most commonly used mouse model of Down syndrome (DS) - the goal of this study is two-pronged: 1) to conduct a thorough assessment of DS-related genotypic, physiological, behavioral, and phenotypic measures in a longitudinal manner; and 2) to measure the effects of chronic GTE-EGCG on these measures in the Ts65Dn mouse model. Corroborating results from several previous studies on Ts65Dn mice, findings of this study show confirm the Ts65Dn mouse model exhibits the suite of traits associated with DS. The findings also suggest that the mouse model might have experienced drift, given the milder phenotypes than those reported by earlier studies. Results of the GTE-EGCG treatment do not support its therapeutic use and instead show that the treatment exacerbated certain DS-related phenotypes.

Strengths:

The authors performed a rigorous assessment of treatment and examined treatment and genotypic alterations at multiple time points during growth and aging. Detailed analysis shows differences in genotype during aging as well as genotype with treatment. This study is solid in the overarching methodological approach (with the exception of RNAseq, described below). The biggest strength of the study is its approach and dataset, which corroborate results from a multitude of past studies on Ts65Dn mice, albeit on adult specimens.

Comments on revised submission:

The authors have made numerous changes to address the concerns of the reviewers. The strengths remain: a large, longitudinal data set for the Ts65Dn mouse model across multiple organ systems. The results also clearly show the impact of GTE-EGCG treatment and do not support its therapeutic use.

The authors should report their a priori power calculations that they used when designing their experiment. This should be added to either the Animals or Statistics subsections of the Methods.

Author Response

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

Public Reviews:

Reviewer #1:

Summary:

In this study, Yan et al. investigate the molecular bases underlying mating type recognition in Tetrahymena thermophila. This model protist possesses a total of 7 mating types/sexes and mating occurs only between individuals expressing different mating types. The authors aimed to characterize the function of mating type proteins (MTA and MTB) in the process of self- and non-self recognition, using a combination of elegant phenotypic assays, protein studies, and imaging. They showed that the presence of MTA and MTB in the same cell is required for the expression of concavalin-A receptors and for tip transformation - two processes that are characteristic of the costimulation phase that precedes cell fusion. Using protein studies, the authors identify a set of additional proteins of varied functions that interact with MTA and MTB and are likely responsible for the downstream signaling processes required for mating. This is a description of a fascinating self- and non-self-recognition system and, as the authors point out, it is a rare example of a system with numerous mating types/sexes. This work opens the door for the further understanding of the molecular bases and evolution of these complex recognition systems within and outside protists.

The results shown in this study point to the unequivocal requirement of MTA and MTB proteins for mating. Nevertheless, some of the conclusions regarding the mode of functioning of these proteins are not fully supported and require additional investigation.

Strengths:

(1) The authors have established a set of very useful knock-out and reporter lines for MT proteins and extensively used them in sophisticated and well-designed phenotypic assays that allowed them to test the role of these proteins in vivo.

(2) Despite their apparent low abundance, the authors took advantage of a varied set of protein isolation and characterization techniques to pinpoint the localization of MT proteins to the cell membrane, and their interaction with multiple other proteins that could be downstream effectors. This opens the door for the future characterization of these proteins and further elucidation of the mating type recognition cascade.

Weaknesses:

The manuscript is structured and written in a very clear and easy-to-follow manner. However, several conclusions and discussion points fall short of highlighting possible models and mechanisms through which MT proteins control mating type recognition:

(1) The authors dismiss the possibility of a "simple receptor-ligand system", even though the data does not exclude this possibility. The model presented in Figure 2 S1, and on which the authors based their hypothesis, assumes the independence of MTA and MTB proteins in the generation of the intracellular cascade. However, the results presented in Figure 2 show that both proteins are required to be active in the same cell. Coupled with the fact that MTA and MTB proteins interact, this is compatible with a model where MTA would be a ligand and MTB a receptor (or vice-versa), and could thus form a receptor-ligand complex that could potentially be activated by a non-cognate MTA-MTB receptor-ligand complex, leading to an intracellular cascade mediated by the identified MRC proteins. As it stands, it is not clear what is the proposed working model, and it would be very beneficial for the reader for this to be clarified by having the point of view of the authors on this or other types of models.

We are very grateful that Reviewer #1 proposed the possibility that MTA and MTB form a receptor-ligand complex in which one acting as the ligand and the other as the receptor. We considered this hypothesis when asking how dose MTRC function, too. However, our current results do not support this idea. For instance, if MTA were a ligand and MTB a receptor, we would expect a mating signal upon treatment with MTAxc protein, but not with MTBxc. Contrary to this expectation, our experiments revealed that both MTAxc and MTBxc exhibit very similar effects (Figure 5, green and blue), and their combined treatment produces a stronger effect (Figure 5, teal). This suggests a mixed function for both proteins. (We incorporated this discussion into the revised version [line 120-121, 240-244].) It is pity that our current knowledge does not provide a detailed molecular mechanism for this intricate system. We are actively investigating the protein structures of MTA, MTB, and the entire MTRC, hoping to gain deeper insights into the molecular functions of MTA and MTB.

Additionally, we also realized that the expression we used in the previous version, “simple receptor-ligand model”, is not clearly defined. As Reviewer #1 pointed out, in this section, we examined whether the individual proteins of MTA and MTB act as a couple of receptor and ligand. We think this is the simplest possibility as a null hypothesis for Tetrahymena mating-type recognition. We have clarified it in the revised version (line 90-91, 104-106). According to this section, we proposed that MTA and MTB may form a complex that serves as a recognizer (functioning as both ligand and receptor) (line 117-118).

(2) The presence of MTA/MTB proteins is required for costimulation (Figure 2), and supplementation with non-cognate extracellular fragments of these proteins (MTAxc, or MTBxc) is a positive stimulator of pairing. However, alone, these fragments do not have the ability to induce costimulation (Figure 5). Based on the results in Figures 5 and 6 the authors suggest that MT proteins mediate both self and non-self recognition. Why do MTAxc and MTBxc not induce costimulation alone? Are any other components required? How to reconcile this with the results of Figure 2? A more in-depth interpretation of these results would be very helpful, since these questions remain unanswered, making it difficult for the reader to extract a clear hypothesis on how MT proteins mediate self- and non-self-recognition.

Several factors could contribute to the inability of MTA/Bxc to induce costimulation. It is highly likely that additional components are necessary, given that MTA/B form a protein complex with other proteins. Moreover, the expression of MTA/Bxc in insect cells, compared with Tetrahymena, might result in differences in post-translational modifications. Additionally, there are variations in protein conditions; on the Tetrahymena membrane, these proteins are arranged regularly and concentrated in a small area, while MTA/Bxc is randomly dispersed in the medium. The former condition could be more efficient. If there is a threshold required to stimulate a costimulation marker, MTA/Bxc may fail to meet this requirement. Much more studies are needed to fully answer this question. We acknowledged this limitation in the revised version (line 244-248).

Reviewer #2:

This manuscript reports the discovery and analysis of a large protein complex that controls mating type and sexual reproduction of the model ciliate Tetrahymena thermophila. In contrast to many organisms that have two mating types or two sexes, Tetrahymena is multi-sexual with 7 distinct mating types. Previous studies identified the mating type locus, which encodes two transmembrane proteins called MTA and MTB that determine the specificity of mating type interactions. In this study, mutants are generated in the MTA and MTB genes and mutant isolates are studied for mating properties. Cells missing either MTA or MTB failed to co-stimulate wild-type cells of different mating types. Moreover, a mixture of mutants lacking MTA or MTB also failed to stimulate. These observations support the conclusion that MTA and MTB may form a complex that directs mating-type identity. To address this, the proteins were epitope-tagged and subjected to IP-MS analysis. This revealed that MTA and MTB are in a physical complex, and also revealed a series of 6 other proteins (MRC1-6) that together with MTA/B form the mating type recognition complex (MTRC). All 8 proteins feature predicted transmembrane domains, three feature GFR domains, and two are predicted to function as calcium transporters. The authors went on to demonstrate that components of the MTRC are localized on the cell surface but not in the cilia. They also presented findings that support the conclusion that the mating type-specific region of the MTA and MTB genes can influence both self- and non-self-recognition in mating.

Taken together, the findings presented are interesting and extend our understanding of how organisms with more than two mating types/sexes may be specified. The identification of the six-protein MRC complex is quite intriguing. It would seem important that the function of at least one of these subunits be analyzed by gene deletion and phenotyping, similar to the findings presented here for the MTA and MTB mutants. A straightforward prediction might be that a deletion of any subunit of the MRC complex would result in a sterile phenotype. The manuscript was very well written and a pleasure to read.

Thanks for the valuable comments and suggestions. We are currently in the process of constructing deletion strains for these genes. As of now, we have successfully obtained ΔMRC1-3 and MRC4-6 knockdown strains. Our preliminary observations indicate that ΔMRC1-3 strains are unable to undergo mating. However, we prefer not to include these results in the current manuscript, as we believe that more comprehensive studies are still needed.

Reviewer #3:

The authors describe the role, location, and function of the MTA and MTB mating type genes in the multi-mating-type species T. thermophila. The ciliate is an important group of organisms to study the evolution of mating types, as it is one of the few groups in which more than two mating types evolved independently. In the study, the authors use deletion strains of the species to show that both mating types genes located in each allele are required in both mating individuals for successful matings to occur. They show that the proteins are localized in the cell membrane, not the cilia, and that they interact in a complex (MTRC) with a set of 6 associated (non-mating type-allelic) genes. This complex is furthermore likely to interact with a cyclin-dependent kinase complex. It is intriguing that T. thermophila has two genes that are allelic and that are both required for successful mating. This coevolved double recognition has to my knowledge not been described for any other mating-type recognition system. I am not familiar with experimental research on ciliates, but as far as I can judge, the experiments appear well performed and mostly support the interpretation of the authors with appropriate controls and statistical analyses.

The results show clearly that the mating type genes regulate non-self-recognition, however, I am not convinced that self-recognition occurs leading to the suppression of mating. An alternative explanation could be that the MTA and MTB proteins form a complex and that the two extracellular regions together interact with the MTA+MTB proteins from different mating types. This alternative hypothesis fits with the coevolution of MTA and MTB genes observed in the phylogenetic subgroups as described by Yan et al. (2021 iScience). Adding MTAxc and/or MTBxc to the cells can lead to the occupation of the external parts of the full proteins thereby inhibiting the formation of the complex, which in turn reduces non-self interactions. Self-recognition as explained in Figure 2S1 suggests an active response, which should be measurable in expression data for example. This is in my opinion not essential, but a claim of self-recognition through the MTA and MTB should not be made.

We express our gratitude to Reviewer #3 for proposing the occupation model and have incorporated this possibility into the manuscript. We believe it is possible that occupation may serve as the molecular mechanism through which self-recognition negatively regulates mating. If there is a physical interaction between mating-type proteins of the same type, but this interaction blocks the recognition machinery rather than initiating mating, it can be considered a form of self-recognition. This aligns with the observation that strains expressing MTA/B6 and MTB2 mate normally with WT cells of all mating types except for VI and II (line 203-204). A concise discussion on this topic is included in the manuscript (line 288-293, 659-661). We are actively investigating the downstream aspects of mating-type recognition, and we hope to provide further insights into this question soon.

The authors discuss that T. thermophila has special mating-type proteins that are large, while those of other groups are generally small (lines 157-160 and discussion). The complex formed is very large and in the discussion, they argue that this might be due to the "highly complex process, given that there are seven mating types in all". There is no argument given why large is more complex, if this is complex, and whether more mating types require more complexity. In basidiomycete fungi, many more mating types than 7 exist, and the homeodomain genes involved in mating types are relatively small but highly diverse (Luo et al. 1994 PMID: 7914671). The mating types associated with GPCR receptors in fungi are arguably larger, but again their function is not that complex, and mating-type specific variations appear to evolve easily (Fowler et al 2004 PMID: 14643262; Seike et al. 2015 PMID: 25831518). The large protein complex formed is reminiscent of the fusion patches that develop in budding or fission yeasts. In these species, the mating type receptors are activated by ligand pheromones from the opposite mating type that induce polarity patch formation (see Sieber et al. 2023 PMID: 35148940 for a recent review). At these patches, growth (shmooing) and fusion occur, which is reminiscent (in a different order) of the tip transformation in T. thermophilia. The fusion of two cells is in all taxa a dangerous and complex event that requires the evolution of very strict regulation and the existence of a system like the MTRC and cyclin-dependent complex to regulate this process is therefore not unexpected. The existence of multiple mating types should not greatly complicate the process, as most of the machinery (except for the MTA and MTB) is identical among all mating types.

We are very grateful that Reviewer #3 provide this insightful view and relevant papers. In response to the feedback, we removed the sentences regarding “multiple mating types greatly complicate the process” in the revised version. Instead, we have introduced a discussion section comparing the mating systems of yeasts and Tetrahymena (line 279-286).

The Tetrahymena/ciliate genetics and lifecycle could be better explained. For a general audience, the system is not easy to follow. For example, the ploidy of the somatic nucleus with regards to the mating type is not clear to me. The MAC is generally considered "polyploid", but how does this work for the mating type? I assume only a single copy of the mating type locus is available in the MAC to avoid self-recognition in the cells. Is it known how the diploid origin reduces to a single mating type? This does not become apparent from Cervantes et al. 2013.

In T. thermophila, the MIC (diploid) contains several mating-type gene pairs (mtGP, i.e., MTA and MTB) organized in a tandem array at the mat locus on each chromosome. In sexual reproduction, the new MAC of the progeny develops from the fertilized MIC through a series of genome editing events, and its ploidy increases to ~90 by endoreduplication. During this process, mtGP loss occurs, resulting in only one mtGP remaining on the MAC chromosome. The mating-type specificity of mtGPs on each chromosome within one nucleus becomes relatively pure through intranuclear coordination. After multiple assortments (possibly caused by MAC amitosis during cell fission), only mtGPs of one mating-type specificity exist in each cell, determining the cell’s mating type.

It is pity that the exact mechanisms involved in this complicated process remain a black box. The loss of mating-type gene pairs is hypothesized to involve a series of homologous recombination events, but this has not been completely proven. Furthermore, there is no clear understanding of how intranuclear coordination and assortment are achieved. While we have made observations confirming these events, a breakthrough in understanding the molecular mechanism is yet to be achieved.

We included more information in the revised version (line 672-683). Given the complexity of these unusual processes, we recommend an excellent review by Prof. Eduardo Orias (PMID: 28715961), which offers detailed explanations of the process and related concepts (line 685-686).

Also, the explanation of co-stimulation is not completely clear (lines 49-60). Initially, direct cell-cell contact is mentioned, but later it is mentioned that "all cells become fully stimulated", even when unequal ratios are used. Is physical contact necessary? Or is this due to the "secrete mating-essential factors" (line 601)? These details are essential, for interpretation of the results and need to be explained better.

Sorry that we didn’t realize the term “contact” is not precise enough. In Tetrahymena, physical contact is indeed necessary, but it can refer to temporary interactions. Unlike yeast, Tetrahymena cells exhibit rapid movement, swimming randomly in the medium. Occasionally, two cells may come into contact, but they quickly separate instead of sticking together. Even newly formed loose pairs often become separated. As a result, one cell can come into contact with numerous others and stimulate them. We have clarified this aspect in the revised version (line 50-51, 57).

Abstract and introduction: Sexes are not mating types. In general, mating types refer to systems in which there is no obvious asymmetry between the gametes, beyond the compatibility system. When there is a physiological difference such as size or motility, sexes are used. This distinction is of importance because in many species mating types and sexes can occur together, with each sex being able to have either (when two) or multiple mating types. An example are SI in angiosperms as used as an example by the authors or mating types in filamentous fungi. See Billiard et al. 2011 [PMID: 21489122] for a good explanation and argumentation for the importance of making this distinction.

We have clarified the expression in the revised version (line 20, 38, 40, 45).

Recommendations for the authors:

Reviewer #1:

I really enjoyed reading this manuscript and I think a few tweaks in the writing/data presentation could greatly improve the experience for the reader:

(1) The information about your previous work in identifying downstream proteins CDK19, CYC9, and CIP1 (lines 170-173) could be directly presented in the introduction.

We have moved this information in the introduction in the revised version (line 74-77).

(2) For a reader who is not familiar with Tetrahymena, a few more details on how reporter and knock-out lines are generated would be beneficial.

We introduced the knock-out method in Figure 2 – figure supplement 1B, HA-tag method in Figure 3A, and MTB2-eGFP construction method in Figure 4E. In addition, we introduced how co-stimulation markers observed in Materials and Methods (line 404-410)

(3) Figures 5 and 6: clarify the types of pairing and treatments that were done directly in the figure (eg. adding additional labels). As of now, it is necessary to go through the text and legend to try and understand in detail what was done.

Cell types and treatments were directly introduced in the revised figure (Figure 5 and 6).

(4) The logical transition in lines 136-142 is hard to follow.

We rewrote this paragraph in the revised version (lines 143-156). Additionally, we added a figure to illustrate the theoretical mating-type recognition model between WT cells and ΔCDK19, ΔCYC9 cells, MTAxc, MTBxc proteins, and ΔMTA, ΔMTB cells (Figure 2 – figure supplement 1D-G).

(5) Lines 191-196: the fact that cells expressing multiple mating types can self goes against an active self-rejection system - if this is the case there should be self-rejection among all expressed mating types. Unless non-self recognition is an active process and self-recognition is simply the absence of non-self recognition. The authors briefly mention this in lines 263-265, but it would be interesting to expand and clarify this.

We appreciate that Reviewer #1 notice the interesting selfing phenotype of the MTB2-eGFP (MTVI background) strain. We further discussed it in the revised manuscript (line 298-306).

(6) The authors briefly mention the possibility of different mating types using different recognition mechanisms (lines 255-260), based on the big differences in the size of the mating-specific region of MT proteins. Following this and the weakness nr. 2, I think it would be pertinent to gather and present more information on the properties and structures of the mating-type specific regions of MT proteins. Simple in silico analysis of motifs, structure, etc. could help clarify the role of these regions. It seems more parsimonious that MT proteins would have variable mating type specific regions that account for the recognition of the different mating types, and conserved cytoplasmic functions that could trigger a single downstream signaling cascade. It would be interesting to know the authors' opinion on this.

We are very grateful for this suggestion. Actually, we are currently working on determining the 3D structure of MTRC. The Alphafold2 prediction indicates that the MT-specific region is comprised of seven global β-sheets, resembling the structure of immunoglobulins (Ig). Our most recent cryo-EM results have revealed a ~15Å structure, aligning well with the prediction. However, the main challenge lies in the low expression levels, both in Tetrahymena and insect/mammal cells. We anticipate obtaining more detailed results soon. Therefore, we prefer to present the MT recognition model with robust experimental evidence in the future, and didn’t discuss too much on this aspect in the current manuscript.

(7) Adding a figure including a proposed model, as well as expanding the discussion on the points presented as "weaknesses" would help clarify the ideas/hypothesis on how the mating recognition works. I think this would really elevate the paper and help highlight the results.

We added a figure to introduce the model and the weaknesses in the revised version (Figure 7, line 656-665).

(8) Line 202-203: It is far-fetched to infer subcellular localization based on the data presented here, couterstaining with other dyes and antibodies specific to certain cell components, as well as negative control images, are required.

Thanks for the suggestion. We attempted to stain cell components using various dyes and antibodies. Unfortunately, we found that cell surface and cilia (especially oral cilia) is very easy to give a false positive signal. We think this issue seriously affects the credibility of the results. It may seem like splitting hairs, but we are trying to be precise.

Meanwhile, we still believe the mating-type proteins localizes to cell surface because MTA-HA is identified in the isolated cell surface proteins.

Regarding negative control, as shown in Fig. 4G, where a MTB2-eGFP cell is pairing with a WT cell, no GFP signal is observed in the WT cell.

(9) Lines 131: clarify the sentence - expression of Con-A receptors requires both MTA and MTB (MTA to receive the signal).

We modified the sentence in the revised version (line 139-140).

Reviewer #2:

Minor points.

(1) Line 194-196. Why are these cells able to self?

These cells able to self may because the MTRC contain heterotypic mating-type proteins (MTA6 and MTB2), which activate mating when they interact with another heterotypic MTRC (line 207-208).

(2) Line 232. What do the authors mean by the term synergistic effect here? Definition and statistics?

Sorry about the confusion. The synergistic effect refers to the effect of MTAxc and MTBxc become stronger when using together. We clarified it in the revised version (line 232).

(3) For Figure 4 panel D, are there antibodies that are available as a control for cilia? If so, then blotting this membrane would show that cilia-associated proteins are in the cilia preparation, which is a standard control for sub-cellular fractionation.

Thanks for the suggestion. Unfortunately, we didn’t find a suitable cilia-specific antibody yet. Instead, we employed MS analysis to confirm the presence of cilia proteins in this sample (line 195-196, Figure 4–Source data 1). We also observed the sample under the microscope, which directly revealed the presence of cilia (Figure 4C).

(4) At least one reference cited in the text was not present in the reference list. The authors should go through the references cited to ensure that all have made it into the reference list.

We have checked all the references.

Some minor edits:

(1) MTA and MTB are presented in both roman and italics (e.g. line 209) in the manuscript. Maybe all should be in italics? Or is this a distinction between the gene and the protein?

The italics word (MTA) refers to gene, and non-italics word (MTA) refers to protein.

(2) Line 251. Change "achieving" to "achieve".

We have corrected this word (line 266).

Reviewer #3:

Line 101. It would help to explain this expectation earlier in this paragraph.

We explained the expectation in the revised version (line 92-97, 104-106).

Line 109. How is a co-receptor different from the MTRC complex?

We have rewritten the relevant sentences to enhance clarity (line 116-119). The molecular function of the MTRC complex could involve acting as a co-receptor or recognizer (functioning as both ligand and receptor). Based on the results presented in this section, we propose that MTA and MTB may function as a complex, but the confirmation of this hypothesis (MTRC) is provided in a later section. Therefore, we did not use the term “MTRC” here. These sentences briefly discuss the molecular function of this complex and explain why MTRC does not appear to function as a co-receptor.

Line 251: which "dual approach" is referred to?

Dual approach is referred to both self and non-self recognition. We explained it in the revised version (line 265-266).

Line 258: what "different mechanisms" do the authors have in mind? Why would a different mechanism be expected? The different sizes could have evolved for (coevolutionary?) selection on the same mechanism.

Sorry about the confusion. We clarified it in the revised version (line 269-278).

What we intended to express is that we are uncertain whether the mating-type recognition model we discovered in T. thermophila is applicable to all Tetrahymena species due to significant differences in the length of the mating-type-specific region. We believe it is important to highlight this distinction to avoid potential misinterpretations in future studies involving other Tetrahymena species. At the same time, we look forward to future research that may provide insights into this question.

Fig 2 C&D. Is it correct that these figures show the strains only after 'preincubation'? This is not apparent from the caption of the text. Additionally, the order of the images is very confusing. Write in the figures (so not just in the caption) what the sub-script means.

These panels are re-organized in the revised version (Fig. 2C&D). There are three kinds of pictures: “not incubated”, “WT pre-incubated by mutant” and “mutant pre-incubated by WT”.

The methods used to generate Figure 5 are not clearly described. I understand that the obtained xc proteins were added to the cells, and then washed, after which a test was performed mixing WT-VI and WT-VII cells. Were both cells treated? Or only one of the strains? The explanation for the reused washing medium is not clear and the method is not indicated.

Both cells are treated. More details are provided in the revised manuscript (line 230-231, 633-634, 637-639, Fig. 5). To prepare the starvation medium containing mating-essential factors, cells were starved in fresh starvation medium for ~16 hours. Subsequently, cells were removed by three rounds of centrifugation (1000 g, 3 min) (line 330-332).

In general, the figures are difficult to understand without repeated inquiries in the captions. Give more information in the figures themselves.

More information is introduced in the figure (Fig. 2C, Fig. 3B, Fig. 4A, B, D, Fig. 5 and Fig. 6).

eLife assessment

This fundamental study provides insight into the fascinating process of self- and non-self-recognition in the protist Tetrahymena thermophila, a species with seven distinct mating types. Using an elegant combination of phenotypic assays, protein studies, and imaging, the authors present convincing evidence that a large multifunctional protein complex at the cell surface mediates both self- and non-self mating-type recognition. This study extends our understanding of how more than two mating types/sexes may be specified in a species, and it will be relevant for anyone interested in sexual systems and cell-cell communication.

Reviewer #1 (Public Review):

Summary:<br /> In this study, Yan et al. investigate the molecular bases underlying mating type recognition in Tetrahymena thermophila. This model protist possesses a total of 7 mating types/sexes and mating occurs only between individuals expressing different mating types. The authors aimed to characterize the function of mating type proteins (MTA and MTB) in the process of self- and non-self recognition, using a combination of elegant phenotypic assays, protein studies, and imaging. They showed that the presence of MTA and MTB in the same cell is required for the expression of concavalin-A receptors and for tip transformation - two processes that are characteristic of the costimulation phase that precedes cell fusion. Using protein studies, the authors identify a set of additional proteins of varied functions that interact with MTA and MTB and are likely responsible for the downstream signaling processes required for mating. This is a description of a fascinating self- and non-self-recognition system and, as the authors point out, it is a rare example of a system with numerous mating types/sexes. This work opens the door for the further understanding of the molecular bases and evolution of these complex recognition systems within and outside protists.

The results shown in this study point to the unequivocal requirement of MTA and MTB proteins for mating. Nevertheless, some of the conclusions regarding the mode of functioning of these proteins are not fully supported and require additional investigation.

Strengths:<br /> (1) The authors have established a set of very useful knock-out and reporter lines for MT proteins and extensively used them in sophisticated and well-designed phenotypic assays that allowed them to test the role of these proteins in vivo.

(2) Despite their apparent low abundance, the authors took advantage of a varied set of protein isolation and characterization techniques to pinpoint the localization of MT proteins to the cell membrane, and their interaction with multiple other proteins that could be downstream effectors. This opens the door for the future characterization of these proteins and further elucidation of the mating type recognition cascade.

Weaknesses:<br /> The manuscript is structured and written in a very clear and easy-to-follow manner. However, several conclusions and discussion points fall short of highlighting possible models and mechanisms through which MT proteins control mating type recognition:

(1) The authors dismiss the possibility of a "simple receptor-ligand system", even though the data does not exclude this possibility. The model presented in Figure 2 S1, and on which the authors based their hypothesis, assumes the independence of MTA and MTB proteins in the generation of the intracellular cascade. However, the results presented in Figure 2 show that both proteins are required to be active in the same cell. Coupled with the fact that MTA and MTB proteins interact, this is compatible with a model where MTA would be a ligand and MTB a receptor (or vice-versa), and could thus form a receptor-ligand complex that could potentially be activated by a non-cognate MTA-MTB receptor-ligand complex, leading to an intracellular cascade mediated by the identified MRC proteins. As it stands, it is not clear what is the proposed working model, and it would be very beneficial for the reader for this to be clarified by having the point of view of the authors on this or other types of models.

(2) The presence of MTA/MTB proteins is required for costimulation (Figure 2), and supplementation with non-cognate extracellular fragments of these proteins (MTAxc, or MTBxc) is a positive stimulator of pairing. However, alone, these fragments do not have the ability to induce costimulation (Figure 5). Based on the results in Figures 5 and 6 the authors suggest that MT proteins mediate both self and non-self recognition. Why do MTAxc and MTBxc not induce costimulation alone? Are any other components required? How to reconcile this with the results of Figure 2? A more in-depth interpretation of these results would be very helpful, since these questions remain unanswered, making it difficult for the reader to extract a clear hypothesis on how MT proteins mediate self- and non-self-recognition.

Reviewer #2 (Public Review):

This manuscript reports the discovery and analysis of a large protein complex that controls mating type and sexual reproduction of the model ciliate Tetrahymena thermophila. In contrast to many organisms that have two mating types or two sexes, Tetrahymena is multi-sexual with 7 distinct mating types. Previous studies identified the mating type locus, which encodes two transmembrane proteins called MTA and MTB that determine the specificity of mating type interactions. In this study, mutants are generated in the MTA and MTB genes and mutant isolates are studied for mating properties. Cells missing either MTA or MTB failed to co-stimulate wild-type cells of different mating types. Moreover, a mixture of mutants lacking MTA or MTB also failed to stimulate. These observations support the conclusion that MTA and MTB may form a complex that directs mating-type identity. To address this, the proteins were epitope-tagged and subjected to IP-MS analysis. This revealed that MTA and MTB are in a physical complex, and also revealed a series of 6 other proteins (MRC1-6) that together with MTA/B form the mating type recognition complex (MTRC). All 8 proteins feature predicted transmembrane domains, three feature GFR domains, and two are predicted to function as calcium transporters. The authors went on to demonstrate that components of the MTRC are localized on the cell surface but not in the cilia. They also presented findings that support the conclusion that the mating type-specific region of the MTA and MTB genes can influence both self- and non-self-recognition in mating.

Taken together, the findings presented are interesting and extend our understanding of how organisms with more than two mating types/sexes may be specified. The identification of the six-protein MRC complex is quite intriguing. It would seem important that the function of at least one of these subunits be analyzed by gene deletion and phenotyping, similar to the findings presented here for the MTA and MTB mutants. A straightforward prediction might be that a deletion of any subunit of the MRC complex would result in a sterile phenotype. The manuscript was very well written and a pleasure to read.

Reviewer #3 (Public Review):

The authors describe the role, location, and function of the MTA and MTB mating type genes in the multi-mating-type species T. thermophila. The ciliate is an important group of organisms to study the evolution of mating types, as it is one of the few groups in which more than two mating types evolved independently. In the study, the authors use deletion strains of the species to show that both mating types genes located in each allele are required in both mating individuals for successful matings to occur. They show that the proteins are localized in the cell membrane, not the cilia, and that they interact in a complex (MTRC) with a set of 6 associated (non-mating type-allelic) genes. This complex is furthermore likely to interact with a cyclin-dependent kinase complex. It is intriguing that T. thermophila has two genes that are allelic and that are both required for successful mating. This coevolved double recognition has to my knowledge not been described for any other mating-type recognition system. I am not familiar with experimental research on ciliates, but as far as I can judge, the experiments appear well performed and mostly support the interpretation of the authors with appropriate controls and statistical analyses.

The results show clearly that the mating type genes regulate non-self-recognition, however, I am not convinced that self-recognition occurs leading to the suppression of mating. An alternative explanation could be that the MTA and MTB proteins form a complex and that the two extracellular regions together interact with the MTA+MTB proteins from different mating types. This alternative hypothesis fits with the coevolution of MTA and MTB genes observed in the phylogenetic subgroups as described by Yan et al. (2021 iScience). Adding MTAxc and/or MTBxc to the cells can lead to the occupation of the external parts of the full proteins thereby inhibiting the formation of the complex, which in turn reduces non-self interactions. Self-recognition as explained in Figure 2S1 suggests an active response, which should be measurable in expression data for example. This is in my opinion not essential, but a claim of self-recognition through the MTA and MTB should not be made.

The authors discuss that T. thermophila has special mating-type proteins that are large, while those of other groups are generally small (lines 157-160 and discussion). The complex formed is very large and in the discussion, they argue that this might be due to the "highly complex process, given that there are seven mating types in all". There is no argument given why large is more complex, if this is complex, and whether more mating types require more complexity. In basidiomycete fungi, many more mating types than 7 exist, and the homeodomain genes involved in mating types are relatively small but highly diverse (Luo et al. 1994 PMID: 7914671). The mating types associated with GPCR receptors in fungi are arguably larger, but again their function is not that complex, and mating-type specific variations appear to evolve easily (Fowler et al 2004 PMID: 14643262; Seike et al. 2015 PMID: 25831518). The large protein complex formed is reminiscent of the fusion patches that develop in budding or fission yeasts. In these species, the mating type receptors are activated by ligand pheromones from the opposite mating type that induce polarity patch formation (see Sieber et al. 2023 PMID: 35148940 for a recent review). At these patches, growth (shmooing) and fusion occur, which is reminiscent (in a different order) of the tip transformation in T. thermophilia. The fusion of two cells is in all taxa a dangerous and complex event that requires the evolution of very strict regulation and the existence of a system like the MTRC and cyclin-dependent complex to regulate this process is therefore not unexpected. The existence of multiple mating types should not greatly complicate the process, as most of the machinery (except for the MTA and MTB) is identical among all mating types.

The Tetrahymena/ciliate genetics and lifecycle could be better explained. For a general audience, the system is not easy to follow. For example, the ploidy of the somatic nucleus with regards to the mating type is not clear to me. The MAC is generally considered "polyploid", but how does this work for the mating type? I assume only a single copy of the mating type locus is available in the MAC to avoid self-recognition in the cells. Is it known how the diploid origin reduces to a single mating type? This does not become apparent from Cervantes et al. 2013. Also, the explanation of co-stimulation is not completely clear (lines 49-60). Initially, direct cell-cell contact is mentioned, but later it is mentioned that "all cells become fully stimulated", even when unequal ratios are used. Is physical contact necessary? Or is this due to the "secrete mating-essential factors" (line 601)? These details are essential, for interpretation of the results and need to be explained better.

Abstract and introduction: Sexes are not mating types. In general, mating types refer to systems in which there is no obvious asymmetry between the gametes, beyond the compatibility system. When there is a physiological difference such as size or motility, sexes are used. This distinction is of importance because in many species mating types and sexes can occur together, with each sex being able to have either (when two) or multiple mating types. An example are SI in angiosperms as used as an example by the authors or mating types in filamentous fungi. See Billiard et al. 2011 [PMID: 21489122] for a good explanation and argumentation for the importance of making this distinction.

Author Response

Reviewer #1 (Public Review):

Summary:

This paper suggests to apply intrinsically-motivated exploration for the discovery of robust goal states in gene regulatory networks.

Strengths:

The paper is well written. The biological motivation and the need for such methods are formulated extraordinarily well. The battery of experimental models is impressive.

We thank the reviewer for sharing interest in the research problem and for recognizing the strengths of our work.

Weaknesses:

(1) The proposed method is compared to the random search. That says little about the performance with regard to the true steady-state goal sets. The latter could be calculated at least for a few simple ODE (e.g., BIOMD0000000454, `Metabolic Control Analysis: Rereading Reder'). The experiment with 'oscillator circuits' may not be directly interpolated to the other models.

The lack of comparison to the ground truth goal set (attractors of ODE) from arbitrary initial conditions makes it hard to evaluate the true performance/contribution of the method. A part of the used models can be analyzed numerically using JAX, while there are models that can be analyzed analytically.

"...The true versatility of the GRN is unknown and can only be inferred through empirical exploration and proxy metrics....": one could perform a sensitivity analysis of the ODEs, identifying stable equilibria. That could provide a proxy for the ground truth 'versatility'.

We agree with the reviewer that one primary concern is to properly evaluate the effectiveness of the proposed method. However, as we move toward complex pathways, knowledge of the “true” steady-state goal sets is often unknown which is where the use of machine learning methods as the one we propose are particularly interesting (but challenging to evaluate).

For simple models whose true steady-state distribution can be derived numerically and/or analytically, it is very likely that their exploration will be much simpler and this is not where a lot of improvement over random search may be found, which explains our focus on more complex models. While we agree that it is still interesting to evaluate exploration methods on these simple models for checking their behavior, it is not clear how to scale this analysis to the targeted more complex systems.

For systems whose true steady state distribution cannot be derived analytically or numerically, we believe that random search is a pertinent baseline as it is commonly used in the literature to discover the attractors/trajectories of a biological network. For instance, Venkatachalapathy et al. [1] initialize stochastic simulations at multiple randomly sampled starting conditions (which is called a kinetic Monte Carlo-based method) to capture the steady states of a biological system. Similarly, Donzé et al. [29] use a Monte Carlo approach to compute the reachable set of a biological network «when the number of parameters is large and their uncertain range is not negligible».

(2) The proposed method is based on `Intrinsically Motivated Goal Exploration Processes with Automatic Curriculum Learning', which assumes state action trajectories [s_{t_0:t}, a_{t_0:t}], (2.1 Notations and Assumptions' in the IMGEP paper). However, the models used in the current work do not include external control actions, but rather only the initial conditions can be set. It is not clear from the methods whether IMGEP was adapted to this setting, and how the exploration policy was designed w/o actual time-dependent actions. What does "...generates candidate intervention parameters to achieve the current goal....", mean considering that interventions 'Sets the initial state...' as explained in Table 2?

We thank the reviewer for asking for clarification, as indeed the IMGEP methodology originates from developmental robotics scenarios which generally focus on the problem of robotic sequential decision-making, therefore assuming state action trajectories as presented in Forestier et al. [65]. However, in both cases, note that the IMGEP is responsible for sampling parameters which then govern the exploration of the dynamical system. In Forestier et al. [65], the IMGEP also only sets one vector at the start (denoted θ∈Θ) which was specifying parameters of a movement (like the initial state of the GRN), which was then actually produced with dynamic motion primitives which are dynamical system equations similar to GRN ODEs, so the two systems are mathematically equivalent. More generally, while in our case the “intervention” of the IMGEP (denoted i ∈I) only controls the initial state of the GRN, future work could consider more advanced sequential interventions simply by setting parameters of an action policy π_i at the start which could be called during the GRN’s trajectory to sample control actions π_i (a_(t+1) 〖|s〗_(t0:t+1),a_t) where s_t would be the state of the GRN. In practice this would also require setting only one vector at the start, so it would remain the same exploration algorithm and only the space of parameters would change, which illustrates the generality of the approach.

(3) Fig 2 shows the phase space for (ERK, RKIPP_RP) without mentioning the typical full scale of ERK, RKIPP_RP. It is unclear whether the path from (0, 0) to (~0.575, ~3.75) at t=1000 is significant on the typical scale of this phase space. is it significant on the typical scale of this phase space?

The purpose of Figure 2 is to illustrate an example of GRN trajectory in transcriptional space, and to illustrate what “interventions” and “perturbations” can be in that context. To that end we have used the fixed initial conditions provided in the BIOMD0000000647, replicating Figure 5 of Cho et al. [56]. While we are not sure of what the reviewer means with “typical” scale of this phase space, we would like to point reviewer toward Figure 8 which shows examples of certain paths that indeed reach further point in the same phase space (up to ~10μM in RKIPP_RP levels and ~300μM in ERK levels). However, while the paths displayed in Figure 8 are possible (and were discovered with the IMGEP), note that they may be “rarer” to occur naturally in the sense that a large portion of the tested initial conditions with random search tend to converge toward smaller (ERK, RKIPP_RP) steady-state values similar to the ones displayed in Figure 2.

(4) Table 2:

a) Where is 'effective intervention' used in the method?

b) in my opinion 'controllability', 'trainability', and 'versatility' are different terms. If their correspondence is important I would suggest to extend/enhance the column "Proposed Isomorphism". otherwise, it may be confusing.

a) We thank the reviewer for pointing out that “effective intervention” is not explicitly used in the method. The idea here is that as we are exploring a complex dynamical system (here the GRN), some of the sampled interventions will be particularly effective at revealing novel unseen outcomes whereas others will fail to produce a qualitative change to the distribution of discovered outcomes. What we show in this paper, for instance in Figure 3a and Figure 4, is that the IMGEP method is particularly sample-efficient in finding those “effective interventions”, at least more than a random exploration. However we agree that the term “effective intervention” is ambiguous (does not say effective in what) and propose to replace it with “salient intervention” in the revised version.

b) We thank the reviewer for highlighting some confusing terms in our chosen vocabulary, and we will try to clarify those terms in the revised version. We agree that controllability/trainability and versatility are not exactly equivalent concepts, as controllability/trainability typically refers to the amount to which a system is externally controllable/trainable whereas versatility typically refers to the inherent adaptability or diversity of behaviors that a system can exhibit in response to inputs or conditions. However, they are both measuring the extent of states that can be reached by the system under a distribution of stimuli/conditions, whether natural conditions or engineered ones, which is why we believe that their correspondence is relevant.

I don't see how this table generalizes "concepts from dynamical complex systems and behavioral sciences under a common navigation task perspective".

We propose to replace “generalize” with “investigate” in the revised version.

Reviewer #2 (Public Review):

Summary:

Etcheverry et al. present two computational frameworks for exploring the functional capabilities of gene regulatory networks (GRNs). The first is a framework based on intrinsically-motivated exploration, here used to reveal the set of steady states achievable by a given gene regulatory network as a function of initial conditions. The second is a behaviorist framework, here used to assess the robustness of steady states to dynamical perturbations experienced along typical trajectories to those steady states. In Figs. 1-5, the authors convincingly show how these frameworks can explore and quantify the diversity of behaviors that can be displayed by GRNs. In Figs. 6-9, the authors present applications of their framework to the analysis and control of GRNs, but the support presented for their case studies is often incomplete.

Strengths:

Overall, the paper presents an important development for exploring and understanding GRNs/dynamical systems broadly, with solid evidence supporting the first half of their paper in a narratively clear way.

The behaviorist point of view for robustness is potentially of interest to a broad community, and to my knowledge introduces novel considerations for defining robustness in the GRN context.

We thank the reviewer for recognizing the strengths and novelty of the proposed experimental framework for exploring and understanding GRNs, and complex dynamical systems more generally. We agree that the results presented in the section “Possible Reuses of the Behavioral Catalog and Framework” (Fig 6-9) can be seen as incomplete along certain aspects, which we tried to make as explicit as possible throughout the paper, and why we explicitly state that these are “preliminary experiments”. Despite the discussed limitations, we believe that these experiments are still very useful to illustrate the variety of potential use-cases in which the community could benefit from such computational methods and experimental framework, and build on for future work.

Some specific weaknesses, mostly concerning incomplete analyses in the second half of the paper:

(1) The analysis presented in Fig. 6 is exciting but preliminary. Are there other appropriate methods for constructing energy landscapes from dynamical trajectories in gene regulatory networks? How do the results in this particular case study compare to other GRNs studied in the paper?

We are not aware of other methods than the one proposed by Venkatachalapathy et al. [1] for constructing an energy landscape given an input set of recorded dynamical trajectories, although it might indeed be the case. We want to emphasize that any of such methods would anyway depend on the input set of trajectories, and should therefore benefit from a set that is more representative of the diversity of behaviors that can be achieved by the GRN, which is why we believe the results presented in Figure 6 are interesting. As the IMGEP was able to find a higher diversity of reachable goal states (and corresponding trajectories) for many of the studied GRNs, we believe that similar effects should be observable when constructing the energy landscapes for these GRN models, with the discovery of additional or wider “valleys” of reachable steady states. We could indeed add other case studies in the supplementary to support the argument for the revised version.

Additionally, it is unclear whether the analysis presented in Fig. 6C is appropriate. In particular, if the pseudopotential landscapes are constructed from statistics of visited states along trajectories to the steady state, then the trajectories derived from dynamical perturbations do not only reflect the underlying pseudo-landscape of the GRN. Instead, they also include contributions from the perturbations themselves.

We agree that the landscape displayed Fig. 6C integrates contributions from the perturbations on the GRN’s behavior, and that it can shape the landscape in various ways, for instance affecting the paths that are accessible, the shape/depth of certain valleys, etc. But we believe that qualitatively or quantitatively analyzing the effect of these perturbations on the landscape is precisely what is interesting here: it might help 1) understand how a system respond to a range of perturbations and to visualize which behaviors are robust to those perturbations, 2) design better strategies for manipulating those systems to produce certain behaviors

(2) In Fig. 7, I'm not sure how much is possible to take away from the results as given here, as they depend sensitively on the cohort of 432 (GRN, Z) pairs used. The comparison against random networks is well-motivated. However, as the authors note, comparison between organismal categories is more difficult due to low sample size; for instance, the "plant" and "slime mold" categories each only have 1 associated GRN. Additionally, the "n/a" category is difficult to interpret.

We acknowledge that this part is speculative as stated in the paper: “the surveyed database is relatively small with respect to the wealth of available models and biological pathways, so we can hardly claim that these results represent the true distribution of competencies across these organism categories”. However, when further data is available, the same methodology can be reused and we believe that the resulting statistical analyses could be very informative to compare organismal (or other) categories.

(3) In Fig. 8, it is unclear whether the behavioral catalog generated is important to the intervention design problem of moving a system from one attractor basin to another. The authors note that evolutionary searches or SGD could also be used to solve the problem. Is the analysis somehow enabled by the behavioral catalog in a way that is complementary to those methods? If not, comparison against those methods (or others e.g. optimal control) would strengthen the paper.

We thank the reviewer for asking to clarify this point, which might not be clearly explained in the paper. Here the behavioral catalog is indeed used in a complementary way to the optimization method, by identifying a representative set of reachable attractors which are then used to define the optimization problem. For instance here, thanks to the catalog, we 1) were able to identify a “disease” region and several possible reachable states in that region and 2) use several of these states as starting points of our optimization problem, where we want to find a single intervention that can successfully and robustly reset all those points, as illustrated in Figure 8. Please note that given this problem formulation, a simple random search was used as an optimization strategy. When we mention more advanced techniques such as EA or SGD, it is to say that they might be more efficient optimizers than random search. However, we agree that in many cases optimizing directly will not work if starting from random or bad initial guess, and this even with EA or SGD. In that case the discovered behavioral catalog can be useful to better initialize this local search and make it more efficient/useful, akin to what is done in Figure 9.

(4) The analysis presented in Fig. 9 also is preliminary. The authors note that there exist many algorithms for choosing/identifying the parameter values of a dynamical system that give rise to a desired time-series. It would be a stronger result to compare their approach to more sophisticated methods, as opposed to random search and SGD. Other options from the recent literature include Bayesian techniques, sparse nonlinear regression techniques (e.g. SINDy), and evolutionary searches. The authors note that some methods require fine-tuning in order to be successful, but even so, it would be good to know the degree of fine-tuning which is necessary compared to their method.

We agree that the analysis presented in Figure 9 is preliminary, and thank the reviewer for the suggestion. We would first like to refer to other papers from the ML literature that have more thoroughly analyzed this issue, such as Colas et al. [74] and Pugh et al. [34], and shown the interest of diversity-driven strategies as promising alternatives. Additionally, as suggested by the reviewer, we added an additional comparison to the CMA-ES algorithm in order to complete our analysis. CMA-ES is an evolutionary algorithm which is self-adaptive in the optimization steps and that is known to be better suited than SGD to escape local minimas when the number of parameters is not too high (here we only have 15 parameters). However, our results showed that while CMA-ES explores more the solution space at the beginning of optimization than SGD does, it also ultimately converges into a local minima similarly to SGD. The best solution converges toward a constant signal (of the target b) but fails to maintain the target oscillations, similar to the solutions discovered by gradient descent. We tried this for a few hyperparameters (init mean and std) but always found similar results. We report the novel results at https://developmentalsystems.org/curious-exploration-of-grn-competencies/tuto2.html (bottom cell, Figure 4). We suggest including the updated figure and caption in the revised version.

eLife assessment

This important study develops a machine learning method to reveal hidden unknown functions and behavior in gene regulatory networks by searching parameter space in an efficient way. The evidence for some parts of the paper is still incomplete and needs systematic comparison to other methods and to the ground truth, but the work will be of broad interest to anyone working in biology of all stripes since the ideas reach beyond gene regulatory networks to revealing hidden functions in any complex system with many interacting parts.

Reviewer #1 (Public Review):

Summary: This paper suggests to apply intrinsically-motivated exploration for the discovery of robust goal states in gene regulatory networks.

Strengths:<br /> The paper is well written. The biological motivation and the need for such methods are formulated extraordinarily well. The battery of experimental models is impressive.

Weaknesses:<br /> (1) The proposed method is compared to the random search. That says little about the performance with regard to the true steady-state goal sets. The latter could be calculated at least for a few simple ODE (e.g., BIOMD0000000454, `Metabolic Control Analysis: Rereading Reder'). The experiment with 'oscillator circuits' may not be directly interpolated to the other models.

The lack of comparison to the ground truth goal set (attractors of ODE) from arbitrary initial conditions makes it hard to evaluate the true performance/contribution of the method. A part of the used models can be analyzed numerically using JAX, while there are models that can be analyzed analytically.

"...The true versatility of the GRN is unknown and can only be inferred through empirical exploration and proxy metrics....": one could perform a sensitivity analysis of the ODEs, identifying stable equilibria. That could provide a proxy for the ground truth 'versatility'.

(2) The proposed method is based on `Intrinsically Motivated Goal Exploration Processes with Automatic Curriculum Learning', which assumes state action trajectories [s_{t_0:t}, a_{t_0:t}], (2.1 Notations and Assumptions' in the IMGEP paper). However, the models used in the current work do not include external control actions, but rather only the initial conditions can be set. It is not clear from the methods whether IMGEP was adapted to this setting, and how the exploration policy was designed w/o actual time-dependent actions. What does "...generates candidate intervention parameters to achieve the current goal...."<br /> mean considering that interventions 'Sets the initial state...' as explained in Table 2?

(3) Fig 2 shows the phase space for (ERK, RKIPP_RP) without mentioning the typical full scale of ERK, RKIPP_RP. It is unclear whether the path from (0, 0) to (~0.575, ~3.75) at t=1000 is significant on the typical scale of this phase space. is it significant on the typical scale of this phase space?

(4) Table 2:<br /> a. Where is 'effective intervention' used in the method?<br /> b. in my opinion 'controllability', 'trainability', and 'versatility' are different<br /> terms. If their correspondence is important I would suggest to extend/enhance the column "Proposed Isomorphism". otherwise, it may be confusing. I don't see how this table generalizes generalizes "concepts from dynamical complex systems and behavioral sciences under a common navigation task perspective".

Reviewer #2 (Public Review):

Summary:<br /> Etcheverry et al. present two computational frameworks for exploring the functional capabilities of gene regulatory networks (GRNs). The first is a framework based on intrinsically-motivated exploration, here used to reveal the set of steady states achievable by a given gene regulatory network as a function of initial conditions. The second is a behaviorist framework, here used to assess the robustness of steady states to dynamical perturbations experienced along typical trajectories to those steady states. In Figs. 1-5, the authors convincingly show how these frameworks can explore and quantify the diversity of behaviors that can be displayed by GRNs. In Figs. 6-9, the authors present applications of their framework to the analysis and control of GRNs, but the support presented for their case studies is often incomplete.

Strengths:<br /> Overall, the paper presents an important development for exploring and understanding GRNs/dynamical systems broadly, with solid evidence supporting the first half of their paper in a narratively clear way.

The behaviorist point of view for robustness is potentially of interest to a broad community, and to my knowledge introduces novel considerations for defining robustness in the GRN context.

Some specific weaknesses, mostly concerning incomplete analyses in the second half of the paper:

(1) The analysis presented in Fig. 6 is exciting but preliminary. Are there other appropriate methods for constructing energy landscapes from dynamical trajectories in gene regulatory networks? How do the results in this particular case study compare to other GRNs studied in the paper?

Additionally, it is unclear whether the analysis presented in Fig. 6C is appropriate. In particular, if the pseudopotential landscapes are constructed from statistics of visited states along trajectories to the steady state, then the trajectories derived from dynamical perturbations do not only reflect the underlying pseudo-landscape of the GRN. Instead, they also include contributions from the perturbations themselves.

(2) In Fig. 7, I'm not sure how much is possible to take away from the results as given here, as they depend sensitively on the cohort of 432 (GRN, Z) pairs used. The comparison against random networks is well-motivated. However, as the authors note, comparison between organismal categories is more difficult due to low sample size; for instance, the "plant" and "slime mold" categories each only have 1 associated GRN. Additionally, the "n/a" category is difficult to interpret.

(3) In Fig. 8, it is unclear whether the behavioral catalog generated is important to the intervention design problem of moving a system from one attractor basin to another. The authors note that evolutionary searches or SGD could also be used to solve the problem. Is the analysis somehow enabled by the behavioral catalog in a way that is complementary to those methods? If not, comparison against those methods (or others e.g. optimal control) would strengthen the paper.

(4) The analysis presented in Fig. 9 also is preliminary. The authors note that there exist many algorithms for choosing/identifying the parameter values of a dynamical system that give rise to a desired time-series. It would be a stronger result to compare their approach to more sophisticated methods, as opposed to random search and SGD. Other options from the recent literature include Bayesian techniques, sparse nonlinear regression techniques (e.g. SINDy), and evolutionary searches. The authors note that some methods require fine-tuning in order to be successful, but even so, it would be good to know the degree of fine-tuning which is necessary compared to their method.

eLife assessment

This study presents a valuable set of calcium imaging data to analyze the dynamics of excitatory and inhibitory responses in the projection neurons of the honeybees during and after odor presentations. The neural circuit model fed with the imaging data recapitulated odor-specific activity in the Kenyon cells in the post-odor period and the timing shift of behavioral response in associative learning. This solid work will be of interest to researchers working on associative learning.

Reviewer #1 (Public Review):

This study by Paoli et al. used a resonant scanning multiphoton microscope to examine olfactory representation in the projection neurons (PNs) of the honeybee with improved temporal resolution. PNs were classified into 9 groups based on their response patterns. Authors found that excitatory repose in the PNs precedes the inhibitory responses for ~40ms, and ~50% of PN responses contain inhibitory components. They built the neural circuit model of the mushroom body (MB) with evolutionally conserved features such as sparse representation, global inhibition, and a plasticity rule. This MB model fed with the experimental data could reproduce a number of phenomena observed in experiments using bees and other insects, including dynamical representations of odor onset and offset by different populations of Kenyon cells, prolonged representations of after-smell, different levels of odor-specificity for early/delay conditioning, and shift of behavioral timing in delay conditioning. The trace conditioning was not modeled and tested experimentally. Also, the experimental result itself is largely confirmatory to preceding studies using other organisms. Nonetheless, the experimental data and the model provide a solid basis for future studies.

Reviewer #2 (Public Review):

The study presented by Paoli et al. explores temporal aspects of neuronal encoding of odors and their perception, using bees as a general model for insects. The neuronal encoding of the presence of an odor is not a static representation; rather, its neuronal representation is partly encoded by the temporal order in which parallel olfactory pathways participate and are combined. This aspect is not novel, and its relevance in odor encoding and recognition has been discussed for more than the past 20 years.

The temporal richness of the olfactory code and its significance have traditionally been driven by results obtained based on electrophysiological methods with temporal resolution, allowing the identification and timing of the action potentials in the different populations of neurons whose combination encodes the identity of an odor. On the other hand, optophysiological methods that enable spatial resolution and cell identification in odor coding lack the temporal resolution to appreciate the intricacies of olfactory code dynamics.

1) In this context, the main merit of Paoli et al.'s work is achieving an optical recording that allows for spatial registration of olfactory codes with greater temporal detail than the classical method and, at the same time, with greater sensitivity to measure inhibitions as part of the olfactory code.

The work clearly demonstrates how the onset and offset of odor stimulation triggers a dynamic code at the level of the first interneurons of the olfactory system that changes at every moment as a natural consequence of the local inhibitory interactions within the first olfactory neuropil, the antennal lobe. This gives rise to the interesting theory that each combination of activated neurons along this temporal sequence corresponds to the perception of a different odor. The extent to which the corresponding postsynaptic layers integrate this temporal information to drive the perception of an odor, or whether this sequence is, in a sense, a journey through different perceptions, is challenging to address experimentally.

In their work, the authors propose a computational approach and olfactory learning experiments in bees to address these questions and evaluate whether the sequence of combinations drives a sequence of different perceptions. In my view, it is a highly inspiring piece of work that still leaves several questions unanswered.

2) In my opinion, the detailed temporal profile of the response of projection neurons and their respective probabilities of occurrence provide valuable information for understanding odor coding at the level of neurons transferring information from the antennal lobes to the mushroom bodies. An analysis of these probabilities in each animal, rather than in the population of animals that were measured, would aid in better comprehending the encoding function of such temporal profiles. Being able to identify the involved glomeruli and understanding the extent to which the sequence of patterns and inhibitions is conserved for each odor across different animals, as it is well known for the initial excitatory burst of activity observed in previous studies without the fine temporal detail, would also be highly significant.

In my view, the computational approach serves as a useful tool to inspire future experiments; however, it appears somewhat simplistic in tackling the complexity of the subject. One question that I believe the researchers do not address is to what extent the inhibitions recorded in the projection neurons are integrated by the Kenyon cells and are functional for generating odor-specific patterns at that level.

Lastly, the behavioral result indicating a difference in conditioned response latency after early or delayed learning protocol is interesting. However, it does not align with the expected time for the neuronal representation that was theoretically rewarded in the delayed protocol. This final result does not support the authors' interpretation regarding the existence of a smell and an after-smell as separate percepts that can serve as conditioned stimuli.

eLife assessment

This study provides important insights into the role of neurexins as regulators of synaptic strength and timing at the glycinergic synapse between neurons of the medial nucleus of the trapezoid body and the lateral superior olive, key components of the auditory brainstem circuit involved in computing sound source location from differences in the intensity of sounds arriving at the two ears. Through an elegant combination of genetic manipulation, fluorescence in-situ hybridization, ex vivo slice electrophysiology, pharmacology, and optogenetics, the authors provide convincing evidence to support their claims. While further work is needed to reveal the mechanistic basis by which neurexins influence glycinergic neurotransmission, this work will be of interest to both auditory and synaptic neuroscientists.

Reviewer #1 (Public Review):

Jiang et al. demonstrated that ablating Neurexins results in alterations to glycinergic transmission and its calcium sensitivity, utilizing a robust experimental system. Specifically, the authors employed rAAV-Cre-EGFP injection around the MNTB in Nrxn1/2/3 triple conditional mice at P0, measuring Glycine receptor-dependent IPSCs from postsynaptic LSO neurons at P13-14. Notably, the authors presented a clear reduction of 60% and 30% in the amplitudes of opto- and electric stimulation-evoked IPSCs, respectively. Additionally, they observed changes in kinetics, alterations in PPR, and sensitivity to lower calcium and the calcium chelator, EGTA, indicating solid evidence for changes in presynaptic properties of glycinergic transmission.

Furthermore, the authors uncovered an unexpected increase in sIPSC frequency without altering amplitude. Despite the reduction in evoked IPSC, immunostaining revealed an increase in GlyT2 and VGAT in TKO mice, supporting the notion of an increase in synapse number. However, the reviewer expresses caution regarding the authors' conclusion that "glycinergic neurotransmission likely by promoting the synapse formation/maintenance, which is distinct from the phenotypes observed in glutamatergic and GABAergic neurons (Chen et al., 2017; Luo et al., 2021)", as outlined in lines 173-175. The reviewer suggests that this statement may be overstated, pointing out the authors' own discussion in lines 254-265, which acknowledges multiple possibilities, including the potential that the increase in synapses is a consequence rather than a causal effect of Nrxn deletion.

Reviewer #2 (Public Review):

Summary:<br /> In this manuscript, Jiang et al., explore the role of neurexins at glycinergic MNTB-LSO synapses. The authors utilize elegant and compelling ex vivo slice electrophysiology to assess how the genetic conditional deletion of Nrxns1-3 impacts inhibitory glycinergic synaptic transmission and found that TKO of neurexins reduced electrically and optically evoked IPSC amplitudes, slowed optically evoked IPSC kinetics and reduced presynaptic release probability. The authors use classic approaches including reduced [Ca2+] in ACSF and EGTA chelation to propose that changes in these evoked properties are likely driven by the loss of calcium channel coupling. Intriguingly, while evoked transmission was impaired, the authors reported that spontaneous IPSC frequency was increased, potentially due to an increased number of synapses in LSO. Overall, this manuscript provides important insight into the role of neurexins at the glycinergic MNTP-LSO synapse and further emphasizes the need for continued study of both the non-redundant and redundant roles of neurexins.

Strengths:<br /> This well-written manuscript seamlessly incorporates mouse genetics and elegant ex vivo electrophysiology to identify a role for neurexins in glycinergic transmission at MNTB-LSO synapses. Triple KO of all neurexins reduced the amplitude and timing of evoked glycinergic synaptic transmission. Further, spontaneous IPSC frequency was increased. The evoked synaptic phenotype is likely a result of reduced presynaptic calcium coupling while the spontaneous synaptic phenotype is likely due to increased synapse numbers. While neuroligin-4 has been identified at glycinergic synapses, this study, to the best of my knowledge, is the first to study Nrxn function at these synapses.

Weaknesses:<br /> The data are compelling and report an intriguing functional phenotype. The role of Neurexins redundantly controls calcium channel coupling has been previously reported. Mechanistic insight would significantly strengthen this study.<br /> The claim that triple KO of Nrxns from MNTB increases the number of synapses in LSO is not strongly supported.<br /> Despite the stated caveats of measuring electrically evoked currents and the more robust synaptic phenotypes observed using optically evoked transmission, the authors rely heavily on electrical stimulation for most measurements.<br /> The differential expression of individual neurexins might indicate that specific neurexins may dominantly regulate synaptic transmission, however, this possibility is not discussed in detail.

Reviewer #3 (Public Review):

Summary:<br /> The authors investigate the hypothesis that neurexins serve a crucial role as regulators of the synaptic strength and timing at the glycinergic synapse between neurons of the medial nucleus of the trapezoid body (MNTB) and the lateral superior olivary complex (LSO). It is worth mentioning that LSO neurons are an integration station of the auditory brainstem circuit displaying high reliability and temporal precision. These features are necessary for computing interaural cues to derive sound source location from comparing the intensities of sounds arriving at the two ears. In this context, the authors' findings build up according to the hypothesis first by displaying that neurexins were expressed in the MNTB at varying levels. They followed this up with the deletion of all neurexins in the MNTB through the employment of a triple knock-out (TKO). Using electrophysiological recordings in acute brainstem slices of these TKO mice, they gathered solid evidence for the role of neurexins in synaptic transmission at this glycinergic synapse primarily by ensuring tight coupling of Ca2+ channels and vesicular release sites. Additionally, the authors uncovered a connection between the deletion of neurexins and a higher number of glycinergic synapses in TKO mice, for which they provided evidence in the form of immunostainings and related it to electrophysiological data on spontaneous release. Consequently, this investigation expands our knowledge on the molecular regulation of synaptic transmission at glycinergic synapses, as well as on the auditory processing at the level of the brainstem.

Strengths:<br /> The authors demonstrate substantial results in support of the hypothesis of a critical role of neurexins for regulating glycinergic transmission in the LSO using various techniques. They provide evidence for the expression of neurexins in the MNTB and consecutively successfully generate and characterize the neurexin TKO. For their study on LSO IPSCs the authors transduced MNTB neurons by co-injection of virus-carrying Cre and ChR2 and subsequently optogenetically evoke release of glycine. As a result, they observed a significant reduction in amplitude and significantly slower rise and decay times of the IPSCs of the TKO in comparison with control mice in which MNTB neurons were only transduced with ChR2. Furthermore, they observed an increased paired pulse ratio (PPR) of LSO IPSCs in the TKO mice, indicating lower release probability. Elaborating on the hypothesis that neurexins are essential for the coupling of synaptic vesicles to Ca2+ channels, the authors show lowered Ca2+ sensitivity in the TKO mice. Additionally, they reveal convincing evidence for the connection between the increased frequency of spontaneous IPSC and the higher number of glycinergic synapses of the LSO in the TKO mice, revealed by immunolabeling against the glycinergic presynaptic markers GlyT2 or VGAT.

Weaknesses:<br /> The major concern is novelty as this work on the effects of pan-neurexin deletion in a glycinergic synapse is quite consistent with the authors' prior work on glutamatergic synapses (Luo et al., 2020). The authors might want to further work out novel aspects and strengthen the comparative perspective. Conceptually, the authors might want to be more clear about interpreting the results on the altered dependence of release on voltage-gated Ca2+ influx (Ca2+ sensitivity, coupling).

eLife assessment

This study provides important evidence supporting the ability of a new type of neuroimaging, OPM-MEG system, to measure beta-band oscillation in sensorimotor tasks on 2-14 years old children and to demonstrate the corresponding development changes, since neuroimaging methods with high spatiotemporal resolution that could be used on small children are quite limited. The evidence supporting the conclusion is solid but lacks clarifications about the much-discussed advantages of OPM-MEG system (e.g., motion tolerance), control analyses (e.g., trial number), and rationale for using sensorimotor tasks. This work will be of interest to the neuroimaging and developmental science communities.

Reviewer #1 (Public Review):

Summary:<br /> Compared with conventional SQUID-MEG, OPM-MEG offers theoretical advantages of sensor configurability (that is, sizing to suit the head size) and motion tolerance (the sensors are intrinsically in the head reference frame). This study purports to be the first to experimentally demonstrate these advantages in a developmental study from age 2 to age 34.

In short, while the theoretical advantages of OPM-MEG are attractive - both in terms of young child sensitivity and in terms of motion tolerance - neither was in fact demonstrated in this manuscript. We are left with a replication of SQUID-MEG observations, which certainly establishes OPM-MEG as "substantially equivalent" to conventional technology but misses the opportunity to empirically demonstrate the much-discussed theoretical advantages/opportunities.

Strengths:<br /> A replication of SQUID-MEG observations, which certainly establishes OPM-MEG as "substantially equivalent" to conventional technology but misses the opportunity to empirically demonstrate the much-discussed theoretical advantages/opportunities.

Weaknesses:<br /> The authors describe 64 tri-axial detectors, which they refer to as 192 channels. This is in keeping with some of the SQUID-MEG description, but possibly somewhat disingenuous. For the scientific literature, perhaps "64 tri-axial detectors" is a more parsimonious description.

A small fraction (<20%) of trials were eliminated for analysis because of "excess interference" - this warrants further elaboration.

Figure 3 shows a reduced beta ERD in the youngest children. Although the authors claim that OPM-MEG would be similarly sensitive for all ages and that SQUID-MEG would be relatively insensitive to young children, one trivial counterargument that needs to be addressed is that OPM has NOT in fact increased the sensitivity to young child ERD. This can possibly be addressed by analogous experiments using a SQUID-based system. An alternative would be to demonstrate similar sensitivity across ages using OPM to a brain measure such as evoked response amplitude. In short, how does Figure 3 demonstrate the (theoretical) sensitivity advantage of OPM MEG in small heads ?

The data do not make a compelling case for the motion tolerance of OPM-MEG. Although an apparent advantage of a wearable system, an empirical demonstration is still lacking. How was motion tracked in these participants?

Furthermore, while the introduction discusses at some length the phenomenon of PMBR, there is no demonstration of the recording of PMBR (or post-sensory beta rebound). This is a shame because there is literature suggesting an age-sensitivity to this, that the optimal sensitivity of OPM-MEG might confirm/refute. There is little evidence in Figure 3 for adult beta rebound. Is there an explanation for the lack of sensitivity to this phenomenon in children/adolescents ? Could a more robust paradigm (button-press) have shed light on this?

Data on functional connectivity are valuable but do not rely on OPM recording. They further do not add strength to the argument that OPM MEG is more sensitive to brain activity in smaller heads - in fact, the OPM recordings seem plagued by the same insensitivity observed using conventional systems.

The discussion of burst vs oscillations, while highly relevant in the field, is somewhat independent of the OPM recording approach and does not add weight to the OPM claims.

In short, while the theoretical advantages of OPM-MEG are attractive - both in terms of young child sensitivity and in terms of motion tolerance, neither was in fact demonstrated in this manuscript. We are left with a replication of SQUID-MEG observations, which certainly establishes OPM-MEG as "substantially equivalent" to conventional technology but misses the opportunity to empirically demonstrate the much-discussed theoretical advantages/opportunities.

Reviewer #2 (Public Review):

Summary:<br /> The authors introduce a new 192-channel OPM system that can be configured using different helmets to fit individuals from 2 to 34 years old. To demonstrate the veracity of the system, they conduct a sensorimotor task aimed at mapping developmental changes in beta oscillations across this age range. Many past studies have mapped the trajectory of beta (and gamma) oscillations in the sensorimotor cortices, but these studies have focused on older children and adolescents (e.g., 9-15 years old) and used motor tasks. Thus, given the study goals, the choice of a somatosensory task was surprising and not justified. The authors recorded a final sample of 27 children (2-13 years old) and 24 adults (21-34 years) and performed a time-frequency analysis to identify oscillatory activity. This revealed strong beta oscillations (decreases from baseline) following the somatosensory stimulation, which the authors imaged to discern generators in the sensorimotor cortices. They then computed the power difference between 0.3-0.8 period and 1.0-1.5 s post-stimulation period and showed that the beta response became stronger with age (more negative relative to the stimulation period). Using these same time windows, they computed the beta burst probability and showed that this probability increased as a function of age. They also showed that the spectral composition of the bursts varied with age. Finally, they conducted a whole-brain connectivity analysis. The goals of the connectivity analysis were not as clear as prior studies of sensorimotor development have not conducted such analyses and typically such whole-brain connectivity analyses are performed on resting-state data, whereas here the authors performed the analysis on task-based data. In sum, the authors demonstrate that they can image beta oscillations in young children using OPM and discern developmental effects.

Strengths:<br /> Major strengths of the study include the novel OPM system and the unique participant population going down to 2-year-olds. The analyses are also innovative in many respects.

Weaknesses:<br /> Several weaknesses currently limit the impact of the study. First, the choice of a somatosensory stimulation task over a motor task was not justified. The authors discuss the developmental motor literature throughout the introduction, but then present data from a somatosensory task, which is confusing. Of note, there is considerable literature on the development of somatosensory responses so the study could be framed with that. Second, the primary somatosensory response actually occurs well before the time window of interest in all of the key analyses. There is an established literature showing mechanical stimulation activates the somatosensory cortex within the first 100 ms following stimulation, with the M50 being the most robust response. The authors focus on a beta decrease (desynchronization) from 0.3-0.8 s which is obviously much later, despite the primary somatosensory response being clear in some of their spectrograms (e.g., Figure 3 in older children and adults). This response appears to exhibit a robust developmental effect in these spectrograms so it is unclear why the authors did not examine it. This raises a second point; to my knowledge, the beta decrease following stimulation has not been widely studied and its function is unknown. The maps in Figure 3 suggest that the response is anterior to the somatosensory cortex and perhaps even anterior to the motor cortex. Since the goal of the study is to demonstrate the developmental trajectory of well-known neural responses using an OPM system, should the authors not focus on the best-understood responses (i.e., the primary somatosensory response that occurs from 0.0-0.3 s)?

Regarding the developmental effects, the authors appear to compute a modulation index that contrasts the peak beta window (.3 to .8) to a later 1.0-1.5 s window where a rebound is present in older adults. This is problematic for several reasons. First, it prevents the origin of the developmental effect from being discerned, as a difference in the beta decrease following stimulation is confounded with the beta rebound that occurs later. A developmental effect in either of these responses could be driving the effect. From Figure 3, it visually appears that the much later rebound response is driving the developmental effect and not the beta decrease that is the primary focus of the study. Second, these time windows are a concern because a different time window was used to derive the peak voxel used in these analyses. From the methods, it appears the image was derived using the .3-.8 window versus a baseline of 2.5-3.0 s. How do the authors know that the peak would be the same in this other time window (0.3-0.8 vs. 1.0-1.5)? Given the confound mentioned above, I would recommend that the authors contrast each of their windows (0.3-0.8 and 1.0-1.5) with the 2.5-3.0 window to compute independent modulation indices. This would enable them to identify which of the two windows (beta decrease from 0.3-0.8 s or the increase from 1.0-1.5 s) exhibited a developmental effect. Also, for clarity, the authors should write out the equation that they used to compute the modulation index. The direction of the difference (positive vs. negative) is not always clear.

Another complication of using a somatosensory task is that the literature on bursting is much more limited and it is unclear what the expectations would be. Overall, the burst probability appears to be relatively flat across the trial, except that there is a sharp decrease during the beta decrease (.3-.8 s). This matches the conventional trial-averaging analysis, which is good to see. However, how the bursting observed here relates to the motor literature and the PMBR versus beta ERD is unclear.

Another weakness is that all participants completed 42 trials, but 19% of the trials were excluded in children and 9% were excluded in adults. The number of trials is proportional to the signal-to-noise ratio. Thus, the developmental differences observed in response amplitude could reflect differences in the number of trials that went into the final analyses.

Finally, the discussion could be improved to focus on the somatosensory literature and how this contributes to that. Currently, the discussion includes very little from the somatosensory literature.

Reviewer #3 (Public Review):

This study demonstrated the application of OPM-MEG in neurodevelopment studies of somatosensory beta oscillations and connections with children as young as 2 years old. It provides a new functional neuroimaging method that has a high spatial-temporal resolution as well wearable which makes it a new useful tool for studies in young children. They have constructed a 192-channel wearable OPM-MEG system that includes field compensation coils which allow free head movement scanning with a relatively high ratio of usable trials. Beta band oscillations during somatosensory tasks are well localized and the modulation with age is found in the amplitude, connectivity, and pan-spectral burst probability. It is demonstrated that the wearable OPM-MEG could be used in children as a quite practical and easy-to-deploy neuroimaging method with performance as good as conventional MEG. With both good spatial (several millimeters) and temporal (milliseconds) resolution, it provides a novel and powerful technology for neurodevelopment research and clinical applications not limited to somatosensory areas.

The conclusions of this paper are mostly well supported by data acquired under the proper method. However, some aspects of data analysis need to be improved and extended.

(1) The colour bars selected for the pseudo-T-static pictures of beta modulation in Figures 2 and 3, which are blue/black and red/black, are not easily distinguished from the anatomical images which are grey-scale. A colour bar without black/white would make these figures better. The peak point locations are also suggested to be marked in Figure 2 and averaged locations in Figure 3 with an error bar.

(2) The data points in plots are not constant across figures. In Figures 3 and 5, they are classified into triangles and circles for children and adults, but all are circles in Figures 4 and 6.

(3) Although MEG is much less susceptible to conductivity inhomogeneity of the head than EEG, the forward modulating may still be impacted by the small head profile. Add more information about source localization accuracy and stability across ages or head size.

eLife assessment

This valuable study advances our understanding of the potential therapeutic strategies for the treatment of pheochromocytomas using single-cell transcriptomics. The authors propose a new molecular classification criterion based on the characterization of tumor microenvironmental features, based on solid evidence. The work, which could be improved further through delineating the choice of the PASS scoring system, will be of broad interest to clinicians, medical researchers, and scientists working in the field of pheochromocytoma.

Author Response

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

eLife assessment

This study presents a valuable finding for the treatment of PCCs by sequencing 16 tumor specimens from five patients with pheochromocytomas by single-cell transcriptomics and proposing a new molecular classification criterion based on the sequencing results and characterization of tumor microenvironmental features. The evidence supporting the claims of the authors is solid, although the inclusion of more patient samples would strengthen the study's conclusions. The work will be of interest to clinicians or medical biologists working on rare pheochromocytomas (PCCs).

Firstly, we sincerely appreciate the positive feedback from the editor and extend our gratitude to the three reviewers for their meticulous review and valuable comments. Our detailed responses to each recommendation are outlined below.

Response to reviewers’ recommendations

Reviewer #1 (Recommendations for The Authors):

1) Transcriptomal clonal dynamics of different PCCs is well written. However for conclusion sample size needs to be more.

Acknowledging the rarity of PCCs with an incidence of approximately 0.2 to 0.6 cases per 100,000 person-years (Farrugia & Charalampopoulos, 2019; Neumann et al, 2019), our study recognizes the limitation in sample size, as discussed in the limitations section (Page 22). In response to this concern, we are committed to undertaking further research with an expanded sample size to bolster the robustness of our conclusions, seeking a more comprehensive understanding of tumor microenvironment characterization and molecular classification in PCCs. We appreciate the valuable guidance provided by the reviewer.

2) Clinical, biochemistry data of 5 cases can be analysed. Any findings in different categories as per postulated classification can be noted for further studies. Example: epinephrine levels

We have now included the clinical information of 5 PCC patients, encompassing signs and symptoms, the tumor size, and laboratory test results in the revised manuscript as Supplemental Table S3 (Page 11-12). Notably, our analysis revealed that the kinase-type PCC patient (P4) exhibited higher blood pressures and plasma levels of catecholamine metabolites (3-methoxytyramine and normetanephrine) compared to metabolism-type PCC patients (P1-P3, and P5). This observation aligns with the elevated expression of phenylethanolamine N-methyltransferase (PNMT), an enzyme involved in the biosynthesis of catecholamine and linked to hypertension, in P4, as identified in the scRNA-seq data (Figure 4B and 4D) (Kennedy et al, 1993; Konosu-Fukaya et al, 2018; Nguyen et al, 2015). As suggested, we plan to conduct further research to explore the correlation of our molecular classification with plasma levels of catecholamine metabolites, and the relevant points have been discussed in the revision (Page 20).

We would like to take this chance to again thank the reviewer for the careful review and very helpful guidance about how to improve our study.

References for Reviewer #1:

Farrugia FA, Charalampopoulos A (2019) Pheochromocytoma. Endocrine regulations 53: 191-212 Neumann HPH, Young WF, Jr., Eng C (2019) Pheochromocytoma and Paraganglioma. The New England journal of medicine 381: 552-565

Kennedy B, Elayan H, Ziegler MG (1993) Glucocorticoid hypertension and nonadrenal phenylethanolamine N-methyltransferase. Hypertension (Dallas, Tex : 1979) 21: 415419

Konosu-Fukaya S, Omata K, Tezuka Y, Ono Y, Aoyama Y, Satoh F, Fujishima F, Sasano H, Nakamura Y (2018) Catecholamine-Synthesizing Enzymes in Pheochromocytoma and Extraadrenal Paraganglioma. Endocrine pathology 29: 302309

Nguyen P, Khurana S, Peltsch H, Grandbois J, Eibl J, Crispo J, Ansell D, Tai TC (2015) Prenatal glucocorticoid exposure programs adrenal PNMT expression and adult hypertension. The Journal of endocrinology 227: 117-127

Reviewer #2 (Recommendations for The Authors):

1) Please revise all references to "malignant potential", "malignant behavior", etc. throughout the article, including the abstract and introduction, and replace them with the word "metastasis" as appropriate. Since all PCCs are malignant non-epithelial neuroendocrine neoplasms originating from the paraganglia, which are themselves malignant tumors, it is unacceptable to describe them as "malignant potential" or "malignant potential". Please review the 2022 WHO/IARC classification and description of pheochromocytoma/paraganglioma (reference: Mete O, Asa SL, Gill AJ, Kimura N, de Krijger RR, Tischler A. Overview of the 2022 WHO Classification of Paragangliomas and Pheochromocytomas. Endocr Pathol. 2022;33(1):90-114. doi:10.1007/s12022-022-09704-6).

As suggested, we have replaced all occurrences of “malignant potential” or “malignant behavior” with “metastasis” throughout the revised manuscript. We have also included a citation to the 2022 WHO/IARC classification for further clarity.

• Similarly, it is not advisable to use the PASS score to predict "malignant" PCC; this type of scoring system evaluates the "metastasis risk" or the "metastasis potential" of PCC.

We appreciate the reviewer for this insight and have revised our statements accordingly.

• Also, "MALIGNANT CHAFFIN CELLS" needs to be modified; in fact, it is the "tumor cell of PCC" that the authors are trying to express.

As suggested, we have amended the term “malignant chromaffin cells” to “PCC cells” in the revised manuscript (Page 9-10).

2) How does the PASS score specifically relate to intra-tumor heterogeneity as reflected by scRNA-seq? In fact, the PASS score evaluates the histological or pathological invasiveness of PCC, and different sections of the same tumor tissue may have different histological manifestations, which may affect the score; however, scRNA-seq analyzes the cellular composition of the tumor, which is not the same as the information reflected by the PASS score. Both represent different levels and dimensions of intra-tumor heterogeneity and should be analyzed together. Please specifically list, one by one, the proportion of each item score of the PASS system and cell type of scRNA-seq for each sample and the results of the comparisons with each other to better present the conclusions.

As suggested, we have included the proportion of each item score from the PASS system in the revised manuscript as Supplemental Table S2 (Page 8). Integrating this data with the cell type composition of each sample from Figure 2B, our analysis suggests that intra-tumor heterogeneity, as assessed by the PASS system, is more extensive compared to scRNA-seq. We concur with the reviewer’s judgement that scRNA-seq analysis and PASS score represent different levels and dimensions of intratumor heterogeneity, and we have adjusted our claim throughout the revised manuscript accordingly (Page 8, 9, and 19).

3) Where is the specific mutation site of the VHL gene in patient 5? Please advise.

The VHL gene mutation site, c.499C>T (missense mutation), in patient 5 was identified through whole exome sequencing (WES) analysis. We have now added the information to Supplemental Table S1 in the revised manuscript (Page 6).

4) Please revise Supplementary Figure 1, the scale should not appear in the picture of the staining result of P5.

As suggested, we have adjusted the position of the scale bar.

Author response image 1.

Hematoxylin-eosin staining and immunohistochemistry staining of CGA marker in formalin-fixed paraffin-embedded PCC tissue sections matched to scRNA-seq specimens. Scale bar, 100 μm.

5) What were the clinical presentation and biochemical findings in the five patients?

The information regarding tumor sizes, signs and symptoms, and plasma levels of catecholamine metabolites [3-methoxytyramine (3-MT), metanephrine (MN), and normetanephrine (NMN)] has been added to the revised manuscript as Supplemental Table S3 (Page 11-12).

• Were there any preoperative symptoms of hypertension?

With the exception of P2, preoperative symptoms of hypertension were observed in all PCC patients. The information has been added to the revised manuscript as Supplemental Table S3 (Page 11-12).

• What was the size and catecholamine secretion phenotype of each tumor? What was the relationship between these data and the scRNA-seq results?

The secretion phenotype showed that the kinase-type PCC patient (P4) exhibited higher plasma levels of catecholamine metabolites (3-methoxytyramine and normetanephrine) compared to metabolism-type PCC patients (P1-P3, and P5). This observation aligns with the elevated expression of phenylethanolamine Nmethyltransferase (PNMT), an enzyme involved in the biosynthesis of catecholamine and linked to hypertension, in P4, as identified in the scRNA-seq data (Figure 4B and 4D) (Kennedy et al, 1993; Konosu-Fukaya et al, 2018; Nguyen et al, 2015). Meanwhile, we have not observed the correlation between tumor sizes and molecular classification. We have now included tumor sizes and laboratory test results of 5 PCC patients in the revised manuscript as Supplemental Table S3 (Page 11-12), and the relevant points have been discussed in the revision (Page 20).

6) Please revise Figure 1A, the meaning shown in the figure appears to dissociate the tissues of the patient's normal adrenal glands, which can be misleading.

We appreciate the reviewer for raising this concern. The schematic in Figure 1A has been revised accordingly.

Author response image 2.

(1A) Schematic of the experimental pipeline. 11 tumor specimens and 5 adjacent normal adrenal medullary specimens were isolated from 5 PCC patients, dissociated into single-cell suspensions, and analyzed using 10x Genomics Chromium droplet scRNA-seq.

• Please revise the figure note for Figure 1B, where the symbol (B) appears twice.

As suggested, we have revised the figure legends for Figure 1B and 1C (Page 42).

7) Please indicate in the figure legends and text what exactly is meant by "adjacent specimens"? medulla? cortex? normal tissue? I believe the authors mean adjacent normal adrenal medullary tissue, please check the article.

As suggested, we have revised the term “adjacent specimens” to “adjacent normal adrenal medullary tissues” throughout the revised manuscript.

8) Please review the pathologic diagnostic criteria of this study in light of the 2022 WHO/IARC guidelines for pathologic diagnosis: "For the pathological diagnosis, the inclusion criteria were neuroendocrine neoplasm originating from the adrenal medulla and retroperitoneal origin, i.e. pheochromocytoma and paraganglioma, with consistent morphologic and immunohistochemical confirmation in relevant cases and positivity for chromogranin A and synaptophysin. The exclusion criteria were adrenocortical neoplasm and metastatic tumors." It is not rigorous enough to diagnose a tumor as PCC based on positive CgA immunohistochemical staining results alone.

We have revised the statements about pathologic diagnostic criteria in accordance with the suggestion and have cited the reference (Page 6).

We would like to express our gratitude to the reviewer for the thorough review and invaluable guidance provided to enhance the quality of our study.

References for Reviewer #2:

Kennedy B, Elayan H, Ziegler MG (1993) Glucocorticoid hypertension and nonadrenal phenylethanolamine N-methyltransferase. Hypertension (Dallas, Tex: 1979) 21: 415419

Konosu-Fukaya S, Omata K, Tezuka Y, Ono Y, Aoyama Y, Satoh F, Fujishima F, Sasano H, Nakamura Y (2018) Catecholamine-Synthesizing Enzymes in Pheochromocytoma and Extraadrenal Paraganglioma. Endocrine pathology 29: 302309

Nguyen P, Khurana S, Peltsch H, Grandbois J, Eibl J, Crispo J, Ansell D, Tai TC (2015) Prenatal glucocorticoid exposure programs adrenal PNMT expression and adult hypertension. The Journal of endocrinology 227: 117-127

Reviewer #3 (Recommendations For The Authors):

I have several concerns and suggestions, which if addressed would improve the manuscript.

1) The statements of “plasmas” in the manuscript and figures are confusing, which should be revised as “plasma cells”.

As suggested, we have revised the terminology from “plasmas” to “plasma cells” throughout the revised manuscript and figures.

2) The marker genes used for defining plasma cells (IGHG1 and IGLC2) showed low expressing percentage in Figure 1D. Please consider providing other genes as the marker of plasma cells.

As suggested, we performed additional analysis to pinpoint marker genes for accurate definition of plasma cells. Applying stricter statistical criteria (cut-off pvalue < 0.05, log2 fold change ≥ 1.5, and expressing percentage ≥ 0.6), we identified XBP1 (a transcription factor playing key roles in the final stages of plasma cell development) and IGKC (a type of light-chain immunoglobulins) (Todd et al, 2009; Poulsen et al, 2002) as top significant differentially expressed genes (DEGs) suitable for defining plasma cells. These data are now presented as Figure 1D in the revised manuscript (Page 7).

Author response image 3.

(1D) Dot plot of representative marker genes for each cell type. The color scale represents the average marker gene expression level; dot size represents the percentage of cells expressing a given marker gene.

3) The statement “Our clustering and cell type annotation analysis identified diverse adrenal cells, stromal cells, and immune cells within the PCC microenvironment” seems not be exhibited in Figure 1, so the clustering result of adrenal cells, stromal cells, and immune cells need to be added.

As suggested, we performed clustering analysis for adrenal cells, stromal cells, and immune cells (including lymphocytes and myeloid cells), and visualized by the Uniform Manifold Approximation and Projection (UMAP) plot. These data have been added to the revised manuscript as Supplemental Figure S3 (Page 8).

Author response image 4.

Integration Analysis across 5 PCC Patients Revealing the Cell Type Composition of the PCC Microenvironment. UMAP plot depicting the distribution of adrenal cells, stromal cells, and immune cells (including lymphocytes and myeloid cells) within the PCC microenvironment.

4) Given the classification of “metabolism-type PCCs” and “kinase-type PCCs” have not been presented in Figure 2D, the statement “Combined with our findings of a higher proportion of neutrophils and monocyts/macrophages in metabolism-type as compared with kinase-type” in Result 6 should be supported by using additional data.

As suggested, we performed additional analysis to evaluate the proportion of neutrophils and monocytes/macrophages in metabolism-type and kinasetype PCC patients. These data have been added to the revised manuscript as Supplemental Figure S4 (Page 14).

Author response image 5.

The frequency distribution of cell types within the microenvironment of metabolism-type and kinase-type PCC patients.

5) What makes the difference of scRNA-seq analysis and multispectral immunofluorescent staining in judging the immune escape of PCCs? Please provide an explanation.

We appreciate the reviewer's concern. scRNA-seq lacks spatial details, and multispectral immunofluorescent staining is constrained in the number of detected proteins. To address this, we employed both methods for analysis. scRNA-seq revealed limited communication between tumor and T cells, with lower HLA-I expression in kinase-type PCCs compared to metabolism-type PCCs. This was supported by multispectral staining using antibodies against CD4+ T cells, CD8+ T cells, M1 macrophages, or M2 macrophages markers, indicating sparse immune cell infiltration around tumor cells, mainly in the stroma (Figure 7A and 7B). This dual approach strengthens our understanding of immune escape in both PCC types. The explanation has been added to the revised manuscript (Page 21).

6) Figure 7G missed the scale bar for the staining results of marker proteins. Please add the scale bar into the figure.

As suggested, we have added to the scale bar accordingly.

7) In the method part of the manuscript, the authors should describe the minimum and maximum number used for quality control of the number of genes and the percentage of mitochondrial genes.

For quality control, we established a minimum threshold of no less than 200 genes and a maximum threshold of no more than 5000 genes. Additionally, the quality control process included a maximum threshold of 30% for mitochondrial genes. These specific criteria have been added to the methods section of the revised manuscript (Page 25-26).

We express our gratitude to the reviewer for their supportive recommendations and invaluable guidance on enhancing the rigor of our data.

References for Reviewer #3:

Todd DJ, McHeyzer-Williams LJ, Kowal C, Lee AH, Volpe BT, Diamond B, McHeyzer-Williams MG, Glimcher LH (2009) XBP1 governs late events in plasma cell differentiation and is not required for antigen-specific memory B cell development. The Journal of experimental medicine 206: 2151-2159

Poulsen TS, Silahtaroglu AN, Gisselø CG, Tommerup N, Johnsen HE (2002) Detection of illegitimate rearrangements within the immunoglobulin light chain loci in B cell malignancies using end sequenced probes. Leukemia 16: 2148-2155

Author Response

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

Reviewer #1 (Recommendations For The Authors):

This is significant work, and you should certainly make the best case you can on the weaknesses discussed.

We thank reviewer for this positive comment on the significance of our work. The referee indicates as weaknesses (i) that the force involving the bent or straight αI-helix is not readily apparent, (ii) the residue types were not varied in the helix mutations, and (iii) that the chemical shift perturbations are indirect observations.

We think we have tried to address a large part of these questions by being very careful in our analysis and by the discussion in the manuscript. The following remarks may help to clarify this further:

(i) The force emanating from the helix is e.g. visualized in the PC2 loadings in Figure 6E of the PCA carried on all observed SH3-SH2-KD resonances for all apo forms of the helix mutants. The SH2 residues identified by these loadings are in direct vicinity to the αI-helix. The respective PC2 scores correlate to 98% with the vmax of the catalytic reaction and to 94 % with the PC1 scores found for imatinib-induced opening. Importantly, the structure of the KD with the straight αI-helix indicates that mostly residues F516, Q517, S520, and I521 would clash with the SH2 domain in a closed core (Figure 6F). Thus, the expected clashes are in direct vicinity of the SH2 residues identified by the PC2 loadings as correlated to vmax and imatinib-induced opening. These data are completely orthogonal and show that most of the force is coming from residues F516, Q517, S520, and I521 in the αI-αI’ turn.

(ii) We agree that we mainly used truncations of the αI-helix to study its involvement in activation. Point (i) makes it clear that a larger part of the αI-helix effects is caused by steric clashes of the residues in the αI-αI’ turn. In the latter region, we don’t expect strong amino acid type-specific effects besides excluded volume. Due to expression problems, we could not vary the helix length between residues 519 and 534. However, in this region we introduced the amino acid type mutation E528K. The latter showed a clear specific effect. Further amino acid type-specific effects may be possible in this region. However, we expect that the identified electrostatic E528-R479 interaction is one of the most important interactions in this region.

(iii) We agree that chemical shift changes of individual resonances are often hard to interpret. However, we want to stress that our conclusions are all drawn from principal component analyses, which in all cases had as input well over 100 if not over 200 1H-15H resonances. The first two principal components of these analyses are robust averages over many residues, which reveal general correlated structural trends.

We assume that chemical shift deposition etc will be pursued.

We are currently depositing a larger collection of our Abl data to the “Biological Magnetic Resonance Data Bank (BMRB)”, which includes the NMR chemical shift data of the present work. A ‘collection’ will be a new feature of the BMRB, and we are in discussion with their staff. We will provide the accession codes as soon as possible (probably within the next month) to be included into the final version of the manuscript. We have amended the Data Availability Section accordingly.

Reviewer #2 (Recommendations For The Authors):

1) The overall discussion of the implications of the described allostery on kinase activation is provided through lenses of imatinib binding, which is used as an experimental trigger to disassemble the autoinhibited core. Can the authors elaborate in the Discussion on what event would play this role in the kinase catalytic cycle, communicating to helix I? Would dissociation of the myristate from the active site be hypothesized to be the first step in kinase activation? While I understand that certainty may be challenging to attain, it would be good to introduce some ideas into the Discussion.

We appreciate the reviewer’s suggestions for the discussion and added the following text to the Conclusion section:

"We have used here imatinib binding to the ATP-pocket as an experimental tool to disassemble the Abl regulatory core. Our previous analysis (Sonti et al., 2018) of the high-resolution Abl transition-state structure (Levinson et al., 2006) indicated that due to the extremely tight packing of the catalytic pocket, binding and release of the ATP and tyrosine peptide substrates is only possible if the P-loop and thereby the N-lobe move towards the SH3 domain by about 1–2 Å. This motion is of similar size and direction as the motion of the N-lobe observed in complexes with imatinib and other type II inhibitors (Sonti et al., 2018). From this we concluded that substrate binding opens the Abl core in a similar way as imatinib. The present NMR and activity data now clearly establish the essential role of the αI-helix both in the imatinib- and substrate-induced opening of the core, thereby further corroborating the similarity of both disassembly processes.

Notably, the used regulatory core construct Abl83-534 lacks the myristoylated N-cap. Although we have previously demonstrated that the latter construct is predominantly assembled (Skora et al., 2013), the addition of the myristoyl moiety is expected to further stabilize the assembled conformation in a similar way as asciminib.

Considering this mechanism, dissociation of myristoyl from the native Abl 1b core may be a first step during activation. However, it should be kept in mind that the Abl 1a isoform lacks the N-terminal myristoylation, and it is presently unclear whether other moieties bind to the myristoyl pocket of Abl 1a during cellular processes."

2) Can the authors comment more on the differentiation between assembled conformations induced by type I inhibitor binding vs apo forms (or AMP-PNP and allosteric inhibitor) reported in Figure 3B? The differences are clearly identified by PCA but not sufficiently discussed.

As indicated in the text, we think two structural effects are intermingled within PC2. Due to this admixture, it is hard to draw strong conclusions and we don’t want to expand on this too much. We have slightly modified the respective paragraph (p.7) as follows):

"As the affected residues react differently to perturbations by type I inhibitors and truncation of the αI’-helix (Figure 3A, right), we attribute this behavior to two effects intermixed into the PC2 detection: (i) a minor rearrangement of the SH3/KD N-lobe interface caused by filling of the ATP pocket with type I inhibitors, which in contrast to the stronger N-lobe motion induced by type II inhibitors does not yet lead to core disassembly and (ii) a small rearrangement of the SH2/KD C-lobe interface caused by shortening and mutations of the αI-helix."

3) The allosteric connection between active site inhibitor binding and the myristate/allosteric inhibitor binding has been observed in the past and noted before, in papers such as Zhang et al, Nature 2010. While the authors reference this paper, they do not acknowledge its specific findings or engage in a broader discussion of how their conclusions relate to this work.

We have modified the beginning of the Conclusion section:

"The allosteric connection between Abl ATP site and myristate site inhibitor binding has been noted before, albeit specific settings such as construct boundaries and the control of phosphorylation vary in published experiments. Positive and negative binding cooperativity of certain ATP-pocket and allosteric inhibitors has been observed in cellular assays and in vitro (Kim et al., 2023; Zhang et al., 2010). Furthermore, hydrogen exchange mass spectrometry has indicated changes around the unliganded ATP pocket upon binding of the allosteric inhibitor GNF-5 (Zhang et al., 2010). Here, we present a detailed high-resolution explanation of these allosteric effects via a mechanical connection between the kinase domain N- and C-lobes that is mediated by the regulatory SH2 and SH3 domains and involves the αI helix as a crucial element.

Specifically, we have established a firm correlation between the kinase activity of the Abl regulatory core, the imatinib (type II inhibitor)-induced disassembly of the core, which is caused by a force FKD–N,SH3 between the KD N-lobe and the SH3 domain, and a force FαI,SH2 exerted by the αI-helix towards the SH2 domain. The FαI,SH2 force is mainly caused by a clash of the αI-αI’ loop with the SH2 domain. Both the FKD–N,SH3 and FαI,SH2 force act on the KD/SH2SH3 interface and may lead to the disassembly of the core, which is in a delicate equilibrium between assembled and disassembled forms. As disassembly is required for kinase activity, the modulation of both forces constitutes a very sensitive regulation mechanism. Allosteric inhibitors such as asciminib and also myristoyl, the natural allosteric pocket binder, pull the αI-αI’ loop away from the SH2 interface, and thereby reduce the FαI,SH2 force and activity. Notably, all observations described here were obtained under nonphosphorylated conditions, as phosphorylation will lead to additional strong activating effects."

4) Figure 6 could do a better job of providing an illustration of steric clashes.

We have revised Figure 6, panel F, in order to better illustrate the steric clashes, and modified the legend accordingly.

5) There is a typo in line 5 from the top on page 11 (dash missing from "83534" superscript).

Thank you. This was fixed.

eLife assessment

This important paper provides solid evidence that the angular gyrus plays a role in insight-based memory updating. The study is well conducted, timely, and presents clear-cut behavioral results. While the study provides robust evidence that transcranial magnetic stimulation to the angular gyrus impacts memory, evidence for the strong claim of a causal contribution of the angular gyrus in particular – apart from other connected regions, including the hippocampus – is not conclusive.

Author Response

Responses to public reviews

Reviewer 1

We thank the reviewer for the valuable and constructive comments and are pleased that the re-viewer finds our study timely and our behavioral results clear.

1) The RSA basically asks on the lowest level, whether neural activation patterns (as measured by EEG) are more similar between linked events compared to non-linked events. At least this is the first question that should be asked. However, on page 11 the authors state: "We ex-amined insight-induced effects on neural representations for linked events [...]". Hence, the critical analysis reported in the manuscript fully ignores the non-linked events and their neu-ral activation patterns. However, the non-linked events are a critical control. If the reported effects do not differ between linked and non-linked events, there is no way to claim that the effects are due to experimental manipulation - neither imagination nor observation. Hence, instead of immediately reporting on group differences (sham vs. control) in a two-way in-teraction (pre vs. post X imagination vs. observation), the authors should check (and re-port) first, whether the critical experimental manipulation had any effect on the similarity of neural activation patterns in the first place.

We completely agree that the non-link items are a critical control. Therefore, we had reported not only the results for linked but also for non-linked events on page 15, lines 336-350. We clarified this important point now on page 12 lines 283-286:

“Subsequently, we examined insight-induced effects on neural representations for linked (vs. non-linked) events by comparing the change from pre- to post-insight (post-pre) and the difference between imagination and observation (imagination - observation) between cTBS and sham groups using an independent cluster-based permutation t-test.”

Moreover, to directly compare linked and non-linked events we performed a four-way in-teraction including link vs. non-link. This analysis yielded a significant four-way interaction, showing that the interaction of time (pre vs. post), mode of insight (imagination vs. obser-vation) and cTBS differed for linked vs. non-linked items. We then report the follow-up analyses, separately for linked and non-linked events. Please see pages 12-13, lines 287-294:

“First, we included the within-subject factors time (pre vs. post), mode of insight (imagina-tion vs. observation) and link (vs. non-link) by calculating the difference waves. Subse-quently we conducted a cluster-based permutation test comparing the cTBS and the sham groups. This analysis yielded a four-way interaction within a negative cluster in a fronto-temporal region (electrode: FT7; p = 0.007, ci-range = 0.00, SD = 0.00). This result indicates that the impact of cTBS over the angular gyrus on the neural pattern reconfiguration follow-ing imagination- vs. observation-based insight may differ between linked and non-linked events. For linked events, this analysis yielded a […]”

2) Overall, the focus on the targeted three-way interaction is poorly motivated. Also, a func-tional interpretation is largely missing.

In order to better explain our motivation for the three-way interaction, we em-phasized in the introduction the importance of disentangling potential differences due to the mode of insight, given the known role of the angular gyrus in imagination on pages 4-5, lines 107-115:

“Considering this involvement of the angular gyrus in imaginative processes, we expected that the effect of cTBS on the change in representational similarity from pre- to post-insight will differ based on the mode of insight – whether this insight was gained via imagination or observation. Specifically, we expected a more pronounced impairment in the neural recon-figurations when insight is gained via imagination, as this function may depend more on an-gular gyrus recruitment than insight gained via observation. Additionally, we expected cTBS to the left angular gyrus to interfere with the increase in neural similarity for linked events and with the decrease of neural similarity for non-linked event.”

As discussed on page 21 (starting from line 478; see also the intro on page 4), we expected that the angular gyrus would be particularly implicated in imagination-based insight, given its known role in imagination (e.g.: Thakral et al., 2017). Moreover, given the angular gyrus’s strong connectivity with other regions, the results observed may not be driven by this re-gion alone but also by interconnected regions, such as the hippocampus. We clarified these important points at the very end of the discussion on pages 23-24, lines 543-560:

“Furthermore, the differential impact of cTBS to the angular gyrus on neural reconfigura-tions between events linked via imagination and those linked via observation may be at-tributed to its crucial role in imaginative processes (Ramanan et al., 2018; Thakral et al., 2017). Another intriguing aspect to consider is that the stimulated site was situated in the more ventral portion of the angular gyrus, recognized for its stronger connectivity to the episodic hippocampal memory system in contrast to its more dorsal counterpart (Seghier, 2013; Uddin et al., 2010). This stronger connectivity between the ventral angular gyrus and the hippocampus may shed light on the greater impact of cTBS to the angular gyrus on im-agination-based insight. Given the angular gyrus’s robust connectivity with other brain re-gions, including the hippocampus (Seghier, 2013), it is plausible that the observed changes might not solely stem from alterations within the angular gyrus itself, but could also origi-nate from these interconnected regions. This notion may bear particular importance given the required accessibility to the hippocampus during imaginative processes (Benoit & Schacter, 2015; Grob et al., 2023a; Zeidman & Maguire, 2016). Interactions between the an-gular gyrus and the hippocampus may give rise to rich memory representations (Ramanan et al., 2018). In line with this, recent studies have demonstrated that cTBS to the angular gy-rus resulted in enhanced hippocampal connectivity and improved associative memory (Hermiller et al., 2019; Tambini et al., 2018; Wang et al., 2014).”

3) "Interestingly, we observed a different pattern of insight-related representational pattern changes for non-linked events." It is not sufficient to demonstrate that a given effect is pre-sent in one condition (linked events) but not the other (non-linked events). To claim that there are actually different patterns, the authors would need to compare the critical condi-tions directly (Nieuwenhuis et al., 2011).

We completely agree and now compared the two conditions directly. Specifical-ly, we now report the significant four-way interaction, including the factor link vs. non-link, before delving into separate analyses for linked and non-linked events on pages 12-13, lines 287-294:

“First, we included the within-subject factors time (pre vs. post), mode of insight (imagina-tion vs. observation) and link (vs. non-link) by calculating the difference waves. Subse-quently we conducted a cluster-based permutation test comparing the cTBS and the sham groups. This analysis yielded a four-way interaction within a negative cluster in a fronto-temporal region (electrode: FT7; p = 0.007, ci-range = 0.00, SD = 0.00). This result indicates that the impact of cTBS over the angular gyrus on the neural pattern reconfiguration follow-ing imagination- vs. observation-based insight may differ between linked and non-linked events. For linked events, this analysis yielded a […]”

4) "This analysis yielded a negative cluster (p = 0.032, ci-range = 0.00, SD = 0.00) in the parieto-temporal region (electrodes: T7, Tp7, P7; Fig. 3B)." (p. 11). The authors report results with specificity for certain topographical locations. However, this is in stark contrast to the fact that the authors derived time X time RSA maps.

We did derive time × time similarity maps for each electrode within each partic-ipant, which allowed us to find a cluster consisting of specific electrodes. We apologize for not making this aspect clear enough and have, therefore, modified the respective part of our methods section on page 38, lines 951-952:

“In total, this analysis produced eight Representational Dissimilarity Matrices (RDMs) for each electrode and each participant.”

5) "These theta power values were then combined to create representational feature vectors, which consisted of the power values for four frequencies (4-7 Hz) × 41 time points (0-2 sec-onds) × 64 electrodes. We then calculated Pearson's correlations to compare the power pat-terns across theta frequency between the time points of linked events (A with B), as well as between the time points of non-linked events (A with X) for the pre- and the post-phase separately, separately for stories linked via imagination and via observation. To ensure un-biased results, we took precautions not to correlate the same combination of stories twice, which prevented potential inflation of the data. To facilitate statistical comparisons, we ap-plied a Fisher z-transform to the Pearson's rho values at each time point. This yielded a global measure of similarity on each electrode site. We, thus, obtained time × time similarity maps for the linked events (A and B) and the non-linked events (A and X) in the pre- and post-phases, separately for the insight gained through imagination and observation." (p. 34+35).

If RSA values were calculated at each time point and electrode, the Pearson correlations would have been computed effectively between four samples only, which is by far not enough to derive reliable estimates (Schönbrodt & Perugini, 2013). The problem is aggra-vated by the fact that due to the time and frequency smoothing inherent in the time-frequency decomposition of the EEG data, nearby power values across neighboring theta frequencies are highly similar to start with. (e.g., Schönauer et al., 2017; Sommer et al., 2022).

Alternative approaches would be to run the correlations across time for each electrode (re-sulting in the elimination of the time dimension) or to run the correlations at each time point across electrodes (resulting in the elimination of topographic specificity).

At least, the authors should show raw RSA maps for linked and non-linked events in the pre- and post-phases separately for the insight gained through imagination and observa-tion in each group, to allow for assessing the suitability of the input data (in the supple-ments?) before progressing to reporting the results of three-way interactions.

Although we do see the reviewer’s point, we think that an RSA specific to the theta range yielding electrode specific time × time similarity maps must be run this way, otherwise, as you pointed out, one or the other dimension is compromised. Running an RSA across time for each electrode will lead to computing a similarity measure between the events without information on when these stimuli become more or less similar, thereby ig-noring the temporal dynamics crucial to EEG data and not taking advantage of the high temporal resolution. Conversely, conducting an RSA across electrodes might result in an overall similarity measure per participant, disregarding the spatial distribution and potential variations among electrodes. Although EEG has limited spatial resolution, different elec-trodes can capture differences that may aid in understanding neural processing. However, as suggested by the reviewer, we included the raw RSA maps for linked and non-linked events separately for pre- and post-phases, imagination and observation and link and non-link in the supplement and refer to these data in the results section on pages 12-13, lines 293-295:

“For linked events, this analysis yielded a negative cluster (p = 0.032, ci-range = 0.00, SD = 0.00) in the parieto-temporal region (electrodes: T7, Tp7, P7; Fig. 3B; Figure 3 – Figure sup-plement 1).”

And on page 15, lines 339-341:

“This analysis yielded a positive cluster (p = 0.035, ci-range = 0.00, SD = 0.00) in a fronto-temporal region (electrode: FT7; Fig. 3C; Figure 3 – Figure supplement 2).”

Reviewer 2

We thank the reviewer for the very helpful and constructive comments and appreciate that the reviewer finds our study relevant to all areas of cognitive research.

1) While the observed memory reconfiguration/changes are attributed to the angular gyrus in this study, it remains unclear whether these effects are solely a result of the AG's role in re-configuration processes or to what extent the hippocampus might also mediate these memory effects (e.g., Tambini et al., 2018; Hermiller et al., 2019).

We agree that, in addition to the critical role of the angular gyrus, there may be an involvement of the hippocampus. We point now explicitly to the modulatory capacities of angular gyrus stimulation on the hippocampus. Please see page 4, lines 81-88:

“One promising candidate that may contribute to insight-driven memory reconfiguration is the angular gyrus. The angular gyrus has extensive structural and functional connections to many other brain regions (Petit et al., 2023), including the hippocampus (Coughlan et al., 2023; Uddin et al., 2010). Accordingly, previous studies have shown that stimulation of the angular gyrus resulted in altered hippocampal activity (Thakral et al., 2020; Wang et al., 2014). Furthermore, the angular gyrus has been implicated in a myriad of cognitive func-tions, including mental arithmetic, visuospatial processing, inhibitory control, and theory-of-mind (Cattaneo et al., 2009; Grabner et al., 2009; Lewis et al., 2019; Schurz et al., 2014).”

We further added a new paragraph to the discussion pointing at the possibility that not solely the angular gyrus but another brain region, such as the hippocampus, may have me-diated the changes observed in our study on pages 23-24, lines 546-562:

“Another intriguing aspect to consider is that the stimulated site was situated in the more ventral portion of the angular gyrus, recognized for its stronger connectivity to the episodic hippocampal memory system in contrast to its more dorsal counterpart (Seghier, 2013; Ud-din et al., 2010). This stronger connectivity between the ventral angular gyrus and the hip-pocampus may shed light on the greater impact of cTBS to the angular gyrus on imagination-based insight. Given the angular gyrus’s robust connectivity with other brain regions, includ-ing the hippocampus (Seghier, 2013), it is plausible that the observed changes might not solely stem from alterations within the angular gyrus itself, but could also originate from these interconnected regions. This notion may bear particular importance given the re-quired accessibility to the hippocampus during imaginative processes (Benoit & Schacter, 2015; Grob et al., 2023a; Zeidman & Maguire, 2016). Interactions between the angular gyrus and the hippocampus may give rise to rich memory representations (Ramanan et al., 2018). In line with this, recent studies have demonstrated that cTBS to the angular gyrus resulted in enhanced hippocampal connectivity and improved associative memory (Hermiller et al., 2019; Tambini et al., 2018; Wang et al., 2014). However, it should be noted that our study detected impaired associative memory following cTBS to the angular gyrus.”

2) Another weakness in this manuscript is the use of different groups of participants for the key TMS intervention, along with underspecified or incomplete hypotheses/predictions.

In our view, the chosen between-subjects design is to be preferred over a crossover design for several reasons. First, our choice aimed to eliminate potential se-quence effects that may have adversely affected performance in the narrative-insight task (NIT). Second, this approach ensured consistency in expectations regarding the story links while also mitigating potential differences induced by fatigue. Additionally, we accounted for the potential advantage of a within-subject design – the stimulation of the same brain – by utilizing neuro-navigated TMS for targeting the stimulation coordinate. Finally, it is im-portant to note that we measured the event representations pre- and post-insight and that also the mode of insight was manipulated within-subject. Thus, our design did include a within-subject component and we are convinced that the chosen paradigm balances the different strengths and weaknesses of within-subject and between-subjects designs in the best possible manner. We specified our rationale for choosing a between-subjects ap-proach in the introduction on page 5, lines 122-126:

“We intentionally adopted a mixed design, combining both between-subjects and within-subject methodologies. The between-subjects approach was chosen to minimize the risk of carry-over effects and sequence biases. Simultaneously, we capitalized on the advantages of a within-subject design by altering the pre- to post-insight comparison and the mode of insight (imagination vs. observation) within each participant.”

Moreover, to provide a comprehensive portrayal of the two groups, we incorporated de-scriptions concerning trait and state variables alongside age and motor thresholds and in-cluded t-test comparisons between these variables on page 7, lines 157-160:

“Notably, the groups did not differ on levels of subjective chronic stress (TICS), state and trait anxiety (STAI-S, STAI-T), depressive mood (BDI), imaginative capacities (FFIS), person-ality dimensions (BFI), age, and motor thresholds (for descriptive statistics see Table 1; all p > 0.053).”

And further included age and motor thresholds as control variables in Table 1 on page 18, lines 402-404:

“Overall, levels of subjective chronic stress, anxiety, and depressive mood were relatively low and not different between groups. The groups did further not differ in terms of per-sonality traits, imagination capacity, age or motor thresholds (all p > 0.053; see Table 1).”

For greater precision in outlining our hypotheses, we specified these at the end of the in-troduction on pages 4-55, lines 107-118:

“Considering this involvement of the angular gyrus in imaginative processes, we expected that the effect of cTBS on the change in representational similarity from pre- to post-insight will differ based on the mode of insight – whether this insight was gained via imagination or observation. Specifically, we expected a more pronounced impairment in the neural recon-figurations when insight is gained via imagination, as this function may depend more on an-gular gyrus recruitment than insight gained via observation. Additionally, we expected cTBS to the left angular gyrus to interfere with the increase in neural similarity for linked events and with the decrease of neural similarity for non-linked events. We further predicted that cTBS to the left angular gyrus would reduce the impact of (imagination-based) insight into the link of initially unrelated events on memory performance during free recall, given its higher variability compared to other memory measures.”

3) Furthermore, in some instances, the types of analyses used do not appear to be suitable for addressing the questions posed by the current study, and there is limited explanation pro-vided for the choice of analyses and questionnaires.

We addressed this concern by inserting a new section “control variables” in the methods explaining our rationale for employing the different questionnaires as control var-iables on pages 40-41, lines 1003-1019:

“Control variables In order to ensure that the observed effects were solely attributable to the TMS manipula-tion and not influenced by other factors, we comprehensively evaluated several trait and state variables. To account for potential variations in anxiety levels that could impact our re-sults, we specifically measured state and trait anxiety using STAI-S and STAI-T (Laux et al., 1981), thus minimizing the potential confounding effects of anxiety on our findings (Char-pentier et al., 2021). Additionally, we evaluated participants’ chronic stress levels using the TICS (Schulz & Schlotz, 1999) to exclude any group variations that might explain the effect on memory, cosidering the well-established impact of stress on memory (Sandi & Pinelo-Nava, 2007; Schwabe et al., 2012). Moreover, we assessed participants’ depressive symp-toms employing the BDI (Hautzinger et al., 2006), to guarantee group comparability on this clinical measure. We further assessed fundamental personality dimensions using the BFI-2 (Danner et al., 2016) to exclude any potential group discrepancies that could account for dif-ferences observed. Lastly, we assessed participants’ imaginative capacities using the FFIS (Zabelina & Condon, 2019), to ensure uniformity across groups regarding this central varia-ble, considering the significant role of imagination in relation to the cTBS-targeted angular gyrus (Thakral et al., 2017).”

We further specified why we chose to analyze our behavioral data using LMMs on page 34, lines 849-85:

“For our behavioral analyses we opted to employ linear-mixed models (LMM), given their high robustness regarding the underlying distribution and high sensitivity to individual varia-tion (Pinheiro & Bates, 2000; Schielzeth et al., 2020).”

Moreover, we added an explanation on why we opted for the RSA approach in the meth-ods section on page 37, lines 920-923:

“This method is ideally suited to measure neural representation changes and was specifical-ly chosen as it has been previously identified as the preferred approach for quantifying in-sight-induced neural changes (Grob et al., 2023b; Milivojevic et al., 2015).”

To clarify on the rationale behind our coherence analysis, we incorporated an explanatory sentence in the methods section on page 39, lines 966-967:

“Due to the robust connectivity between the angular gyrus and other brain regions (Petit et al., 2023; Seghier, 2013), we proceeded with a connectivity analysis as a next step.”

Reviewer 3

We thank the reviewer for the constructive and very helpful comments. We are pleased that the reviewer considered our experimental design to be strong and our behavioral results to be striking.

1) My major criticism relates to the main claim of the paper regarding causality between the angular gyrus and the authors' behavior of interest. Specifically, I am not convinced by the evidence that the effects of stimulation noted in the paper are attributable specifically to the angular gyrus, and not other regions/networks.

While our results showed specific changes after cTBS over the angular gyrus, demonstrating a causal involvement of the angular gyrus in these effects, we completely agree that this does not rule out an involvement of additional areas. In particular, there is evidence suggesting that cTBS over parietal regions, such as the angular gyrus, could poten-tially influence hippocampal functioning. We address this issue now in a new paragraph that we have added to the discussion, on pages 23-24, lines 546-564:

“Another intriguing aspect to consider is that the stimulated site was situated in the more ventral portion of the angular gyrus, recognized for its stronger connectivity to the episodic hippocampal memory system in contrast to its more dorsal counterpart (Seghier, 2013; Ud-din et al., 2010). This stronger connectivity between the ventral angular gyrus and the hip-pocampus may shed light on the greater impact of cTBS to the angular gyrus on imagination-based insight. Given the angular gyrus’s robust connectivity with other brain regions, includ-ing the hippocampus (Seghier, 2013), it is plausible that the observed changes might not solely stem from alterations within the angular gyrus itself, but could also originate from these interconnected regions. This notion may bear particular importance given the re-quired accessibility to the hippocampus during imaginative processes (Benoit & Schacter, 2015; Grob et al., 2023a; Zeidman & Maguire, 2016). Interactions between the angular gyrus and the hippocampus may give rise to rich memory representations (Ramanan et al., 2018). In line with this, recent studies have demonstrated that cTBS to the angular gyrus resulted in enhanced hippocampal connectivity and improved associative memory (Hermiller et al., 2019; Tambini et al., 2018; Wang et al., 2014). However, it should be noted that our study detected impaired associative memory following cTBS to the angular gyrus. Expanding upon this idea, it is conceivable that targeting a more dorsal segment of the angular gyrus might exert a stronger influence on observation-based linking – an aspect that warrants future in-vestigations.”

Responses to reviewer recommendations

Reviewer 1

1) On page 26, the authors write: "[...] different video events (A, B, and X) were recalled from day one [...]". I may have missed this point, but I had the impression that the task was con-ducted within one day.

Indeed, this study was conducted within a single day. We rephrased the respec-tive statement accordingly. Please see page 7, lines 149-153:

“To test this hypothesis and the causal role of the angular gyrus in insight-related memory reconfigurations, we combined the life-like video-based narrative-insight task (NIT) with representational similarity analysis of EEG data and (double-blind) neuro-navigated TMS over the left angular gyrus in a comprehensive investigation within a single day.”

We further included this information in the methods section on page 27, lines 634-635:

“In total, the experiment took about 4.5 hours per participant and was completed within a single day. ”

Reviewer 2

1) There is a substantial disconnection between the introduction and the methods/results sec-tion. One reason is that there is not sufficient detail regarding the hypotheses/predictions and the specific types of analyses chosen to test these hypotheses/predictions. Additionally, it is not explained what comparisons and outcomes would be informative/expected. This should be made clear. Second and related to the above, the rationale for conducting certain types of analyses (correlation, coherence, see below) sometimes is not specified.

To address this concern, we elaborated on our hypotheses incorporating specif-ic predictions for the free recall, given its higher variability than the other memory measures, and for imagination vs. observation at the end of the introduction on pages 4-5, lines 107-122:

“Considering this involvement of the angular gyrus in imaginative processes, we expected that the effect of cTBS on the change in representational similarity from pre- to post-insight will differ based on the mode of insight – whether this insight was gained via imagination or observation. Specifically, we expected a more pronounced impairment in the neural recon-figurations when insight is gained via imagination, as this function may depend more on an-gular gyrus recruitment than insight gained via observation. Additionally, we expected cTBS to the left angular gyrus to interfere with the increase in neural similarity for linked events and with the decrease of neural similarity for non-linked events. We further predicted that cTBS to the left angular gyrus would reduce the impact of (imagination-based) insight into the link of initially unrelated events on memory performance during free recall, given its higher variability compared to other memory measures. Considering the high connectivity profile of the angular gyrus within the brain (Seghier, 2013), we conducted an EEG connec-tivity analysis building upon prior findings concerning alterations in neural reconfigurations. To establish a link between neural and behavioral findings, we chose a correlational ap-proach to relate observations from these two domains.”

Moreover, we made our rationale for the employed analyses more explicit and specified why we chose to analyze our behavioral data using LMMs on page 34, lines 849-851:

“For our behavioral analyses we opted to employ linear-mixed models (LMM), given their high robustness regarding the underlying distribution and high sensitivity to individual varia-tion (Pinheiro & Bates, 2000; Schielzeth et al., 2020).”

Moreover, we added an explanation on why we opted for the RSA approach in the meth-ods section on page 37, lines 920-923:

“This method is ideally suited to measure neural representation changes and was specifical-ly chosen as it has been previously identified as the preferred approach for quantifying in-sight-induced neural changes (Grob et al., 2023b; Milivojevic et al., 2015).”

To clarify on the rationale behind our coherence analysis, we incorporated an explanatory sentence in the methods section on page 39, lines 966-967:

“Due to the robust connectivity between the angular gyrus and other brain regions (Petit et al., 2023; Seghier, 2013), we proceeded with a connectivity analysis as a next step.”

2) The authors suggest that besides Branzi et al. (2021), this is one of the first studies showing that memory update is linked to the AG. I suggest having a look at work from Tambini, Nee, & D'Esposito, 2018, JoCN, and other papers from Joel Voss' group that target a similar re-gion of AG/Inferior parietal cortex. Many studies, using multiple TMS protocols, have now shown this brain region is causally involved in episodic and associative memory encoding.

As mentioned above, further consideration of this literature is important as it delves into the region's hippocampal connectivity (and other network properties), and how that mediates the memory effects. Indeed because of the nature of the methods employed in this study, we do not know if the memory-related behavioural effects are due to TMS-changes induced at the AG's versus the hippocampal' s level, or both. How do the current findings square with the existing TMS effects from this region? Can the connectivity profile of the target re-gion highlighted by previous studies provide further insight into how the current behaviour-al effect arises? Some comments on this could be added to the discussion.

We completely agree that the other studies showing enhanced associative memory after TMS to parietal regions need to be addressed. Therefore, we updated the discussion on page 20, lines 449-453:

“Interestingly, recent work has additionally indicated that targeting parietal regions with TMS led to alterations in hippocampal functional connectivity, thereby enhancing associa-tive memory (Nilakantan et al., 2017; Tambini et al., 2018; Wang et al., 2014), potentially shedding light on the underlying mechanisms involved.”

Moreover, we included a section specifically addressing the possibility that the effects ob-served may pertain to having modulated other regions via the targeted region and updated the discussion on pages 23-24, lines 543-562:

“Furthermore, the differential impact of cTBS to the angular gyrus on neural reconfigura-tions between events linked via imagination and those linked via observation may be at-tributed to its crucial role in imaginative processes (Ramanan et al., 2018; Thakral et al., 2017). Another intriguing aspect to consider is that the stimulated site was situated in the more ventral portion of the angular gyrus, recognized for its stronger connectivity to the episodic hippocampal memory system in contrast to its more dorsal counterpart (Seghier, 2013; Uddin et al., 2010). This stronger connectivity between the ventral angular gyrus and the hippocampus may shed light on the greater impact of cTBS to the angular gyrus on im-agination-based insight. Given the angular gyrus’s robust connectivity with other brain re-gions, including the hippocampus (Seghier, 2013), it is plausible that the observed changes might not solely stem from alterations within the angular gyrus itself, but could also origi-nate from these interconnected regions. This notion may bear particular importance given the required accessibility to the hippocampus during imaginative processes (Benoit & Schacter, 2015; Grob et al., 2023a; Zeidman & Maguire, 2016). Interactions between the an-gular gyrus and the hippocampus may give rise to rich memory representations (Ramanan et al., 2018). In line with this, recent studies have demonstrated that cTBS to the angular gy-rus resulted in enhanced hippocampal connectivity and improved associative memory (Hermiller et al., 2019; Tambini et al., 2018; Wang et al., 2014). However, it should be noted that our study detected impaired associative memory following cTBS to the angular gyrus.”

3) Another comment I have regards the results observed for the observation vs imagination insight conditions. The authors mention that the 'changes in representational similarity for the observation condition should be interpreted with caution, as these seemingly opposite changes appeared to be at least in part driven by group differences already in the pre-phase before participants gained insight.' I wonder what these group differences are and whether the authors have any hypothesis about what factors determined them.

We could only speculate about the basis of the observed pre-insight phase dif-ferences. However, we provide now the raw RSA data as supplemental material to make the pattern of the (raw) RSA findings in the pre- and post-insight phases more transparent. We refer the interested reader to this material on pages 12-13, lines 293 to 295:

“For linked events, this analysis yielded a negative cluster (p = 0.032, ci-range = 0.00, SD = 0.00) in the parieto-temporal region (electrodes: T7, Tp7, P7; Fig. 3B; Figure 3 – Figure sup-plement 1).”

And on page 15, lines 339-341:

“This analysis yielded a positive cluster (p = 0.035, ci-range = 0.00, SD = 0.00) in a fronto-temporal region (electrode: FT7; Fig. 3C; Figure 3 – Figure supplement 2).”

Furthermore, the age of participants is not reported separately for the two groups (cTBS to AG vs Sham), I think. This should be reported including a t-test showing that the two groups have the same age.

We agree and report now explicitly that groups did not significantly differ in rel-evant control variables including age. Please see page 7, lines 157-160:

“Notably, the groups did not differ on levels of subjective chronic stress (TICS), state and trait anxiety (STAI-S, STAI-T), depressive mood (BDI), imaginative capacities (FFIS), person-ality dimensions (BFI), age, and motor thresholds (for descriptive statistics see Table 1; all p > 0.053).”

And further included age and motor thresholds as control variables in Table 1 on page 18, lines 402-412:

“Overall, levels of subjective chronic stress, anxiety, and depressive mood were relatively low and not different between groups. The groups did further not differ in terms of per-sonality traits, imagination capacity, age or motor thresholds (all p > 0.053; see Table 1).”

The fact this study is not a within-subject design makes difficult the interpretation of the results and this should be recognised as an important limitation of the study.

As outlined above, a within-subject design would in our view come with several disadvantages, such as significant sequence/carry-over effects. Moreover, the neural rep-resentation change was measured in a pre-post design, enabling us to measure the insight-driven neural reconfiguration at the individual level.

We clarify our rationale for the between-subjects factor TMS in the introduction on page 5, lines 122-126:

“We intentionally adopted a mixed design, combining both between-subjects and within-subject methodologies. The between-subjects approach was chosen to minimize the risk of carry-over effects and sequence biases. Simultaneously, we capitalized on the advantages of a within-subject design by altering the pre- to post-insight comparison and the mode of insight (imagination vs. observation) within each participant.”

Furthermore, we included our rationale for choosing a between-subjects approach for the crucial TMS manipulation in the methods section on page 25, lines 601-604:

“We implemented a mixed-design including the within-subject factors link (linked vs. non-linked events), session (pre- vs. post-link), and mode (imagination vs. observation) as well as the between-subjects factor group (cTBS to the angular gyrus vs. sham) to mitigate the risk of carry-over effects and sequence biases of the crucial cTBS manipulation.”

4) The angular gyrus is a heterogeneous region with multiple graded subregions. The one tar-geted in the present study is the ventral AG which has strong connections with the episodic-hippocampal memory system. I was wondering if this might explain why the AG TMS ef-fects on representational changes have been observed for events linked via imagination but not direct observation. Perhaps the stimulation of a more 'visual' AG subregion (see Hum-phreys et al., 2020, Cerebral Cortex) would have resulted in a different (opposite) pattern of results. It would be good to add some comments on this in the discussion.

We appreciate this interesting perspective offered regarding the potential out-comes of our study, particularly in relation to the activation of a more ventral sub region of the angular gyrus. We incorporated this idea into our discussion, alongside considerations regarding the potential effects of a more dorsal angular gyrus stimulation on observation-based linking. However, caution is warranted recognizing the inherent limitations posed by the precision of TMS manipulations, which is further underscored by our electric field simu-lations, utilizing a 10 mm radius. We included this section in the discussion on pages 23-24, lines 546-569:

“Another intriguing aspect to consider is that the stimulated site was situated in the more ventral portion of the angular gyrus, recognized for its stronger connectivity to the episodic hippocampal memory system in contrast to its more dorsal counterpart (Seghier, 2013; Ud-din et al., 2010). This stronger connectivity between the ventral angular gyrus and the hip-pocampus may shed light on the greater impact of cTBS to the angular gyrus on imagina-tion-based insight. Given the angular gyrus’s robust connectivity with other brain regions, including the hippocampus (Seghier, 2013), it is plausible that the observed changes might not solely stem from alterations within the angular gyrus itself, but could also originate from these interconnected regions. This notion may bear particular importance given the re-quired accessibility to the hippocampus during imaginative processes (Benoit & Schacter, 2015; Grob et al., 2023a; Zeidman & Maguire, 2016). Interactions between the angular gyrus and the hippocampus may give rise to rich memory representations (Ramanan et al., 2018). In line with this, recent studies have demonstrated that cTBS to the angular gyrus resulted in enhanced hippocampal connectivity and improved associative memory (Hermiller et al., 2019; Tambini et al., 2018; Wang et al., 2014). However, it should be noted that our study detected impaired associative memory following cTBS to the angular gyrus. Expanding upon this idea, it is conceivable that targeting a more dorsal segment of the angular gyrus might exert a stronger influence on observation-based linking – an aspect that warrants future in-vestigations. Yet, while acknowledging the functional heterogeneity within the angular gy-rus (Humphreys et al., 2020), pinpointing specific sub regions via TMS remains challenging due to its limited focal precision at the millimeter level (Deng et al., 2013; Thielscher & Kammer, 2004), as reinforced by our electric field simulations utilizing a 10 mm radius. Hence, drawing definitive conclusions regarding distinct angular gyrus sub regions requires future research employing rigorous checks to assess the focality of their stimulation.”

5) Regarding the methods section, I have the following specific queries. It is unclear what is the purpose of the coherence and correlation analyses (pages 35, 36). Could the authors pro-vide further clarification on this? These analyses seem not to be mentioned anywhere in the introduction. This should be clarified briefly in the introduction and then in the methods sec-tion. The same for the questionnaires (anxiety, stress, etc): It is unclear the reason for col-lecting this type of data. This should be clarified in the introduction as well.

We agree, and have updated the introduction as follows on page 5, lines 118-122:

“Considering the high connectivity profile of the angular gyrus within the brain (Seghier, 2013), we conducted an EEG connectivity analysis building upon findings from the RSA anal-yses concerning alterations in neural reconfigurations. To establish a link between neural and behavioral findings, we chose a correlational approach to relate observations from these two domains.”

We additionally provided an explanation for including these questionnaires in the introduc-tion on page 5, lines 126-129:

“To control for any group differences beyond the TMS manipulation, we gathered various control variables through questionnaires, including trait- and state-anxiety, depressive symptoms, chronic stress levels, personality dimensions, and imaginative capacities.”

Moreover, we elaborated on the underlying rationale guiding our chosen analytical ap-proaches. Therefore, we specified why we chose to analyze our behavioral data using LMMs on page 34, lines 849-851:

“For our behavioral analyses we opted to employ linear-mixed models (LMM), given their high robustness regarding the underlying distribution and high sensitivity to individual varia-tion (Pinheiro & Bates, 2000; Schielzeth et al., 2020).”

Furthermore, we added an explanation on why we opted for the RSA approach in the methods section on page 37, lines 920-923:

“This method is ideally suited to measure neural representation changes and was specifical-ly chosen as it has been previously identified as the preferred approach for quantifying in-sight-induced neural changes (Grob et al., 2023b; Milivojevic et al., 2015).”

To clarify on the rationale behind our coherence analysis, we incorporated an explanatory sentence in the methods section on page 39, lines 966-967:

“Due to the robust connectivity between the angular gyrus and other brain regions (Petit et al., 2023; Seghier, 2013), we proceeded with a connectivity analysis as a next step.”

6) The preregistration webpage is in German. This is not ideal as it means that the information is available only to German speakers.

This webpage can easily be switched to English by changing the settings in the top right corner:

To address this issue, we included a description of how to set the webpage to English in the methods section on page 25, lines 581-582:

“For translation to English, please adjust the page settings located in the top right corner.”

7) Page 18. 'NIT' and 'MAT' - avoid abbreviations when possible.

We included the full name for the narrative-insight task (NIT) on page 7, line 151, line 153, and line 165, page 8 lines 177-178 and line 187, page 19 on line 427, page 26 on line 615, line 629 and line 632, page 27, line 653, page 30, lines 730-731, page 31, line 754, page 35, line 870, line 873, and page 36 and line 885.

We further included the full name for the multi-arrangements task (MAT) on page 19, lines 428-429.

8) Line 21....we further observed DECREASED...should be replaced with INCREASED, if I am not wrong.

We checked the sentence again and it looks correct to us, since it describes the change for observation-based insight, not imagination-based insight. We clarified that this finding pertains to observation-based linking by modifying the sentence on page 23, lines 525-528, as follows:

“Following cTBS to the angular gyrus, we further observed decreased pattern similarity for non-linked events in the observation-based condition, resembling the pattern change ob-served in the sham group for linked events, which may highlight the role of the angular gy-rus in representational separation during observation-based linking”

Reviewer 3

1) The major claim of the paper is that the angular gyrus is causally involved in insight-driven memory reconfiguration. To the authors' credit, they localized stimulation to the angular gyrus using an anatomical scan, the strength of the estimated electromagnetic field in the angular gyrus correlated with their behavioral results, and there were also brain-behavior correlations involving sensors located in the parietal lobe. However, the minimum evidence needed to claim causality is 1) evidence of a behavioral change (which the authors found) and 2) evidence of target engagement in the angular gyrus. It is also important to show brain-behavior correlations between target engagement and behavior. Although the au-thors stimulated the angular gyrus, that does not mean that rTMS specifically affected this region or that the behavioral results can be attributed to rTMS effects on the angular gyrus. As the authors point out, the angular gyrus has dense connections with other regions such as the hippocampus. In fact, several studies have shown that angular gyrus (or near AG) stimulation affects the hippocampal network (Wang et al., 2014, Science; Freedberg et al. 2019, eNeuro; Thakral et al., 2020, PNAS). EEG also has a poor spatial resolution, so even though the results were attributable to parieto-temporal sensors, this is not sufficient evi-dence to claim that the angular gyrus was modulated. Source localization would be re-quired to reconstruct the signal specifically from the AG. Thus, with the manuscript written as is, the authors can claim that "cTBS to the angular gyrus modulates insight-driven memory reconfiguration," but the current claim is not sufficiently substantiated.

While acknowledging the potential role of the angular gyrus in driving the ob-served changes, we recognize that the available evidence may not be sufficient. Conse-quently, we have introduced several modifications within our manuscript to address this concern.

In the revised Introduction, we now explicitly address the possibility of a stimulation of the hippocampus via the angular gyrus on page 4, lines 84-85:

“Accordingly, previous studies have shown that stimulation of the angular gyrus resulted in altered hippocampal activity (Thakral et al., 2020; Wang et al., 2014).”

Additionally, we included relevant evidence demonstrating previous instances of targeted stimulation of the angular gyrus, which led to alterations in hippocampal connectivity and associative memory. These insights have been included in the discussion on page 20, lines 449-453:

“Interestingly, recent work has additionally indicated that targeting parietal regions with TMS led to alterations in hippocampal functional connectivity, thereby enhancing associa-tive memory (Nilakantan et al., 2017; Tambini et al., 2018; Wang et al., 2014), potentially shedding light on the underlying mechanisms involved.”

Next, we have integrated crucial modifications essential for establishing a conclusive infer-ence of causality in our study. Moreover, we now explore the potential mediation of the effects observed from angular gyrus stimulation through other brain regions, like the hip-pocampus. In addition, we have highlighted prior work where such stimulation coincided with alterations in associative memory. For the updated discussion section, please see pag-es 23-24, lines 538-562:

“Although our study provided evidence suggesting a causal role of the angular gyrus in in-sight-driven memory reconfigurations – highlighted by behavioral changes after cTBS to the angular gyrus, neural changes in left parietal regions, and relevant brain-behavior associa-tions – it is important to acknowledge the limitations imposed by the spatial resolution of EEG. Consequently, the precise source of the observed signal changes in the parietal re-gions remains uncertain, potentially tempering the definitive nature of these findings. Fur-thermore, the differential impact of cTBS to the angular gyrus on neural reconfigurations between events linked via imagination and those linked via observation may be attributed to its crucial role in imaginative processes (Ramanan et al., 2018; Thakral et al., 2017). An-other intriguing aspect to consider is that the stimulated site was situated in the more ven-tral portion of the angular gyrus, recognized for its stronger connectivity to the episodic hippocampal memory system in contrast to its more dorsal counterpart (Seghier, 2013; Ud-din et al., 2010). This stronger connectivity between the ventral angular gyrus and the hip-pocampus may shed light on the greater impact of cTBS to the angular gyrus on imagina-tion-based insight. Given the angular gyrus’s robust connectivity with other brain regions, including the hippocampus (Seghier, 2013), it is plausible that the observed changes might not solely stem from alterations within the angular gyrus itself, but could also originate from these interconnected regions. This notion may bear particular importance given the re-quired accessibility to the hippocampus during imaginative processes (Benoit & Schacter, 2015; Grob et al., 2023a; Zeidman & Maguire, 2016). Interactions between the angular gyrus and the hippocampus may give rise to rich memory representations (Ramanan et al., 2018). In line with this, recent studies have demonstrated that cTBS to the angular gyrus resulted in enhanced hippocampal connectivity and improved associative memory (Hermiller et al., 2019; Tambini et al., 2018; Wang et al., 2014). However, it should be noted that our study detected impaired associative memory following cTBS to the angular gyrus.”

We further replaced terms that imply inhibition of the angular gyrus with a more operation-ally descriptive phrase:

“cTBS to the angular gyrus”

2) The authors frequently claim that cTBS is "inhibitory stimulation" and that inhibition of the angular gyrus caused their effects. There is a common misconception within the cognitive neuroscience literature that stimulation is either "inhibitory" or "excitatory," but there is no such thing as either. The effects of rTMS are dependent on many physiological, state, and trait-specific variables and the location of stimulation. For example, while cTBS does repro-ducibly inhibit behavior supported by the motor cortex (Wilkinson et al., 2010, Cortex; Rosenthal et al., 2009, J Neurosci), cTBS of the posterior parietal cortex reproducibly en-hances hippocampal network functional connectivity and episodic memory (Hermiller et al., 2019, Hippocampus; Hermiller et al., 2020, J Neurosci). The authors reference the Huang et al. (2005) paper as evidence of its inhibitory effects but work in this paper is not sufficient to broadly categorize cTBS as inhibitory. First, Huang et al. stimulated the motor cortex and measured the effects on corticospinal excitability, which is significantly different from what the current authors are measuring. Furthermore, this oft-cited study only included 9 sub-jects. Other studies have found that the effects of theta-burst are significantly more varia-ble when more subjects are used. For example, intermittent theta-burst, which is assumed to be excitatory based on the Huang paper, was found to produce unreliable excitatory ef-fects when more subjects were examined (Lopez-Alonso, 2014, Brain Stimulation). Thus, the a priori assumption that stimulation would be inhibitory is weak and cTBS should not be dis-cussed as "inhibitory."

We agree and included now a statement in the methods section that explicitly states that cTBS effects may be region-specific on page 33, lines 817-819:

“Nonetheless, the effects of cTBS appear to vary based on the targeted region, with cTBS to parietal regions demonstrating the capability to enhance hippocampal connectivity (Hermiller et al., 2019, 2020).”

We further substituted all terminology suggestive of an inhibitory effect with the phrase:

“cTBS to the angular gyrus”.

However, it is important to note, that while other studies (Hermiller et al., 2019; Tambini et al., 2018; Wang et al., 2014) found increased hippocampal connectivity after rTMS to a parie-tal region as well as enhanced associative memory, we observed impaired memory for the linked events. We included this clarification in the discussion on page 24, lines 558-562:

“In line with this, recent studies have demonstrated that cTBS to the angular gyrus resulted in enhanced hippocampal connectivity and improved associative memory (Hermiller et al., 2019; Tambini et al., 2018; Wang et al., 2014). However, it should be noted that our study detected impaired associative memory following cTBS to the angular gyrus.”

3) The hypothesis at the end of the introduction did not strike me as entirely clear. From this hypothesis, it seems that the authors are just comparing the differences in memory and re-configuration during imagination-based insight links. However, the authors also include ob-servation-based links and a non-linking condition, which seem ancillary to the main hy-pothesis. Thus, I am confused about why these extra factors were included and exactly what statistical results would confirm the authors' hypothesis.

We agree, and have clarified our hypotheses on pages 4-5, lines 107-115:

“Considering this involvement of the angular gyrus in imaginative processes, we expected that the effect of cTBS on the change in representational similarity from pre- to post-insight will differ based on the mode of insight – whether this insight was gained via imagination or observation. Specifically, we expected a more pronounced impairment in the neural recon-figurations when insight is gained via imagination, as this function may depend more on an-gular gyrus recruitment than insight gained via observation. Additionally, we expected cTBS to the left angular gyrus to reduce the increase in neural similarity for linked events and in-crease of neural dissimilarity for non-linked events.”

4) Many of the distributions throughout the paper do not look normal. Was normality checked? Are non-parametric stats warranted?

We evaluated and reported the normality assumption in our behavioral anal-yses. Despite the non-normal distribution of our data, we chose to utilize linear-mixed models due to their robust performance even in case of deviations from normal distribu-tions. This update in our methods section can be found on page 36, lines 890-896:

“After outlier correction, we identified non-normality in our data using a Shapiro-Wilk test (narrative-insight task: W = 0.92, p < 0.001; multi-arrangements task: W = 0.94, p < 0.001; forced-choice recognition: W = 0.50, p < 0.001; free recall details: W = 0.85, p < 0.001; free recall naming of linking events: W = 0.94, p < 0.001). However, we mitigated this by employ-ing linear-mixed models (LMMs), recognized for their robustness even with non-normally distributed data (Schielzeth et al., 2020).”

We recalculated the correlational analysis between the RSA data and the behavioral recall of linking events by using the Spearman method on page 13, lines 306-308:

“Furthermore, to address a deviation from the normality assumption, the correlational analysis was repeated using the Spearman method, which indicated an even stronger cor-relation (r(59) = 0.32, p = 0.012).”

We further recalculated the correlation between the change in coherence for linked events and the recall of details for events linked via imagination on page 16, lines 376-378:

“Please note that for addressing a deviation from the normality assumption, the correla-tional analysis was repeated using the Spearman method, which yielded a significant corre-lation of similar strength (r(59) = 0.31, p = 0.015).”

Our EEG analyses , including RSA and coherence analyses, utilized a cluster-based permuta-tion test (Fieldtrip; Oostenveld et al., 2011). These tests do not assume a normal distribu-tion by utilizing empirical sampling for statistical inference. This approach ensures robust-ness without constraints imposed by specific distributional assumptions. Subsequent t-tests, stemming from significant clusters identified in the initial non-parametric analyses, were extensions of the robust non-parametric approach and did not require additional normality testing.

5) Can the authors include more detail about the sham coil? Was it subthreshold? Did the EMF cross the skull?

The sham coil, also obtained from MAG & More GmbH, München, Germany, provided a similar sensory experience; however, the company did not specify any field strength (n.a.) as this coil was purposefully designed to prevent the induction of an elec-tromagnetic field (EMF) capable of penetrating the skull, thereby ensuring it had no impact on the brain. We clarified on this point in the methods section on pages 31-32, lines 772-778:

“Two identically looking but different 70 mm figure-of-eight-shaped coils were used de-pending on the TMS condition: The PMD70-pCool coil (MAG & More GmbH, München, Germany) with a 2T maximum field strength was used for cTBS, while the PMD70-pCool-SHAM coil (MAG & More GmbH, München, Germany), with minimal magnetic field strength, was employed for sham, providing a similar sensory experience, with stimulation pulses being scattered over the scalp and not penetrating the skull.”

6) There are differences between exclusion criteria in pre-registration and report. For example, BMI is an exclusion factor in the report, but not in the pre-registration. Can the authors provide a reason for this deviation?

This discrepancy is due to (partial) participant recruitment from previous fMRI studies conducted in our lab that involved a stress induction protocol (as a structural MRI image was needed for the ‘neuronavigated’ TMS). Owing to the distinct cortisol stress reac-tivity observed in individuals with varying body mass indices (BMIs), participants with a BMI below 19 or above 26 kg/m² were excluded from these studies. To maintain consistency within our sample, only participants meeting these criteria were included. We elaborated on this point in the methods section on page 25, lines 586-592:

“Participants were screened using a standardized interview for exclusion criteria that com-prised a history of neurological and psychiatric disease, medication use and substance abuse, cardiovascular, thyroid, or renal disease, evidence of COVID-19 infection or expo-sure, and any contraindications to MRI examination or TMS. Additionally, participants with a body mass index (BMI) below 19 or above 26 kg/m² were excluded. This decision stemmed from recruiting some participants from prior studies that incorporated stress induction pro-tocols, which imposed this specific criterion (Herhaus & Petrowski, 2018; Schmalbach et al., 2020).”

7) Were impedances monitored and minimized during EEG?

Yes, they were monitored. We clarified this point in the methods section on page 34, lines 845-847:

“We maintained impedances within a range of ± 20 μV using the common mode sense (CMS) and driven right leg (DRL) electrodes, serving as active reference and ground, re-spectively”

8) I think there may be a typo related to the Thakral coordinates. I believe Thakral used MNI coordinates -48,-64, 30, whereas the authors stated they used -48,-67,30. Is this a mistake?

Upon reevaluation of our study coordinates, we identified a slight deviation in our stimulation coordinates compared to those reported by Thakral et al. (2017; +3mm on the y-axis). This variance resulted from the required MNI to Talairach (TAL) transformations necessary for utilizing the neuronavigation software Powermag View! (MAG & More GmbH, München, Germany). Notably, this deviation was consistent across all participants in our study. While TMS is more precise than tDCS, its focality is not as fine-grained down to the millimeter level. Despite this, our electric field simulations, adopting a 10mm radius, ef-fectively encompassed the original coordinates specified by Thakral et al. (2017). This radius ensured coverage over the intended target area, mitigating the impact of this minor devia-tion on the overall study outcomes. We updated the methods section accordingly on page 33, lines 800-806:

“Based on the individual T1 MR images, we created 3D reconstructions of the participants' heads, allowing us to precisely locate the left angular gyrus coordinate (MNI: -48, -67, 30), initially derived from previous work (Thakral et al., 2017), for TMS stimulation. Despite a mi-nor deviation in coordinates due to necessary MNI to Talairach transformations for soft-ware compatibility (Powermag View! by MAG & More GmbH, München, Germany), our methodology ensured precise localization of the angular gyrus target area.”

9) How was the tail of the coil positioned during stimulation? Was it individualized so that the lobes of the coil are perpendicular to the nearest gyrus, as is commonly done?

The coil handle always pointed upwards to maintain optimal positioning with the coil holder. We followed the positioning procedure in the neuronavigation software Powermag View!, which did not indicate any positioning of the coil handle but specified the position and angle of the coil itself. To incorporate this aspect, we updated the legend of figure 2 on page 11, lines 260-261:

“Please note that in the study, the coil handle was oriented upwards; however, in this illus-tration, it has been intentionally depicted as pointing downwards for better visibility pur-poses.”

We further updated the method section on page 33, lines 723-824:

“The coil was positioned tangentially on the head and mechanically fixed in a coil holder, with its handle pointing upwards to maintain its position”

Reviewer #2 (Public Review):

The formation of long-term memory representations requires the continuous updating of ongoing representations. Various studies have shown that the left angular gyrus (AG) may support this cognitive operation. However, this study demonstrates that this brain region plays a causal role in the formation of long-term memory representations, affecting both the neural and behavioural measures of information binding.

A significant strength of this work is that it is the first one to test the hypothesis that the left angular gyrus has a causal role in the reconfiguration and binding of long-term memory representations by comparing when insights are primarily derived from direct observation versus imagination. Consequently, the results from this manuscript have the potential to be informative for all areas of cognitive research, including basic perception, language cognition and memory.

Furthermore, this study presents a comprehensive set of measurements on the same individuals, encompassing various task-related behavioural measures, EEG data, and questionnaire responses.

A weakness of the manuscript is the use of different groups of participants for the key TMS intervention.

Author Response

Many thanks for handling our manuscript (eLife-RP-RA-2023-93968) entitled "Allosteric modulation of the CXCR4:CXCL12 axis by targeting receptor nanoclustering via the TMV-TMVI domain", by García-Cuesta et al. We are delighted to hear your willingness to consider our manuscript following appropriate revision. We have carefully read the referees' commentaries and have organized new experiments to address their specific queries.

Reviewer #1 (Public Review)

The computational methodology is going to be carefully reviewed. In particular to justify the software and techniques used in this manuscript. We will also describe the method for identifying the pocket on the CXCR4 structure as well as the workflow used to explain the transition from docking evaluation to MD analyses. Additionally, we will conduct experiments to enhance the results and address the specific feedback provided, ultimately improving the overall reliability.

Reviewer #2 (Public Review)

Although the paper was initiated by titrating the compounds in migration experiments, we are going to add new kinetics and titration of concentrations in these experiments. In addition, we are going to change the way in which we present the data from the singlemolecule tracking experiments. We will add a representative video of each experimental condition, and include some of the mean square displacement curves to support our data on the analysis of the diffusion coefficient (D1-4) to give a more conclusive view of receptor clustering. Regarding the tumorigenesis experiments we will include the individual data points and we will try to perform kinetics with distinct concentrations of the drug.

eLife assessment

This important study identifies novel small molecule antagonists of CXCR4 that disrupt nanocluster formation and chemotactic function without blocking CXCL12 binding and downstream signals. The conclusions are based on solid evidence, but the work could be improved by including kinetic and dose information on the most active inhibitors. We also note that modeling and mutagenesis implicate helix V and VI in an allosteric mechanism, but that the description of the modeling is not sufficiently detailed such that others could replicate it.

Reviewer #1 (Public Review):

Summary:<br /> The manuscript, titled "Allosteric Modulation of the CXCR4:CXCL12 Axis by Targeting Receptor Nanoclustering via the TMV-TMVI Domain," presents a compelling investigation into the development of a potential anti-cancer therapeutic agent. The study focuses on targeting specific CXCR4 intermolecular interactions via an allosteric antagonist which binds proximal to the orthosteric ligand binding site. The novel compounds developed aim to mitigate tumor dissemination, proliferation, and metastasis in transgenic Zebrafish models implanted with HeLa cells.

Strengths:<br /> The study holds significant promise, offering a novel approach to addressing the targeted modulation of CXCR4. The multidisciplinary methodology employed is commendable, providing a comprehensive understanding of the underlying molecular interactions. The proposed workflow, although requiring some adjustments, is reasonable and has the potential to make a substantial impact in the field.

Weaknesses:<br /> Despite the brilliance of the concept and its potential impact, the computational approach appears somewhat superficial and lacks essential considerations. A comprehensive revision of the computational methodology is strongly recommended, with a focus on addressing key points. Additionally, the experimental section should be modified accordingly to align with the refined results. While the study's foundations are promising, its current state warrants a thorough revision to enhance its scientific rigor and overall robustness.

Reviewer #2 (Public Review):

Summary:<br /> This work describes a new pharmacological targeting approach to inhibit selective functions of the ubiquitously expressed chemokine receptor CXCR4, a potential target of immunomodulatory or anti-cancer treatments. Overall, the results build a strong case for the potential of this new compound to target specific functions of CXCR4, particularly linked to tumorigenesis. However, a more thorough evaluation of the function of the compound as well as future studies in mammalian model systems are needed to better assess the promise of the compound.

Strengths:<br /> The work elegantly utilizes in silico drug modelling to propose new small molecule compounds with specific features. This way, the authors designed compound AGR1.137, which abolishes ligand-induced CXCR4 receptor nanoclustering and the subsequent directed cell migration without affecting ligand binding itself or some other ligand-induced signaling pathways. The authors have used a relatively broad set of experiments to validate and demonstrate the effects of the drug. Importantly, the authors also test AGR1.137 in vivo, using a zebrafish model of tumorigenesis and metastasis. A relatively strong inhibitory effect of the compound is reported.

Weaknesses:<br /> The data would be significantly strengthened by adding kinetics and titration of concentrations. This is particularly important as it is the first description of these particular compounds and would help to evaluate the potency and possible side effects of the drug.

The authors carry out single-molecule tracking experiments to analyze nanoclustering of CXCR4 upon ligand binding. This complex data is presented in a sub-optimal manner. Representative images of the data should be included together with more thorough analysis tools like autocorrelation function or mean square displacement to get a more conclusive view of receptor clustering and the effects of the compound.

In the in vivo tumorigenesis experiments, again more kinetics and different concentrations of the drug would generate more convincing data. Also, the individual data points should be visualized to allow full evaluation of the data, throughout the experiments.

Author Response

Reviewer #1 (Public Review):

This manuscript from Kavanjoo et al examines the role of macrophages within the fetal liver beyond erythrocyte maturation. Using single-cell sequencing, high-resolution imaging, and inducible genetic deletion of yolk-sac (YS) derived macrophages, the authors demonstrate that heterogeneous fetal liver macrophages regulate erythrocyte enucleation, interact physically with fetal HSCs, and may regulate neutrophil accumulation in the fetal liver. The data as presented do not strongly support the authors’ conclusion that fetal macrophages in the liver regulate the HSC niche or granulopoiesis from HSCs.

Fetal-derived resident tissue macrophages are increasingly implicated in regulation of adult tissue function and homeostasis, but considerably less is known regarding the function of fetal macrophages during development. Macrophages in the fetal liver have been shown to form erythroblastic islands, where they regulate erythrocyte maturation. Here, the authors performed single-cell sequencing on fetal liver macrophages (Cd11b-lo) to gain insight into heterogeneity and utilized previously published pre-Mac signatures from the YS to focus on YS-derived macrophages. These clusters were then further cross-referenced with surface protein expression as determined by multidimensional flow cytometry to hone in on a very specific subset of three groups of F4/80hi macrophages defined by multiple surface markers. Fate-mapping with three models (Tnfrsf11a-Cre - YS pMAC derived; Ms4a3Cre - FL monocyte derived; CXCR4-Cre-ERT2 - definitive HSC derived) revealed that three major subsets are all derived from YS pMACs.

We thank the reviewer for the comments and have addressed all points below. If certain points were mentioned twice, we responded at the position where the point was raised the first time.

However, the relative frequencies of these specific populations are not shown, and because the single sequencing analysis goes through so many iterations of re-clustering that initiates by focusing specifically on pMAC signatures, this result is not surprising.

Probing gene expression within each of the three clusters revealed ligand expression suggesting cell-cell interactions, and cross-referencing with a fetal LT-HSC gene expression dataset revealed potential receptor-ligand interactions. Microscopic investigation of physical interactions between specific macrophage subsets and HSCs was not particularly convincing. In Figure 3C, for example, Cluster C is very difficult to visualize. It would again be helpful to know what the ratios are within the FL for each cluster. Data in Figure 3F are not well represented by Data in Figure 3E.

We showed frequencies after CODEX in the original manuscript (Fig. S3A, now Figure 4 - figure supplement 1A) since isolation of cells often induces an artifact, and relative frequencies after scRNA-seq experiments never represent the actual cell numbers present in situ. However, also the CODEX analysis has its weakness, especially in dense tissues, as the automated gating method may not catch every macrophage due to its star-shaped structure. Thus, we have now included the absolute numbers of macrophage subpopulations in Figure 7C. We have tried to improve the visualization of the clusters in Figure 3C (now Figure 4C) by zooming into a specific region. The Voronoi diagram is a powerful method that allows for an overall spatial visualization of cell distribution in large tissue pieces. In the high-resolution PDF that we provide, zooming into the PDF file should allow the reader to see each cluster in great detail.

To improve the data of macrophage-HSC interaction we have performed 3D reconstructions and quantified the distance of CD150+ and Iba1+ cells in 3D (new Figure 3C-E) as the thin cryosectioning used for CODEX is not suitable to reconstruct these interactions properly (see also lines 328-331). Thus, Figure 3E was not able and also not meant to represent data shown in Figure 3F (now Figure 4E and 4F). Figure 3E is just meant to show examples of all clusters sitting in proximity to CD150+ HSCs.

Furthermore, deletion of YS pMAC-derived macrophages the Tnfrsf11a-Cre X Spi1fl/fl resulted in broad macrophage depletion - although the authors did not demonstrate this using the carefully refined phenotypes they had defined earlier in the manuscript. Nonetheless, the authors demonstrate that macrophage depletion did affect erythroid enucleation, as expected, and the authors also showed some effect of macrophage deletion on LT-HSC gene expression by bulk transcription analysis. These effects were relatively small, however, and this was clear in the absence of effects on hematopoiesis in vivo or HSC proliferation ex vivo. To further investigate the effects of macrophage deletion on downstream hematopoieisis, the authors re-assessed the myeloid compartment following macrophage deletion, and identified and specifically focused on an observed increase in neutrophils in response to macrophage depletion. Based on this increase, they tested HSC differentiation using a colony-forming assay, which shows a slight increase in GM colonies that is also reflective of a slight but insignificant increase in total colony forming capability. The authors concluded that loss of fetal macrophages causes a reprogramming of HSCs to the granulocytic lineage. However, the colony-forming assay and subtle differences in gene expression are not sufficient to conclude that fetal HSCs have been reprogrammed towards granulocytic lineage by macrophage deletion.

We thank the reviewer for this comment and have improved the manuscript accordingly: We have performed the colony-forming assay again with n=5 embryos per genotype that were harvested on the same day, which resulted in a similar phenotype as before, with the differences of GM colonies now being significant. Further, we quantified the depletion of all macrophage subpopulations in the Tnfrsf11a-Cre X Spi1fl/fl model (Fig. 7C). To strengthen the point that the transient lack of macrophages when HSCs arrive in the fetal liver leads to their reprograming, we included flow cytometry data from E16.5 and E18.5 where we still see an increase of neutrophils in the fetal liver, despite the fact that macrophages are repopulating the empty niche (Fig. 7E, F). To show that this is a cell-intrinsic effect, we have performed adoptive transfer experiments supporting our claim that loss of macrophages reprograms HSCs toward the granulocytic lineage (Fig. 7H, I)

Overall, there are some interesting pieces of data in this manuscript, including the classification of new subsets of macrophages in the liver, their fate-mapping to the YS, and gene expression analysis. However, the data as presented do not strongly support a role for these particular macrophage subsets in regulating HSCs or fetal hematopoiesis within the fetal liver niche. Although there may be specific subsets of fetal liver macrophages that more closely physically interact with HSCs, deletion of what appeared to be a vast majority of macrophages in the FL did not appear to affect cellularity of hematopoietic stem and progenitor cells in vivo, and was not shown to convincingly affect HSC function. The mechanism by which macrophage deletion affected granulopoiesis could be independent from HSCs, and would be interesting to further explore.

We hope that with new set of experiments we were able to convince the reviewer of the importance of macrophages in the HSC niche.

Reviewer #2 (Public Review):

Using a single-cell omics approach combined with spatial proteomics and genetic fate mapping, Kayvanjoo et al found that fetal liver (FL) macrophages cluster into distinct yolk sac-derived subpopulations and that some of the HSCs in FL preferentially associate with one of the identified macrophage subpopulations. FLs lacking macrophages show a delay in erythropoiesis. The authors also try to identify a role of macrophages for HSCs function in FL, and claim that macrophages affect myeloid differentiation of HSCs. Experimental support for the function of macrophages on HSCs remains weak. Taken together, their data provide a precise map of FL macrophage subpopulations, which is novel and will serve the field well.

We thank the reviewer for the positive assessment. We have now strengthened the data regarding the impact of granulopoiesis by performing additional CFU assays and adoptive transfers.

Author Response

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

Public Reviews:

Reviewer #1 (Public Review):

In this study, the researchers aimed to investigate the cellular landscape and cell-cell interactions in cavernous tissues under diabetic conditions, specifically focusing on erectile dysfunction (ED). They employed single-cell RNA sequencing to analyze gene expression patterns in various cell types within the cavernous tissues of diabetic individuals. The researchers identified decreased expression of genes associated with collagen or extracellular matrix organization and angiogenesis in several cell types, including fibroblasts, chondrocytes, myofibroblasts, valve-related lymphatic endothelial cells, and pericytes. They also discovered a newly identified marker, LBH, that distinguishes pericytes from smooth muscle cells in mouse and human cavernous tissues. Furthermore, the study revealed that pericytes play a role in angiogenesis, adhesion, and migration by communicating with other cell types within the corpus cavernosum. However, these interactions were found to be significantly reduced under diabetic conditions. The study also investigated the role of LBH and its interactions with other proteins (CRYAB and VIM) in maintaining pericyte function and highlighted their potential involvement in regulating neurovascular regeneration. Overall, the manuscript is well-written and the study provides novel insights into the pathogenesis of ED in patients with diabetes and identifies potential therapeutic targets for further investigation.

Reviewer #2 (Public Review):

Summary: In this manuscript, the authors performed single cell RNA-sequencing of cells from the penises of healthy and diabetes mellitus model (STZ injection-based) mice, identified Lbh as a marker of penis pericytes, and report that penis-specific overexpression of Lbh is sufficient to rescue erectile function in diabetic animals. In public human single cell RNA-sea datasets, the authors report that LBH is similarly specific to pericytes and down regulated in diabetic patients. Additionally, the authors report discovery of CRYAB and VIM1 as protein interacting partners with LBH.

The authors contributions are of interest to the erectile dysfunction community and their Lbh overexpression experiments are especially interesting and well-conducted. However, claims in the manuscript regarding the specificity of Lbh as a pericyte marker, the mechanism by which Lbh overexpression rescues erectile function, cell-cell interactions impaired by diabetes, and protein-interaction partners require qualification or further evidence to justify.

Major claims and evidence:

1) Marker gene specificity and quantification: One of the authors' major contributions is the identification of Lbh as a marker of pericytes in their data. The authors present qualitative evidence for this marker gene relationship, but it is unclear from the data presented if Lbh is truly a specific marker gene for the pericyte lineage (either based on gene expression or IF presented in Fig. 2D, E). Prior results (see Tabula Muris Consortium, 2018) suggest that Lbh is widely expressed in non-pericyte cell types, so the claims presented in the manuscript may be overly broad. Even if Lbh is not a globally specific marker, the authors' subsequent intervention experiments argue that it is still an important gene worth studying.

Answer: We appreciate this comment. In our scRNAseq data for the mouse cavernosum tissues, previously known markers such as Rgs5, Pdgfrb, Cspg4, Kcnj8, Higd1b, and Cox4i2 were found to be expressed not exclusively in pericytes, while Lbh exhibited specific expression patterns in pericytes (Fig. 2 and Supplementary Fig. 5). LBH expression was easily distinguishable from α-SMA, not only in mouse cavernosum but also in dorsal artery and dorsal vein tissues within penile tissues. This distinctive expression pattern of LBH was also observed in the human cavernous pericytes (Fig. 5). Then, we examined Lbh expression patterns in various mouse tissues using the mouse single-cell atlas (Tabula Muris), although endothelial and pericyte clusters were not subclustered in most tissues from Tabula Muris. To identify pericytes, we relied on the expression pattern of known marker genes (Pecam1 for endothelial cells, Rgs5, Pdgfrb, and Cspg4 for pericytes). Lbh was expressed in pericytes of the bladder, heart and aorta, kidney, and trachea but not as specifically in penile pericytes (Supplementary Fig. 6A-D). However, it is worth noting that other known pericyte markers were also did not exhibit exclusive expression in pericytes across all the tissues we analyzed. Therefore, in certain tissues, particularly in mouse penile tissues, Lbh may be a valuable marker in conjunction with other established pericyte marker genes for distinguishing pericytes.

2) Cell-cell communication and regulon activity changes in the diabetic penis: The authors present cell-cell communication analysis and TF regulon analysis in Fig 3 and report differential activities in healthy and DM mice. These results are certainly interesting, however, no statistical analyses are performed to justify claimed changes in the disease state and no validations are performed. It is therefore challenging to interpret these results, and the relevant claims do not seem well supported.

Answer: In response to these helpful suggestions, we calculated statistical significance and performed experimental validation. CellphoneDB permutes the cluster labels of all cells 1000 times and calculates the mean(mean(molecule 1 in cluster X), mean(molecule 2 in cluster Y)) at each time for each interaction pair, for each pairwise comparison between two cell types. We only considered interactions in which the difference in means calculated by these permutations were greater than 0.25-fold between diabetes and normal. Also, we considered that the interactions with P-value < 0.05 were significant.

To assess differential regulon activities of transcription factor (SCENIC) between diabetic and normal pericytes, we utilized a generalized linear model with scaled activity scores for each cell as input. These scaled regulon activity values for angiogenesis-related TFs exhibited differences between diabetic and normal pericytes. The results of the generalized linear model revealed that Klf5, Egr1, and Junb were TFs with significantly altered regulon activities in diabetic pericytes. Experimental data indicated that the expression level of Lmo2, Junb, Elk1, and Hoxd10 was higher (Hoxd10) or lower (Lmo2, Junb, Elk1) in diabetic pericytes compared to normal pericytes (Supplementary Fig. 9). We have added the scaled regulon activity values and statistical significance in Fig. 3E.

3) Rescue of ED by Lbh overexpression: This is a striking and very interesting result that warrants attention. By simple overexpression of the pericyte marker gene Lbh, the authors report rescue of erectile function in diabetic animals. While mechanistic details are lacking, the phenomenon appears to have a large effect size and the experiments appear sophisticated and well conducted. If anything, the authors appear to underplay the magnitude of this result.

Answer: We appreciate this comment. Therefore, we have added relevant clarification in the revised manuscript discussion section to emphasize the importance of LBH overexpression on rescuing ED as follows: “To test our hypothesis, we utilized the diabetes-induced ED mouse model, commonly employed in various studies focusing on microvascular complications associated with type 1 diabetes. We observed that the overexpression of LBH in diabetic mice led to the restoration of reduced erectile function by enhancing neurovascular regeneration. However, this study primarily demonstrated the observed phenomenon without delving into the detailed mechanisms. Nonetheless, these results of LBH on erections provide us with new strategies for treating ED and should be of considerable concern.” (Please see revised ‘Discussion’)

4) Mechanistic claims for rescue of ED by Lbh overexpression: The authors claim that cell type-specific effects on MPCs are responsible for the rescue of erectile function induced by Lbh overexpression. This causal claim is unsupported by the data, which only show that Lbh overexpression influences MPC performance. In vivo, it's likely that Lbh is being over expressed by diverse cell types, any of which could be the causal driver of ED rescue. In fact, the authors report rescue of cell type abundance in endothelial cells and neuronal cells. Therefore, it cannot be concluded that MPC effects alone or in principal are responsible for ED rescue.

Answer: We agree with these claims. Therefore, we have added relevant clarifications in the discussion section of the revised manuscript. Our findings suggest that LBH can affect the function of cavernous pericytes, although we cannot definitively specify which particular cavernous cell types are affected by the overexpressed LBH, whether it be cavernous endothelial cells, smooth muscle cells, or others. Subsequent research will be required to conduct more comprehensive mechanistic investigations, such as in vitro studies using cavernous endothelial cells, smooth muscle cells, and fibroblasts to address these knowledge gaps. (Please see revised ‘Discussion’)

5) Protein interaction data: The authors claim that CRYAB and VIM1 are novel interacting partners of LBH. However, the evidence presented (2 blots in Fig. 6A,B) lack the relevant controls. It is possible that CRYAB and VIM1 are cross-reactive with the anti-LBH antibody or were not washed out completely. The abundance of bands on the Coomassie stain in Fig. 6A suggests that either event is plausible. Therefore, the evidence presented is insufficient to support the claim that CRYAB and VIM1 are protein interacting partners of LBH.

Answer: We agree with these claims. Therefore, we have added the relevant controls(Input) and performed Co-IP (IP: CRYAB or VIM, WB: LBH) to demonstrate CRYAB and VIM1 are not simply cross-reactive antigens to their LBH antibody. Our results show that we can detect the expression of CRYAB and VIM after LBH IP, and we also detect the expression of LBH after CRYAB and VIM IP. In addition, it can be seen from our results that the binding of LBH to VIM is higher than that of CRYAB. Regardless, these results indicate that the binding of CRYAB or VIM to LBH is not a random phenomenon. (Please see revised ‘Result’ and ‘Figure 6B’)

Impact: These data will trigger interest in Lbh as a target gene within the erectile dysfunction community.

Reviewer #3 (Public Review):

Bae et al. described the key roles of pericytes in cavernous tissues in diabetic erectile dysfunction using both mouse and human single-cell transcriptomic analysis. Erectile dysfunction (ED) is caused by dysfunction of the cavernous tissue and affects a significant proportion of men aged 40-70. The most common treatment for ED is phosphodiesterase 5 inhibitors; however, these are less effective in patients with diabetic ED. Therefore, there is an unmet need for a better understanding of the cavernous microenvironment, cell-cell communications in patients with diabetic ED, and the development of new therapeutic treatments to improve the quality of life.

Pericytes are mesenchymal-derived mural cells that directly interact with capillary endothelial cells (ECs). They play a vital role in the pathogenesis of erectile function as their interactions with ECs are essential for penile erection. Loss of pericytes has been associated with diabetic retinopathy, cancer, and Alzheimer's disease and has been investigated in relation to the permeability of cavernous blood vessels and neurovascular regeneration in the authors' previous studies. This manuscript explores the mechanisms underlying the effect of diabetes on pericyte dysfunction in ED. Additionally, the cellular landscape of cavernous tissues and cell type-specific transcriptional changes were carefully examined using both mouse and human single-cell RNA sequencing in diabetic ED. The novelty of this work lies in the identification of a newly identified pericyte (PC)-specific marker, LBH, in mouse and human cavernous tissues, which distinguishes pericytes from smooth muscle cells. LBH not only serves as a cavernous pericyte marker, but its expression level is also reduced in diabetic conditions. The LBH-interacting proteins (Cryab and Vim) were further identified in mouse cavernous pericytes, indicating that these signaling interactions are critical for maintaining normal pericyte function. Overall, this study demonstrates the novel marker of pericytes and highlights the critical role of pericytes in diabetic ED.

Reviewer #1 (Recommendations For The Authors):

1) The methods are poorly written. It lacks specific information on the sample size, experimental design, and data analysis methods employed. The absence of these crucial details makes it difficult to evaluate the robustness and reliability of the findings.

Answer: We agree with the reviewer’s suggestion, now we revised the methods of our manuscript, and added detailed information or references. For sample size we have added detailed information in Figure legend (Please see revised ‘Method’ , Figure Legend, and Supplementary information.)

2) The cell number in the scRNA-seq analysis is small (~12000) and some minor cell types are probably underrepresented. It is not clear whether the authors pooled the cells from different mice as one sample, or replicates in different groups have been included. It will be helpful to label different samples in the UMAP. The authors should repeat the experiments with more replicates to increase the cell number and validate the findings.

Answer: We understand the reviewer's concern, but due to the small size of mouse penile tissue, we had to pool 5 corpus cavernosum tissues for each group (using pooled samples) for scRNA-seq analysis. Moreover, owing to the unique nature of mouse penile tissue, which is highly resistant, it posed challenges for the dissolution and isolation of single cells using conventional single-cell separation methods. Consequently, we had to increase the concentration of the enzyme to finally obtain 12,894 cells. Rather than conducting a repetitive scRNAseq analysis on the same mouse model, we validated our findings in human cavernous single-cell transcriptome data. This analysis allowed us to confirm the presence of pericyte in human corpus cavernosum, specific expression of LBH in human cavernous pericytes, and the identification of relevant GO terms associated with pericyte functions (Figure 5). We have add these information in ‘Method’ (Please see revised ‘Method’).

3) Functional studies are lacking to justify how manipulating LBH expression or its interacting proteins might lead to effective therapeutic approaches for diabetic ED.

Answer: We have performed the functional study to evaluate LBH expression might lead to effective therapeutic approaches for diabetic ED as showed in Figure 4G. Assessment of intracavernous pressure (ICP) is the most representative test for evaluating erectile function. Therefore, we modulated LBH expression in the penis of diabetic mice and assessed the erectile function of the mice by intracavernous pressure. However, we have not performed ICP studies and relative in vitro studies (migration, survival experiment) to assess whether LBH-interacting proteins have the same effect.

4) Although the abstract identifies novel targets for potential interventions, such as LBH and its interacting proteins, the clinical relevance of these findings remains uncertain. The authors should include a discussion regarding the translation of these discoveries into therapeutic strategies or their potential impact on patients with diabetes and ED.

Answer: We appreciate the reviewer's suggestion and have added a discussion as per the reviewer’s recommendation (Please see revised ‘Discussion’).

5) While the study highlights the importance of pericytes in penile erection, it fails to mention the broader context of other cell types involved in the pathogenesis of ED. Neglecting to discuss potential contributions from endothelial cells, smooth muscle cells, or neural elements limits the comprehensive understanding of the cellular interactions underlying diabetic ED.

Answer: We agree with the reviewer's suggestion and have added a discussion regarding the significance of other cell populations in penile tissues, such as endothelial cells, smooth muscle cells fibroblasts, and neural elements, along with the rationale for our focus on pericytes. (Please see revised ‘Discussion’).

Reviewer #2 (Recommendations For The Authors):

We congratulate the authors on an interesting study. We were especially excited to see their Lbh overexpression results. However, we felt other claims in the paper could benefit from additional investigation, analysis, and statistical rigor. We have provided a set of suggestions for improvement below.

Major points:

1) Pericyte marker gene proposal: See public review for commentary on the following suggested experiments. The authors should perform binary classification analysis using Lbh and report the performance of this gene as a marker (e.g. using the area under the receiver operating characteristic, accuracy, precision and recall). Further, they should consider performing this analysis for all other genes in their data to determine whether Lbh is the best marker gene.

Answer: We appreciate this comment. AUC scores of Rgs5, Pln, Ednra, Npylr, Atp1b2, and Gpc3 for ability of a binary classifier to distinguish between pericyte and the other cell types in mouse penile tissues were measured by using FindMarkers function. Rgs5 had the highest AUC, but Rgs5 was also expressed in SMCs in our data. Pln, Ednra, Gpc3, and Npy1r also seemed to be candidate markers, but the literature search excluded these genes as they are also expressed in the SMCs of other tissues or different cell types. The AUC score of Lbh was over 0.7, and expression in SMC was not identified in previous studies, and ultimately, we experimentally identified that Lbh is penis pericyte specific. We have added this to the manuscript.

Author response table 1.

Robust differential expression analysis should also be performed for this gene (if not all) and the statistics should be reported, given known issues with the statistical approach used by the authors for differential expression (see: Squair 2021, 10.1038/s41467-021-25960-2). The authors' should also report the number of cells involved in these comparisons, as the number of pericytes in the data (Fig 1B) appears quite small.

Answer: We appreciate this comment. We used “MAST” to identify differentially expressed genes. This test is often used to find DEGs in single-cell RNA data. However, because the pseudobulk method has advantages over the single cell DEG method (Squair 2021, 10.1038/s41467-021-25960-2), we additionally performed DEG analysis with DESeq2 to confirm whether Lbh can distinguish pericytes from other cell types in the penile. As a result, even when tested with DESeq2, Lbh expression was significantly higher in pericytes than in other cell types in penile (adjusted p-value = 2.694475e-07 in Pericyte vs SMC, adjusted P-value = 3.700118e-58 in Pericyte vs the other cell types). Mouse penile tissue is small in size, and the number of pericytes in mouse penile tissue is relatively smaller compared to fibroblasts and chondrocytes. In our mouse penile scRNAseq data, the number of pericytes is as follows: normal: 58, diabetes: 116. Despite the limited number of cells, we were able to establish statistical significance in our analyses.

Immunostaining results in Fig. 2D, E should likewise be quantified. At present, it's unclear that LBH and aSMA are mutually exclusive as claimed. The authors should also investigate Lbh expression in public single cell genomics data, rather than performing candidate gene literature searches. For example, the Tabula Muris suggests Lbh is expressed widely outside pericytes.

Answer: For Figure 2D and E, the aim of these analyses was to assess the distribution of LBH and other cellular markers to see if they overlap and if they can be distinguished. We think that some of the overlapping staining in the tissue may be caused by multilayered cellular structures, so staining within cells would be more convincing. Therefore, we quantified the percentage of LBH- or α-SMA-expressed pericytes and relative expression in smooth muscle cells in cell staining (Supplementary Fig. 5E). We found that only 3% of smooth muscle cells expressed LBH, 67% of mouse cavernous pericytes (MCPs) expressed α-SMA, and more than 97% of MCPs expressed LBH. Therefore, these results may illustrate the specific expression of LBH in MCPs. These information was added as ‘Supplementary Fig. 5E’ (Please see revised ‘Supplementary information’). We also examined Lbh expression patterns in various mouse tissues using the public mouse single-cell atlas (Tabula Muris), and provided a detailed response in reviewer 2’s public review 1.

Even if Lbh is not the best marker, the authors' intervention experiment still motivates study of the gene, but these analyses would help contextualize the result for readers.

2) Statistical anslyses for cell-cell communication and TF regulon analysis: See public review for context on these comments. The authors should perform statistical tests to evaluate the significance of differences detected for each of these analysis. For example, generalized linear models can be used to assess the significance of TF regulon activity scores from SCENIC, and permutation tests can be used to measure the significance of cell-cell interaction score changes. Without these statistical tests, it's challenging for a reader to interpret whether the results reported are meaningful or within the realm of experimental noise.

Answer: We appreciate this comment. We calculated statistical significance TF regulon analyses as suggested by the reviewer and described a detailed statistical calculation method for cell-cell communication. We provided a detailed response in reviewer 2’s public review 2.

3) Mechanism of ED rescue by Lbh overexpression: To support this claim, the authors would need to perform an experiment where Lbh is over expressed specifically in MPCs (using e.g. a specific promoter on their LTV construct, or a transgenic line with a cell type-specific Cre-Lox system). Absent these data, the claim should be removed.

Answer: We agree with the reviewer's suggestion and we have reworked the claim that ‘LBH overexpression is affected by pericytes during ED recovery’ and have added relevant clarification in the Discussion section to clearly state that LBH overexpression may affect many cavernosum cells, such as cavernous endothelial cells, smooth muscle cells, fibroblasts, and pericytes (Please see revised ‘Result’ and ‘Discussion’)

4) Protein interaction claims: This experiment would require that the authors perform a similar pull-down with LBH KO cells and or a reciprocal Co-IP (e.g. IP: CRYAB or VIM1, WB: LBH) to demonstrate CRYAB and VIM1 are not simply cross-reactive antigens to their LBH antibody. Further, these experiments appear to only have a single replicate for each condition. The authors should either remove associated claims, or perform a Co-IP experiment with the relevant controls with sufficient replication.

Answer: We agree with the claims. Therefore, we have included the necessary controls (Input) and performed Co-IP (IP: CRYAB or VIM1, WB: LBH) to demonstrate that CRYAB and VIM1 are not simply cross-reactive antigens to their LBH antibody. Our results show that we can detect the expression of CRYAB and VIM after LBH IP, and we also detect the expression of LBH after CRYAB and VIM IP. In addition, it can be seen from our results that the binding of LBH to VIM is higher than that of CRYAB. Regardless, these results indicate that the binding of CRYAB or VIM to LBH is not a random phenomenon. Additionally, all IP experiments were replicated at least three times. (Please see revised ‘Result’ and ‘Figure 6B’)

Minor Points:

• The reference "especially in men" on line 56 seems odd given that only males can experience penile erectile dysfunction.

Answer: We agree with the reviewer's suggestion and have removed the description 'especially male' (Please see revised ‘Introduction’)

• Line 109, it's unclear what genes showed altered expression in Schwann cells.

Answer: We apologize for the confusion. There was no significant differentially expressed genes between normal and diabetes in Schwann cells. We revised this part in the manuscript. (Schwann cells showed an increased expression compared to normal cells in diabetes, though not significant. In Schwann cells, there were no significant DEGs between diabetic and normal cells.)

• It would be helpful for readers to see an analysis of the cell types that are transduced in the Lbh overexpression experiment in vivo. At present, some pericyte specificity is implied, but not demonstrated.

Answer: We appreciate this comment. Our findings suggest that LBH can affect the function of cavernous pericytes, although we cannot definitively conclude which specific-cavernous cell types are affected by the overexpressed LBH, whether it be cavernous endothelial cells, smooth muscle cells, or others. Subsequent research will be required to conduct more comprehensive mechanistic investigations, such as in vitro studies using cavernous endothelial cells, smooth muscle cells, and fibroblasts to address these knowledge gaps. These were also mentioned in the manuscript.

• To improve clarity and enhance readability, define abbreviations before their initial usage in the text. For instance, in the second paragraph of the Introduction, the abbreviation 'ECs' is used without prior definition. It can be inferred that it is referring to endothelial cells, mentioned in parentheses in the subsequent sentence.

Answer: We agree with the reviewer's suggestion to expand acronyms and ensure that all acronyms are defined in the revised manuscript before they are used for the first time in the text (Please see revised Manuscript).

• It is important to include relevant references that align with the content being discussed. For example, in the Introduction, pericytes are described as being involved in various processes such as angiogenesis, vasoconstriction, and permeability. The text refers to a single reverence, a review by Gerhardt and Besholtz, which primarily focuses on pericyte's role in regulating angiogenesis. Adding additional sources, such as the review by Bergers and Song (Neuro Oncol., 2005) is recommended.

Answer: We agree with the reviewer's suggestion, and have added the reference as reviewer recommended (Please see revised Manuscript and reference).

• Figure 3E: it is stated that a panel of 53 angiogenesis factors were tested, it is stated that only MMP3 showed increased expression. However, various unlabeled spots appear to show changed expression patterns. It would be helpful to show a summary graph with the relative intensities of the full array of factors tested.

Answer: We agree with the reviewer’s suggestion, now we showed all spots density in angiogenesis array as Supplementary Table 1. The condition of the spots we selected was that the expression density was at least above 1500, and the change ratio was greater than 1.2. (Please see revised ‘Supplementary information’)

Reviewer #3 (Recommendations For The Authors):

Detailed statistical power calculation

Data availability statement( were both mouse and human scRNA deposited in GEO with a taken and when will they be released to the public?)

Answer: Human scRNA data have been deposited in GEO under accession number GSE206528. Our mouse scRNA dataset has been uploaded to KoNA and is available for download (https://www.kobic.re.kr/kona/review?encrypt_url=amlod2FucGFya3xLQUQyMzAxMDEz)

Major concerns about this work

1) The single cell RNAseq data collected for mouse diabetic ED(Fig 1B), FB are the most abundant cell population compared to PC, EC, SMC and other clusters. The rationale for studying FB clusters (in Figure 1, D-F) instead of PC cluster is unclear. Which cluster DEG did the authors annotate for Fig 1G-H?

Answer: We understand the reviewer's suggestion and confusion. Although other major cell populations in penile tissue such as smooth muscle cells, endothelial cell, and fibroblasts have been extensively studied, pericytes have mainly been investigated in the context of the central nervous system (CNS). For example, in the CNS, pericytes are involved in maintaining the integrity of the brain's blood-brain barrier (BBB) [PMID: 27916653], regulating blood flow at capillary junctions [PMID: 33051294], and promoting neuroinflammatory processes [PMID: 31316352], whose dysfunction is considered an important factor in the progression of vascular diseases such as Alzheimer's disease [PMID: 24946075]. But little is known about the role of pericytes in penile tissue [PMID: 35865945; PMID: 36009395; PMID: 26044953]. In order to explore the role of pericytes in repairing the corpus cavernosum vascular and neural tissues damaged by DM, we focused on pericytes, which are multipotent perivascular cells that contribute to the generation and repair of various tissues in response to injury. Although recent studies have shown that pericytes are involved in physiological mechanisms of erection, little is known about their detailed mechanisms. We have also added this rationale in discussion.

Single cell level study has not been conducted in mouse penile tissues. Therefore, before delving into pericytes, we aimed to identify overall transcriptome differences between normal and diabetic conditions in mouse penile tissues. We presented the analyses of FB, which make up the largest proportion among the cell types in the mouse penis, in Fig. 1D-F. The analysis of other cell types is provided in Supplementary Fig. 1-4. Fig. 1G-H are GO terms for Fibroblasts clusters. We added this information in the figure.

2) Fig 2 is the critical data to show Lbh is a cavernous PC specific marker. More PC violin plots to identify PC cluster such as Cspg4, Kcnj8, Higd1b, Cox4i2 and more SMC violin plots to identify SMC cluster such as Acta2, Myh11, Tagln, Actg2 should be used for inclusion and exclusion of PC( the same concern applied to human scRNAseq in Fig 5B).

Answer: We appreciate this comment. We examined the expression of other marker genes of pericytes and SMCs. Although some marker genes were rarely expressed in the mouse penis data (Kcnj8, Higd1b), the expression of marker genes tended to be relatively high in each cluster. The expression of Cspg4 and Cox4i2 was higher in pericytes than in SMCs, while the expression of Acta2, Myh11,and Tagln was higher in SMCs than in pericytes. Actag2 was specifically expressed in SMCs. Through the gene set enrichment test as well as the expression of known cell type marker genes, we identified that the annotation of pericyte and SMC was appropriate (Fig. 2B and Fig. 5C). We added the violin plots of these marker genes in Supplementary Fig. 5.

Author response image 1.

(Mouse)

In human penis data, ACTA2 and MYH11 were expressed in SMCs, pericytes, and myofibroblasts, as in the previous paper [PMID: 35879305]. Among pericyte markers, the number of cells expressing KCNJ8 and HIGD1B was small. The cluster we annotated as pericyte was double positive for pericyte markers CSPG4 and COX4I2. ACTG2, a marker for SMC, was expressed more highly in SMC than in pericytes and myofibroblasts. As in the mouse penis data, we identified that the annotation of each cell type was appropriate through the gene set enrichment test in the human penis data. We added the violin plots of CSPG4, COX4I2, and ACTG2 in Supplementary Fig. 11.

Author response image 2.

(Human)

When exploring Lbh expression levels in "Database of gene expression in adult mouse brain and lung vascular and perivascular cells" from https://betsholtzlab.org/VascularSingleCells/database.html, Lbh is not uniquely expressed in PC, suggesting its tissue-specific expression level. This difference should be discussed in the Discussion section.

Answer: We appreciate this valuable comment. For the answer to this comment, we extensively analyzed Lbh expression patterns in various mouse tissues using the public mouse single-cell atlas (Tabula Muris) as also suggested by Reviewer 2. Please see our detailed response in reviewer 2’s public review 1.

3) In prior studies on PC morphology and location (PMID: 21839917), they reside in capillaries (diameter less than 10um) or distal vessels (diameter less than 25um) and have oval cell body and long processes. Due to the non-specificity of Pdgfrb, SMC are positive for Pdgfrb staining (this has been shown in many publications that SMC are Pdgfrb+; unfortunately, NG2 antibody also stains for both PC and SMC). Therefore, the LBH immunostaining (in Fig 2D and 2E of large-sized vessels) are very likely for SMC identity, not PC. PC should be in close contact with CD31+ ECs in healthy conditions. The LBH immunostaining of PC in both mouse and human tissues (Fig 4) must be replaced and better characterized.

Answer: We agree with the reviewer's suggestion. As it is widely known, peicytes are primarily located in capillaries, where they surround endothelial cells of blood vessels. However, recent discoveries have identified cells with pericyte-like characteristics in the walls of large blood vessels, challenging the traditional concept [PMID: 27268036]. In our study, we observed minimal overlap in staining between LBH and α-SMA, suggesting that the cells expressing LBH were not smooth muscle cells but possibly pericyte-like cells in large vessels. In small vessels within the bladder, kidney, and even the aorta, we found LBH-expressing cells surrounding CD31-expressing vessels, consistent with the known characteristics of pericytes. Further research is needed to comprehend the differences in LBH expression and its characteristics in both large and small blood vessels. We have added discussions and references for this issue (Please see revised ‘Discussion’ and ‘Reference’)

4) How do mouse cavernous pericytes isolate? How is purity?

Answer: As the reviewer points out, we isolated mouse spongiform pericytes following our and other previously published methods. We used pigment epithelium-derived factor (PEDF), which removes non-pericytic cells [PMID: 30929324, 23493068]. Although there are no purity study results such as FACS, other staining results thoroughly support the notion that this method yields pericytes with a notably high level of purity. (Please see ‘Method’ section).

5) Can mouse scRNAseq cell-cell communication in Fig 3 be reproducible in human scRNAseq cell-cell communication? The results in human ED are more clinically significant than in mouse data.

Answer: In human scRNAseq data, the difference between angiogenesis-related interactions between normal and diabetes was not as significant as that in mouse data. Because the cell type composition of the human and mouse penis is not completely identical, there are limitations in comparing cell-cell interactions. However, in the human penis data, some interactions related to angiogenesis between pericytes and other cell types were decreased in diabetes compared to normal (boxed parts).

Author response image 3.

6) Fibroblasts also express Vim. Murine PC VIM/CRYAB( should be written as Vim/Cryab as mouse proteins) direct interaction with Lbh is unclear from Lbh IP as Fig 6A red boxes showed a wide range of sizes. Where is the band for Lbh? Do human PC LBH interact with VIM/CRYAB?

Answer: We agree with the reviewer's comment. VIM is a type III intermediate filament protein expressed in many cell types. We have added the relevant controls (Input) and performed Co-IP (IP: CRYAB or VIM, WB: LBH) to demonstrate CRYAB and VIM are not simply cross-reactive antigens to their LBH antibody. In western blot study, the LBH band was expressed between 35 kDa-48 kDa. From Figure 6A, we detected CRYAB in band 1 and VIM in bands 2 and 3. This may be due to the formation of dimers or multimers by VIM. We did not use human PCs for IP studies because IP requires large amounts of protein, making IP studies using human pericyte challenging. Nevertheless, the interaction between LBH and CRYAB in humans has been reported through fluorescent resonance energy transfer assay and affinity chromatography technology assay [PMID:34000384, PMID:20587334].

7) In Fig 6H and I, why does CRYAB expression significantly reduce in vitro and in vivo under diabetic conditions, whereas VIM expression significantly increases?

Answer: As the reviewer pointed out, and we have discussed on this issue in the manuscript, CRYAB is known to promote angiogenesis. Diabetes reduces CRYAB expression, so angiogenesis may be impaired. Furthermore, since VIM is a multifunctional protein, it interacts with several other proteins with multiple functions under various pathophysiological conditions. There are many relevant literatures showing that VIM expression is increased under diabetic conditions [PMID: 28348116 and PMID: 32557212]. And VIM deficiency protects against obesity and insulin resistance in patients with type 2 diabetes. Therefore, we hypothesize that exogenous LBH may have the ability to bind to the increased VIM in diabetic conditions and inactivate the effects of VIM. Thereby achieving the protective effect. This needs to be proved in further studies.

8) The therapeutic strategies targeting (Lbh-Cryab-Vim) on mouse diabetic ED model is not investigated and need to be further validated and discussed.

Answer: As the reviewers pointed out, in this study, we did not evaluate the targeted therapeutic strategy for LBH-CRYAB-VIM in a mouse diabetic ED model. We only identified the binding potential of these three proteins. Evaluation of this treatment strategy requires further study. For example, we can employ shRNA lentivirus, either alone or in combination, to downregulate CRYABexpression [PMID: 31612679] in normal mice, utilize a lentiviral vector CMV-GFP-puro-vimentin to overexpress Vimentin [PMID: 36912679], and then treat it with LBH to evaluate whether the LBH effect still exists (in vivo erectile function study and in vitro angiogenesis assay). We include this information in the Discussion section as a limitation of this study (Please see revised ‘Discussion’).

9) The Discussion of current knowledge of pericytes in diabetic ED and other diseases and the significance of this study as well as clinical implications, should be expanded.

Answer: As the reviewers pointed out, we have expanded the current knowledge of pericytes in diabetic ED and other diseases (CNS disease) and clinical implications as follows: “Although other major cell populations in penile tissue such as smooth muscle cells, endothelial cell, and fibroblasts have been extensively studied, pericytes have mainly been investigated in the context of the central nervous system (CNS). For example, in the CNS, pericytes are involved in maintaining the integrity of the brain's blood-brain barrier (BBB), regulating blood flow at capillary junctions, and promoting neuroinflammatory processes, whose dysfunction is considered an important factor in the progression of vascular diseases such as Alzheimer's disease. But little is known about the role of pericytes in penile tissue.” (Please see revised ‘Discussion’).

10) How many clinical samples were used? How many times did each experiment repeat?

Answer: As the reviewers pointed out, the clinical samples’ information was added in ‘method’ section. A total four human samples were used in this study (‘human corpus cavernosum tissues were obtained from two patients with congenital penile curvature (59-year-old and 47-year-old) who had normal erectile function during reconstructive penile surgery and two patients with diabetic ED (69-year-old and 56-year-old) during penile prosthesis implantation.’). For in vivo study, we quantified four different fields from human samples.

Minor concerns

1) Fig 1A, why normal mouse's body size is the same as DM?

Answer: As the reviewer pointed out, in Figure 1A, while the size of normal mice and DM mice may not appear significantly different, there are indeed notable difference in body weight and size. The normal mice body weigh we used was about 30 grams, while DM mice body weigh was generally less than 24 grams. We found that we missed information on physiological and metabolic parameters from in vivo studies (ICP function study). Therefore, we have added it in Supplementary Table 2 (Please see revised ‘Supplementary information’)

2) The label and negative, and positive controls for Fig 6B are missing.

Answer: We thank for pointing out this. We have added the relevant controls (Input) and performed Co-IP (IP: CRYAB or VIM1, WB: LBH) to demonstrate CRYAB and VIM1 are not simply cross-reactive antigens to their LBH antibody and all IP was replicated for at least 3 times. (Please see revised ‘Result’ and ‘Figure 6B’)

3) The limitation of this study and future work should be discussed.

Answer: As the reviewer pointed out, we have added the limitation of this study and future direction in the discussion section (Please see revised ‘Discussion’).

eLife assessment

The authors have made important contributions to our understanding of the pathogenesis of erectile dysfunction (ED) in diabetic patients. They have identified the gene Lbh, expressed in pericytes of the penis and decreased in diabetic animals. Overexpression of Lbh appears to counteract ED in these animals. The authors also confirm Lbh as a potential marker in cavernous tissues in both humans and mice. While solid evidence supports Lbh's functional role as a marker gene, further research is needed to elucidate the specific mechanisms by which it exerts its effects. This work is of interest to those working in the fields of ED and angiogenesis.

Reviewer #1 (Public Review):

In this study, the researchers aimed to investigate the cellular landscape and cell-cell interactions in cavernous tissues under diabetic conditions, specifically focusing on erectile dysfunction (ED). They employed single-cell RNA sequencing to analyze gene expression patterns in various cell types within the cavernous tissues of diabetic individuals. The researchers identified decreased expression of genes associated with collagen or extracellular matrix organization and angiogenesis in several cell types, including fibroblasts, chondrocytes, myofibroblasts, valve-related lymphatic endothelial cells, and pericytes. They also discovered a newly identified marker, LBH, that distinguishes pericytes from smooth muscle cells in mouse and human cavernous tissues. Furthermore, the study revealed that pericytes play a role in angiogenesis, adhesion, and migration by communicating with other cell types within the corpus cavernosum. However, these interactions were found to be significantly reduced under diabetic conditions. The study also investigated the role of LBH and its interactions with other proteins (CRYAB and VIM) in maintaining pericyte function and highlighted their potential involvement in regulating neurovascular regeneration. Overall, the manuscript is well-written and the study provides novel insights into the pathogenesis of ED in patients with diabetes and identifies potential therapeutic targets for further investigation.

Comments on revised version:

For Figure 4, immunofluorecent staining of LBH following intracavernous injections with lentiviruses is required to justify overexpression and tissue specificity.

Reviewer #3 (Public Review):

Bae et al. described the key roles of pericytes in cavernous tissues in diabetic erectile dysfunction using both mouse and human single-cell transcriptomic analysis. Erectile dysfunction (ED) is caused by dysfunction of the cavernous tissue and affects a significant proportion of men aged 40-70. The most common treatment for ED is phosphodiesterase 5 inhibitors; however, these are less effective in patients with diabetic ED. Therefore, there is an unmet need for a better understanding of the cavernous microenvironment, cell-cell communications in patients with diabetic ED, and the development of new therapeutic treatments to improve the quality of life.

Pericytes are mesenchymal-derived mural cells that directly interact with capillary endothelial cells (ECs). They play a vital role in the pathogenesis of erectile function as their interactions with ECs are essential for penile erection. Loss of pericytes has been associated with diabetic retinopathy, cancer, and Alzheimer's disease and has been investigated in relation to the permeability of cavernous blood vessels and neurovascular regeneration in the authors' previous studies. This manuscript explores the mechanisms underlying the effect of diabetes on pericyte dysfunction in ED. Additionally, the cellular landscape of cavernous tissues and cell type-specific transcriptional changes were carefully examined using both mouse and human single-cell RNA sequencing in diabetic ED. The novelty of this work lies in the identification of a newly identified pericyte (PC)-specific marker, LBH, in mouse and human cavernous tissues, which distinguishes pericytes from smooth muscle cells. LBH not only serves as a cavernous pericyte marker, but its expression level is also reduced in diabetic conditions. The LBH-interacting proteins (Cryab and Vim) were further identified in mouse cavernous pericytes, indicating that these signaling interactions are critical for maintaining normal pericyte function. Overall, this study demonstrates the novel marker of pericytes and highlights the critical role of pericytes in diabetic ED.

Comments on revised version:

Bae and colleagues substantially improved the data quality and revised their manuscript "Pericytes contribute to pulmonary vascular remodeling via HIF2a signaling". While these revisions clarify some of the concerns raised, others remain. In my view, the following question must be addressed.

In my prior question on #3, I completely disagree with the statement that "identified cells with pericyte-like characteristics in the walls of large blood vessels". The staining that authors provided for LBH, was clearly stained for SMCs, not pericytes. Per Fig 2E, the authors are correct that LBH is colocalized with SMA+ cells( SMCs). However, the red signal from LBH clearly stains endothelial cells. In the rest of 2E and 2D, LBH is CD31- and their location suggests LBH stained for SMCs in the Aorta, Kidney vasculature, Dorsal vein, and Dorsal Artery.

Author Response

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

REVIEWER 1

The claim that olivooid-type feeding was most likely a prerequisite transitional form to jet-propelled swimming needs much more support or needs to be tailored to olivooids. This suggests that such behavior is absent (or must be convergent) before olivooids, which is at odds with the increasing quantities of pelagic life (whose modes of swimming are admittedly unconstrained) documented from Cambrian and Neoproterozoic deposits. Even among just medusozoans, ancestral state reconstruction suggests that they would have been swimming during the Neoproterozoic (Kayal et al., 2018; BMC Evolutionary Biology) with no knowledge of the mechanics due to absent preservation.

Thanks for your suggestions. Yes, we agree with you that the ancestral swimming medusae may appear before the early Cambrian, even at the Neoproterozoic deposits. However, discussions on the affinities of Ediacaran cnidarians are severely limited because of the lack of information concerning their soft anatomy. So, it is hard to detect the mechanics due to absent preservation. Olivooids found from the basal Cambrian Kuanchuanpu Formation can be reasonably considered as cnidarians based on their radial symmetry, external features, and especially the internal anatomies (Bengtson and Yue 1997; Dong et al. 2013; 2016; Han et al. 2013; 2016; Liu et al. 2014; Wang et al. 2017; 2020; 2022). The valid simulation experiment here was based on the soft tissue preserved in olivooids.

While the lack of ambient flow made these simulations computationally easier, these organisms likely did not live in stagnant waters even within the benthic boundary layer. The absence of ambient unidirectional laminar current or oscillating current (such as would be found naturally) biases the results.

Many thanks for your suggestion concerning the lack of ambient flow in the simulations. We revised the section “Perspectives for future work and improvements” (lines 381-392 in our revised version of manuscript). Conducting the simulations without ambient flow can reduce the computational cost and, of course, making the simulation easier, while adding ambient flow can lead to poorer convergency and more technical issues. Meanwhile, we strongly agreed that these (benthic) organisms did not live in stagnant waters, as discussed in Liu et al. 2022. However, reducing computational complexity is not the main reason that the ambient flow was not incorporated in the simulations. As we discussed in section “Perspectives for future work and improvements”, our work focuses on the theoretical effect caused by the dynamics (based on fossil observation and hypothesis) of polyp on ambient environment (i.e., how fast the organism inhales water from ambient environment) rather than effect caused by ambient flow on organism (e.g., drag forces), which was what previous palaeontological CFD simulations mainly focused based on fossil morphology and hydrodynamics. To this end, we mainly concern the flow velocity above or near peridermal aperture (and vorticity computed in this paper) generated only by polyp’s dynamics itself without the interference of ambient flow (as many CFD simulations for modern jellyfish, i.e., McHenry & Jed 2003; Gemmell et al. 2013; Sahin et al. 2009. All those simulations were conducted under hydrostatic conditions). Adding ambient flow to our simulations “biases” the flow velocity profiles we expect to obtain in this case.

Nevertheless, we do agree that the ambient unidirectional laminar current or oscillating current plays an important role in feeding and respiration behavior of Quadrapyrgites. Further investigations need to be realized by designing a set of new insightful simulations and is beyond the scope of this work. We conducted CFD simulations incorporated with a randomly generated surface that imitated uneven seabed, where unidirectional laminar current and oscillating current (or vortex) were formed and exerted on Quadrapyrgites located in different places on the surface (Zhang et al. 2022). We assumed that combining the method we used in Zhang et al. 2022 and the velocity profiles collected in this work to conduct new simulations may be a promising way to further investigate the effect of the ambient current on organisms’ active feeding behavior.

There is no explanation for how this work could be a breakthrough in simulation gregarious feeding as is stated in the manuscript.

Thanks for your suggestion. We revised the section “Perspectives for future work and improvements” (lines 396-404 in our revised version of manuscript).

Conducting simulations of gregarious active feeding behavior generally need to model multi (or clustered) organisms, which is beyond the present computational capability. However, exploiting the simulation result and thus building a simplified model can be possible to realize that, as we may apply an inlet or outlet boundary condition to the peridermal aperture of Quadrapyrgites with corresponding exhale or inhale flow velocity profiles collected in this work. By doing this we can obtain a simplified version of an active feeding Quadrapyrgites model without using computational expensive moving mesh feature. Such a model can be used solely or in cluster to investigate gregarious feeding behavior incorporated with ambient current. Those above are explicit explanations for how this work could be a “breakthrough” in simulation gregarious feeding. However, we modified the corresponding description in section “Perspectives for future work and improvements” to make it more appropriate.

Throughout the manuscript there are portions that are difficult to digest due to grammar, which I suspect is due to being written in a second language. This is particularly problematic when the reader is attempting to understand if the authors are stating an idea is well documented versus throwing out hypotheses/interpretations.

Thanks. Our manuscript was checked and corrected by a native speaker of English again.

Line-by-line:

L023: "Although fossil evidence suggests..."

L026: "demonstrated" instead of "proven"

We corrected them accordingly.

L030: "The hydrostatic simulations show that the..." Maybe I'm confused by the wording, but shouldn't this be the case since it's a set part of the model?

As is demonstrated in our manuscript, all the simulations were conducted under “hydrostatic” environment. We originally intend to use the description “hydrostatic” here to emphasize the simulation condition we set in our work. However, it can literally lead to misunderstanding that some of the simulations we conducted are “hydrostatic” while the others are not. To this end, deleting the word “hydrostatic” here (line 30) may be appropriate to eliminate confusion.

L058: "lacking soft tissue" Haootia preservation suggests it is soft tissue (Liu et al., 2014), unless the preceding sentence is not including Haootia, in which case this section is confusingly worded

Thank you. We deleted the sentence “However, their affinities are not without controversy as the lacking soft tissue.”

L085: change "proxy"

Yes, we changed to “Considering their polypoid shape and cubomedusa-type anatomy, the hatched olivooids appear to a type of periderm-bearing polyp-shaped medusa (Wang et al. 2020) (lines 86-88).”

L092: "assist in feeding" has this been stated before? Citation needed, else this interpretation should primarily be in the discussion

Yes, you are right. We cited the reference at the end of the mentioned sentence (lines 91-94).

L095: Remove "It is suggested that"

Thanks for your suggestions. We corrected it.

L100: "Probably the..." here to the end belongs in the discussion and not introduction.

Thanks for your suggestions. We corrected the sentences.

L108: "an abapical"

Thanks for your suggestions. We revised it in line 107.

L112: "for some distance" be specific or remove

Yes, we deleted “for some distance” in line 111.

L133: I can't find a corresponding article to Zhang et al., 2022. Is this the correct reference?

The article Zhang et al. 2022 (entitled “Effect of boundary layer on simulation of microbenthic fossils in coastal and shallow seas”.) was in press at the time when we first submitted this manuscript. We complemented the corresponding term in References with the doi (10.13745/j.esf.sf.2023.5.32), which may help readers to locate this article easier.

L138: You can't be positive that your simulations "provide a good reproduction of the movement." You have attempted to reconstruct said movement, but the language here is overly firm - as is "pave a new way"

Thanks for your suggestions. We corrected the corresponding description (lines 138-140) to make it more rigorous.

L149: "No significant change" implies statistics were computed that are not presented here.

The statistics were computed by using built-in function of Excel and presented in Table supplement 2 (deposited in figshare, https://doi.org/10.6084/m9.figshare.23282627.v2) rather than in manuscript. To be specific, the error computations are followed by the formula of relative error, which is defined by:

where u_z denotes the velocity profile collected on each cut point z with the current mesh parameters, u_z^* denotes the velocity profile collected on each cut point z with the next finer mesh parameters, i denotes each time step (from 0.01 to 4.0). In this case, the total average error was computed by averaging the sum of each 〖error〗_i on corresponding time step. The results are red marked in Table supplement 2. We revised the corresponding description in lines 140-146

L152: "line graphs" >> "profiles"

Thanks for your suggestions. We corrected it in line 144.

L159: remove "significant" unless statistics are being reported, in which case those need to be explained in detail.

Thanks for your suggestions. We removed "significant" and corrected the corresponding sentences in lines 150-153 to make them more rigorous.

L159: I would recommend including a supplemental somewhere that shows how tall the modeled Quadrapyrgites is and where the cut lines exist above it.

Many thanks for your suggestions. Corresponding complementation was made in the last paragraph of section “Computational fluid dynamics” (line 455 and line 535). We agree that it is appropriate to elucidate the height of modeled Quadrapyrgites and the position of each cut point. Hence, we add a supplementary figure (entitled Figure supplement 1) to illustrate those above.

L183: "The maximum vorticity magnitude was set..." I do not follow what this threshold is based on the current phrasing.

The vorticity magnitude mentioned here is the visualisation range of the color scalebar, which can be set manually set in the software. The positive number represent the vortex rotated counterclockwise, while the negative number represent that rotated clockwise on the cut plane. In this case, the visualisation range is [-0.001,0.001] (i.e., the absolute value of 0.001 is the threshold), as the color scalebar in Figure 7. Decreasing the threshold, for example, setting the visualisation range to [-0.0001,0.0001], can capture smaller vorticity on the cut plane, as the figure below on the left. Otherwise, setting the range to [-0.01,0.01] will focus on bigger vorticity, as the figure below on the right. We found [-0.001,0.001] could be an appropriate parameter to visualize the vortex near periderm based on our trial. To be more rigorous and to avoid confusion, we modified the description in the corresponding place of the manuscript (lines 172-174).

Author response image 1.

L201: "3.9-4 s"

Thanks, we corrected it in line 191.

L269: "Sahin et al.,..." add to the next paragraph

Yes, we rearranged the corresponding two paragraphs (lines 258-289).

L344: "Higher expansion-contraction..." this needs references and/or more justification.

Thanks. We deleted the sentence.

L446: two layers of hexahedral elements is a very low number for meshing boundary layer flow

Many thanks for your question. We agree that an appropriate hexahedral elements mesh for boundary layer is essential to recover boundary flow, especially in cases where turbulence model incorporated with wall function is adopted such as the standard k-epsilon model. In this case, the boundary flow is not the main point since the velocity profile was collected above periderm aperture rather than near no-slip wall region. What else, we do not need drag (related to sheer stress and pressure difference) computations in this case, which requires a more accurate flow velocity reconstruction near no-slip walls as what previous palaeontological CFD simulations have done. Thus, we think two layers of hexahedral elements are enough. What else, hexahedral elements added to periderm aperture domain, as illustrated in figure below, can let the velocity near wall vary smoothly and thus can benefit the convergency of simulations.

Author response image 2.

L449: similar to comments regarding lines 146-148, key information is missing here. Figure 3C appears to be COMSOL's default meshing routine. While it is true that the domain is discretized in a non-uniform manner, no information is provided as to what mesh parameters were "tuned" to determine "optimal settings" or what those settings are (or how they are optimal).

Many thanks for your question. Specific mesh parameters were listed in Table supplement 3 and corresponding descriptions and modifications were made both in lines 475-479 and lines 542-549. In most CFD cases, the mesh parameters need to be tuned to ensure a balance between computational cost and accuracy. If the difference of the result obtained from present mesh and that obtained from the next finer mesh ranges from 5% -10%, the present mesh is expected to be “optimal”. To achieve this, we prescribed several sets of different mesh (mainly concerning maximum and minimum element size) to each subdomain (domain of the inner cavity, domain of the peridermal aperture and domain outside of fossil model) of the whole computational domain in the test model. Subsequently, we refined the mesh step by step as much as possible and adjust the element size of subdomains to find suitable mesh parameters, that is how the mesh parameters were "tuned". We agree that we should explicit what mesh parameters were tuned and what those settings are.

Figure 7 should have the timesteps included and the scaling of the arrows should be explicit in the caption

Many thanks for your suggestions. We intended to use the white arrows to represent the velocity orientation rather than true velocity scale in Figure 7 (Instead, the white arrows in Animation supplement 1 represent a normalized velocity profile). To avoid confusion, we revised Figure 7 with timesteps and arrows represent a normalized velocity profile, making it consistent with Animation supplement 1. Corresponding modification is also made in the caption of Figure 7.

The COMSOL simulation files (raw data) are missing from the supplemental data. These should be posted to Dryad or here.

We uploaded the files to Dryad (https://datadryad.org/stash/share/QGDSqLh8HOll7ofl6JWVrqM57Rp62ZPjvZU0AQQHwTY), and added the corresponding link to section “Data Availability Statement”.

REVIEWER 2

Lines 319-334: The omission in this paragraph of Paraconularia ediacara Leme, Van Iten and Simoes (2022) from the terminal Ediacaran of Brazil is a serious matter, as (1) the medusozoan affinities of this fossil are every bit as well established as those of anabaritids, Sphenothallus, Cambrorhytium and Byronia, and (2) P. ediacara was a large (centimetric) polyp, the presence of which in Precambrian times is thus a problem for the simple evolutionary scenario (very small polyps followed later in evolutionary history by large polyps) outlined in the paragraph. Thus, Paraconularia ediacara must be mentioned in this paper, both in connection with the early evolution of size in cnidarian polyps and in other places where the early evolution of cnidarians is discussed.

Thanks for your important suggestions. We added some sentences in lines 323-326 as following: “Significantly, the large-bodied, skeletonized conulariids-like Paraconularia found from the terminal Ediacaran Tamengo Formation of Brazil confirmed their ancient predators like the extant medusozoans and suggested the origin of cnidarians even farther into the deep evolutionary scenario (Leme et al. 2022).”

Line 23. Delete the word, been.

Line 25. Replace conjecture with conjectural.

Line 26. Delete the word, the before calyx-like.

Line 32. Replace consisting with consistent.

Thanks for your suggestions. We all corrected them.

eLife assessment

This important study advances our understanding of early Cambrian cnidarian paleoecology and suggests that the reconstructed ancestral feeding and respiration mechanisms predate jet-propelled swimming utilized by modern jellyfish. The work combines solid evidence of fluid and structural mechanics modeling, simulating for the first time the feeding and respiratory capacities in a microfossil (Quadrapyrgites), which in turn opens new possibilities using this approach for paleontological research. Assuming that the prior interpretations and assumptions concerning the modeled organism's soft part and skeletal anatomy are correct, the hypotheses that (1) the organism could alternately contract and expand the oral region and (2) such movement increased feeding efficiency seem plausible.

Reviewer #1 (Public Review):

Summary:<br /> The authors utilize fluid-structure interaction analyses to simulation fluid flow within and around the Cambrian cnidarian Quadrapyrgites to reconstruct feeding/respiration dynamics. Based on vorticity and velocity flow patterns, the authors suggest that the polyp expansion and contraction ultimately develop vortices around the organism that are like what modern jellyfish employ for movement and feeding. Lastly, the authors suggest that this behavior is likely a prerequisite transitional form to swimming medusae.

Strengths:<br /> While fluid-structure-interaction analyses are common in engineering, physics, and biomedical fields, they are underutilized in the biological and paleobiological sciences. Zhang et al. provide a strong approach to integrating active feeding dynamics into fluid flow simulations of ancient life. Based on their data, it is entirely likely the described vortices would have been produced by benthic cnidarians feeding/respiring under similar mechanisms. However, some of the broader conclusions require additional justification.

Weaknesses:

1. The claim that olivooid-type feeding was most likely a prerequisite transitional form to jet-propelled swimming needs much more support or needs to be tailored to olivooids. This suggests that such behavior is absent (or must be convergent) before olivooids, which is at odds with the increasing quantities of pelagic life (whose modes of swimming are admittedly unconstrained) documented from Cambrian and Neoproterozoic deposits. Even among just medusozoans, ancestral state reconstruction suggests that they would have been swimming during the Neoproterozoic (Kayal et al., 2018; BMC Evolutionary Biology) with no knowledge of the mechanics due to absent preservation.<br /> 2. While the lack of ambient flow made these simulations computationally easier, these organisms likely did not live in stagnant waters even within the benthic boundary layer. The absence of ambient unidirectional laminar current or oscillating current (such as would be found naturally) biases the results.<br /> 3. There is no explanation for how this work could be a breakthrough in simulation gregarious feeding as is stated in the manuscript.

Despite these weaknesses the authors dynamic fluid simulations convincingly reconstruct the feeding/respiration dynamics of the Cambrian Quadrapyrgites, though the large claims of transitionary stages for this behavior are not adequately justified. Regardless, the approach the authors use will be informative for future studies attempting to simulate similar feeding and respiration dynamics.

The following text is directly in response to the revised version of the manuscript.<br /> Dynamic simulations of feeding and respiration of the early Cambrian periderm-bearing cnidarian polyps

Revision 1

I think this manuscript has been improved by the authors, and I appreciate their time and effort in considering my earlier comments. While most of my line by line comments have been incorporated, I do feel that some of my larger points have been insufficiently addressed. Those are repeated with additional clarifications below.

Original comment: The claim that olivooid-type feeding was most likely a prerequisite transitional form to jet-propelled swimming needs much more support or needs to be tailored to olivooids. This suggests that such behavior is absent (or must be convergent) before olivooids, which is at odds with the increasing quantities of pelagic life (whose modes of swimming are admittedly unconstrained) documented from Cambrian and Neoproterozoic deposits. Even among just medusozoans, ancestral state reconstruction suggests that they would have been swimming during the Neoproterozoic (Kayal et al., 2018; BMC Evolutionary Biology) with no knowledge of the mechanics due to absent preservation.

Author response: Thanks for your suggestions. Yes, we agree with you that the ancestral swimming medusae may appear before the early Cambrian, even at the Neoproterozoic deposits. However, discussions on the affinities of Ediacaran cnidarians are severely limited because of the lack of information concerning their soft anatomy. So, it is hard to detect the mechanics due to absent preservation. Olivooids found from the basal Cambrian Kuanchuanpu Formation can be reasonably considered as cnidarians based on their radial symmetry, external features, and especially the internal anatomies (Bengtson and Yue 1997; Dong et al. 2013; 2016; Han et al. 2013; 2016; Liu et al. 2014; Wang et al. 2017; 2020; 2022). The valid simulation experiment here was based on the soft tissue preserved in olivooids.

Reviewer response: This response does not sufficiently address my earlier comment. While the authors are correct that individual Ediacaran affinities are an area of active research and that Olivooids can reasonably be considered cnidarians, this doesn't address the actual critique in my comment. Most (not all) Ediacaran soft-bodied fossils are considered to have been benthic, but pelagic cnidarian life is widely acknowledged to at least be present during later White Sea and Nama assemblages (and earlier depending on molecular clock interpretations). The authors have certainly provided support for the mechanics of this type of feeding being co-opted for eventual jet-propulsion swimming in Olivooids. They have not provided sufficient justifications within the manuscript for this to be broadened beyond this group.

Original comment: There is no explanation for how this work could be a breakthrough in simulation gregarious feeding as is stated in the manuscript.

Author response: Thanks for your suggestion. We revised the section "Perspectives for future work and improvements" (lines 396-404 in our revised version of MS). Conducting simulations of gregarious active feeding behavior generally need to model multi (or clustered) organisms, which is beyond the present computational capability. However, exploiting the simulation result and thus building a simplified model can be possible to realize that, as we may apply an inlet or outlet boundary condition to the peridermal aperture of Quadrapyrgites with corresponding exhale or inhale flow velocity profiles collected in this work. By doing this we can obtain a simplified version of an active feeding Quadrapyrgites model without using computational expensive moving mesh feature. Such a model can be used solely or in cluster to investigate gregarious feeding behavior incorporated with ambient current. Those above are explicit explanations for how this work could be a "breakthrough" in simulation gregarious feeding. However, we modified the corresponding description in section "Perspectives for future work and improvements" to make it more appropriate.

Reviewer response: I think I understand where the authors are trying to take this next step. If the authors were to follow up on this study with the proposed implementation of inhalant/exhalent velocities profiles (or more preferably velocity/pressure fields), then that study would be a breakthrough in simulating such gregarious feeding. Based on what has been done within the present study, I think the term "breakthrough" is instead overly emphatic.<br /> An additional note on this. The authors are correct that incorporating additional models could be used to simulation a population (as has been successfully done for several Ediacaran taxa despite computational limitations), but it's not the only way. The authors might explore using periodic boundary conditions on the external faces of the flow domain. This could require only a single Olivooid model to assess gregarious impacts - see the abundant literature of modeling flow through solar array fields.

Original comment: L446: two layers of hexahedral elements is a very low number for meshing boundary layer flow

Author response: Many thanks for your question. We agree that an appropriate hexahedral elements mesh for boundary layer is essential to recover boundary flow, especially in cases where turbulence model incorporated with wall function is adopted such as the standard k-epsilon model. In this case, the boundary flow is not the main point since the velocity profile was collected above periderm aperture rather than near no-slip wall region. What else, we do not need drag (related to sheer stress and pressure difference) computations in this case, which requires a more accurate flow velocity reconstruction near no-slip walls as what previous palaeontological CFD simulations have done. Thus, we think two layers of hexahedral elements are enough. What else, hexahedral elements added to periderm aperture domain, as illustrated in figure below, can let the velocity near wall vary smoothly and thus can benefit the convergency of simulations.

Reviewer response: As the authors point out in the main text, these organisms are small (millimeters in scale) and certainly lived within the boundary layer range of the ocean. While the boundary layer is not the main point, it still needs to be accurately resolved as it should certainly affect the flow further towards the far field at this scale. I'm not suggesting the authors need to perfectly resolve the boundary layer or focus on using turbulence models more tailored to boundary layer flows (such as k-w), but the flow field still needs sufficient realism for a boundary bounded flow. The authors really should consider quantitatively assessing the number of hexahedral elements within their mesh refinement study.

Reviewer #2 (Public Review):

Summary: The authors seek to elucidate the early evolution of cnidarians through computer modeling of fluid flow in the oral region of very small, putative medusozoan polyps. They propose that the evolutionary advent of the free-swimming medusoid life stage was preceded by a sessile benthic life stage equipped with circular muscles that originally functioned to facilitate feeding and that later became co-opted for locomotion through jet propulsion.

Strengths: Assumptions of the modeling exercise laid out clearly; interpretations of the results of the model runs in terms of functional morphology plausible. An intriguing investigation that should stimulate further discussion and research.

Weaknesses: Speculation on the origin of the medusoid life stage in cnidarians heavily dependent on prior assumptions concerning the soft part anatomy and material properties of the skeleton of the modeled fossil organism that may be open to alternative interpretations. Logically, of course, the hypothesis that cnidarian medusae originated from benthic polyps must be evaluated along with the alternative hypotheses that the medusa came first and that the ancestral cnidarian exhibited both life stages.

eLife assessment

This useful study examines how deletion of a major DNA repair gene in bacteria may facilitate the rise of mutations that confer resistance against a range of different antibiotics. Although the phenotypic evidence is intriguing, the interpretation of the phenotypic data presented and the proposed mechanism by which these mutations are generated are incomplete, relying on untested assumptions and suboptimal methodology. If substantially improved, this work could be of interest to microbiologists studying antibiotic resistance, genome integrity, and evolution, but as yet is of unclear significance.

Reviewer #1 (Public Review):

Summary:<br /> Jin et al. investigated how the bacterial DNA damage (SOS) response and its regulator protein RecA affect the development of drug resistance under short-term exposure to beta-lactam antibiotics. Canonically, the SOS response is triggered by DNA damage, which results in the induction of error-prone DNA repair mechanisms. These error-prone repair pathways can increase mutagenesis in the cell, leading to the evolution of drug resistance. Thus, inhibiting the SOS regulator RecA has been proposed as a means to delay the rise of resistance.

In this paper, the authors deleted the RecA protein from E. coli and exposed this ∆recA strain to selective levels of the beta-lactam antibiotic, ampicillin. After an 8-hour treatment, they washed the antibiotic away and allowed the surviving cells to recover in regular media. They then measured the minimum inhibitory concentration (MIC) of ampicillin against these treated strains. They note that after just 8-hour treatment with ampicillin, the ∆recA had developed higher MICs towards ampicillin, while by contrast, wild-type cells exhibited unchanged MICs. This MIC increase was also observed in subsequent generations of bacteria, suggesting that the phenotype is driven by a genetic change.

The authors then used whole genome sequencing (WGS) to identify mutations that accounted for the resistance phenotype. Within resistant populations, they discovered key mutations in the promoter region of the beta-lactamase gene, ampC; in the penicillin-binding protein PBP3 which is the target of ampicillin; and in the AcrB subunit of the AcrAB-TolC efflux machinery. Importantly, mutations in the efflux machinery can impact the resistance towards other antibiotics, not just beta-lactams. To test this, they repeated the MIC experiments with other classes of antibiotics, including kanamycin, chloramphenicol, and rifampicin. Interestingly, they observed that the ∆recA strains pre-treated with ampicillin showed higher MICs towards all other antibiotics tested. This suggests that the mutations conferring resistance to ampicillin are also increasing resistance to other antibiotics.

The authors then performed an impressive series of genetic, microscopy, and transcriptomic experiments to show that this increase in resistance is not driven by the SOS response, but by independent DNA repair and stress response pathways. Specifically, they show that deletion of the recA reduces the bacterium's ability to process reactive oxygen species (ROS) and repair its DNA. These factors drive the accumulation of mutations that can confer resistance to different classes of antibiotics. The conclusions are reasonably well-supported by the data, but some aspects of the data and the model need to be clarified and extended.

Strengths:<br /> A major strength of the paper is the detailed bacterial genetics and transcriptomics that the authors performed to elucidate the molecular pathways responsible for this increased resistance. They systemically deleted or inactivated genes involved in the SOS response in E. coli. They then subjected these mutants to the same MIC assays as described previously. Surprisingly, none of the other SOS gene deletions resulted in an increase in drug resistance, suggesting that the SOS response is not involved in this phenotype. This led the authors to focus on the localization of DNA PolI, which also participates in DNA damage repair. Using microscopy, they discovered that in the RecA deletion background, PolI co-localizes with the bacterial chromosome at much lower rates than wild-type. This led the authors to conclude that deletion of RecA hinders PolI and DNA repair. Although the authors do not provide a mechanism, this observation is nonetheless valuable for the field and can stimulate further investigations in the future.

In order to understand how RecA deletion affects cellular physiology, the authors performed RNA-seq on ampicillin-treated strains. Crucially, they discovered that in the RecA deletion strain, genes associated with antioxidative activity (cysJ, cysI, cysH, soda, sufD) and Base Excision Repair repair (mutH, mutY, mutM), which repairs oxidized forms of guanine, were all downregulated. The authors conclude that down-regulation of these genes might result in elevated levels of reactive oxygen species in the cells, which in turn, might drive the rise of resistance. Experimentally, they further demonstrated that treating the ∆recA strain with an antioxidant GSH prevents the rise of MICs. These observations will be useful for more detailed mechanistic follow-ups in the future.

Weaknesses:<br /> Throughout the paper, the authors use language suggesting that ampicillin treatment of the ∆recA strain induces higher levels of mutagenesis inside the cells, leading to the rapid rise of resistance mutations. However, as the authors note, the mutants enriched by ampicillin selection can play a role in efflux and can thus change a bacterium's sensitivity to a wide range of antibiotics, in what is known as cross-resistance. The current data is not clear on whether the elevated "mutagenesis" is driven ampicillin selection or by a bona fide increase in mutation rate.

Furthermore, on a technical level, the authors employed WGS to identify resistance mutations in the treated ampicillin-treated wild-type and ∆recA strains. However, the WGS methodology described in the paper is inconsistent. Notably, wild-type WGS samples were picked from non-selective plates, while ΔrecA WGS isolates were picked from selective plates with 50 μg/mL ampicillin. Such an approach biases the frequency and identity of the mutations seen in the WGS and cannot be used to support the idea that ampicillin treatment induces higher levels of mutagenesis.

Finally, it is important to establish what the basal mutation rates of both the WT and ∆recA strains are. Currently, only the ampicillin-treated populations were reported. It is possible that the ∆recA strain has inherently higher mutagenesis than WT, with a larger subpopulation of resistant clones. Thus, ampicillin treatment might not in fact induce higher mutagenesis in ∆recA.

Reviewer #2 (Public Review):

Summary:<br /> This study aims to demonstrate that E. coli can acquire rapid antibiotic resistance mutations in the absence of a DNA damage response. To investigate this, the authors employed a sophisticated experimental framework based on a modified Adaptive Laboratory Evolution (ALE) workflow. This workflow involves numerous steps culminating in the measurement of antibiotic resistance. The study presents evidence that a recA strain develops ampicillin resistance mutations more quickly than the wild-type, as shown by measuring the Minimum Inhibitory Concentration (MIC) and mutation frequency. Whole-genome sequencing of 15 recA- colonies resistant to ampicillin revealed predominantly inactivation of genes involved in the multi-drug efflux pump system, whereas, in the wild-type, mutations appear to enhance the activity of the chromosomal ampC cryptic promoter. By analyzing mutants involved in the SOS response, including a lexA3 mutant incapable of inducing the SOS response, the authors conclude that the rapid evolution of antibiotic resistance occurs in an SOS-independent manner when recA is absent.

Furthermore, RNA sequencing (RNA-seq) of the four experimental conditions suggests that genes related to antioxidative responses drive the swift evolution of antibiotic resistance in the recA- strain.

Weaknesses:<br /> However, a potential limitation of this study is the experimental design used to determine the 'rapid' evolution of antibiotic resistance. It may introduce a significant bottleneck in selecting ampicillin-resistant mutants early on. A recA mutant could be more susceptible to ampicillin than the wild-type, and only resistant mutants might survive after 8 hours, potentially leading to their enrichment in subsequent steps. To address this concern, it would be critical to perform a survival analysis at various time points (0h, 2h, 4h, 6h, and 8h) during ampicillin treatment for both recA and wild-type strains, ensuring there is no difference in viability.

The observation that promoter mutations are absent in recA strains could be explained by previous research indicating that amplification of the AmpC genes is a mechanism for E. coli resistance to ampicillin, which does not occur in a recA-deficient background (PMID# 19474201).

The section describing Figure 3 is poorly articulated, and the conclusions drawn are apparent. The inability of a recA strain to induce the SOS response is well-documented (lines 210 and 278). The data suggest that merely blocking SOS induction is insufficient to cause 'rapid' evolution in their experimental conditions. To investigate whether SOS response can be induced independently of lexA cleavage by recA, alternative experiments, such as those using a sulA-GFP fusion, might be more informative.

In Figure 4E, the lack of increased SulA gene expression in the wild-type strain treated with ampicillin is unexpected, given that SulA is an SOS-regulated gene. The fact that polA (Pol I) is going down should be taken into account in the interpretation of Figures 2D and 2E.

The connection between compromised DNA repair, the accumulation of Reactive Oxygen Species (ROS) based on RNA-seq data, and accelerated evolution is merely speculative at this point and not experimentally established.

Reviewer #3 (Public Review):

Summary:<br /> In the present work, Zhang et al investigate the involvement of the bacterial DNA damage repair SOS response in the evolution of beta-lactam drug resistance evolution in Escherichia coli. Using a combination of microbiological, bacterial genetics, laboratory evolution, next-generation, and live-cell imaging approaches, the authors propose short-term drug resistance evolution that can take place in RecA-deficient cells in an SOS response-independent manner. They propose the evolvability of drug resistance is alternatively driven by the oxidative stress imposed by the accumulation of reactive oxygen species and inhibition of DNA repair. Overall, this is a nice study that addresses a growing and fundamental global health challenge (antimicrobial resistance). However, although the authors perform several multi-disciplinary experiments, there are several caveats to the authors' proposal that ultimately do not fully support their interpretation that the observed antimicrobial resistance evolution phenotype is due to compromised DNA repair.

Strengths:<br /> The authors introduce new concepts to antimicrobial resistance evolution mechanisms. They show short-term exposure to beta-lactams can induce durably fixed antimicrobial resistance mutations. They propose this is due to comprised DNA repair and oxidative stress. This is primarily supported by their observations that resistance evolution phenotypes only exist for recA deletion mutants and not other genes in the SOS response

Weaknesses:<br /> The authors do not show any direct evidence (1) that these phenotypes exist in strains harboring deletions in other DNA repair genes outside of the SOS response, (2) that DNA damage is increased, (3) that reactive oxygen species accumulate, (4) that accelerated resistance evolution can be reversed by anything other than recA complementation. The authors do not directly test alternative hypotheses. The conclusions drawn are therefore premature.

eLife assessment

By assessing what it means to replicate a null finding, and by proposing two methods that can be used to evaluate whether null findings have been replicated (frequentist equivalence testing, and Bayes factors), this article represents an important contribution to work on reproducibility. Through a compelling re-analysis of results from the Reproducibility Project: Cancer Biology, the authors demonstrate that even when 'replication success' is reduced to a single criterion, different methods to assess replication of a null finding can lead to different conclusions.

Reviewer #1 (Public Review):

Summary:<br /> The goal of Pawel et al. is to provide a more rigorous and quantitative approach for judging whether or not an initial null finding (conventionally with p >= 0.05) has been replicated by a second similarly null finding. They discuss important objections to relying on the qualitative significant/non-significant dichotomy to make this judgement. They present two complementary methods (one frequentist and the other Bayesian) which provide a superior quantitative framework for assessing the replicability of null findings.

Strengths:<br /> Clear presentation; illuminating examples drawn from the well-known Reproducibility Project: Cancer Biology data set; R-code that implements suggested analyses. Using both methods as suggested provides a superior procedure for judging the replicability of null findings.

Weaknesses:<br /> The frequentist and the Bayesian methods can be used to make binary assessments of an original finding and its replication. The authors clarify, though, that they can also be used to make continuous quantitative judgements about strength of evidence. I believe that most will use the methods in a binary fashion, but the availability of more nuanced assessments is welcome. This revision has addressed what I initially considered a weakness.

Reviewer #2 (Public Review):

Summary and strengths:

1) The work provides significant insights because usually non-significant studies can be considered replicated by their null replications as well. The work discuss and provide data demonstrating that when analyzing studies with p > 0.05 for the result to be replicated, equivalence tests and bayes factor approaches are more suitable, since studies can be underpowered even if replications use larger samples than their original studies in general. Non-significant p-values are highly expected even with 80% of power for a true effect.

2) The evidence used features methods and analyses more rigorous than current state-of-the-art research on replicability.

Weaknesses:<br /> I am satisfied with the revisions made by the authors in response to my initial suggestions, as well as their subsequent responses to my observations throughout the reviewing process.

eLife assessment

In this important study by Theriot et al., the authors utilize an impressive set of innovative approaches to conduct a CRISPRi pooled screen in human cells using large-scale microscopy screen data. They leverage an improved barcoding approach to identify genes targeted in specific cells and examine the effects on cell morphology using high-dimensional phenotypic analysis. The method and data presented are compelling.

Reviewer #1 (Public Review):

Summary:<br /> Theriot et al. are proposing here a technically very impressive screening method. Their optimization of single-cell sgRNA barcode sequencing/reading is fundamental progress towards the use of CRISPRi technology in phenotypic screening.

The biology side of the manuscript focuses on cell morphology and cytoskeleton. For this, others are also proposing innovative methods for phenotypic quantification and analysis. The output of the phenotypic analysis shows interesting hit correlations between the methods used and identifies well-known hit genes. Nevertheless, the strength and the validity of the results are yet difficult to assess. The complexity and the amount of features extracted from the cell images do not always seem justified. Indeed, the visual conclusion from the authors at the end mostly refers to basic features (cell size, shape, nuclear localization, actin network polarity), which in my opinion could be quantified in a more straightforward way, which then would facilitate the ultimate goal of such a work, which is the biological interpretation of the screening campaign.

Strengths:<br /> A very impressive technology work on molecular biology, microscopy, image analysis, and data analysis. The investment of such efforts seems fundamental for the development of phenotypic and CRISPR screens.

Weaknesses:<br /> The phenotypic analysis method seems too complex in regard to the actual output. The biological interpretation of the screen is therefore suffering from this complexity. Having said that the quantification of cell morphology and actin network phenotype is a very risky and complex task.

Reviewer #2 (Public Review):

Summary:<br /> In this study, the authors present a robust pipeline that integrates high-content phenotypic imaging of a targeted pool of 366 CRISPRi-screened genes with in situ sequencing of single cells, achieving a resolution for 1.3 million cells. The application of this pipeline on the U2OS cell line effectively screens for nuclear and actin morphology changes. One study's strength lies in the utilization of a barcode system, enabling efficient imaging and genotype determination for 85% of cells. The authors employ two distinct approaches to delineate phenotypic changes. In the first approach, cells are characterized by approximately 1,000 morphological features, with dimensionality reduction via PCA using 25 principal components and a novel image sampling method called VIEWED (Visual Interpretation of Embedding by Constrained Walkthrough Sampling). The second approach employs a deep learning technique, specifically the Beta-variational encoder, to identify morphological differences, offering a generative AI approach for visualizing interpreted distinctions learned through the algorithm. While the Beta-variational encoder is deemed simpler to use and interpret, the classical PCA approach demonstrates superiority due to its heightened sensitivity in identifying more genes with phenotypic changes. Both methods, however, successfully identify shared phenotypic gene hits, showing consistent replication across multiple individual guides for each gene hit. Key phenotypic clusters are identified and replicated similarly by both the conventional PCA feature approach and the Beta-variational encoder approach.

Strengths:<br /> - A novel barcode methodology for efficient genotyping via in situ sequencing, minimizing rounds of imaging and genotyping 85% of cells.<br /> - Use of a beta variational autoencoder, generative AI approach to facilitate detection of morphological change in cells, gene hits, and phenotypic gene clusters.

Weaknesses:<br /> Although the outcome is reproduced with 3 gRNA/gene, no biological replicate is presented and is as such limiting on convincing on reproducibility of the phenotypic detection approach.

The presented work is highly compelling as it employs an optical pooled CRISPRi screen, showcasing the capability to conduct pool screening beyond the typical frequency count of guides with the next-generation sequencing approach, effectively establishing a direct link between cell images and guide RNAs in the pool screen approach. This achievement, typically associated with arrayed screens, sets the study apart. Moreover, the study offers captivating images of individual cells that vividly portray convincing phenotypic changes. Additionally, the work effectively highlights the potency of generative AI in interpreting cell phenotypic changes detected by the algorithm. This aspect of the study is particularly relevant in the present time, as it introduces a potentially highly valuable methodology. Overall, the research provides a robust demonstration of innovative techniques and methodologies, contributing significantly to the field.

Reviewer #3 (Public Review):

Summary:<br /> Pooled optical screening has recently emerged as a powerful approach to associate complex phenotypic information from microscope images with specific genetic perturbations at the single-cell level. This is achieved by amplifying and sequencing DNA barcodes within individual cells through in-situ sequencing. This paper leverages these advances in pooled screening technology to examine the effects of gene knockdowns on high-dimensional cell morphological phenotypes beyond binary readouts.

A key challenge is how to effectively distill meaningful phenotypic dimensions from information-rich image data to connect genotype to phenotype. By screening 366 genes using CRISPRi and analyzing tens of thousands of single-cell images, this paper provides insights into genetic regulators of morphology in osteosarcoma cells. In developing this screen and analyzing its readout, the authors make several notable contributions.

First, the authors tested and optimized molecular inversion probes (MIPs) to improve rolling circle amplification and barcode imaging. Through these optimization experiments, they identified a shortened MIP design that yielded 11-fold more visible amplicons, enabling more robust barcode readout from complex images. Second, the authors address several unresolved questions regarding how to work with single-cell images at this scale. A critical aspect of this is the need to develop analysis strategies using single-cell data rather than commonly used current methodologies that condense down to an agglomerated perturbation level cell morphology information. The authors compare morphological profiling using curated feature extraction and an unsupervised deep learning approach called a β-variational autoencoder on single-cell imaging data, suggesting that the latter can capture salient aspects of variation without requiring much human input. Finally, and perhaps more importantly, the authors develop an approach, Visual Interpretation of Embeddings by constrained Walkthrough Sampling (VIEWS), towards sampling cells at the end of distributions such as a principal component dimension in a reduction of curated features or a latent space dimension extracted from an autoencoder. This allows for a rapid and efficient way of understanding extremes of morphological profiles and allows for quick interpretability of extracted morphological signal which in turn assists with downstream functional understandings of groups of genes that similarly alter a cell's morphology.

Strengths and Weaknesses:<br /> This is an interesting and rigorous paper that provides an important advance in conducting large-scale microscopy-based approaches. The methods development and computational analyses described in this paper are strong and innovative. However, the screening conducted in this paper did not identify a large number of modifiers of general U2OS cell morphology. As the authors rightly point out, several factors could contribute to the modest hit rate, including variable CRISPRi knockdown efficiency and limited phenotypic readout from just two imaging channels. Despite these limitations, the paper makes several key methodological contributions and in the opinion of this reviewer merits revision or benchmarking.

eLife assessment

The findings of this study are valuable as they challenge the dogma regarding the link between lowered bacterial metabolism and tolerance to aminoglycosides. The authors propose that the well-known tolerance to AG of mutants such as those of complexes I and II is not due to a decrease in the proton motive force and thus antibiotic uptake. The results presented here are solid but incomplete and the conclusions require additional experimental support.

Reviewer #1 (Public Review):

Summary:<br /> In this study, the authors investigate the tolerance of aminoglycosides in E. coli mutants deleted in the Krebs cycle and respiratory chain enzymes. The motivation for this study is unclear. Transport of aminoglycosides is pmf-dependent, as the authors correctly note, and knocking out energy-producing components leads to tolerance of aminoglycosides, this has been well established. In S. aureus, clinically relevant "small colony" strains selected for in the course of therapy with aminoglycosides acquire null mutations in the biosynthesis of heme or ubiquinone, and have been studied in detail. In E. coli, such knockouts have not been reported in clinical isolates, probably due to severe fitness costs. At the same time, single-cell analysis has shown that individual cells with a decrease in the expression of Krebs cycle enzymes are tolerant of antibiotics and have lower ATP (Manuse et al., PLoS Biol 19: e3001194). The authors of the study under review report that knocking out ICD, isocitrate dehydrogenase that catalyzes the rate-limiting step in the Krebs cycle, has little effect on aminoglycoside tolerance and actually leads to an increase in the level of ATP over time. This observation does not seem to make much sense and contradicts previous reports, specifically that E. coli ICD is tolerant of antibiotics and, not surprisingly, produces Less ATP (Kabir and Shimizu, Appl Micro-biol Biotechnol. 2004; 65(1):84-96; Manuse et al., PLoS Biol 19: e3001194). Mutations in other Krebs cycle enzymes, unlike ICD, do lead to a dramatic increase in tolerance of aminoglycosides according to the paper under review. This is all very confusing.

Apart from the confusing data, it is not clear what useful information may be obtained from the choice of the experimental system. The authors examine exponentially growing cells of E. coli for tolerance of aminoglycosides. The population at this stage of growth is highly susceptible to aminoglycosides, and only some rare persister cells can survive. However, the authors do not study persisters. A stationary population of E. coli is tolerant of aminoglycosides, and this is clinically relevant, but this is not the subject of the study.

Reviewer #2 (Public Review):

Summary:<br /> This interesting study challenges a dogma regarding the link between bacterial metabolism decrease and tolerance to aminoglycosides (AG). The authors demonstrate that mutants well-known for being tolerant to AG, such as those of complexes I and II, are not so due to a decrease in the proton motive force (PMF) and thus antibiotic uptake, as previously reported in the literature.

Strengths:<br /> This is a complete study. These results are surprising and are based on various read-outs, such as ATP levels, pH measurement, membrane potential, and the uptake of fluorophore-labeled gentamicin. Utilizing a proteomic approach, the authors show instead that in tolerant mutants, there is a decrease in the levels of proteins associated with ribosomes (targets of AG), causing tolerance.

Weaknesses:<br /> The use of a single high concentration of aminoglycoside: my main comment on this study concerns the use of an AG concentration well above the MIC (50 µg/ml or 25 µg/ml for uptake experiments), which is 10 times higher than previously used concentrations (Kohanski, Taber) in study showing a link with PMF. This significant difference may explain the discrepancies in results. Indeed, a high concentration of AG can mask the effects of a metabolic disruption and lead to less specific uptake. However, this concentration highlights a second molecular level of tolerance. Adding experiments using lower concentrations (we propose 5 µg/ml to compare with the literature) would provide a more comprehensive understanding of AG tolerance mechanisms during a decrease in metabolism.

Another suggestion would be to test iron limitation (using an iron chelator as DIP), which has been shown to induce AG tolerance. Can the authors demonstrate if this iron limitation leads to a decrease in ribosomal proteins? This experiment would validate their hypothesis in the case of a positive result. Otherwise, it would help distinguish two types of molecular mechanisms for AG tolerance during a metabolic disruption: (i) PMF and uptake at low concentrations, (ii) ribosomal proteins at high concentrations.

eLife assessment

This study presents a valuable finding on the possible use of vilazodone in the management of thrombocytopenia through regulating 5-HT1A receptor signaling. The evidence supporting the claims of the authors is solid, with the combined use of computational methods and biochemical assays. The work will be of broad interest to scientists working in the field of thrombocytopenia.

Reviewer #1 (Public Review):

Summary:<br /> This is well-performed research with solid results and thorough controls. The authors did a good job of finding the relationship between the 5-HT1A receptor and megakaryocytopoiesis, which demonstrated the potential of vilazodone in the management of thrombocytopenia. The paper emphasizes the regulatory mechanism of 5-HT1A receptor signaling on hematopoietic lineages, which could further advance the field of thrombocytopenia for therapeutic purposes.

Strengths:<br /> This is comprehensive and detailed research using multiple methods and model systems to determine the pharmacological effects and molecular mechanisms of vilazodone. The authors conducted in vitro experiments using HEL and Meg-01 cells and in vivo experiments using Zebrafish and Kunming-irradiated mice. The experiments and bioinformatics analysis have been performed with a high degree of technical proficiency. The authors demonstrated how vilazodone binds to 5-HTR1A and regulates the SRC/MAPK pathway, which is inhibited by particular 5-HTR1A inhibitors. The authors determined this to be the mechanistic underpinning for the effects of vilazodone in promoting megakaryocyte differentiation and thrombopoiesis.

Weaknesses:<br /> 1. Which database are the drug test sets and training sets for the creation of drug screening models obtained from? What criteria are used to grade the results?

2. What is the base of each group in Figure 3b for the survival screening of zebrafish? The positivity rate of GFP-labeled platelets is too low, as indicated by the quantity of eGFP+ cells. What gating technique was used in Figure 3e?

3. In Figure 4C, the MPV values of each group of mice did not show significant downregulation or upregulation. The possible reasons for this should be explained.

4. The PPI diagram and the KEGG diagram in Figure 6 both provide a possible mechanism pathway for the anti-thrombocytopenia effect of vilazodone. How can the authors analyze the differences in their results?

5. 5-HTR1A protein expression is measured only in the Meg-01 cells assay. Similar quantitation through western blot is not shown in other cell models.

Reviewer #2 (Public Review):

Summary:<br /> The authors tried to understand the mechanism of how a drug candidate, VLZ, works on a receptor, 5-HTR1A, by activating the SRC/MAPK pathway to promote the formation of platelets.

Strengths:<br /> The authors used both computational and experimental methods. This definitely saves time and funds to find a useful drug candidate and its therapeutic marker in the subfield of platelets reduction in cancer patients. The authors achieved the aim of explaining the mechanism of VLZ in improving thrombocytopenia by using two cell lines and two animal models.

Weaknesses:<br /> Only two cell lines, HEL and Meg-01 cells, were evaluated in this study. However, using more cell lines is really depending on the workflow and the grant situations of the current research team.

eLife assessment

This manuscript is a valuable contribution to our understanding of foraging behaviors in marine bacteria. The authors present a conceptual model for how a marine bacterial species consumes an abundant polysaccharide. Using experiments in microfluidic devices and through measurements of motility and gene expression, the authors offer solid evidence that the degradation products of polysaccharide digestion can stimulate motility.

Reviewer #1 (Public Review):

Summary:<br /> The authors attempt to understand how cells forage for spatially heterogeneous complex polysaccharides. They aimed to quantify the foraging behavior and interrogate its genetic basis. The results show that cells aggregate near complex polysaccharides, and disperse when simpler byproducts are added. Dispersing cells tend to move towards the polysaccharide. The authors also use transcriptomics to attempt to understand which genes support each of these behaviors - with motility and transporter-related genes being highly expressed during dispersal, as expected.

Strengths:<br /> The paper is well written and builds on previous studies by some of the authors showing similar behavior by a different species of bacteria (Caulobacter) on another polysaccharide (xylan). The conceptual model presented at the end encapsulates the findings and provides an interesting hypothesis. I also find the observation of chemotaxis towards the polysaccharide in the experimental conditions interesting.

Weaknesses:<br /> Much of the genetic analysis, as it stands, is quite speculative and descriptive. I found myself confused about many of the genes (e.g., quorum sensing) that pop up enriched during dispersal quite in contrast to my expectations. While the authors do mention some of this in the text as worth following up on, I think the analysis as it stands adds little insight into the behaviors studied. However, I acknowledge that it might have the potential to generate hypotheses and thus aid future studies. Further, I found the connections to the carbon cycle and marine environments in the abstract weak --- the microfluidics setup by the authors is nice, but it provides limited insight into naturalistic environments where the spatial distribution and dimensionality of resources are expected to be qualitatively different.

Reviewer #2 (Public Review):

Summary:<br /> The paper sets out to understand the mechanisms underlying the colonization and degradation of marine particles using a natural Vibrio isolate as a model. The data are measurements of motility and gene expression using microfluidic devices and RNA sequencing. The results reveal that degradation products of alginate do stimulate motility but not chemotaxis. The evidence for these claims is strong. The story of how particle degradation occurs through colonization and dispersal has modest support in the data. A quantitative description of these dynamics awaits future studies.

Strengths:<br /> The microfluidic and transcriptional measurements are the central strengths of the paper as they allow the delineation of phenotypes at the cellular and molecular levels in the presence of polymer and byproducts of polymer degradation.

Weaknesses:<br /> The explanation of the microfluidics measurements is somewhat confusing but I think this could be easily remedied. The quantitative interpretation of the dispersal data could also be improved and I'm not clear if the data support the claim made.

Reviewer #3 (Public Review):

Summary:<br /> In this manuscript, Stubbusch and coauthors examine the foraging behavior of a marine species consuming an abundant marine polysaccharide. Laboratory experiments in a microfluidic setup are complemented with transcriptomic analyses aiming at assessing the genetic bases of the observed behavior. Bacterial cells consuming the polysaccharide form cohesive aggregates, while they start dispersing away when the byproduct of the digestion of the polysaccharide starts accumulating. Dispersing cells, tend to be attracted by the polysaccharide. Expression data show that motility genes are enriched during the dispersal phase, as expected. Counterintuitively, in the same phase, genes for transporters and digestions of polysaccharides are also highly expressed.

Strengths:<br /> The manuscript is very well written and easy to follow. The topic is interesting and timely. The genetic analyses provide a new, albeit complex, angle to the study of foraging behaviors in bacteria, adding to previous studies conducted on other species.

Weaknesses:<br /> I find this paper very descriptive and speculative. The results of the genetic analyses are quite counterintuitive; therefore, I understand the difficulty of connecting them to the observations coming from experiments in the microfluidic device. However, they could be better placed in the literature of foraging - dispersal cycles, beyond bacteria. In addition, the interpretation of the results is sometimes confusing.

Reviewer #1 (Public Review):

Questions and concerns:

The abstract is hard to follow. The authors there refer to a previous experiment showing that "overnight fasting diminishes excessive avoidance and speeds up fear extinction by decreasing subjective relief during threat omissions" (L26). They go on to say that "relief tracks the reward prediction error signal that governs safety learning" (L28). This is puzzling. While getting less relief/safety from avoidance actions will surely diminish avoidance (because avoidance actions are less reinforced), getting less relief/safety from omissions of an unconditioned stimulus (US) in fear extinction should slow down (not speed up) fear extinction. In the same vein, why are "lower activations [in fMRI] in the ventromedial prefrontal cortex and nucleus accumbens in response to threat omissions signaled by a safe cue" (L34) associated with "increased effective avoidance and sped up fear extinction" (L33)? This clearly goes against the existing literature on reward prediction errors (PEs) in fear learning paradigms, where these PEs in the mesolimbic dopamine system drive extinction, that is, they are associated with better extinction (and should therefore also be associated with more avoidance). For instance, in the rodent, Luo et al., 2018 (DOI: 10.1038/s41467-018-04784-7) and Salinas-Hernandez et al., 2018 (DOI: https://doi.org/10.7554/eLife.388181 of 25RESEARCH ARTICLE) and 2023 (https://doi.org/10.1016/j.neuron.2023.08.025ll) have in various constellations optogenetically enhanced and diminished, respectively, the PE signal at the time of US omission in extinction in either VTA or nucleus accumbens and thereby sped up and slowed down, respectively, extinction learning. If the results of the current experiment contradict established knowledge, the reader must be clearly informed about this. By contrast, the abstract gives the impressions as if the current results were to be expected and in line with the literature ("since relief tracks the reward prediction error signal ..., we hypothesized ...").

It would also help the reader if it was clarified that the finding of "increased effective avoidance" (L33) went counter to the hypothesis, e.g., by saying "Contrary to our hypothesis, we observed ...".

Introduction:

L51: The presentation of exposure therapy is a bit misleading and may create confusion. While it is probably correct that exposure works by "promoting safety learning", this is generally thought to be the case only for Pavlovian associations (CS-US), that is, for extinction (where safety learning creates the new association of CS and "no US"). It is, however, not generally considered to be the case for the instrumental action-outcome associations that underlie avoidance learning ("I do this or that, then I do not have to experience the feared object or situation"). Therapists try to prevent this type of learning from happening, exactly by promoting the confrontation with fear objects or situations in the absence of any avoidance action.

Generally, I think the introduction suffers from the absence of a short explanation of what avoidance and extinction learning are, behaviorally, and what types of mechanisms are believed to drive them, and that the one (avoidance) is thought to contribute to the maintenance of fears whereas the other (extinction) reduces fear. The non-specialist reader is somehow left in the dark.

In the same vein, on L63, presenting the results of their previous fasting study that serves as a discovery study for the present experiment, the authors make a distinction between "unnecessary avoidance during a signal of safety" and "effective avoidance during a signal of upcoming threat". It is really expecting too much from the reader that they will understand at this stage that a CS can become a signal of safety through extinction or that a CS not paired with a US during conditioning (a "CS-") is a safety signal and that it is not necessary to avoid such a signal, whereas a non-extinguished CS (signaling threat) may well be avoided. (At least, this is how I understood the distinction.)

I was then really confused by the following statement (L65) that "the decrease in unnecessary avoidance was mediated by lower levels of relief ... during omissions of threat". If a CS is already extinguished (has no remaining or only little threat value, that is, is a safety stimulus), there is no longer threat omission when the US does not occur, and no relief. There should also be no relief to US omission after a CS-. More importantly even, if fasted participants reported lower levels of relief from threat omission, why did they not also show less effective avoidance (which is driven by the reinforcement provided by the relief that occurs when a successful avoidance action has prevented a US from occurring after or during the CS)?

L69: Also the statement "a faster decline in relief ... ratings during ... extinction, suggesting faster decrease of threat expectancies" can only be understood by the reader if they already know what a PE is and by what rules PE-driven learning is governed (that is, essentially, if they know Rescorla-Wagner). I think the authors must explain, in order to allow a non-specialist reader to follow their text, that the PE (supposed to be indexed by the relief rating) reflects the discrepancy between the magnitude of an outcome expectation (e.g., here, expectation of the US) and the obtained outcome (here, US or not); that, therefore, a PE is generated when a subject expects a US (as a result of prior conditioning) but does not get it; that this leads to a proportional update (reduction) of the US expectation in the next trial; and that this in turn leads to a diminished PE when the US again does not occur. Notably, the reader must be made aware that the higher the PE, the higher the reduction and the faster the extinction (proportionality).

The reader must also be made aware that the update is additionally determined by some multiplicatory "transmission" function or constant (e.g., learning rate in Rescorla-Wagner) that defines the size of the relationship between the magnitude of the PE and the magnitude of the update (reduction). Hence, in two individuals, even if the magnitude of the PE is identical, the magnitude of the update may differ because of individual differences in the learning rate (to take the Rescorla-Wagner implementation). The authors, however, seem to ignore the possibility that fasting changes the learning rate.

Both the dynamics of the PE and the learning rate, of course, add complexity to the interpretation of the past and present data. But I think the authors cannot avoid this when they want to make sense of a treatment (fasting) that they believe affects safety learning. Speaking of "lower levels of relief" (L66) must be qualified by whether these lower ratings were observed initially (when the first PEs were registered at initial threat omissions, meaning that safety learning should be relatively slowed down by fasting) or on average or later during a safety learning experiment (which could indicate that learning under fasting was relatively quicker/more successful).

Following upon this, in L74, the conclusion from observations of lower levels of relief during avoidance and faster decline in relief during extinction in the previous study that "overnight fasting decreased the reward value of safety (less relief pleasantness)" may be wrong if the faster decline and the resulting lower average levels of relief were the consequence of a higher initial PE in the fasting group, as would be expected from the Rescorla-Wagner rule. If the latter were the case, this would suggest that subjects actually registered more safety (a higher discrepancy to their threat expectation) in early trials. This could also explain why fasting sped up extinction in that study (see Abstract). It might also explain why "effective avoidance" (L64) was at least maintained (although it should actually also be sped up). It might make less parsimonious explanations ("fasting biases .. to focus on food at the expense of safety", L79), requiring the presence of a food source and a utility function of accepting a threat in the obtainment of food, unnecessary.

All this, however, rests on whether I think I have understood what the authors want to say about their relief measurements and the way the operationalized avoidance in the previous study.

More unclarities due to not giving full information: L91: "... extinction and avoidance learning. Accordingly, human fMRI studies have found ... activations in the ventral striatum and the VTA during threat omissions that might contribute to establishing a new safety CS-->noUS memory that reduces the initial fear response." However, in avoidance, it is an action that is reinforced by the US omission and hence an action-->noUS memory that is being formed. The CS keeps its threat value acquired during the preceding conditioning phase, and the reduction of fear during CS presentations is contingent upon the exertion of the avoidance action.

L99: "Because overnight fasting decreased relief rating particularly during omissions after safety signals". Again, if a US is omitted after a safety signal (an extinguished CS or a CS-), there should be no PE and no relief. If there were still relief ratings at US omission after a safety signal, this would suggest extinction did not fully work or differential conditioning was not successful. In any case, it is not clear at all why relief was specifically decreased during omissions after safety signals and not (and much more so) during omissions after threat signals, where there is clearly a PE. If this was not the case, one has to wonder if something went wrong in the discovery study.

The paragraph starting L103 and the associated figure 1 could be a bit more precise and give a bit more information in order to provide the reader a proper understanding of key experimental manipulations, in particular the ART task. Please define abbreviations "CS+unav", "CS+av". L108 ff.: One gets the impression there is only one CS+, whereas there are two. Say explicitly that one CS+ remains unavoidable during the Avoidance phase (CS+unav). What is the purpose of this stimulus? Do participants learn during the Avoidance phase that the CS+unav is unavoidable and the CS+av is avoidable or is this instructed? Do participants have to press the button within a certain time after CS+unav onset in order to avoid the US, or with a certain force? Is avoidance in case of successful button pressing deterministic or probabilistic? Say that the frame with the non-lit lamp is the ITI.

Relief ratings (Figure 1b): The rating says "How pleasant was the relief that you felt?". That is, the experimenter insinuates that the participant will have felt relief and only wants to know how pleasant that relief was. The subjects has no chance to indicate there was no relief. This may be the reason why, in the discovery study, subjects indicated relief to safe stimuli, see above. Why did the authors not simply ask about the degree of relief felt, which would give a subject the chance to say there was no relief? I think this is a major flaw.

L119: "We previously found that overnight fasting reduces avoidance and relief mostly to a safe CS-." If this is really the only thing that the authors found, then the fasting manipulation in their previous study failed to modulate avoidance of CS+s and the PE signaling at the time of US omissions after CS+s, that is, after actual threat stimuli. The procedure then clearly is not suited to study influences of fasting on avoidance learning. Whatever it does manipulate, it is not relief-based avoidance learning.

L130: It makes absolutely no sense to hypothesize that a manipulation reducing relief in extinction learning will decrease activation in the neural PE circuitry at the time of US omission more after the CS- than after the CS+. Of course, the PE is highest when the US is not given after the CS+, and this is where any relief manipulation should have an effect. As said above, the authors must also specify their hypothesis with respect of timing (early or late extinction? See the animal papers cited above.)

Author Response

eLife assessment

This potentially valuable study uses classic neuroanatomical techniques and synchrotron X-ray tomography to investigate the mapping of the trunk within the brainstem nuclei of the elephant brain. Given its unique specializations, understanding the somatosensory projections from the elephant trunk would be of general interest to evolutionary neurobiologists, comparative neuroscientists, and animal behavior scientists. However, the anatomical analysis is inadequate to support the authors' conclusion that they have identified the elephant trigeminal sensory nuclei rather than a different brain region, specifically the inferior olive.

Comment: We are happy that our paper is considered to be potentially valuable. Also, the editors highlight the potential interest of our work for evolutionary neurobiologists, comparative neuroscientists, and animal behavior scientists. The editors are more negative when it comes to our evidence on the identification of the trigeminal nucleus vs the inferior olive. We have five comments on this assessment. (i) We think this assessment is heavily biased by the comments of referee 2. We will show that the referee’s comments are more about us than about our paper. Hence, the referee failed to do their job (refereeing our paper) and should not have succeeded in leveling our paper. (ii) We have no ad hoc knock-out experiments to distinguish the trigeminal nucleus vs the inferior olive. Such experiments (extracellular recording & electrolytic lesions, viral tracing would be done in a week in mice, but they cannot and should not be done in elephants. (iii) We have extraordinary evidence. Nobody has ever described a similarly astonishing match of body (trunk folds) and myeloarchitecture in the trigeminal system before. (iv) We will show that our assignment of the trigeminal nucleus vs the inferior olive is more plausible than the current hypothesis about the assignment of the trigeminal nucleus vs the inferior olive as defended by referee 2. We think this is why it is important to publish our paper. (v) We think eLife is the perfect place for our publication because the deviating views of referee 2 are published along.

Change: We performed additional peripherin-antibody staining to differentiate the inferior olive and trigeminal nucleus. Peripherin is a cytoskeletal protein that is found in peripheral nerves and climbing fibers. Specifically, climbing fibers of various species (mouse, rabbit, pig, cow, and human; Errante et al., 1998) are stained intensely with peripherin-antibodies. What is tricky for our purposes is that there is also some peripherin-antibody reactivity in the trigeminal nuclei (Errante et al., 1998). Such peripherin-antibody reactivity is weaker, however, and lacks the distinct axonal bundle signature that stems from the strong climbing fiber peripherin-reactivity as seen in the inferior olive (Errante et al., 1998). As can be seen in Author response image 1, we observe peripherin-reactivity in axonal bundles (i.e. in putative climbing fibers), in what we think is the inferior olive. We also observe weak peripherin-reactivity, in what we think is the trigeminal nucleus, but not the distinct and strong labeling of axonal bundles. These observations are in line with our ideas but are difficult to reconcile with the views of the referee. Specifically, the lack of peripherin-reactive axon bundles suggests that there are no climbing fibres in what the referee thinks is the inferior olive.

Errante, L., Tang, D., Gardon, M., Sekerkova, G., Mugnaini, E., & Shaw, G. (1998). The intermediate filament protein peripherin is a marker for cerebellar climbing fibres. Journal of neurocytology, 27, 69-84.

Author response image 1.

The putative inferior olive but not the putative trigeminal nucleus contains peripherin-positive axon bundles (presumptive climbing fibers). (A) Overview picture of a brainstem section stained with anti-peripherin-antibodies (white color). Anti-peripherin-antibodies stain climbing fibers in a wide variety of mammals. The section comes from the posterior brainstem of African elephant cow Bibi; in this posterior region, both putative inferior olive and trigeminal nucleus are visible. Note the bright staining of the dorsolateral nucleus, the putative inferior olive according to Reveyaz et al., and the trigeminal nucleus according to Maseko et al., 2013. (B) High magnification view of the dorsolateral nucleus (corresponding to the upper red rectangle in A). Anti-peripherin-positive axon bundles (putative climbing fibers) are seen in support of the inferior olive hypothesis of Reveyaz et al. (C) High magnification view of the ventromedial nucleus (corresponding to the lower red rectangle in A). The ventromedial nucleus is weakly positive for peripherin but contains no anti-peripherin-positive axon bundles (i.e. no putative climbing fibers) in support of the trigeminal nucleus hypothesis of Reveyaz et al. Note that myelin stripes – weakly visible as dark omissions – are clearly anti-peripherin-negative.

Reviewer #1:

Summary:

This fundamental study provides compelling neuroanatomical evidence underscoring the sensory function of the trunk in African and Asian elephants. Whereas myelinated tracts are classically appreciated as mediating neuronal connections, the authors speculate that myelinated bundles provide functional separation of trunk folds and display elaboration related to the "finger" projections. The authors avail themselves of many classical neuroanatomical techniques (including cytochrome oxidase stains, Golgi stains, and myelin stains) along with modern synchrotron X-ray tomography. This work will be of interest to evolutionary neurobiologists, comparative neuroscientists, and the general public, with its fascinating exploration of the brainstem of an icon sensory specialist.

Comment: We are incredibly grateful for this positive assessment.

Changes: None.

Strengths:

• The authors made excellent use of the precious sample materials from 9 captive elephants.

• The authors adopt a battery of neuroanatomical techniques to comprehensively characterize the structure of the trigeminal subnuclei and properly re-examine the "inferior olive".

• Based on their exceptional histological preparation, the authors reveal broadly segregated patterns of metabolic activity, similar to the classical "barrel" organization related to rodent whiskers.

Comment: The referee provides a concise summary of our findings.

Changes: None.

Weaknesses:

• As the authors acknowledge, somewhat limited functional description can be provided using histological analysis (compared to more invasive techniques).

• The correlation between myelinated stripes and trunk fold patterns is intriguing, and Figure 4 presents this idea beautifully. I wonder - is the number of stripes consistent with the number of trunk folds? Does this hold for both species?

Comment: We agree with the referee’s assessment. We note that cytochrome-oxidase staining is an at least partially functional stain, as it reveals constitutive metabolic activity. A significant problem of the work in elephants is that our recording possibilities are limited, which in turn limits functional analysis. As indicated in Figure 4 for the African elephant Indra, there was an excellent match of trunk folds and myelin stripes. Asian elephants have more, and less conspicuous trunk folds than African elephants. As illustrated in Figure 6, Asian elephants have more, and less conspicuous myelin stripes. Thus, species differences in myelin stripes correlate with species differences in trunk folds.

Changes: We clarify the relation of myelin stripe and trunk fold patterns in our discussion of Figure 6.  

Reviewer #2 (Public Review):

The authors describe what they assert to be a very unusual trigeminal nuclear complex in the brainstem of elephants, and based on this, follow with many speculations about how the trigeminal nuclear complex, as identified by them, might be organized in terms of the sensory capacity of the elephant trunk.

Comment: We agree with the referee’s assessment that the putative trigeminal nucleus described in our paper is highly unusual in size, position, vascularization, and myeloarchitecture. This is why we wrote this paper. We think these unusual features reflect the unique facial specializations of elephants, i.e. their highly derived trunk. Because we have no access to recordings from the elephant brainstem, we cannot back up all our functional interpretations with electrophysiological evidence; it is therefore fair to call them speculative.

Changes: None.

The identification of the trigeminal nuclear complex/inferior olivary nuclear complex in the elephant brainstem is the central pillar of this manuscript from which everything else follows, and if this is incorrect, then the entire manuscript fails, and all the associated speculations become completely unsupported.

Comment: We agree.

Changes: None.

The authors note that what they identify as the trigeminal nuclear complex has been identified as the inferior olivary nuclear complex by other authors, citing Shoshani et al. (2006; 10.1016/j.brainresbull.2006.03.016) and Maseko et al (2013; 10.1159/000352004), but fail to cite either Verhaart and Kramer (1958; PMID 13841799) or Verhaart (1962; 10.1515/9783112519882-001). These four studies are in agreement, but the current study differs.

Comment & Change: We were not aware of the papers of Verhaart and included them in the revised ms.

Let's assume for the moment that the four previous studies are all incorrect and the current study is correct. This would mean that the entire architecture and organization of the elephant brainstem is significantly rearranged in comparison to ALL other mammals, including humans, previously studied (e.g. Kappers et al. 1965, The Comparative Anatomy of the Nervous System of Vertebrates, Including Man, Volume 1 pp. 668-695) and the closely related manatee (10.1002/ar.20573). This rearrangement necessitates that the trigeminal nuclei would have had to "migrate" and shorten rostrocaudally, specifically and only, from the lateral aspect of the brainstem where these nuclei extend from the pons through to the cervical spinal cord (e.g. the Paxinos and Watson rat brain atlases), the to the spatially restricted ventromedial region of specifically and only the rostral medulla oblongata. According to the current paper, the inferior olivary complex of the elephant is very small and located lateral to their trigeminal nuclear complex, and the region from where the trigeminal nuclei are located by others appears to be just "lateral nuclei" with no suggestion of what might be there instead.

Comment: We have three comments here:

1) The referee correctly notes that we argue the elephant brainstem underwent fairly major rearrangements. In particular, we argue that the elephant inferior olive was displaced laterally, by a very large cell mass, which we argue is an unusually large trigeminal nucleus. To our knowledge, such a large compact cell mass is not seen in the ventral brain stem of any other mammal.

2) The referee makes it sound as if it is our private idea that the elephant brainstem underwent major rearrangements and that the rest of the evidence points to a conventional ‘rodent-like’ architecture. This is far from the truth, however. Already from the outside appearance (see our Figure 1B and Figure 6A) it is clear that the elephant brainstem has huge ventral bumps not seen in any other mammal. An extraordinary architecture also holds at the organizational level of nuclei. Specifically, the facial nucleus – the most carefully investigated nucleus in the elephant brainstem – has an appearance distinct from that of the facial nuclei of all other mammals (Maseko et al., 2013; Kaufmann et al., 2022). If both the overall shape and the constituting nuclei of the brainstem are very different from other mammals, it is very unlikely if not impossible that the elephant brainstem follows in all regards a conventional ‘rodent-like’ architecture.

3) The inferior olive is an impressive nucleus in the partitioning scheme we propose (Author response image 1). In fact – together with the putative trigeminal nucleus we describe – it’s the most distinctive nucleus in the elephant brainstem. We have not done volumetric measurements and cell counts here, but think this is an important direction for future work. What has informed our work is that the inferior olive nucleus we describe has the serrated organization seen in the inferior olive of all mammals. We will discuss these matters in depth below.

Changes: None.

Such an extraordinary rearrangement of brainstem nuclei would require a major transformation in the manner in which the mutations, patterning, and expression of genes and associated molecules during development occur. Such a major change is likely to lead to lethal phenotypes, making such a transformation extremely unlikely. Variations in mammalian brainstem anatomy are most commonly associated with quantitative changes rather than qualitative changes (10.1016/B978-0-12-804042-3.00045-2).

Comment: We have two comments here:

1) The referee claims that it is impossible that the elephant brainstem differs from a conventional brainstem architecture because this would lead to lethal phenotypes etc. Following our previous response, this argument does not hold. It is out of the question that the elephant brainstem looks very different from the brainstem of other mammals. Yet, it is also evident that elephants live. The debate we need to have is not if the elephant brainstem differs from other mammals, but how it differs from other mammals.

2). In principle we agree with the referee’s thinking that the model of the elephant brainstem that is most likely correct is the one that requires the least amount of rearrangements to other mammals. We therefore prepared a comparison of the model the referee is proposing (Maseko et al., 2013; see Author response table 1 below) with our proposition. We scored these models on their similarity to other mammals. We find that the referee’s ideas (Maseko et al., 2013) require more rearrangements relative to other mammals than our suggestion.

Changes: Inclusion of Author response table 1, which we discuss in depth below.

The impetus for the identification of the unusual brainstem trigeminal nuclei in the current study rests upon a previous study from the same laboratory (10.1016/j.cub.2021.12.051) that estimated that the number of axons contained in the infraorbital branch of the trigeminal nerve that innervate the sensory surfaces of the trunk is approximately 400 000. Is this number unusual? In a much smaller mammal with a highly specialized trigeminal system, the platypus, the number of axons innervating the sensory surface of the platypus bill skin comes to 1 344 000 (10.1159/000113185). Yet, there is no complex rearrangement of the brainstem trigeminal nuclei in the brain of the developing or adult platypus (Ashwell, 2013, Neurobiology of Monotremes), despite the brainstem trigeminal nuclei being very large in the platypus (10.1159/000067195). Even in other large-brained mammals, such as large whales that do not have a trunk, the number of axons in the trigeminal nerve ranges between 400,000 and 500,000 (10.1007/978-3-319-47829-6_988-1). The lack of comparative support for the argument forwarded in the previous and current study from this laboratory, and that the comparative data indicates that the brainstem nuclei do not change in the manner suggested in the elephant, argues against the identification of the trigeminal nuclei as outlined in the current study. Moreover, the comparative studies undermine the prior claim of the authors, informing the current study, that "the elephant trigeminal ganglion ... point to a high degree of tactile specialization in elephants" (10.1016/j.cub.2021.12.051). While clearly, the elephant has tactile sensitivity in the trunk, it is questionable as to whether what has been observed in elephants is indeed "truly extraordinary".

Comment: These comments made us think that the referee is not talking about the paper we submitted, but that the referee is talking about us and our work in general. Specifically, the referee refers to the platypus and other animals dismissing our earlier work, which argued for a high degree of tactile specialization in elephants. We think the referee’s intuitions are wrong and our earlier work is valid.

Changes: We prepared a Author response image 2 (below) that puts the platypus brain, a monkey brain, and the elephant trigeminal ganglion (which contains a large part of the trunk innervating cells) in perspective.

Author response image 2

The elephant trigeminal ganglion is comparatively large. Platypus brain, monkey brain, and elephant ganglion. The elephant has two trigeminal ganglia, which contain the first-order somatosensory neurons. They serve mainly for tactile processing and are large compared to a platypus brain (from the comparative brain collection) and are similar in size to a monkey brain. The idea that elephants might be highly specialized for trunk touch is also supported by the analysis of the sensory nerves of these animals (Purkart et al., 2022). Specifically, we find that the infraorbital nerve (which innervates the trunk) is much thicker than the optic nerve (which mediates vision) and the vestibulocochlear nerve (which mediates hearing). Thus, not everything is large about elephants; instead, the data argue that these animals are heavily specialized for trunk touch.

But let's look more specifically at the justification outlined in the current study to support their identification of the unusually located trigeminal sensory nuclei of the brainstem.

(1) Intense cytochrome oxidase reactivity.

(2) Large size of the putative trunk module.

(3) Elongation of the putative trunk module.

(4) The arrangement of these putative modules corresponds to elephant head anatomy.

(5) Myelin stripes within the putative trunk module that apparently match trunk folds.

(6) Location apparently matches other mammals.

(7) Repetitive modular organization apparently similar to other mammals.

(8) The inferior olive described by other authors lacks the lamellated appearance of this structure in other mammals.

Comment: We agree those are key issues.

Changes: None.

Let's examine these justifications more closely.

(1) Cytochrome oxidase histochemistry is typically used as an indicative marker of neuronal energy metabolism. The authors indicate, based on the "truly extraordinary" somatosensory capacities of the elephant trunk, that any nuclei processing this tactile information should be highly metabolically active, and thus should react intensely when stained for cytochrome oxidase. We are told in the methods section that the protocols used are described by Purkart et al (2022) and Kaufmann et al (2022). In neither of these cited papers is there any description, nor mention, of the cytochrome oxidase histochemistry methodology, thus we have no idea of how this histochemical staining was done. To obtain the best results for cytochrome oxidase histochemistry, the tissue is either processed very rapidly after buffer perfusion to remove blood or in recently perfusion-fixed tissue (e.g., 10.1016/0165-0270(93)90122-8). Given: (1) the presumably long post-mortem interval between death and fixation - "it often takes days to dissect elephants"; (2) subsequent fixation of the brains in 4% paraformaldehyde for "several weeks"; (3) The intense cytochrome oxidase reactivity in the inferior olivary complex of the laboratory rat (Gonzalez-Lima, 1998, Cytochrome oxidase in neuronal metabolism and Alzheimer's diseases); and (4) The lack of any comparative images from other stained portions of the elephant brainstem; it is difficult to support the justification as forwarded by the authors. The histochemical staining observed is likely background reactivity from the use of diaminobenzidine in the staining protocol. Thus, this first justification is unsupported.

Comment: The referee correctly notes the description of our cytochrome-oxidase reactivity staining was lacking. This is a serious mistake of ours for which we apologize very much. The referee then makes it sound as if we messed up our cytochrome-oxidase staining, which is not the case. All successful (n = 3; please see our technical comments in the recommendation section) cytochrome-oxidase stainings were done with elephants with short post-mortem times (≤ 2 days) to brain removal/cooling and only brief immersion fixation (≤ 1 day). Cytochrome-oxidase reactivity in elephant brains appears to be more sensitive to quenching by fixation than is the case for rodent brains. We think it is a good idea to include a cytochrome-oxidase staining overview picture because we understood from the referee’s comments that we need to compare our partitioning scheme of the brainstem with that of other authors. To this end, we add a cytochrome-oxidase staining overview picture (Author response image 3) along with an alternative interpretation from Maseko et al., 2013.

Changes: 1) We added details on our cytochrome-oxidase reactivity staining protocol and the cytochrome-oxidase reactivity in the elephant brain in general recommendation.

2) We provide a detailed discussion of the technicalities of cytochrome-oxidase staining below in the recommendation section, where the referee raised further criticisms.

3) We include a cytochrome-oxidase staining overview picture (Author response image 2) along with an alternative interpretation from Maseko et al., 2013.

Author response image 3.

Cytochrome-oxidase staining overview along with the Maseko et al. (2013) scheme Left, coronal cytochrome-oxidase staining overview from African elephant cow Indra; the section is taken a few millimeters posterior to the facial nucleus. Brown is putatively neural cytochrome-reactivity, and white is the background. Black is myelin diffraction and (seen at higher resolution, when you zoom in) erythrocyte cytochrome-reactivity in blood vessels (see our Figure 1E-G); such blood vessel cytochrome-reactivity is seen, because we could not perfuse the animal. There appears to be a minimal outside-in-fixation artifact (i.e. a more whitish/non-brownish appearance of the section toward the borders of the brain). This artifact is not seen in sections from Indra that we processed earlier or in other elephant brains processed at shorter post-mortem/fixation delays (see our Figure 1C). Right, coronal partitioning scheme of Maseko et al. (2013) for the elephant brainstem at an approximately similar anterior-posterior level.

The same structures can be recognized left and right. The section is taken at an anterior-posterior level, where we encounter the trigeminal nuclei in pretty much all mammals. Note that the neural cytochrome reactivity is very high, in what we refer to as the trigeminal-nuclei-trunk-module and what Maseko et al. refer to as inferior olive. Myelin stripes can be recognized here as white omissions.

At the same time, the cytochrome-oxidase-reactivity is very low in what Maseko et al. refer to as trigeminal nuclei. The indistinct appearance and low cytochrome-oxidase-reactivity of the trigeminal nuclei in the scheme of Maseko et al. (2013) is unexpected because trigeminal nuclei stain intensely for cytochrome-oxidase-reactivity in most mammals and because the trigeminal nuclei represent the elephant’s most important body part, the trunk. Staining patterns of the trigeminal nuclei as identified by Maseko et al. (2013) are very different at more posterior levels; we will discuss this matter below.

Justifications (2), (3), and (4) are sequelae from justification (1). In this sense, they do not count as justifications, but rather unsupported extensions.

Comment: These are key points of our paper that the referee does not discuss.

Changes: None.

(4) and (5) These are interesting justifications, as the paper has clear internal contradictions, and (5) is a sequelae of (4). The reader is led to the concept that the myelin tracts divide the nuclei into sub-modules that match the folding of the skin on the elephant trunk. One would then readily presume that these myelin tracts are in the incoming sensory axons from the trigeminal nerve. However, the authors note that this is not the case: "Our observations on trunk module myelin stripes are at odds with this view of myelin. Specifically, myelin stripes show no tapering (which we would expect if axons divert off into the tissue). More than that, there is no correlation between myelin stripe thickness (which presumably correlates with axon numbers) and trigeminal module neuron numbers. Thus, there are numerous myelinated axons, where we observe few or no trigeminal neurons. These observations are incompatible with the idea that myelin stripes form an axonal 'supply' system or that their prime function is to connect neurons. What do myelin stripe axons do, if they do not connect neurons? We suggest that myelin stripes serve to separate rather than connect neurons." So, we are left with the observation that the myelin stripes do not pass afferent trigeminal sensory information from the "truly extraordinary" trunk skin somatic sensory system, and rather function as units that separate neurons - but to what end? It appears that the myelin stripes are more likely to be efferent axonal bundles leaving the nuclei (to form the olivocerebellar tract). This justification is unsupported.

Comment: The referee cites some of our observations on myelin stripes, which we find unusual. We stand by the observations and comments. The referee does not discuss the most crucial finding we report on myelin stripes, namely that they correspond remarkably well to trunk folds.

Changes: None.

(6) The authors indicate that the location of these nuclei matches that of the trigeminal nuclei in other mammals. This is not supported in any way. In ALL other mammals in which the trigeminal nuclei of the brainstem have been reported they are found in the lateral aspect of the brainstem, bordered laterally by the spinal trigeminal tract. This is most readily seen and accessible in the Paxinos and Watson rat brain atlases. The authors indicate that the trigeminal nuclei are medial to the facial nerve nucleus, but in every other species, the trigeminal sensory nuclei are found lateral to the facial nerve nucleus. This is most salient when examining a close relative, the manatee (10.1002/ar.20573), where the location of the inferior olive and the trigeminal nuclei matches that described by Maseko et al (2013) for the African elephant. This justification is not supported.

Comment: The referee notes that we incorrectly state that the position of the trigeminal nuclei matches that of other mammals. We think this criticism is justified.

Changes: We prepared a comparison of the Maseko et al. (2013) scheme of the elephant brainstem with our scheme of the elephant brainstem (see Author response table 1). Here we acknowledge the referee’s argument and we also changed the manuscript accordingly.

(7) The dual to quadruple repetition of rostrocaudal modules within the putative trigeminal nucleus as identified by the authors relies on the fact that in the neurotypical mammal, there are several trigeminal sensory nuclei arranged in a column running from the pons to the cervical spinal cord, these include (nomenclature from Paxinos and Watson in roughly rostral to caudal order) the Pr5VL, Pr5DM, Sp5O, Sp5I, and Sp5C. However, these nuclei are all located far from the midline and lateral to the facial nerve nucleus, unlike what the authors describe in the elephants. These rostrocaudal modules are expanded upon in Figure 2, and it is apparent from what is shown that the authors are attributing other brainstem nuclei to the putative trigeminal nuclei to confirm their conclusion. For example, what they identify as the inferior olive in Figure 2D is likely the lateral reticular nucleus as identified by Maseko et al (2013). This justification is not supported.

Comment: The referee again compares our findings to the scheme of Maseko et al. (2013) and rejects our conclusions on those grounds. We think such a comparison of our scheme is needed, indeed.

Changes: We prepared a comparison of the Maseko et al. (2013) scheme of the elephant brainstem with our scheme of the elephant brainstem (see Author response table 1).

(8) In primates and related species, there is a distinct banded appearance of the inferior olive, but what has been termed the inferior olive in the elephant by other authors does not have this appearance, rather, and specifically, the largest nuclear mass in the region (termed the principal nucleus of the inferior olive by Maseko et al, 2013, but Pr5, the principal trigeminal nucleus in the current paper) overshadows the partial banded appearance of the remaining nuclei in the region (but also drawn by the authors of the current paper). Thus, what is at debate here is whether the principal nucleus of the inferior olive can take on a nuclear shape rather than evince a banded appearance. The authors of this paper use this variance as justification that this cluster of nuclei could not possibly be the inferior olive. Such a "semi-nuclear/banded" arrangement of the inferior olive is seen in, for example, giraffe (10.1016/j.jchemneu.2007.05.003), domestic dog, polar bear, and most specifically the manatee (a close relative of the elephant) (brainmuseum.org; 10.1002/ar.20573). This justification is not supported.

Comment: We carefully looked at the brain sections referred to by the referee in the brainmuseum.org collection. We found contrary to the referee’s claims that dogs, polar bears, and manatees have a perfectly serrated (a cellular arrangement in curved bands) appearance of the inferior olive. Accordingly, we think the referee is not reporting the comparative evidence fairly and we wonder why this is the case.

Changes: None.

Thus, all the justifications forwarded by the authors are unsupported. Based on methodological concerns, prior comparative mammalian neuroanatomy, and prior studies in the elephant and closely related species, the authors fail to support their notion that what was previously termed the inferior olive in the elephant is actually the trigeminal sensory nuclei. Given this failure, the justifications provided above that are sequelae also fail. In this sense, the entire manuscript and all the sequelae are not supported.

Comment: We disagree. To summarize:

(1) Our description of the cytochrome oxidase staining lacked methodological detail, which we have now added; the cytochrome oxidase reactivity data are great and support our conclusions.

(2)–(5)The referee does not really discuss our evidence on these points.

(6) We were wrong and have now fixed this mistake.

(7) The referee asks for a comparison to the Maseko et al. (2013) scheme (agreed, see Author response image 4 4 and Author response table 1).

(8) The referee bends the comparative evidence against us.

Changes: None.

A comparison of the elephant brainstem partitioning schemes put forward by Maseko et al 2013 and by Reveyaz et al.

To start with, we would like to express our admiration for the work of Maseko et al. (2013). These authors did pioneering work on obtaining high-quality histology samples from elephants. Moreover, they made a heroic neuroanatomical effort, in which they assigned 147 brain structures to putative anatomical entities. Most of their data appear to refer to staining in a single elephant and one coronal sectioning plane. The data quality and the illustration of results are excellent.

We studied mainly two large nuclei in six (now 7) elephants in three (coronal, parasagittal, and horizontal) sectioning planes. The two nuclei in question are the two most distinct nuclei in the elephant brainstem, namely an anterior ventromedial nucleus (the trigeminal trunk module in our terminology; the inferior olive in the terminology of Maseko et al., 2013) and a more posterior lateral nucleus (the inferior olive in our terminology; the posterior part of the trigeminal nuclei in the terminology of Maseko et al., 2013).

Author response image 4 gives an overview of the two partitioning schemes for inferior olive/trigeminal nuclei along with the rodent organization (see below).

Author response image 4.

Overview of the brainstem organization in rodents & elephants according to Maseko et. (2013) and Reveyaz et al. (this paper).

The strength of the Maseko et al. (2013) scheme is the excellent match of the position of elephant nuclei to the position of nuclei in the rodent (Author response image 4). We think this positional match reflects the fact that Maseko et al. (2013) mapped a rodent partitioning scheme on the elephant brainstem. To us, this is a perfectly reasonable mapping approach. As the referee correctly points out, the positional similarity of both elephant inferior olive and trigeminal nuclei to the rodent strongly argues in favor of the Maseko et al. (2013), because brainstem nuclei are positionally very conservative.

Other features of the Maseko et al. (2013) scheme are less favorable. The scheme marries two cyto-architectonically very distinct divisions (an anterior indistinct part) and a super-distinct serrated posterior part to be the trigeminal nuclei. We think merging entirely distinct subdivisions into one nucleus is a byproduct of mapping a rodent partitioning scheme on the elephant brainstem. Neither of the two subdivisions resemble the trigeminal nuclei of other mammals. The cytochrome oxidase staining patterns differ markedly across the anterior indistinct part (see our Author response image 4) and the posterior part of the trigeminal nuclei and do not match with the intense cytochrome oxidase reactivity of other mammalian trigeminal nuclei (Referee Figure 3). Our anti-peripherin staining indicates that there probably no climbing fibers, in what Maseko et al. think. is inferior olive; this is a potentially fatal problem for the hypothesis. The posterior part of Maseko et al. (2013) trigeminal nuclei has a distinct serrated appearance that is characteristic of the inferior olive in other mammals. Moreover, the inferior olive of Maseko et al. (2013) lacks the serrated appearance of the inferior olive seen in pretty much all mammals; this is a serious problem.

The partitioning scheme of Reveyaz et al. comes with poor positional similarity but avoids the other problems of the Maseko et al. (2013) scheme. Our explanation for the positionally deviating location of trigeminal nuclei is that the elephant grew one of the if not the largest trigeminal systems of all mammals. As a result, the trigeminal nuclei grew through the floor of the brainstem. We understand this is a post hoc just-so explanation, but at least it is an explanation.

The scheme of Reveyaz et al. was derived in an entirely different way from the Maseko model. Specifically, we were convinced that the elephant trigeminal nuclei ought to be very special because of the gigantic trigeminal ganglia (Purkart et al., 2022). Cytochrome-oxidase staining revealed a large distinct nucleus with an elongated shape. Initially, we were freaked out by the position of the nucleus and the fact that it was referred to as inferior olive by other authors. When we found an inferior-olive-like nucleus at a nearby (although at an admittedly unusual) location, we were less worried. We then optimized the visualization of myelin stripes (brightfield imaging etc.) and were able to collect an entire elephant trunk along with the brain (African elephant cow Indra). When we made the one-to-one match of Indra’s trunk folds and myelin stripes (Figure 4) we were certain that we had identified the trunk module of the trigeminal nuclei. We already noted at the outset of our rebuttal that we now consider such certainty a fallacy of overconfidence. In light of the comments of Referee 2, we feel that a further discussion of our ideas is warranted. A strength of the Reveyaz model is that nuclei look like single anatomical entities. The trigeminal nuclei look like trigeminal nuclei of other mammals, the trunk module has a striking resemblance to the trunk and the inferior olive looks like the inferior olive of other mammals.

We evaluated the fit of the two models in the form of a table (Author response table 1; below). Unsurprisingly, Author response table 1 aligns with our views of elephant brainstem partitioning.

Author response table 1.

Qualitative evaluation of elephant brainstem partitioning schemes

Author response table 1 suggests two conclusions to us. (i) The Reveyaz et al. model has mainly favorable properties. The Maseko et al. (2013) model has mainly unfavorable properties. Hence, the Reveyaz et al. model is more likely to be true. (ii) The outcome is not black and white, i.e., both models have favorable and unfavorable properties. Accordingly, we overstated our case in our initial submission and toned down our claims in the revised manuscript.

What the authors have not done is to trace the pathway of the large trigeminal nerve in the elephant brainstem, as was done by Maseko et al (2013), which clearly shows the internal pathways of this nerve, from the branch that leads to the fifth mesencephalic nucleus adjacent to the periventricular grey matter, through to the spinal trigeminal tract that extends from the pons to the spinal cord in a manner very similar to all other mammals. Nor have they shown how the supposed trigeminal information reaches the putative trigeminal nuclei in the ventromedial rostral medulla oblongata. These are but two examples of many specific lines of evidence that would be required to support their conclusions. Clearly, tract tracing methods, such as cholera toxin tracing of peripheral nerves cannot be done in elephants, thus the neuroanatomy must be done properly and with attention to detail to support the major changes indicated by the authors.

Comment: The referee claims that Maseko et al. (2013) showed by ‘tract tracing’ that the structures they refer to trigeminal nuclei receive trigeminal input. This statement is at least slightly misleading. There is nothing of what amounts to proper ‘tract tracing’ in the Maseko et al. (2013) paper, i.e. tracing of tracts with post-mortem tracers. We tried proper post-mortem tracing but failed (no tracer transport) probably as a result of the limitations of our elephant material. What Maseko et al. (2013) actually did is look a bit for putative trigeminal fibers and where they might go. We also used this approach. In our hands, such ‘pseudo tract tracing’ works best in unstained material under bright field illumination, because myelin is very well visualized. In such material, we find: (i) massive fiber tracts descending dorsoventrally roughly from where both Maseko et al. 2013 and we think the trigeminal tract runs. (ii) These fiber tracts run dorsoventrally and approach, what we think is the trigeminal nuclei from lateral.

Changes: Ad hoc tract tracing see above.

So what are these "bumps" in the elephant brainstem?

Four previous authors indicate that these bumps are the inferior olivary nuclear complex. Can this be supported?

The inferior olivary nuclear complex acts "as a relay station between the spinal cord (n.b. trigeminal input does reach the spinal cord via the spinal trigeminal tract) and the cerebellum, integrating motor and sensory information to provide feedback and training to cerebellar neurons" (https://www.ncbi.nlm.nih.gov/books/NBK542242/). The inferior olivary nuclear complex is located dorsal and medial to the pyramidal tracts (which were not labeled in the current study by the authors but are clearly present in Fig. 1C and 2A) in the ventromedial aspect of the rostral medulla oblongata. This is precisely where previous authors have identified the inferior olivary nuclear complex and what the current authors assign to their putative trigeminal nuclei. The neurons of the inferior olivary nuclei project, via the olivocerebellar tract to the cerebellum to terminate in the climbing fibres of the cerebellar cortex.

Comment: We agree with the referee that in the Maseko et al. (2013) scheme the inferior olive is exactly where we expect it from pretty much all other mammals. Hence, this is a strong argument in favor of the Maseko et al. (2013) scheme and a strong argument against the partitioning scheme suggested by us.

Changes: Please see our discussion above.

Elephants have the largest (relative and absolute) cerebellum of all mammals (10.1002/ar.22425), this cerebellum contains 257 x109 neurons (10.3389/fnana.2014.00046; three times more than the entire human brain, 10.3389/neuro.09.031.2009). Each of these neurons appears to be more structurally complex than the homologous neurons in other mammals (10.1159/000345565; 10.1007/s00429-010-0288-3). In the African elephant, the neurons of the inferior olivary nuclear complex are described by Maseko et al (2013) as being both calbindin and calretinin immunoreactive. Climbing fibres in the cerebellar cortex of the African elephant are clearly calretinin immunopositive and also are likely to contain calbindin (10.1159/000345565). Given this, would it be surprising that the inferior olivary nuclear complex of the elephant is enlarged enough to create a very distinct bump in exactly the same place where these nuclei are identified in other mammals?

Comment: We agree with the referee that it is possible and even expected from other mammals that there is an enlargement of the inferior olive in elephants. Hence, a priori one might expect the ventral brain stem bumps to the inferior olive, this is perfectly reasonable and is what was done by previous authors. The referee also refers to calbindin and calretinin antibody reactivity. Such antibody reactivity is indeed in line with the referee’s ideas and we considered these findings in our Referee Table 1. The problem is, however, that neither calbindin nor calretinin antibody reactivity are highly specific and indeed both nuclei in discussion (trigeminal nuclei and inferior olive) show such reactivity. Unlike the peripherin-antibody staining advanced by us, calbindin nor calretinin antibody reactivity cannot distinguish the two hypotheses debated.

Changes: Please see our discussion above.

What about the myelin stripes? These are most likely to be the origin of the olivocerebellar tract and probably only have a coincidental relationship with the trunk. Thus, given what we know, the inferior olivary nuclear complex as described in other studies, and the putative trigeminal nuclear complex as described in the current study, is the elephant inferior olivary nuclear complex. It is not what the authors believe it to be, and they do not provide any evidence that discounts the previous studies. The authors are quite simply put, wrong. All the speculations that flow from this major neuroanatomical error are therefore science fiction rather than useful additions to the scientific literature.

Comment: It is unlikely that the myelin stripes are the origin of the olivocerebellar tract as suggested by the referee. Specifically, the lack of peripherin-reactivity indicates that these fibers are not climbing fibers (Referee Figure 1). In general, we feel the referee does not want to discuss the myelin stripes and obviously thinks we made up the strange correspondence of myelin stripes and trunk folds.

Changes: Please see our discussion above.

What do the authors actually have?

The authors have interesting data, based on their Golgi staining and analysis, of the inferior olivary nuclear complex in the elephant.

Comment: The referee reiterates their views.

Changes: None.

Reviewer #3 (Public Review):

Summary:

The study claims to investigate trunk representations in elephant trigeminal nuclei located in the brainstem. The researchers identified large protrusions visible from the ventral surface of the brainstem, which they examined using a range of histological methods. However, this ventral location is usually where the inferior olivary complex is found, which challenges the author's assertions about the nucleus under analysis. They find that this brainstem nucleus of elephants contains repeating modules, with a focus on the anterior and largest unit which they define as the putative nucleus principalis trunk module of the trigeminal. The nucleus exhibits low neuron density, with glia outnumbering neurons significantly. The study also utilizes synchrotron X-ray phase contrast tomography to suggest that myelin-stripe-axons traverse this module. The analysis maps myelin-rich stripes in several specimens and concludes that based on their number and patterning they likely correspond with trunk folds; however, this conclusion is not well supported if the nucleus has been misidentified.

Comment: The referee gives a concise summary of our findings. The referee acknowledges the depth of our analysis and also notes our cellular results. The referee – in line with the comments of Referee 2 – also points out that a misidentification of the nucleus under study is potentially fatal for our analysis. We thank the referee for this fair assessment.

Changes: We feel that we need to alert the reader more broadly to the misidentification concern. We think the critical comments of Referee 2, which will be published along with our manuscript, will go a long way in doing so. We think the eLife publishing format is fantastic in this regard. We will also include pointers to these concerns in the revised manuscript.

Strengths:

The strength of this research lies in its comprehensive use of various anatomical methods, including Nissl staining, myelin staining, Golgi staining, cytochrome oxidase labeling, and synchrotron X-ray phase contrast tomography. The inclusion of quantitative data on cell numbers and sizes, dendritic orientation and morphology, and blood vessel density across the nucleus adds a quantitative dimension. Furthermore, the research is commendable for its high-quality and abundant images and figures, effectively illustrating the anatomy under investigation.

Comment: Again, a very fair and balanced set of comments. We are thankful for these comments.

Changes: None.

Weaknesses:

While the research provides potentially valuable insights if revised to focus on the structure that appears to be the inferior olivary nucleus, there are certain additional weaknesses that warrant further consideration. First, the suggestion that myelin stripes solely serve to separate sensory or motor modules rather than functioning as an "axonal supply system" lacks substantial support due to the absence of information about the neuronal origins and the termination targets of the axons. Postmortem fixed brain tissue limits the ability to trace full axon projections. While the study acknowledges these limitations, it is important to exercise caution in drawing conclusions about the precise role of myelin stripes without a more comprehensive understanding of their neural connections.

Comment: The referee points out a significant weakness of our study, namely our limited understanding of the origin and targets of the axons constituting the myelin stripes. We are very much aware of this problem and this is also why we directed high-powered methodology like synchrotron X-ray tomograms to elucidate the structure of myelin stripes. Such analysis led to advances, i.e., we now think, what looks like stripes are bundles and we understand the constituting axons tend to transverse the module. Such advances are insufficient, however, to provide a clear picture of myelin stripe connectivity.

Changes: We think solving the problems raised by the referee will require long-term methodological advances and hence we will not be able to solve these problems in the current revision. Our long-term plans for confronting these issues are the following: (i) Improving our understanding of long-range connectivity by post-mortem tracing and MR-based techniques such as Diffusion-Tensor-Imaging. (ii) Improving our understanding of mid and short-range connectivity by applying even larger synchrotron X-ray tomograms and possible serial EM.

Second, the quantification presented in the study lacks comparison to other species or other relevant variables within the elephant specimens (i.e., whole brain or brainstem volume). The absence of comparative data for different species limits the ability to fully evaluate the significance of the findings. Comparative analyses could provide a broader context for understanding whether the observed features are unique to elephants or more common across species. This limitation in comparative data hinders a more comprehensive assessment of the implications of the research within the broader field of neuroanatomy. Furthermore, the quantitative comparisons between African and Asian elephant specimens should include some measure of overall brain size as a covariate in the analyses. Addressing these weaknesses would enable a richer interpretation of the study's findings.

Comment: The referee suggests another series of topics, which include the analysis of brain parts volumes or overall brain size. We agree these are important issues, but we also think such questions are beyond the scope of our study.

Changes: We hope to publish comparative data on elephant brain size and shape later this year.  

Author Response

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

Public Reviews:

We thank the reviewers for their insightful comments on our manuscript. We have addressed the reviewers’ comments below and in the revised manuscript.

Reviewer #1:

Comment #1: The authors found differences in the initial spike doublet of action potentials between cortical neurons in experimental and control conditions (Figure 2e). The action potential firing frequency of the first two APs (instant firing frequency) of recorded neurons shall be quantified to investigate whether there are statistical differences between the action potential firing frequency in cortical neurons in different experimental groups versus control conditions.

Response: As suggested by the reviewer, we have quantified the first interspike interval (ISI; time between the 1st and 2nd action potential). The data is included in Fig. 2h as well as in Fig. S3e and Table 1. The Results and Methods have also been updated accordingly.

Comment #2: The mTORS12215Y induced the largest changes in Ih current amplitudes in cortical neurons compared with other experimental conditions. Whether the HCN4 channel expression is regulated by mTOR pathway activation, or could there be possible interactions between the HCN channel and mTORS12215Y mutant protein?

Response: Our previous findings using the RhebS16H mutation support the idea that increased expression of HCN4 channels is regulated by mTOR pathway activation. This is evidenced by its sensitivity to rapamycin (a mTOR inhibitor) and expression of constitutively active 4E-BP1 (a translational repressor downstream of mTORC1). Since mTORS2215Y directly hyperactivates mTORC1 and there are no known interactions between HCN channels and mTORS2215Y, our data strongly suggests that abnormal HCN4 channel expression occurs via mTORC1 hyperactivation in this condition. We have revised our Discussion to make this point clearer.

Comment #3: A comparison of the electrophysiological characteristics of cortical neurons in different experimental conditions in the present study and pathological neurons in human FCD reported in previous literature could be interesting. Inducing pathological gene mutations or knocking out key genes in mTOR pathway in the rodent cortex - which approach could better model human FCD?

Response: We agree with the reviewer and have added a new paragraph in the Discussion to compare our electrophysiology results to those of previous studies done on human FCDII and TSC cytomegalic neurons. With regards to the reviewer’s question about which of the two approaches in the rodent cortex – inducing pathological gene mutations or knocking out key genes in the mTOR pathway – would better model human FCD, our study emphasizes the importance of considering gene-specific mechanisms in FCDII. Thus, modeling the genetically distinct FCDIIs will require using gene-specific manipulations. We have revised our Discussion to include this point. With that said, for some phenotypes that are generalized across FCDII independent of the mTOR pathway genes, using pathogenic mutations of mTOR activators or knockout of negative mTOR regulators would likely both be appropriate models. Of note, as discussed in the manuscript, there are also technical factors to be considered when choosing to use a pathogenic gene mutation versus knocking out a gene (the latter which would depend on the half-life of the proteins).

Reviewer #2:

Comment #1: The authors postulate that all the findings are dependent on mTORC1-related effects but don't assess whether some of the differences could be due to effects on mTORC2 signaling. mTORC2 is an important and poorly understood alternative isoform of mTOR (due to rictor binding) that has effects on distinct cell signaling pathways and in particular actin polymerization. This doesn't diminish the effects of the current analysis of mTORC1 but could explain genotypic differences in each variable. A few prior studies have assessed the role of mTORC2 in epileptogenesis and cortical malformations (Chen et al., 2019).

Response: We agree with the reviewer and have revised our Discussion to include the possibility of mTORC2 contribution to the gene-specific phenotypic differences.

Comment #2: The slice recordings were performed in the usual recording aCSF buffer conditions but there is no assessment of the role of amino acids or nutrients in the bath. While it is clear that valuable and viable acute slice recordings can be made in aCSF, the role of the mTOR pathway is to modulate cell growth in response to nutrient conditions. Thus, one variable that could be manipulated and assessed currently in this study is the levels of amino acids i.e., leucine and arginine added to the bath since DEPDC5 and TSC1 are responsive to ambient amino acid levels.

Response: We thank the reviewer for this great suggestion, and we intend to pursue this as part of another study.

Comment #3: The analysis concedes that the role of somatic mutations in cortical malformations may depend not only on genotypic effects but also on allelic load and cellular subtype affected by the mutation. Thus, it would be interesting to see if electroporation either at E14 or E16, thereby affecting a distinct pool of progenitors, would mitigate or accentuate differences between mTOR pathway genes.

Response: We agree with the reviewer. This is a crucial experiment that we hope to perform in the future. We have also added a paragraph in our Discussion to address this important point.

Comment #4: Treatment with rapamycin and zatebradine in each condition would have added to the strength of the findings to determine the mTOR-dependence and reversibility of HCN4 effects.

Response: We previously demonstrated the mTORC1 dependence of HCN4 expression in the RhebS16H condition using rapamycin and expression of constitutively active 4E-BP1. 4E-BP1 is a translational repressor downstream of mTORC1. In the 4E-BP1 study, we used a conditional system to express 4EBP1F113A (mutation that resists inactivation by mTORC1) in adolescent mice while RhebS16H (and thus mTORC1 activation) was expressed embryonically. 4E-BP1F113A expression suppressed Ih current and HCN4 expression, suggesting that aberrant HCN4 expression can be reversed by decreasing mTORC1regulated translation. Based on these data and the findings that rapamycin suppressed abnormal HCN4 expression, we postulate that increased HCN4 expression in the different gene conditions examined in the present study occurs via the mTORC1 pathway. However, we agree with the reviewer that treating each of the conditions with rapamycin would provide direct evidence of their mTORC1 dependence. Additionally, treating each condition with the HCN channel blocker zatebradine would also add strength to the findings. We have added a comment in the Discussion to acknowledge this point.

Reviewer #1 (Recommendations For The Authors):

Comment #1: The authors found increased frequency or amplitudes of spontaneous postsynaptic currents in different experimental cohorts. These data may not be sufficient to conclude increased synaptic excitability, because there are no pharmacological experiments to verify whether the recorded inward currents are excitatory or inhibitory postsynaptic currents. An alternative approach could be analyzing the decay time of spontaneous postsynaptic currents, the excitatory postsynaptic currents had relatively faster decay time compared with inhibitory postsynaptic currents.

Response: Thank you for the comment. We apologize for the lack of clarity and have added the following text in the Results to clarify: “To separate sEPSCs from spontaneous inhibitory postsynaptic currents (sIPSCs), we used an intracellular solution rich in K-gluconate to impose a low intracellular Cl- concentration and recorded at a holding potential of -70 mV, which is near the Cl- reversal potential. The 90%-10% decay time of the measured synaptic currents ranged between 4-8 ms in all conditions (mean ± SD: control: 4.9 ± 1.6; RhebY35L: 5.2 ± 1.4; mTORS2215Y: 7.4 ± 1.4; control: 6.8 ± 0.7; Depdc5KO: 7.4 ± 1.0; PtenKO: 8.1 ± 0.9; Tsc1KO: 7.4 ± 0.9), consistent with the expected decay time for sEPSCs and shorter than the decay time for sIPSCs (Kroon et al, 2019). The recorded synaptic currents were therefore considered to be sEPSCs.”

Comment #2: There are typos of Depdc5 in the text and figure legends.

Response: Thank you for noticing this error. We have corrected the typos in the manuscript.

eLife assessment

This manuscript examines shared and divergent mechanisms of disruptions of five different mTOR pathway genes on embryonic mouse brain neuronal development. The significance of the manuscript is important, because it bridges several different genetic causes of focal malformations of cortical development. The strength of evidence is compelling, relying on both gain and loss of function, demonstrating differential impact on excitatory synaptic activity, conferring gene-specific mechanisms of hyperexcitability. The results have both theoretical and practical implications for the field of developmental neurobiology and clinical epilepsy.

Author Response

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

Reviewer #1

Strengths:

This study uses a carefully constructed experiment design and decision-making task that allows separation of multiple electroencephalographic (EEG) signals thought to track different stages of decision-making. For example, the steady-state visual evoked potential measures can be cleanly dissociated from more anterior beta-band activity over the motor cortex. They also allow evaluation of how cued expectancy effects may unfold over a number of testing sessions. This is important because the most consistent evidence of expectation-related modulations of electrophysiological measures (using EEG, local field potentials, or single neuron firing rates) is from studies of nonhuman primates that involved many days of cue-stimulus contingency learning, and there is a lack of similar work using several testing sessions in humans. Although there were several experimental conditions included in the study, careful trial-balancing was conducted to minimise biases due to incidental differences in the number of trials included for analyses across each condition. Performance for each individual was also carefully calibrated to maximise the possibility of identifying subtle changes in task performance by expectation and avoid floor or ceiling effects.

We would like to thank Reviewer 1 for these very positive comments.

Weaknesses:

Although the experiment and analysis methods are cohesive and well-designed, there are some shortcomings that limit the inferences that can be drawn from the presented findings.

Comment #1

The first relates to the measures of SSVEPs and their relevance for decision-making in the task. In order to eliminate the influence of sporadic pulses of contrast changes that occurred during stimulus presentation, a time window of 680-975 ms post-stimulus onset was used to measure the SSVEPs. The mean response times for the valid and neutral cues were around 850-900 ms for correct responses, and within the same time window for errors in the invalid cue condition. In addition, a large portion of response times in perceptual decision-making tasks are substantially faster than the mean due to right-skewed response time distributions that are typically observed. As it has also been estimated to require 70-100 ms to execute a motor action (e.g., a keypress response) following the commitment to a decision. This raises some concerns about the proportion of trials in which the contrast-dependent visual responses (indexed by the SSVEPs) indexed visual input that was actually used to make the decision in a given trial. Additional analyses of SSVEPs that take the trial-varying pulses into account could be run to determine whether expectations influenced visual responses earlier in the trial.

The reviewer raises a very valid point and, indeed, it is an issue that we grappled with in our analyses. Actually, in this study, the RT distributions were not right-skewed, but appear to be relatively normal (RT distributions shown below). This is something that we have previously observed when using tasks that involve an initial zero-evidence lead in at the start of each trial which means that participants cannot start accumulating at stimulus onset and must rely on their knowledge of the lead-in duration to determine when the physical evidence has become available (e.g. Kelly et al 2021, Nat Hum Beh). We agree that it is important to establish whether the reported SSVEP modulations occur before or after choice commitment. In our original submission we had sought to address this question through our analysis of the response-locked ‘difference SSVEP’. Figure 4D clearly indicates that the cue modulations are evident before as well as after response.

However, we have decided to include an additional Bayesian analysis of the response-locked signal to offer more evidence that the cue effect is not a post-response phenomenon.

Manuscript Changes

To quantify the evidence that the cue effect was not driven by changes in the signal after the response, we ran Bayesian one-way ANOVAs on the SSVEP comparing the difference across cue conditions before and after the response. If the cue effect only emerged after the response, we would expect the difference between invalid and neutral or invalid and valid cues to increase in the post-response window. There was no compelling evidence of an increase in the effect when comparing invalid to neutral (BF10 = 1.58) or valid cues (BF10 = 0.32).

Comment #2

Presenting response time quantile plots may also help to determine the proportions of motor responses (used to report a decision) that occurred during or after the SSVEP measurement window.

We agree that it may be helpful for the reader to be able to determine the proportion of responses occurring at different phases of the trial, so we have included the requested response time quantile plot (shown below) as a supplementary figure.

Author response image 1.

Reaction time quantiles across cue conditions. The plot illustrates the proportion of trials where responses occurred at different stages of the trial. The SSVEP analysis window is highlighted in purple.

Comment #3

In addition, an argument is made for changes in the evidence accumulation rate (called the drift rate) by stimulus expectancy, corresponding to the observed changes in SSVEP measures and differences in the sensory encoding of the stimulus. This inference is limited by the fact that evidence accumulation models (such as the Diffusion Decision Model) were not used to test for drift rate changes as could be determined from the behavioural data (by modelling response time distributions). There appear to be ample numbers of trials per participant to test for drift rate changes in addition to the starting point bias captured in earlier models. Due to the very high number of trials, models could potentially be evaluated for each single participant. This would provide more direct evidence for drift rate changes than the findings based on the SSVEPs, particularly due to the issues with the measurement window relating to the response times as mentioned above.

The focus of the present study was on testing for sensory-level modulations by predictive cues, rather than testing any particular models. Given that the SSVEP bears all the characteristics of a sensory evidence encoding signal, we believe it is reasonable to point out that its modulation by the cues would very likely translate to a drift rate effect. But we do agree with the reviewer that any connection between our results and previously reported drift rate effects can only be confirmed with modelling and we have tried to make this clear in the revised text. We plan to comprehensively model the data from this study in a future project. While we do indeed have the benefit of plenty of trials, the modelling process will not be straightforward as it will require taking account of the pulse effects which could have potentially complicated, non-linear effects. In the meantime, we have made changes to the text to qualify the suggestion and stress that modelling would be necessary to determine if our hypothesis about a drift rate effect is correct.

Manuscript Changes

(Discussion): [...] We suggest that participants may have been able to stabilise their performance across task exposure, despite reductions in the available sensory evidence, by incorporating the small sensory modulation we detected in the SSVEP. This would suggest that the decision process may not operate precisely as the models used in theoretical work describe. Instead, our study tentatively supports a small number of modelling investigations that have challenged the solitary role of starting point bias, implicating a drift bias (i.e. a modulation of the evidence before or upon entry to the decision variable) as an additional source of prior probability effects in perceptual decisions (Dunovan et al., 2014; Hanks et al., 2011; Kelly et al., 2021; van Ravenzwaaij et al., 2012 Wyart et al., 2012) and indicates that these drift biases could, at least partly, originate at the sensory level. However, this link could only be firmly established with modelling in a future study.

Recommendations For The Authors:

Comment #4

The text for the axis labels and legends in the figures is quite small relative to the sizes of the accompanying plots. I would recommend to substantially increase the sizes of the text to aid readability.

Thank you for this suggestion. We have increased the size of the axis labels and made the text in the figure legends just 1pt smaller than the text in the main body of the manuscript.

Comment #5

It is unclear if the scalp maps for Figure 5 (showing the mu/beta distributions) are on the same scale or different scales. I assume they are on different scales (adjusted to the minimum/maximum within each colour map range), as a lack of consistent signals (in the neutral condition) would be expected to lead to a patchy pattern on the scalp as displayed in that figure (due to the colour range shrinking to the degree of noise across electrodes). I would recommend to include some sort of colour scale to show that, for example, in the neutral condition there are no large-amplitude mu/ beta fluctuations distributed somewhat randomly across the scalp.

Thank you to the reviewer for pointing this out. They were correct, the original topographies were plotted according to their own scale. The topographies in Figure 5 have now been updated to put them on a common scale and we have included a colour bar (as shown below). The caption for Figure 5 has also been updated to confirm that the topos are on a common scale.

Author response image 2.

Manuscript Changes

(Figure 5 Caption): [...] The topography of MB activity in the window - 200:0 ms before evidence onset is plotted on a common scale for neutral and cued conditions separately.

Comment #6

In Figure 2, the legend is split across the two panels, despite the valid/invalid/neutral legend also applying to the first panel. This gives an initial impression that the legend is incomplete for the first panel, which may confuse readers. I would suggest putting all of the legend entries in the first panel, so that all of this information is available to readers at once.

We are grateful to the reviewer for spotting this. Figure 2 has been updated so that the full legend is presented in the first panel, as shown below.

Author response image 3.

Comment #7

Although linear mixed-effects models (using Gaussian families) for response times are standard in the literature, they incorrectly specify the distributions of response times to be Gaussian instead of substantially right-skewed. Generalised linear mixed-effects models using gamma families and identity functions have been shown to more accurately model distributions of response times (see Lo and Andrews, 2015. Frontiers in Psychology). The authors may consider using these models in line with good practice, although it might not make a substantial difference relating to the patterns of response time differences.

We appreciate this thoughtful comment from Reviewer 1. Although RT distributions are often right skewed, we have previously observed that RT distributions can be closer to normal when the trial incorporates a lead-in phase with no evidence (e.g. Kelly et al 2021, Nat Hum Beh). Indeed, the distributions we observed in this study were markedly Gaussian (as shown in the plot below). Given the shape of these distributions and the reviewer’s suggestion that adopting alternative models may not lead to substantial differences to our results, we have decided to leave the mixed effects models as they are in the manuscript, but we will take note of this advice in future work.

Author response image 4.

Reviewer #2

Strengths:

The work is executed expertly and focuses cleverly on two features of the EEG signals that can be closely connected to specific loci of the perceptual decision-making process - the SSVEP which connects closely to sensory (visual) encoding, and Mu-Beta lateralisation which connects closely to movement preparation. This is a very appropriate design choice given the authors' research question.

Another advantage of the design is the use of an unusually long training regime (i.e., for humans) - which makes it possible to probe the emergence of different expectation biases in the brain over different timecourses, and in a way that may be more comparable to work with nonhuman animals (who are routinely trained for much longer than humans).

We are very grateful for these positive comments from Reviewer 2.

Weaknesses:

In my view, the principal shortcoming of this study is that the experimental task confounds expectations about stimulus identity with expectations about to-be-performed responses. That is, cues in the task don't just tell participants what they will (probably) see, but what they (probably) should do.

In many respects, this feature of the paradigm might seem inevitable, as if specific stimuli are not connected to specific responses, it is not possible to observe motor preparation of this kind (e.g., de Lange, Rahnev, Donner & Lau, 2013 - JoN).

However, the theoretical models that the authors focus on (e.g., drift-diffusion models) are models of decision (i.e., commitment to a proposition about the world) as much as they are models of choice (i.e., commitment to action). Expectation researchers interested in these models are often interested in asking whether predictions influence perceptual processing, perceptual decision, and/ or response selection stages (e.g., Feuerriegel, Blom & Hoogendorn, 2021 - Cortex), and other researchers have shown that parameters like drift bias and start point bias can be shifted in paradigms where observers cannot possibly prepare a response (e.g., Thomas, Yon, de Lange & Press, 2020 - Psych Sci).

The present paradigm used by Walsh et al makes it possible to disentangle sensory processing from later decisional processes, but it blurs together the processes of deciding about the stimulus and choosing/initiating the response. This ultimately limits the insights we can draw from this study - as it remains unclear whether rapid changes in motor preparation we see reflect rapid acquisition of new decision criterion or simple cue-action learning. I think this would be important for comprehensively testing the models the authors target - and a good avenue for future work.

Thank you to Reviewer 2 for these observations. We adopted this paradigm because it is typical of the perceptual decision making literature and our central focus in this study was to test for a sensory-level modulation as a source of a decision bias. We are pleased that the Reviewer agrees that the paradigm successfully disentangles sensory encoding from later decisional processes since this was our priority. However, we agree with Reviewer 2 that because the response mapping was known to the participants, the cues predicted both the outcome of the perceptual decision (“Is this a left- or right-tilted grating?”) and the motor response that the participant should anticipate making (“It’s probably going to be a left click on this trial”). They are correct that this makes it difficult to know whether the changes in motor preparation elicited by the predictive cues reflect action-specific preparation or a more general shift in the boundaries associated with the alternate perceptual interpretations. We fully agree that it remains an interesting and important question and in our future work we hope to conduct investigations that better dissect the distinct components of the decision process during prior-informed decisions. In the interim, we have made some changes to the manuscript to reflect the Reviewer’s concerns and better address this limitation of the study design (these are detailed in the response to the comment below).

Recommendations For The Authors:

Comment #8

As in my public review, my main recommendation to the authors is to think a bit more in the presentation of the Introduction and Discussion about the difference between 'perceiving', 'deciding', and 'responding'.

The paper is presently framed in terms of the debates around whether expectations bias decision or bias perception - and these debates are in turn mapped onto different aspects of the driftdiffusion model. Biases in sensory gain, for instance, are connected to biases in the drift rate parameter, while decisional shifts are connected to parameters like start points.

In line with this kind of typology, the authors map their particular EEG signals (SSVEP and MB lateralisation) onto perception and decision. I see the logic, but I think the reality of these models is more nuanced.

In particular, strictly speaking, the process of evidence accumulation to bound is the formation of a 'decision' (i.e., a commitment to having seen a particular stimulus). Indeed, the dynamics of this process have been beautifully described by other authors on this paper in the past. Since observers in this task simultaneously form decisions and prepare actions (because stimuli and responses are confounded) it is unclear whether changes in motor preparation are reflecting changes in what perceivers 'decide' (i.e., changes in what crosses the decision threshold) or what they 'do' (i.e., changes in the motor response threshold). This is particularly important for the debate around whether expectations change 'perception' or 'decision' because - in some accounts - is the accumulation of evidence to the bound that is hypothesised to cause the perceptual experience observers actually have (Pereira, Perrin & Faivre, 2022 - TiCS). The relevant 'bound' here though is not the bound to push the button, but the bound for the brain to decide what one is actually 'seeing'.

I completely understand the logic behind the authors' choices, but I would have liked more discussion of this issue. In particular, it seems strange to me to talk about the confounding of stimuli and responses as a particular 'strength' of this design in the manuscript - when really it is a 'necessary evil' for getting the motor preparation components to work. Here is one example from the Introduction:

"While some have reported expectation effects in humans using EEG/MEG, these studies either measured sensory signals whose relevance to the decision process is uncertain (e.g. Blom et al., 2020; Solomon et al., 2021; Tang et al., 2018) and/or used cues that were implicit or predicted a forthcoming stimulus but not the correct choice alternative (e.g. Aitken et al., 2020; Feuerriegel et al., 2021b; Kok et al., 2017). To assess whether prior probabilities modulate sensory-level signals directly related to participants' perceptual decisions, we implemented a contrast discrimination task in which the cues explicitly predicted the correct choice and where sensory signals that selectively trace the evidence feeding the decision process could be measured during the process of deliberation."

I would contend that this design allows you to pinpoint signals related to participant's 'choices' or 'actions' but not necessarily their 'decisions' in the sense outlined above.

As I say though, I don't think this is fatal and I think the paper is extremely interesting in any case. But I think it would be strengthened if some of these nuances were discussed a bit more explicitly, as a 'perceptual decision' is more than pushing a button. Indeed, the authors might want to consider discussing work that shows the neural overlap between deciding and acting breaks down when Ps cannot anticipate which actions to use to report their choices ahead of time (Filimon, Philiastides, Nelson, Kloosterman & Heekeren, 2013 - JoN) and/or work which has combined expectations with drift diffusion modelling to show how expectations change drift bias (Yon, Zainzinger, de Lange, Eimer & Press, 2020 - JEP:General) and/or start bias (Thomas, Yon, de Lange & Press, 2020 - Psych Sci) even when Ps cannot prepare a motor response ahead of time.

While our focus was on testing for sensory-level modulations, we think the question of whether the motor-level effects we observed are attributable to the task design or represents a more general perceptual bound adjustment is an important question for future research. In our previous work, we have examined this distinction between abstract, movement-independent evidence accumulation (indexed by the centro-parietal positivity, CPP) and response preparation in detail. The CPP has been shown to trace evidence accumulation irrespective of whether the sensory alternatives are associated with a specific response or not (Twomey et al 2016, J Neurosci). When speed pressure is manipulated in tasks with fixed stimulus-response mappings we have found that the CPP undergoes systematic adjustments in its pre-response amplitude that closely accord with the starting-level modulations observed in mu/beta, suggesting that motor-level adjustments do still translate to differences at the perceptual level under these task conditions (e.g. Kelly et al 2021, Nat Hum Beh; Steinemann et al., 2018, Nat Comms). We have also observed that the CPP and mu-beta exhibit corresponding adjustments in response to predictive cues (Kelly et al., 2021) that are consistent with both a starting-point shift and drift rate bias. However, the Kelly et al. study did not include a signature of sensory encoding and therefore could not test for sensory-level modulations.

We have added some remarks to the discussion to acknowledge this issue with the interpretation of the preparatory shifts in mu-beta activity we observed when the predictive cues were presented, and we have included references to the papers that the reviewer helpfully provided. We have also offered some additional consideration of the features of the task design that may have influenced the SSVEP results.

Manuscript Changes

An implication of using cues that predict not just the upcoming stimulus, but the most likely response, is that it becomes difficult to determine if preparatory shifts in mu-beta (MB) activity that we observed reflect adjustments directly influencing the perceptual interpretation of the stimulus or simply preparation of the more probable action. When perceptual decisions are explicitly tied to particular modes of response, the decision state can be read from activity in motor regions associated with the preparation of that kind of action (e.g. de Lafuente et al., 2015; Ding & Gold, 2012; Shadlen & Newsome, 2001; Romo et al., 2004), but these modules appear to be part of a constellation of decision-related areas that are flexibly recruited based on the response modality (e.g. Filimon et al., 2013). When the response mapping is withheld or no response is required, MB no longer traces decision formation (Twomey et al., 2015), but an abstract decision process is still readily detectable (e.g. O’Connell et al., 2012), and modelling work suggests that drift biases and starting point biases (Thomas et al., 2020; Yon et al., 2021) continue to influence prior-informed decision making. While the design of the present study does not allow us to offer further insight about whether the MB effects we observed were inherited from strategic adjustments at this abstract level of the decision process, we hope to conduct investigations in the future that better dissect the distinct components of prior-informed decisions to address this question.

Several other issues remain unaddressed by the present study. One, is that it is not clear to what extent the sensory effects may be influenced by features of the task design (e.g. speeded responses under a strict deadline) and if these sensory effects would generalise to many kinds of perceptual decision-making tasks or whether they are particular to contrast discrimination.

Comment #9

On a smaller, unrelated point - I thought the discussion in the Discussion section about expectation suppression was interesting, but I did not think it was completely logically sound. The authors suggest that they may see relative suppression (rather than enhancement) of their marginal SSVEP under a 'sharpening' account because these accounts suggest that there is a relative suppression of off-channel sensory units, and there are more off-channel sensory units than onchannel sensory units (i.e., there are usually more possibilities we don't expect than possibilities that we do, and suppressing the things we don't expect should therefore yield overall suppression).

However, this strikes me as a non-sequitur given that the marginal SSVEP only reflects featurespecific visual activity (i.e., activity tuned to one of the two grating stimuli used). The idea that there are more off-channel than on-channel units makes sense for explaining why we would see overall signal drops on expected trials e.g., in an entire visual ROI in an fMRI experiment. But surely this explanation cannot hold in this case, as there is presumably an equal number of units tuned to each particular grating?

My sense is that this possibility should probably be removed from the manuscript - and I suspect it is more likely that the absence of a difference in marginal SSVEP for Valid vs Neutral trials has more to do with the fact that participants appear to be especially attentive on Neutral trials (and so any relative enhancement of feature-specific activity for expected events is hard to detect against a baseline of generally high-precision sensory evidence on these highly attentive, neutral trials).

We thank the reviewer for flagging that we did not clearly articulate our thoughts in this section of the manuscript. Our primary purpose in mentioning this sharpening account was simply to point out that, where at first blush our results seem to conflict with expectation suppression effects in the fMRI literature, the sharpening account provides an explanation that can reconcile them. In the case of BOLD data, the sharpening account proposes that on-channel sensory units are boosted and off-channel units are suppressed and, due to the latter being more prevalent, this leads to an overall suppression of the global signal. In the case of the SSVEP, the signal isolates just the onunits and so the sharpening account would predict that when there is a valid cue, the SSVEP signal associated with the high-contrast, expected stimulus should be boosted and the SSVEP signal associated with the low-contrast, unexpected stimulus should be weakened; this would result in a larger difference between these signals and therefore, a larger ‘marginal SSVEP’. Conversely, when there is an invalid cue, the SSVEP signal associated with the, now unexpected, high-contrast stimulus should be relatively weakened and the SSVEP signal associated with the expected, but low-contrast stimulus should be relatively boosted; this would result in a smaller difference between these signals and therefore, a lower amplitude marginal SSVEP. We do not think that this account needs to make reference to any channels beyond those feature-specific channels driving the two SSVEP signals. Again our central point is simply that the sharpening account offers a means of reconciling our SSVEP findings with expectation suppression effects previously reported in the fMRI literature.

We suspect that this was not adequately explained in the discussion. We have adjusted the way this section is phrased to make it clear that we are not invoking off-channel activity to explain the SSVEP effect we observed and we thank the Reviewer for pointing out that this was unclear in the original text.

Manuscript Changes

An alternative account for expectation suppression effects, which is consistent with our SSVEP results, is that they arise, not from a suppression of expected activity, but from a ‘sharpening’ effect whereby the response of neurons that are tuned to the expected feature are enhanced while the responses of neurons tuned to unexpected features are suppressed (de Lange et al., 2018). On this account, the expectation suppression commonly reported in fMRI studies arises because voxels contain intermingled populations with diverse stimulus preferences and the populations tuned to the unexpected features outnumber those tuned to the expected feature. In contrast to these fMRI data, the SSVEP represents the activity of sensory units driven at the same frequency as the stimulus, and thus better isolates the feature-specific populations encoding the task-relevant sensory evidence. Therefore, according to the sharpening account, an invalid cue would have enhanced the SSVEP signal associated with the low contrast grating and weakened the SSVEP signal associated with the high contrast grating. As this would result in a smaller difference between these signals, and therefore, a lower amplitude marginal SSVEP compared to the neutral cue condition, this could explain the effect we observed. 

Reviewer #3

Observers make judgements about expected stimuli faster and more accurately. How expectations facilitate such perceptual decisions remains an ongoing area of investigation, however, as expectations may exert their effects in multiple ways. Expectations may directly influence the encoding of sensory signals. Alternatively (or additionally), expectations may influence later stages of decision-making, such as motor preparation, when they bear on the appropriate behavioral response.

In the present study, Walsh and colleagues directly measured the effect of expectations on sensory and motor signals by making clever use of the encephalogram (EEG) recorded from human observers performing a contrast discrimination task. On each trial, a predictive cue indicated which of two superimposed stimuli would likely be higher contrast and, therefore, whether a left or right button press was likely to yield a correct response. Deft design choices allowed the authors to extract both contrast-dependent sensory signals and motor preparation signals from the EEG. The authors provide compelling evidence that, when predictive cues provide information about both a forthcoming stimulus and the appropriate behavioral response, expectation effects are immediately manifest in motor preparation signals and only emerge in sensory signals after extensive training.

Future work should attempt to reconcile these results with related investigations in the field. As the authors note, several groups have reported expectation-induced modulation of sensory signals (using both fMRI and EEG/MEG) on shorter timescales (e.g. just one or two sessions of a few hundred trials, versus the intensive multi-session study reported here). One interesting possibility is that perceptual expectations are not automatic but demand the deployment of feature-based attention, while motor preparation is comparatively less effortful and so dominates when both sources of information are available, as in the present study. This hypothesis is consistent with the authors' thoughtful analysis showing decreased neural signatures of attention over posterior electrodes following predictive cues. Therefore, observing the timescale of sensory effects using the same design and methods (facilitating direct comparison with the present work), but altering task demands slightly such that cues are no longer predictive of the appropriate behavioral response, could be illuminating.

We would like to thank Reviewer 3 for their positive comments and thoughtful suggestions for future work.

Recommendations For The Authors:

Comment #10

In the methods, the term 'session' is used early on but only fleshed out at the end of the 'Procedure' subsection and never entirely explained (e.g., did sessions take place over multiple days?). A brief sentence laying this out early on, perhaps in 'Participants' after the (impressive) trial counts are reported, might be helpful.

Thank you to Reviewer 3 for pointing out that this was not clear in the original draft. We have amended the text in the Methods section to better explain the relationship between sessions, days, and trial bins.

Manuscript Changes

(Methods - Participants): [...] All procedures were approved by the Trinity College Dublin School of Psychology Ethics Committee and were in accordance with the Declaration of Helsinki. Participants completed between 4 and 6 testing sessions, each on a different day. While the sample size was small, on average, participants completed 5750 (SD = 1066) trials each.

(Methods - Data Analysis): [...] As there were two lengths of testing session and participants completed different numbers of sessions, we analysed the effect of task exposure by pooling trials within-subjects and dividing them into five ‘trial bins’. The first bin represents the participants’ earliest exposure to the task and the final bin represents trials at the end of their participation, when they had had substantial task exposure. All trials with valid responses and reaction times greater than 100 ms were included in the analyses of behavioural data and the SSVEP.

Comment #11

On a related note: participants completed a variable number of trials/sessions. To facilitate comparison across subjects, training effects are reported by dividing each subject's data into 5 exposure bins. This is entirely reasonable but does leave the reader wondering about whether you found any effects of rest or sleep between sessions.

We agree with the reviewer that this is an interesting question that absolutely merits further investigation. As different participants completed different numbers of sessions, different session lengths, and had variable gaps between their sessions, we do not think a per-session analysis would be informative. We think it may be better addressed in a future study, perhaps one with a larger sample where we could collect data specifically about sleep and more systematically control the intervals between testing sessions.

Comment #12

Fig 2B: the 'correct' and 'neutral' labels in the legend are switched

Thank you to the reviewer for spotting that error, the labels in Figure 2 have been corrected.

Comment #13

Fig 4B: it's a bit difficult to distinguish which lines are 'thick' and 'thin'

We have updated Figure 4.B to increase the difference in line thickness between the thick and thin lines (as shown below).

Author response image 5.

Comment #14

Fig 4C: missing (I believe?) the vertical lines indicating median reaction time

We have updated Figure 4.C to include the median reaction times.

Author response image 6.

Author Response

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

Reviewer #1 (Public Review):

Summary:

This study investigated behavioural performance on a competing speech task and neural attentional filtering over the course of two years in a group of middle-aged to older adults. Neural attentional filtering was quantified using EEG by comparing neural envelope tracking to an attended vs. an unattended sentence. This dataset was used to examine the stability of the link between behavior and neural filtering over time. They found that neural filtering and behavior were correlated during each measurement, but EEG measures at the first time point did not predict behavioural performance two years later. Further, while behavioural measures showed relatively high test-retest reliability, the neural filtering reliability was weak with an r-value of 0.21. The authors conclude that neural tracking-based metrics have limited ability to predict longitudinal changes in listening behavior.

Strengths:

This study is novel in its tracking of behavioural performance and neural envelope tracking over time, and it includes an impressively large dataset of 105 participants. The manuscript is clearly written.

Weaknesses:

The weaknesses are minor, primarily concerning how the reviewers interpret their data. Specifically, the envelope tracking measure is often quite low, close to the noise floor, and this may affect testretest reliability. Furthermore, the trajectories may be affected by accelerated age-related declines that are more apparent in neural tracking than in behaviour.

We thank the reviewer for their supportive assessment of our work. We describe in detail how we have addressed the two main concerns raised here—neural filtering’s low test-retest reliability and differences in age-related behavioural vs. neural change—in our response to the more detailed recommendations below.

To briefly summarise here:

(1) In Figure 5, we now illustrate more transparently how the employed structural equation framework helps to overcome the issue of low test-retest reliability of neural filtering as originally reported.

(2) We include two additional control analyses, one of which relates neural tracking of attended speech (featuring a moderately high T1–T2 correlation of r = .64 even outside of latent modelling) to behavioural change. Importantly, this analysis provides critical empirical support for the apparent independence of neural and behavioural trajectories.

(3) We more clearly describe how the latent-variable modelling strategy accounts for differences in age-related change along the neural and behavioural domain. Moreover, the results of the of 18 additional control analysis also suggest that the absence of a change-change relationship is not primarily due to differential effects of age on brain and behaviour.

Reviewer #1 (Recommendations For The Authors):

1) Figure 3:

Does the 70-year range reach a tipping point?

Is that why neural filtering drops dramatically in this age group, whereas the other groups do not change or increase slightly?

This can also be seen with behavioral accuracy to a lesser extent. Perhaps test-retest reliability is affected by accelerated age-related declines in older listeners, as was found for envelope tracking measures in Decruy et al. 2019.

We agree with the reviewer that at first glance the data seem to suggest a critical tipping point in the age range above 70 years. It is important to emphasize, however, that the four age bins were not based on equal number of data points. In fact, the >70 age group included the fewest participants, leading to a less reliable estimate of change. Together with the known observation of increasing interindividual differences with increasing age, the results do not allow for any strong conclusions regarding a potential tipping point. For the same reasons, we used the four age bins for illustrative purposes, only, and did not include them in any statistical modelling.

We did however include chronological age as a continuous predictor in latent change score modelling. Here, we modelled its influence on participants’ T1 neural and behavioural status, as well as its effect on their respective change, thereby accounting for any differential (linear) effects of age on neural vs. behavioural functioning and its change.

On p.14 of the revised manuscript, we now state more clearly that the latent change score model did in fact account for the potential influence of age on the change-related relationships:

"In line with our hypotheses, we modelled the longitudinal impact of T1 neural functioning on the change in speed, and tested for a change-change correlation. Since the analyses conducted up to this point have either directly shown or have suggested that longitudinal change per domain may be affected by age, we included individuals’ age as a time-invariant covariate in the final model. We modelled the influence of age on neural and behavioural functioning at T1 but also on individual change per domain. By accounting for linear effects of age on longitudinal change, we also minimize its potential impact on the estimation of change-change relationship of interest. Note that we refrained from fitting separate models per age group due to both limited and different number of data points per age group."

2) Would good test-retest reliability be expected when the actual values of envelope tracking for attended vs. unattended speech are so low? The investigators address this by including measurement errors in the models, but I am not certain this kind adequately deals with envelope tracking values that are close to the noise floor.

We thank the reviewer for this comment. We addressed the concerns regarding the low re-test reliability of our neural-attentional metric (and its potential impact on observing a systematic changechange relationship) in two separate ways.

The major outcome of these tests is that low re-test reliability of neural tracking is (i) not generally true, and (ii) is not the cause of the main finding, i.e., a low or absent correlations of behavioural vs. neural changes over time.

In more detail, to show how latent change score modelling improves test-retest reliability by explicitly modelling measurement error, we first extracted and correlated T1 and T2 latent factors scores from the respective univariate models of neural filtering and response speed.

Indeed, at the latent level, the correlation of T1–T2 neural filtering was moderately high at r = .65 (compared to r = .21 at the manifest level). The correlation of T1–T2 response speed was estimated as r = .75 (compared to r = .71).

Figure 5A, reproduced below for the reviewer’s convenience, now includes insets quantifying these latent-level correlations over time.

Author response image 1.

Modelling of univariate and bivariate change. A Univariate latent change score models for response speed (left) and neural filtering (right). All paths denoted with Latin letters refer to freely estimated but constrained to be equal parameters of the respective measurement models. Greek letters refer to freely estimated parameters of the structural model. Highlighted in black is the estimated mean longitudinal change from T1 to T2. Scatterplots in the top left corner illustrate how capturing T1 and T2 neural and behavioural functioning as latent factors improves their respective test-retest reliability. B Latent change score model (LCSM) relating two-year changes in neural filtering strength to changes in response speed. Black arrows indicate paths or covariances of interest. Solid black arrows reflect freely estimated and statistically significant effects, dashed black arrows reflect non-significant effects. All estimates are standardised. Grey arrows show paths that were freely estimated or fixed as part of the structural model but that did not relate to the main research questions. For visual clarity, manifest indicators of the measurement model and all symbols relating to the estimated mean structure are omitted but are identical to those shown in panel A. p<.001, p<.01, p<.05, p=.08. C Scatterplots of model-predicted factor scores that refer to the highlighted paths in panel B. Top panel shows that baseline-level neural filtering did not predict two-year change in behavioural functioning, bottom panel shows the absence of a significant change-change correlation.

Second, we ran a control analysis that includes the neural tracking of attended speech in selectiveattention trials rather than the neural filtering index averaged across all trials. The results are shown as part of a new main figure (and two new supplemental figures) reproduced below (see in particular Figure 6, panels C and D).

This analysis serves two purposes: On the one hand, it allows for a more direct evaluation of the actual strength of neural speech tracking as quantified by the Pearson’s correlation coefficient. Note that these individual averages fall well within the to be expected range given that the neural tracking estimates are based on relatively short sentences (i.e., duration of ~2.5 sec) (O’Sullivan et al., 2014).

On the other hand, neural tracking of attended speech showed a moderately high, r = .64, T1–T2 correlation even outside of latent modelling. Note that the magnitude of this T1–T2 reliability is close to the short-term test-retest reliability recently reported by Panela et al. (2023). Still, when including neural tracking of attended speech in the bivariate model of change, the change-change correlation with response speed was now estimated as close to 0 (𝜙 = –.03, n.s). This observation suggests that manifest-level high re-test reliability does not necessarily improve chances of observing a significant change-change correlation.

Lastly, we would like to point out that these bivariate model results also help to shed light on the question of whether non-linear effects of age on neural / behavioural change may affect the chance of observing a systematic change-change relationship. As shown in Fig. 6C, for neural tracking of attended speech, we observed a fairly consistent longitudinal increase across age groups. Yet, as detailed above, the change-change correlation was virtually absent.

In sum, these new results provide compelling evidence for the absence of a systematic changechange relationship.

The respective control analysis results section reads as follows, and is accompanied by Figure 6 reproduced below:

"Control analyses: The weak correlation of behavioural and neural change is robust against different quantifications of neural filtering

Taken together, our main analyses revealed that inter-individual differences in behavioural change could only be predicted by baseline age and baseline behavioural functioning, and did not correlate with contemporaneous neural changes.

However, one could ask in how far core methodological decisions taken in the current study, namely our focus on (i) the differential neural tracking of relevant vs. irrelevant speech as proxy of neural filtering, and (ii) on its trait-level characterization that averaged across different spatial-attention conditions may have impacted these results. Specifically, if the neural filtering index (compared to the neural tracking of attended speech alone) is found to be less stable generally, would this also impact the chances of observing a systematic change-change relationship? Relatedly, did the analysis of neural filtering across all trials underestimate the effects of interest?

To evaluate the impact of these consideration on our main findings, we conducted two additional control analyses: First, we repeated the main analyses using the neural filtering index (and response speed) averaged across selective-attention trials, only. Second, we repeated the main analyses using the neural tracking of attended speech, again averaged across selective-attention trials, only.

As shown in Figure 6, taken together, the control analyses provide compelling empirical support for the robustness of our main results: Linking response speed and neural filtering under selective attention strengthened their relationship at T1 (𝜙 = .54, SE = .15, Dc2(df = 1) = 2.74, p = .1; see. Fig 6B) but did not yield any significant effects for the influence of T1 neural filtering on behavioural change (β = .13, SE = .21, Dc2(df = 1) = .43, p = .51), or for the relationship of neural and behavioural change (𝜙 = .26, SE = .14, Dc2(df = 1) = 3.1, p = .08; please note the close correspondence to path estimates reported in Fig. 5). The second control analysis revealed a substantially higher manifest-level test-retest reliability of neural tracking of attended speech (r = .65, p<.001; Fig. 6C) compared to that of the neural tracking index. However, when linked to longitudinal changes in response speed, this analysis provided even less evidence for systematic change-related relationships: Baseline-levels of attended-speech tracking did not predict future change in response speed (β = .18, SE = .11, Dc2(df = 1) = 2.73, p = .10), and changes in neural and behavioural functioning occurred independently of one another (𝜙 = –.03, SE = .12, Dc2(df = 1) = .06, p = .81).

In sum, the two control analyses provide additional empirical support for the results revealed by our main analysis."

Author response image 2.

Control analyses corroborate the independence of neural and behavioural trajectories under selective attention. Cross-sectional and longitudinal change in neural filtering (A) and neural tracking of attended speech (C) averaged across selective-attention trials, only. Coloured vectors (colour-coding four age groups for illustrative purposes, only) in the left subpanels show individual T1–T2 change along with the cross-sectional trend plus 95% confidence interval (CI) separately for T1 (dark grey) and T2 (light grey). Top right, correlation of T1 and T2 as measure of test-retest reliability along with the 45° line (grey) and individual data points (black circles). Bottom right, mean longitudinal change per age group and grand mean change (grey). B, D Latent change score model (LCSM) relating two-year changes in neural filtering (B) /neural tracking (D) strength to changes in response speed. Black arrows show the paths or covariances of interest that were freely estimates, grey arrows show paths that were freely estimated or fixed as part of the structural model but did not relate to the main research questions. Solid arrows indicate statistically significant effects, dashed arrows reflect nonsignificant paths. All estimates are standardised. p<.001, p<.01, p<.05.

b

3) The authors conclude that the temporal instability of the neural filtering measure precludes its use for diagnostic/therapeutic intervention. I agree that test-retest reliability is needed for a clinical intervention. However, given the relationship with behavior at a specific point in time, would it not be a possible target for intervention to improve performance? Even if there are different trajectories, an individual may benefit from enhanced behavioral performance in the present.

We thank the reviewer for this comment. We would agree that the observation of robust betweensubject (or even more desirable: within-subject) brain–behaviour relationships is a key desideratum in identifying potential interventional targets. At the same time, we would argue that the most direct way of evaluating a neural signature’s translational potential is by focusing on how it predicts or is linked to individual change. In revising both the Introduction and Discussion section, we hope to now better motivate our reasoning.

Other minor comments:

4) Lines 106-107 What is the basis for the prediction regarding neural filtering?

In our previous analysis of T1 data (Tune et al., 2021), we found inter-individual differences in neural filtering itself, and also in its link to behaviour, to be independent of chronological age and hearing loss. On the basis of these results, we did not expect any systematic decrease or increase in neural filtering over time.<br /> We rephrased the respective sentence as follows:

Since we previously observed inter-individual differences in neural filtering to be independent of age and hearing status, we did not expect any systematic longitudinal change in neural filtering.

5) Line 414: Replace "relevant" with "relevance".

Thank you, this has been corrected.

6) What was the range of presentation levels? Stimuli presented at 50 dB above individual sensation level could result in uncomfortably loud levels for people with mild to moderate hearing loss.

Unfortunately, we didn’t have the means to estimate the precise dB SPL level at which our stimuli were presented. Due to the use of in-ear headphones, we did not aim to measure the exact sound pressure level of presentation but instead ensured that even if stimuli were presented at the maximally possible intensity given our hardware, this would not result in subjectively uncomfortably loud stimulus presentation levels. The described procedure estimated per individual how far the maximal sound pressure level needed to be attenuated to arrive at a comfortable and easy-tounderstand presentation level.

Reviewer #2 (Public Review):

Summary:

This study examined the longitudinal brain-behaviour link between attentional neural filtering and listening behaviour among a sample of aging individuals. The results based on the latent change score modeling showed that neither attentional neural filtering at T1 nor its T1-T2 change predicted individual two-year listening performance change. The findings suggest that neural filtering and listening behaviour may follow independent developmental trajectories. This study focuses on an interesting topic and has the potential to contribute a better understanding of the neurobiological mechanisms of successful communication across the lifespan.

Strengths:

Although research suggests that speech comprehension is neurally supported by an attentionguided filter mechanism, the evidence of their causal association is limited. This study addresses this gap by testing the longitudinal stability of neural filtering as a neural mechanism upholding listening performance, potentially shedding light on translational efforts aiming at the preservation of speech comprehension abilities among aging individuals.

The latent change score modeling approach is appropriately used as a tool to examine key developmental questions and distinguish the complex processes underlying lifespan development in brain and behaviour with longitudinal data.

Weaknesses:

Although the paper does have strengths in principle, the weaknesses of the paper are that the findings are merely based on a single listening task. Since both neural and behavioral indicators are derived from the same task, the results may be applicable only to this specific task, and it is difficult to extrapolate them to cognitive and listening abilities measured by the other tasks. Therefore, more listening tasks are required to comprehensively measure speech comprehension and neural markers.

The age span of the sample is relatively large. Although no longitudinal change from T1 to T2 was found at the group-level, from the cross-sectional and longitudinal change results (see Figure 3), individuals of different age groups showed different development patterns. Particularly, individuals over the age of 70 show a clear downward trend in both neural filtering index and accuracy. Therefore, different results may be found based on different age groups, especially older groups. However, due to sample limitations, this study was unable to examine whether age has a moderating effect on this brain-behaviour link.

In the Dichotic listening task, valid and invalid cues were manipulated. According to the task description, the former could invoke selective attention, whereas the latter could invoke divided attention. It is possible that under the two conditions, the neural filtering index may reflect different underlying cognitive processes, and thus may differ in its predictive effect on behavioral performance. The author could perform a more in-depth data analysis on indicators under different conditions.

We thank the reviewer for their critical yet positive assessment of our work that also appreciates its potential to further our understanding of key determinants of successful communication in healthy aging. Please also see our more in-depth responses to the detailed recommendations that relate to the three main concern raised above.

Regarding the first concern of the reviewer about the limited generalizability of our brain–behaviour results, we would argue that there are two sides to this argument.

On the one hand, the results do not directly speak to the generalizability of the observed complex brain–behaviour relationships to other listening tasks. This may be perceived as a weakness. Unfortunately, as part of our large-scale projects, we did not collect data from another listening task suitable for such a generalization test. Using any additional cognitive tests would shift the focus away from the goal of understanding the determinants of successful communication, and rather speak more generally to the relationship of neural and cognitive change.

On the other hand, we would argue the opposite, namely that the focus on the same listening task is in fact a major strength of the present study: The key research questions were motivated by our timepoint 1 findings of a brain-behaviour link both at the within-subject (state) and at the between subject (trait) level (Tune et al., 2021). Notably, in the current study, we show that both, the state- and the trait-level results, were replicated at timepoint 2. This observed stability of results provides compelling empirical evidence for the functional relevance of neural filtering to the listening outcome and critically sets the stage for the inquiry into the complex longitudinal change relationships. We now spell this out more clearly in the Introduction and the Discussion.

Here, we briefly summarise how we have addressed the two remaining main concerns.

(1) Please refer to our response R1’s comment #1 on the influence of (differential) age effects on brain and behaviour. These effects were in fact already accounted for by our modelling strategy which included the continuously (rather than binned by age group) modelled effect of age. We now communicate this more clearly in the revised manuscript.

(2) We added two control analyses, one of which replicated the main analysis using selective attention trials, only. Critically, as shown in Figure 6, while the strength of the relationship of neural filtering and behaviour at a given timepoint increased, the key change-related relationships of interest remained not only qualitatively unchanged, but resulted in highly similar quantitative estimates.

Reviewer #2 (Recommendations For The Authors):

1) Theoretically, the relationship between brain and behavior may not be just one-way, but probably bi-directional. In this study, the authors only considered the unidirectional predictive effect of neural filtering on changes in listening task performance. However, it is possible that lower listening ability may limit information processing in older adults, which may lead to a decline in neural filtering abilities. The authors may also consider this theoretical hypothesis.

We thank the reviewer for this comment. While we did not have any specific hypotheses about influence of the behavioural state at timepoint 1 on the change in neural filtering, we ran control analysis that freely estimates the respective path (rather than implicitly assuming it to be 0). However, the results did not provide evidence for such a relationship. We report the results on p. 14 of the revised manuscript:

"We did not have any a priori hypotheses on the influence of T1 speed on the individual T1–T2 change in neural filtering. Still in a control analysis that freely estimated the respective path, we found that an individual’s latent T1 level of response speed was not predictive of the ensuing latent T1–T2 change in neural filtering (β = –.11, SE = .21, Dc2(df = 1) = .31, p = .58)."

2) The necessity of exploring the longitudinal relationship between attentional neural filtering and listening behaviour needs to be further clarified. That is, why choose attentional filtering (instead of the others) as an indicator to predict listening performance?

We are not quite certain we understood which ‘other’ metrics the reviewer was referring to here exactly. But we would like to reiterate our argument from above: we believe that focusing on neural and behavioural metrics that are (i) derived from the same task, and (ii) were previously shown to be linked at both the trait- and state-level provided strong empirical ground for our inquiries into their longitudinal change-related relationships.

Please note that we agree that the neural filtering index as a measure of attention-guided neural encoding of relevant vs. irrelevant speech signals is only one potential candidate neural measure but one that was clearly motivated by previous results. Nevertheless, in the revised manuscript we now also report on the relationship of neural tracking of attended speech and listening performance (see also our response to the reviewer’s comment #5 below).

Apart of this, by making the entire T1–T2 dataset openly available, we invite researchers to conduct any potential follow-up analyses focused on metrics not reported here.

3) Regarding the Dichotic listening task, further clarification is needed.

(1) The task procedure and key parameters need to be supplemented.

We have added a new supplemental Figure S6 which details the experimental design and procedure. We have also added further listening task details to the Methods section on p.23:

At each timepoint, participants performed a previously established dichotic listening task20. We provide full details on trial structure, stimulus construction, recording and presentation in our previously published study on the first (N = 155) wave of data collection (but see also Fig. S6)12.

In short, in each of 240 trials, participants listened to two competing, dichotically presented five-word sentences spoken by the same female speaker. They were probed on the sentence-final noun in one of the two sentences. Participants were instructed to respond within a given 4 s time window beginning with the onset of a probe screen showing four alternatives. They were not explicitly instructed to respond as quickly as possible. The probe screen showed four alternative words presented either on the left or right side of the screen, indicating the probed ear. Two visual cues preceded auditory presentation (…)

We also note that the task and key parameters have been published additionally in (Tune et al., 2021) and Alavash et al. (2019). We have made sure these citations are placed prominently at the beginning of the methods section.

Author response image 3.

Experimental design and procedure.

(2) Prior to the task, were the participants instructed to respond quickly and correctly? Was there a speed-accuracy trade-off? Was it possible to consider an integrated ACC-RT indicator?

We instructed participants to respond within a 4-sec time window following the response screen onset but we did not explicitly instruct them to respond as quickly as possible. We also state this more explicitly in the revised Method section on p. 23 (see also our response to comment #3 by R3 on p. 15 below).

In a between-subjects analysis we observed, both within T1 and T2, a significant positive correlation (rT1 = .33, p<.01; rT2 = .40, p<.001) of participants’ overall accuracy and response speed, speaking against a speed-accuracy trade-off. For this reason, we did not consider an integrated speed–accuracy measure as behavioural indicator for modelling.

(3) The correlation between neural filtering at T1 and T2 was weak, which may be due to the low reliability of this indicator. The generally low reliability of the difference score is a notorious measurement problem recognized in the academic community.

We fully agree with the reviewer on their assessment of notoriously noisy difference scores. It is the very reason that motivated our application of the latent change score model approach. This framework elegantly supersedes the manual calculation of differences scores, and by explicitly

modelling measurement error also removes the impact of varying degrees of reliability on the estimation of change and how it varies as a function of different influences.

While we had already detailed this rationale in the original manuscript, we now more prominently describe the advantages of the latent variable approach in the first paragraph of the Results section:

Third and final, we integrate and extend the first two analysis perspectives in a joint latent change score model (LCSM) to most directly probe the role of neural filtering ability as a predictor of future attentive listening ability. Addressing our key change-related research questions at the latent rather than the manifest level supersedes the manual calculation of notoriously noisy differences scores, and effectively removes the influence of each metric’s reliability on the estimation of change-related relationships.

We also kindly refer the reviewer to our in-depth response to R1’s comment #2 regarding the concern of neural filtering’s low test-rest reliability and its impact on estimating change-change relationships.

1. For the latent change score model, it is recommended that the authors:<br /> (1) Supplement the coefficients of each path in Figure 5. For details, please refer to the figures in the papers of Kievit et al. (2017, 2019)

This information has been added to Figure 5.

(2) In Figure 5 and Figure S2, why should the two means of the observed 2nd half scores be estimated?

In longitudinal modelling, special care needs to be applied to the pre-processing/transformation of raw data for the purpose of change score modelling. While it is generally desirable to bring all variables onto the same scale (typically achieved by standardising all variables), one needs to be careful not to remove the mean differences of interest in such a data transformation step. We therefore followed the procedure recommended by Little (2013) and rescaled variables stacked across T1 and T2 using the proportion of maximum scale (‘POMS’) methods. This procedure, however, results in mean values per timepoint ≠ 0, so the mean of the second half needed to be freely estimated to avoid model misfit. Note that the mean of the first half manifest variables was set to 0 (using the ‘marker method’; see Little, 2013) to ensure model identification.

We have added the following more detailed description to the Method section on p. 26:

To bring all manifest variables onto the same scale while preserving mean differences over time, we first stacked them across timepoint and then rescaled them using the proportion of maximum scale (‘POMS’) method99,100 (…) Given our choice of POMS-transformation of raw to preserve mean differences over time, the mean of the second manifest variable had to be freely estimated (rather than implicitly assumed to be 0) to avoid severe model misfit.

(3) The authors need to clarify whether the latent change factor in Figure 5 is Δ(T1-T2) or Δ(T2-T1)?

Thank you for this comment. Our notation here was indeed confusing. The latent change factor quantifies the change from T1 to T2, so it is Δ(T2–T1). We have accordingly re-named the respective latent variables in all corresponding figures.

1. For data analysis, the author combined the trials under different conditions (valid and invalid cues) in the dichotic listening task and analyzed them together, which may mask the variations between different attention levels (selective vs. divided attention). It is recommended that the authors analyze the relationship between various indicators under different conditions.

We thank the reviewer for this comment which prompted us to (i) more clearly motivate our decision to model neural filtering across all trials, and (ii) nevertheless report the results of an additional control analyses that focused on neural filtering (or the neural tracking of attended speech) in selective-attention trials, only.

Our decision to analyse neural filtering across all spatial-attention conditions was motivated by two key considerations: First, previous T1 results (Tune et al., 2021) suggested that irrespective of the spatial-attention condition, stronger neural filtering boosted behavioural performance. Second, analysing neural filtering (and associated behaviour) across all trials provided the most direct way of probing the trait-like nature of individual neural filtering ability. <br /> We have included the following paragraph to the Results section on p. 6 to motivate this decision more clearly:

Our main analyses focus on neural filtering and listening performance averaged across all trials and thereby also across two separate spatial-attention conditions. This choice allowed us to most directly probe the trait-like nature and relationships of neural filtering. It was additionally supported by our previous observation of a general boost in behavioural performance with stronger neural filtering, irrespective of spatial attention.

On the other hand, one could argue that the effects of interest are underestimated by jointly analysing neural and behavioural functioning derived from both selective- and divided-attention conditions. After all, it is reasonable to expect a more pronounced neural filtering response in selective-attention trials.

For this reason, we now report, in the revised version, two additional control analyses that replicate the key analyses for the neural filtering index and for the tracking of attended speech, both averaged across selective-attention trials, only: In summary, analysing neural filtering under selective attention strengthened the brain-behaviour link within a given time-point but resulted in highly similar quantitative estimated for the key relationships of interest. The analysis of attended speech tracking notably improved the neural metric’s manifest-level re-test reliability (r = .64, p<.001) – but resulted in an estimated change-change correlation close to 0.

Taken together, these control analyses provide compelling support for our main conclusion that neural and behavioural functioning follow largely independent developmental trajectories.

We kindly refer the reviewer to our detailed response to R1 for the text of the added control analysis section on p. 4f. above. The additional Figure 6 is reproduced again below for the reviewer’s convenience.

Author response image 4.

Control analyses corroborate the independence of neural and behavioural trajectories under selective attention. Cross-sectional and longitudinal change in neural filtering (A) and neural tracking of attended speech (C) averaged across selective-attention trials, only. Coloured vectors (colour-coding four age groups for illustrative purposes, only) in the left subpanels show individual T1–T2 change along with the cross-sectional trend plus 95% confidence interval (CI) separately for T1 (dark grey) and T2 (light grey). Top right, correlation of T1 and T2 as measure of test-retest reliability along with the 45° line (grey) and individual data points (black circles). Bottom right, mean longitudinal change per age group and grand mean change (grey). B, D Latent change score model (LCSM) relating two-year changes in neural filtering (B) /neural tracking (D) strength to changes in response speed. Black arrows show the paths or covariances of interest that were freely estimates, grey arrows show paths that were freely estimated or fixed as part of the structural model but did not relate to the main research questions. Solid arrows indicate statistically significant effects, dashed arrows reflect nonsignificant paths. All estimates are standardised. p<.001, p<.01, p<.05.

Figure 6 has also been supplemented by two additional figures showing behavioural functioning (Fig. S4) and neural tracking of ignored speech (Fig. S5) under selective-attention trials, only. These figures are reproduced below for the reviewer’s convenience.

Author response image 5.

Cross-sectional and longitudinal change in listening behaviour under selective attention.

Author response image 6.

Cross-sectional and longitudinal change in neural tracking of ignored speech under selective attention.

6) As can be seen from the Methods section, there were still other cognitive tasks in this database that can be included in the data analysis to further determine the predictive validity of neural filtering.

We kindly refer the reviewer to our response to their public review and comment # 2 above where we motivate our decision to focus on manifest indicators of neural and behavioural functioning that are derived from the same task.

We believe that the analysis of several additional indicators of cognitive functioning would have distracted from our main goal of the current study focused on understanding how individual trajectories of listening performance may be explained and predicted.

7) "Magnitudes > 1 are taken as moderate, > 2.3 as strong evidence for either of the alternative or null hypotheses, respectively." Which papers are referenced by these criteria? The interpretation of BF values seems inconsistent with existing literature.

It may deserve emphasis that these are log Bayes Factors (logBF). Our interpretation of logarithmic Bayes Factors (logBF) follows Lee and Wagenmakers’ (2013) classic heuristic scheme for the interpretation of (non-logarithmic, ‘raw’) BF10 values. We have added the respective reference to the manuscript.

Reviewer #3 (Public Review):

Summary:

The study investigates the longitudinal changes in hearing threshold, speech recognition behavior, and speech neural responses in 2 years, and how these changes correlate with each other. A slight change in the hearing threshold is observed in 2 years (1.2 dB on average) but the speech recognition performance remains stable. The main conclusion is that there is no significant correlation between longitudinal changes in neural and behavioral measures.

Strengths:

The sample size (N>100) is remarkable, especially for longitudinal studies.

Weaknesses:

The participants are only tracked for 2 years and relatively weak longitudinal changes are observed, limiting how the data may shed light on the relationships between basic auditory function, speech recognition behavior, and speech neural responses.

Suggestions

First, it's not surprising that a 1.2 dB change in hearing threshold does not affect speech recognition, especially for the dichotic listening task and when speech is always presented 50 dB above the hearing threshold. For the same listener, if the speech level is adjusted for 1.2 dB or much more, the performance will not be influenced during the dichotic listening task. Therefore, it is important to mention in the abstract that "sensory acuity" is measured using the hearing threshold and the change in hearing threshold is only 1.2 dB.

We thank the reviewer for this comment. We have added the respective information to the abstract and have toned down our interpretation of the observed behavioural stability despite the expected decline in auditory acuity.

Second, the lack of correlation between age-related changes in "neuronal filtering" and behavior may not suggest that they follow independent development trajectories. The index for "neuronal filtering" does not seem to be stable and the correlation between the two tests is only R = 0.21. This low correlation probably indicates low test-retest reliability, instead of a dramatic change in the brain between the two tests. In other words, if the "neuronal filtering" index only very weakly correlates with itself between the two tests, it is not surprising that it does not correlate with other measures in a different test. If the "neuronal filtering" index is measured on two consecutive days and the index remains highly stable, I'm more convinced that it is a reliable measure that just changes a lot within 2 years, and the change is dissociated with the changes in behavior.

The authors attempted to solve the problem in the section entitled "Neural filtering reliably supports listening performance independent of age and hearing status", but I didn't follow the logic. As far as I could tell, the section pooled together the measurements from two tests and did not address the test-retest stability issue.

Please see our detailed response to R1’s comment #2 regarding the concern of how low (manifestlevel) reliability of our neural metric may have impacted the chance of observing a significant changechange correlation.

In addition, we would like to emphasize that the goal of the second step of our analysis procedure, featuring causal mediation analysis, was not to salvage the perhaps surprisingly low reliability of neural filtering. Instead, this section addressed a different research question, namely, whether the link of neural filtering to behaviour would hold across time, irrespective of the observed stability of the measure itself. The stability of the observed between-subjects brain-behaviour relationships was assessed by testing for an interaction with timepoint.

We have revised the respective Results section to more clearly state our scientific questions, and how our analysis procedure helped to address them:

"The temporal instability of neural filtering challenges its status as a potential trait-like neural marker of attentive listening ability. At the same time, irrespective of the degree of reliability of neural filtering itself, across individuals it may still be reliably linked to the behavioural outcome (see Fig. 1). This is being addressed next.

On the basis of the full T1–T2 dataset, we aimed to replicate our key T1 results and test whether the previously observed between-subjects brain-behaviour relationship would hold across time: We expected an individual’s neural filtering ability to impact their listening outcome (accuracy and response speed) independently of age or hearing status12. (…) To formally test the stability of direct and indirect relationships across time, we used a moderated mediation analysis. In this analysis, the inclusion of interactions by timepoint tested whether the influence of age, sensory acuity, and neural filtering on behaviour varied significantly across time."

Third, the behavioral measure that is not correlated with "neuronal filtering" is the response speed. I wonder if the participants are asked to respond as soon as possible (not mentioned in the method). If not, the response speed may strongly reflect general cognitive function or a personal style, which is not correlated with the changes in auditory functions. This can also explain why the hearing threshold affects speech recognition accuracy but not the response speed (lines 263-264).

Participants were asked to response within a given time window limited to 4 s but were not implicitly instructed to respond as quickly as possible. This is now stated more clearly in the Methods section (please also refer to our response to R2 on a similar question). It is important to emphasize—as shown in Figure 4A and Figure 5B —both at the manifest and latent variable level neural filtering (and in fact also the neural tracking of attended speech, see Fig. 6C) was reliably linked to response speed at T1 and T2. These results providing important empirical ground for the question of whether changes in neural filtering are systematically related to changes in response speed, and whether the fidelity of neural filtering at T1 represents a precursor of behavioural changes.

Moreover, an interpretation of response speed as an indicator of general cognitive function is not at all incompatible with the cognitive demands imposed by the task. As the reviewer rightly stated above, performance in a dichotic listening task does not simply hinge on how auditory acuity may limit perceptual encoding of speech inputs but also on how the goal-directed application of attention modulates the encoding of relevant vs. irrelevant inputs. We here focus on one candidate neural strategy we here termed ‘neural filtering’ in line with an influential metaphor of how auditory attention may be neurally implemented (Cherry, 1953; Erb & Obleser, 2020; Fernandez-Duque & Johnson, 1999).

Reviewer #3 (Recommendations For The Authors):

Other issues:

The authors should consider using terminology that the readers are more familiar with and avoid unsubstantiated claims.

For example, the Introduction mentions that "The observation of such brain-behaviour relationships critically advances our understanding of the neurobiological foundation of cognitive functioning. Their translational potential as neural markers predictive of behaviour, however, is often only implicitly assumed but seldomly put to the test. Using auditory cognition as a model system, we here overcome this limitation by testing directly the hitherto unknown longitudinal stability of neural filtering as a neural compensatory mechanism upholding communication success."

For the first sentence, please be clear about which aspects of "our understanding of the neurobiological foundation of cognitive functioning" is critically advanced by such brain-behaviour relationships, and why such brain-behaviour relationships are so critical given that so many studies have analyzed brain-behaviour relationships. The following two sentences seem to suggest that the current study is a translational study, but the later questions do not seem to be quite translational.

The uncovering of robust between- and within-subject brain behaviour-relationships is a key scientific goal that unites basic and applied neuroscience. From a basic neuroscience standpoint, the observation of such brain–behaviour links provides important mechanistic insight into the neurobiological implementation of higher order cognition – here the application of auditory spatial attention in the service of speech comprehension. At the same time, they provide fruitful ground for translational inquiries of applied neuroscience. We therefore don’t consider it contradictory at all that the current study addressed both more basic and applied/translational neuroscientific research questions.

We have rephrased the respective section as follows:

"The observation of such brain–behaviour relationships critically advances our understanding of the neurobiological foundation of cognitive functioning by showing, for example, how neural implementations of auditory selective attention support attentive listening. They also provide fruitful ground for scientific inquiries into the translational potential of neural markers. However, the potency of neural markers to predict future behavioural outcomes is often only implicitly assumed but seldomly put to the test15."

More importantly, "neuronal filtering" is a key concept in the paper but I'm not sure what it means. The authors have only mentioned that auditory cognition is a model system for "neuronal filtering", but not what "neuronal filtering" is. Even for auditory cognition, I'm not sure what "neuronal filtering" is and why the envelope response is representative of "neuronal filtering".

As spelled out in the Introduction, we define our ‘neural filtering’ metric of interest as neural manifestation of the attention-guided segregation of behaviourally relevant from irrelevant sounds. By terming this signature neural ‘filtering’, we take up on a highly influential algorithmic metaphor of how auditory attention may be implemented at the neurobiological level (Cherry, 1953; Erb & Obleser, 2020; Fernandez-Duque & Johnson, 1999).

We now provide more mechanistic detail in our description of the neural filtering signature analysed in the current study:

"Recent research has focused on the neurobiological mechanisms that promote successful speech comprehension by implementing ‘neural filters’ that segregate behaviourally relevant from irrelevant sounds. Such neural filter mechanisms act by selectively increasing the sensory gain for behaviourally relevant inputs or by inhibiting the processing of irrelevant inputs5-7. A growing body of evidence suggests that speech comprehension is neurally supported by an attention-guided filter mechanism that modulates sensory gain and arises from primary auditory and perisylvian brain regions: By synchronizing its neural activity with the temporal structure of the speech signal of interest, the brain ‘tracks’ and thereby better encodes behaviourally relevant auditory inputs to enable attentive listening 8-11."

Figure 1C should be better organized and the questions mentioned in the Introduction should be numbered.

We have revised both the respective section of the Introduction and corresponding Figure 1 in line with the reviewer’s suggestions. The revised text and figure are reproduced below for the reviewer’s convenience:

"First, by focusing on each domain individually, we ask how sensory, neural, and behavioural functioning evolve cross-sectionally across the middle and older adult life span (Fig. 1B). More importantly, we also ask how they change longitudinally across the studied two-year period (Fig. 1C, Q1), and whether aging individuals differ significantly in their degree of change (Q2). We expect individuals’ hearing acuity and behaviour to decrease from T1 to T2. Since we previously observed inter-individual differences in neural filtering to be independent of age and hearing status, we did not expect any systematic longitudinal change in neural filtering.

Second, we test the longitudinal stability of the previously observed age- and hearing-loss–independent effect of neural filtering on both accuracy and response speed (Fig. 1A). To this end, we analyse the multivariate direct and indirect relationships of hearing acuity, neural filtering and listening behaviour within and across timepoints.

Third, leveraging the strengths of latent change score modelling16,17, we fuse cross-sectional and longitudinal perspectives to probe the role of neural filtering as a precursor of behavioural change in two different ways: we ask whether an individual’s T1 neural filtering strength can predict the observed behavioural longitudinal change (Q3), and whether two-year change in neural filtering can explain concurrent change in listening behaviour (Q4). Here, irrespective of the observed magnitude and direction of T1–T2 developments, two scenarios are conceivable: Intra-individual neural and behavioural change may be either be correlated—lending support to a compensatory role of neural filtering—or instead follow independent trajectories18 (see Fig. 1C)."

Author response image 7.

Schematic illustration of key assumptions and research questions. A Listening behaviour at a given timepoint is shaped by an individuals’ sensory and neural functioning. Increased age decreases listening behaviour both directly, and indirectly via age-related hearing loss. Listening behaviour is supported by better neural filtering ability, independently of age and hearing acuity. B Conceptual depiction of individual two-year changes along the neural (blue) and behavioural (red) domain. Thin coloured lines show individual trajectories across the adult lifespan, thick lines and black arrows highlight two-year changes in a single individual. C Left, Schematic diagram highlighting the key research questions detailed in the introduction and how they are addressed in the current study using latent change score modelling. Right, across individuals, co-occurring changes in the neural and behavioural domain may be correlated (top) or independent of one another (bottom).

Figure 3, the R-value should also be labeled on the four main plots.

This information has been added to Figure 3, reproduced below.

Author response image 8.

Characterizing cross-sectional and longitudinal change along the auditory sensory (A), neural (B), and behavioural (C, D) domain. For each domain, coloured vectors (colour-coding four age groups for illustrative purposes, only) in the respective left subpanels show an individual’s change from T1 to T2 along with the cross-sectional trend plus 95% confidence interval (CI) separately for T1 (dark grey) and T2 (light grey). Top right subpanels: correlation of T1 and T2 as measure of test-retest reliability along with the 45° line (grey) and individual data points (black circles). Bottom right panels: Mean longitudinal change per age group (coloured vectors) and grand mean change (grey). Note that accuracy is expressed here as proportion correct for illustrative purposes, but was analysed logit-transformed or by applying generalized linear models.

T1 and T2 should be briefly defined in the abstract or where they first appear.

We have changed the abstract accordingly.

References

Alavash, M., Tune, S., & Obleser, J. (2019). Modular reconfiguration of an auditory control brain network supports adaptive listening behavior. [Clinical Trial]. Proceedings of the National Academy of Science of the United States of America, 116(2), 660-669. https://doi.org/10.1073/pnas.1815321116

Cherry, E. C. (1953). Some experiments on the recognition of speech, with one and with two ears. The Journal of the Acoustical Society of America, 25(5), 975-979. https://doi.org/10.1121/1.1907229

Erb, J., & Obleser, J. (2020). Neural filters for challening listening situations. In M. Gazzaniga, G. R. Mangun, & D. Poeppel (Eds.), The cognitive neurosciences (6th ed.). MIT Press.

Fernandez-Duque, D., & Johnson, M. L. (1999). Attention metaphors: How metaphors guide the cognitive psychology of attention. Cognitive Science, 23(1), 83-116. https://doi.org/10.1207/s15516709cog2301_4<br /> O’Sullivan, J. A., Power, A. J., Mesgarani, N., Rajaram, S., Foxe, J. J., Shinn-Cunningham, B. G., Slaney, M., Shamma,

S. A., & Lalor, E. C. (2014). Attentional Selection in a Cocktail Party Environment Can Be Decoded from Single-Trial EEG. Cerebral Cortex, 25(7), 1697-1706. https://doi.org/10.1093/cercor/bht355

Panela, R. A., Copelli, F., & Herrmann, B. (2023). Reliability and generalizability of neural speech tracking in younger and older adults. Nature Communications, 2023.2007.2026.550679. https://doi.org/10.1101/2023.07.26.550679

Tune, S., Alavash, M., Fiedler, L., & Obleser, J. (2021). Neural attentional-filter mechanisms of listening success in middle-aged and older individuals. Nature Communications, 1-14. https://doi.org/10.1038/s41467021-24771-9