Reviewer #1 (Public Review):

This study explores the relationship between neurodegeneration's most common spatial patterns and the density of different cell types in the cerebral cortex. The authors present data showing that atrophy patterns in Alzheimer's disease and Frontotemporal dementia (FTD) strongly associated with the abundance of astrocytes and microglia. This work (the original manuscript and the revision) takes a step in the right direction by emphasizing the critical role that cells other than neurons play in the degeneration patterns observable with neuroimaging.

Comments on revised version:

I appreciate the revisions the authors made to address my main comments:<br /> - adding whole-brain maps showing cellular abundance and atrophy<br /> - stratifying the FTD group into the three clinically defined categories bvFTD (behavior-variant), nfvPPA (nonfluent/agrammatic-variant primary progressive aphasia), and svPPA (semantic-variant primary progressive aphasia).

I reiterate my agreement with the authors that this work demonstrates the need to "surpass the current neuro-centric view of brain diseases and the imperative for identifying cell-specific therapeutic targets in neurodegeneration".

Reviewer #3 (Public Review):

This study is a fine example of a recent productive trend in the integration of neuroimaging and molecular biology of the brain: in brief, overlaying some neuroimaging data (usually from a large cohort) onto the high spatial resolution gene expression in the Allen Human Brain Atlas data, derived from 6 individuals. By projecting structural MRI images over cell type proportions identified in the Allen data, the authors can represent various diseases in terms of their spatially-associated cell types. The result has implications for prioritizing the contributions of various cell types to each disease and creates an even-handed cell type profile through which the 11 diseases can be compared.

eLife assessment

This important work identifies a previously uncharacterized capacity for songbird to recover vocal targets even without sensory experience. The evidence supporting this claim is convincing, with technically difficult and innovative experiments exploring goal-directed vocal plasticity in deafened birds. This work has broad relevance to the fields of vocal and motor learning.

Reviewer #3 (Public Review):

Summary:

Zai et al. test whether birds can modify their vocal behavior in a manner consistent with planning. They point out that while some animals are known to be capable of volitional control of vocalizations, it has been unclear if animals are capable of planning vocalizations-that is, modifying vocalizations towards a desired target without the need to learn this modification by practicing and comparing sensory feedback of practiced behavior to the behavioral target. They study zebra finches that have been trained to shift the pitch of song syllables away from their baseline values. It is known that once this training ends, zebra finches have a drive to modify pitch so that it is restored back to its baseline value. They take advantage of this drive to ask whether birds can implement this targeted pitch modification in a manner that looks like planning, by comparing the time course and magnitude of pitch modification in separate groups of birds who have undergone different manipulations of sensory and motor capabilities. A key finding is that birds who are deafened immediately before the onset of this pitch restoration paradigm, but after they have been shifted away from baseline, are able to shift pitch partially back towards their baseline target. In other words, this targeted pitch shift occurs even when birds don't have access to auditory feedback, which argues that this shift is not due to reinforcement-learning-guided practice, but is instead planned based on the difference between an internal representation of the target (baseline pitch) and current behavior (pitch the bird was singing immediately before deafening).

The authors present additional behavioral studies arguing that this pitch shift requires auditory experience of song in its state after it has been shifted away from baseline (birds deafened early on, before the initial pitch shift away from baseline, do not exhibit any shift back towards baseline), and that a full shift back to baseline requires auditory feedback. The authors synthesize these results to argue that different mechanisms operate for small shifts (planning, which does not need auditory feedback) and large shifts (through a mechanism that requires auditory feedback).

The authors also make a distinction between two kinds of planning: covert-not requiring any motor practice-and overt-requiring motor practice, but without access to auditory experience from which target mismatch could be computed. They argue that birds plan overtly, based on these deafening experiments as well as an analogous experiment involving temporary muting, which suggest that indeed motor practice is required for pitch shifts.

Strengths:

The primary finding (that partially restorative pitch shift occurs even after deafening) rests on strong behavioral evidence. It is less clear to what extent this shift requires practice, since their analysis of pitch after deafening takes the average over within the first two hours of singing. If this shift is already evident in the first few renditions then this would be evidence for covert planning. Technical hurdles, such as limited sample sizes and unstable song after surgical deafening, make this difficult to test. (Similarly, the authors could test whether the first few renditions after recovery from muting already exhibit a shift back towards baseline.)

This work will be a valuable addition to others studying birdsong learning and its neural mechanisms. It documents features of birdsong plasticity that are unexpected in standard models of birdsong learning based on reinforcement and are consistent with an additional, perhaps more cognitive, mechanism involving planning. As the authors point out, perhaps this framework offers a reinterpretation of the neural mechanisms underlying a prior finding of covert pitch learning in songbirds (Charlesworth et al., 2012).

A strength of this work is the variety and detail in its behavioral studies, combined with sensory and motor manipulations, which on their own form a rich set of observations that are useful behavioral constraints on future studies.

Weaknesses:

The argument that pitch modification in deafened birds requires some experience hearing their song in its shifted state prior to deafening (Fig. 4) is solid, but has an important caveat. Their argument rests on comparing two experimental conditions: one with and one without auditory experience of shifted pitch. However, these conditions also differ in the pitch training paradigm: the "with experience" condition was performed using white noise training, while the "without experience" condition used "lights off" training (Fig. 4A). It is possible that the differences in ability for these two groups to restore pitch to baseline reflects the training paradigm, not whether subjects had auditory experience of the pitch shift. Ideally, a control study would use one of the training paradigms for both conditions, which would be "lights off" or electrical stimulation (McGregor et al. 2022), since WN training cannot be performed in deafened birds. In the Discussion, in response to this point the authors point out that birds are known to recover their pitch shift if those shifts are driven using electrical stimulation as reinforcement (McGregor et al. 2022); however, it is arguably still relevant to know whether a similar recovery occurs for the "lights off" paradigm used here.

eLife assessment

This important study assesses anatomical, behavioral, physiological, and neurochemical effects of early-life seizures in rats, describing a striking astrogliosis and deficits in cognition and electrophysiological parameters. The solid results come from a wide range of convergent techniques that were used to understand the effects of early-life seizures on behavior as well as hippocampal prefrontal cortical dynamics. This paper will be of interest to neurobiologists, epileptologists, and behavioral scientists.

Reviewer #1 (Public Review):

Summary:

In this paper, Ruggiero, Leite and colleagues assess the effects of early life seizures on a large number of anatomical, physiological, behavioral and neurochemical measures. They find that prolonged early life seizures do not lead to obvious cell loss, but lead to astrogliosis, working memory deficits on the radial arm maze, increased startle response, decreased paired pulse inhibition, and increased hippocampal-PFC LTP. There was a U-shaped relationship between LTP and cognitive deficits. There is increased theta power during the awake state in ELS animals but reduced PFC theta-gamma coupling and reduced theta HPC-PFC coherence. Theta coherence seems to be similar in ACT and REM states in ELS animals while in decreases in active relative REM in controls.

Strengths:

The main strength of the paper is the number of convergent techniques used to understand how hippocampal PFC neural dynamics and behavior change after early life seizures. The sheer scale, breadth and reach of the experiments are praiseworthy. It is clear that the paper is a major contribution to the field as far as understanding the impact of early life seizures. The LTP findings are robust and provide an important avenue for future study. The experiments are performed carefully and the analysis is appropriate. The paper is well-written and the figures are clear.

Weaknesses:

The main weakness of the paper remains the lack of causal manipulations to determine whether prevention or augmentation of any of the findings have any impact on behavior or cognition. Alternatively, if other manipulations would enhance working memory in ELS animals, it would have been interesting to see the effects on any of these parameters measured in the paper. The authors now discuss the lack of causal manipulations in the discussion but have not performed new experiments to address this weakness. Also, I find the sections where correlations and dimensionality reduction techniques are used to compare all possible variables to each other less compelling than the rest of the paper (with the exception of the findings of U shaped relationship of cognition to LTP). In fact, I think these sections take away from the impact of the actual findings. The rationale for the apomorphine experiments are now explained more fully.

eLife assessment

This is an important study examining the neural profile of weak and strong fear memories using a variety of imagining and interrogation neural techniques. The data are convincing in detailing the neural profile of neutral, aversive and fear conditioned stimuli in the LC and its input to the dorsal hippocampus and support the conclusion that dopaminergic input from the LC is the key instigator of trace fear conditioning in hippocampus. This paper is of interest to behavioural and neuroscience researchers studying learning, memory and neural networks.

Reviewer #3 (Public Review):

Summary:

The manuscript examines an important question, namely how the brain associates events spaced in time. It uses a variety of neural methods including fiber photometry as well as area-specific and pathway-silencing methods with the exquisite dissociation of norepinephrine and dopamine. The data show that neurons in the locus coeruleus (LC) respond to auditory cue onset, offset, and shock. These responses are stronger if the cue is paired with shock in a trace procedure. Optogenetic stimulation similar to the neural response captured by fiber photometry enhances associative learning. LC terminals in the dorsal hippocampus also showed phasic responses during fear conditioning and drove dopamine and norepinephrine responses. Pharmacological methods revealed that dopamine and not norepinephrine are critical for fear learning.

Strengths:

The examination of the neural signal to different tone intensities, different shock intensities, repeated tone presentation (habituation), and conditioning, offers an unprecedented account of the neural signal to non-associative and associative processes. This kind of deconstruction of the elements of conditioning offers a strong account of how the brain processes the stimuli used and their interaction during learning.

Excellent use of data acquired with fiber photometry in the optogenetic interrogation study.

The use of pharmacology to disentangle dopamine and norepinephrine was excellent.

Comments on revised version:

The authors have thoroughly and thoughtfully addressed my prior concerns.

eLife assessment

These important findings stand out from other similar studies via some convincing demonstration of behavioural and neural relationships between two helping tasks - one focusing more on social perception, one more on its influence on social behaviour - that were performed more than 300 days apart. The claims however would be more convincing with a larger sample size.

Reviewer #1 (Public Review):

Summary:

The authors conducted two tasks at 300 days separation. First, a social perception task, where Ps responded whether a pictured person either deserved or needed help. Second, an altruism task, where Ps are offered monetary allocations for themselves and a partner. Ps decide whether to accept, or a default allocation of 20 dollars each. The partners differed in perceived merit, such that they were highly deserving, undeserving or unknown. This categorisation was decided on the basis of a prisoners dilemma game the partner played beforehand. "Need" was also manipulated, by altering the probability that the partner must have their hand in cold water at the end of the experiment and this partner can use the money to buy themselves out. These two tasks were conducted to assess the perception of need/merit in the first instance, and how this relates to social behaviour in the second. fMRI data were collected alongside behavioural.

The authors present many analyses of behaviour (including DDM results) and fMRI. E.g., they demonstrate that they could decode across the mentalising network whether someone was making a need or deserving judgement vs control judgements but couldn't decode need vs deserving. And that brain responses during merit inferences (merit - control) systematically covaried with participants' merit sensitivity scores in the rTPJ. They also found relationships between behaviour and rTPJ in the altruism task. And that merit sensitivity in the perception task predicted influence of merit on social behaviour in the altruism task.

Strengths:

This manuscript represents a sensible model to predict social perceptions and behaviours, and a tidy study design with interesting findings. The introduction introduced the field especially brilliantly for a general audience.

Weaknesses:

These are small samples. This is especially the case for the correlational questions. The limitation is acknowledged, but does mean that we cannot conclude much from absent relationships, where the likelihood of Type II error is high.

Decoding analyses. The authors decode need vs merit, and need+merit vs control, not the content of these inferences. The logic of these analyses implies that there is a distributed representation of merit that does not relate to its content but is an abstracted version that applies to all merit judgements. However, these analyses are not central to the authors' aims and conclusions, so this is just a minor point.

Reviewer #2 (Public Review):

When people help others is an important psychological and neuroscientific question. It has received much attention from the psychological side, but comparatively less from neuroscience. The paper translates some ideas from a social Psychology domain to neuroscience using a neuroeconomically oriented computational approach. In particular, the paper is concerned with the idea that people help others based on perceptions of merit/deservingness, but also because they require/need help. To this end, the authors conduct two experiments with an overlapping participant pool:

(1) A social perception task in which people see images of people that have previously been rated on merit and need scales by other participants. In a blockwise fashion, people decide to whether the depicted person a) deserves help, b) needs help, and c) whether the person uses both hands (== control condition)<br /> (2) In an altruism task, people make costly helping decisions by deciding between giving a certain amount of money to themselves or another person. It is manipulated how much the other person needs and deserves the money.<br /> The authors use sound and robust computational modelling approach for both tasks using evidence accumulation models. They analyse behavioural data for both tasks, showing that the behaviour is indeed influenced, as expected, by the deservingness and the need of the shown people. Neurally, the authors use a block-wise analysis approach to find differences in activity levels across conditions of the social perception task. The authors do find large activation clusters in areas related to theory of mind. Interestingly, they also find that activity in TPJ that relates to the deservingness condition correlates with people's deservingness ratings while they do the task, but also with computational parameters related to helping others in the second task, the one that was conducted many months later. Also some behavioural parameters correlate across the two tasks, suggesting that how deserving of help others are perceived reflects a relatively stable feature that translates into concrete helping decisions later-on.

The conclusions of the paper are overall well supported by the data.

(1) I found that the modelling was done very thoroughly for both tasks. Overall, I had the impression that the methods are very solid with many supplementary analyses. The computational modelling is done very well.<br /> (2) A slight caveat, however, regarding this aspect, is that, in my view, the tasks are relatively simplistic, so that even the complex computational models do not as much as they can in the case of more complex paradigms. For example, the bias term in the model seems to correspond to the mean response rate in a very direct way (please correct me if I am wrong).<br /> (3) Related to the simple tasks: The fMRI data is analysed in a simple block-fashion. This is in my view not appropriate to discern the more subtle neural substrates of merit/need-based decision making or person perception. Correspondingly, the neural activation patterns (merit > control, need > control) are relatively broad and unspecific. They do not seem to differ in the classic theory of mind regions, that are the focus of the analyses.<br /> (4) However, the relationship between neural signal and behavioural merit sensitivity in TPJ is noteworthy.<br /> (5) The latter is even more the case, as the neural signal and aspects of the behaviour are correlated across subjects with the second task that is conducted much later. Such a correlation is very impressive and suggests that the tasks are sensitive for important individual differences in helping perception/behaviour.<br /> (6) That being said, the number of participants in the latter analyses are at the lower end of the number of participants that are these days used for across-participant correlations.

Reviewer #3 (Public Review):

Summary: The paper aims at providing a neurocomputational account on how social perception translates in prosocial behaviors. Participants first completed a novel social perception task during fMRI scanning, in which were asked to judge the merit or need of people depicted in different situations. Second , a separate altruistic choice task was used to examine how the perception of merit and need influences the weights people place on themselves, others and fairness when deciding to provide help. Finally, a link between perception and action was drawn in those participants who completed both tasks.

Strengths: The paper is overall very well written and presented, leaving the reader at ease when describing complex methods and results. The approach used by the author is very compelling, as it combines computational modeling of behavior and neuroimaging data analyses. Despite not being able to comment on the computational model, I find the approach used (to disentangle sensitivity and biases, for merit and need) very well described and derived from previous theoretical work. Results are also clearly described and interpreted.

Weaknesses: in the social perception task, merit and need are evaluated by means of very different cues that rely on different cognitive processes (more abstract thinking for merit than need). Despite this limitation of the task, the authors were able to argue convincingly in the revised version about the solidity of their findings. Sample size is quite small for study 2, nevertheless the results provide convincing evidence.

eLife assessment

This useful study addresses the interesting and challenging problem of how neural networks (including possibly the brain) can optimize performance while multi-tasking. The authors address this problem by introducing an information-theoretic framework that balances the costs of control and of automaticity to achieve a desired level of overall performance. They present detailed analyses of this framework, but overall the manuscript is not easily accessible to a broad audience, and the supporting evidence is currently incomplete (but could be greatly improved with substantial revisions). They use information-theoretic terminology in non-standard ways that are not clearly explained, leading to difficulties in interpreting the framework and comparing it to other computational approaches, and the relationship between their findings and empirical data is not always clear.

Reviewer #1 (Public Review):

Summary:<br /> A long literature in cognitive neuroscience studies how humans and animals adjudicate between conflicting goals. However, despite decades of research on the topic, a clear computational account of control has been difficult to pin down. In this project, Petri, Musslick, & Cohen attempt to formalize and quantify the problem of control in the context of toy neural networks performing conflicting tasks.

This manuscript builds on the formalism introduced in Petri et al (2021), "Topological limits to the parallel processing capability of network architectures", which describes a set of tasks as a graph in which input nodes (stimuli) are connected to output nodes (responses). Each edge in this graph links an input node to an output node, representing a "task"; i.e. a word reading task connects the input node "word" to the output node "read". Cleverly, patterns of interference and conflict between tasks can be quantified from this graph. In the current manuscript, the authors extend this framework by converting these graphs into neural networks and a) allowing edges to be continuous rather than binary; b) introducing "hidden layers" of units between input and output nodes; and c) introducing a "control" signal that modulates edge weights. The authors then examine how, in such a network, optimal behavior may involve serial versus parallel execution of different sets of tasks.

Strengths:<br /> There is a longstanding belief in cognitive neuroscience that "control" manages conflicts by scheduling tasks to be executed in parallel versus serially; I applaud the efforts of the authors to give these intuitions a more concrete computational grounding.

My main scientific concern is that the authors focus on what seems like an arbitrary set of network architectures. The networks considered here are derived by converting task graphs, which represent a multitasking problem, into networks for _performing_ that multitasking problem. Frankly, these networks do not look like any neural network a computer scientist would use to actually solve a problem, nor do they seem biologically realistic. Furthermore, adding hidden layers to these networks only ever seems to make performance worse (Figures 4, 11), introducing unnecessary noise and interference; it would seem more useful to study a network architecture in which hidden layers fulfilled some useful purpose (as they do in the brain and machine learning).

However, this scientific concern is secondary to the major problem with this paper, which is clarity.

Major problem: A lack of clarity

I found this paper extremely difficult to read. To illustrate my difficulty, I will describe a subset of my confusion.

The authors define the "entropy" of an action in equation 1, but the content of the equation gives what is sometimes referred to as the "surprisal" of the action. Conventionally (as per Wikipedia and any introductory textbook I am familiar with), entropy is the "expected surprisal" of a random variable, not the surprisal of a single action. This creates immediate confusion going into the results. Furthermore, defining "entropy" this way means that "information" is functionally equivalent to accuracy for the purposes of this paper, in which case I do not know what has been gained by this excursion into (non-standard) information-theoretic terminology.

They next assert that equation 1 is the information _cost_ of an action. No motivation is given for this statement and I do not know what it means. In what sense is a "cost" associated with the negative logarithm of a probability?

In the next section II.B, the authors introduce a new formalism in which responses are represented by task graph nodes _R_. What is the relationship between an action _a_ and the responses _R_? Later, in section II.C, edges _f_ in the task graph are used as seemingly drop-in replacements for actions _a_.

I simply have no idea what is going on in equations 31 through 33. Where are the functions _R_ (not to be confused with the response nodes _R_) and _S_ defined? Or how are they approximated? What does the variable _t_ mean and why does it appear and disappear from equations seemingly at random?

Response times seem to be important, but as far as I can tell, nowhere do the authors actually describe how response times are calculated for the simulated networks.

Similar issues persist through the rest of the paper: unconventional formalism is regularly introduced using under-explained notation and without a clear relationship to the scientific questions at hand. As a result, the content and significance of the findings are largely inscrutable to me, and I suspect also to the vast majority of readers.

Reviewer #2 (Public Review):

Summary:<br /> The authors develop a normative account of automaticity-control trade-offs using the mathematics of information theory, which they apply to abstract neural networks. They use this framework to derive optimal trade-off solutions under particular task conditions.

Strengths:<br /> On the positive side, I appreciate the effort to rigorously synthesize ideas about multi-tasking within an information-theoretic framework. There is potentially a lot of promise in this approach. The analyis is quite comprehensive and careful.

Weaknesses:<br /> Generally speaking, the paper is very long and dense. I don't in principle mind reading long and dense papers (though conciseness is a virtue); it becomes more of a slog when it's not clear what new insights are being gained from laboring through the math. For example, after reading the Stroop section, I wasn't sure what new insight was provided by the information-theoretic formalism which goes beyond earlier models. Is this just an elegant formalism for expressing previously conceived ideas, or is there something fundamentally new here that's not predicted by other frameworks? The authors cite multiple related frameworks addressing the same kinds of data, but there is no systematic comparison of predictions or theoretical interpretations. Even in the Discussion, where related work is directly addressed, I didn't see much in terms of explaining how different models made different predictions, or even what predictions any of them make.

After a discussion of the Stroop task early in the paper, the analysis quickly becomes disconnected from any empirical data. The analysis could be much more impactful if it was more tightly integrated with relevant empirical data.

eLife assessment

The manuscript describes human intracranial neural recordings in the auditory cortex during speech production, showing that the effects of delayed auditory feedback correlate with the degree of underlying speech-induced suppression. This is an important finding, as previous work has suggested that speech suppression and feedback sensitivity often do not co-localize and may be distinct processes, in contrast with findings in non-human primates where there is a strong correlation. The strength of the evidence is solid, with appropriate experimental methods, data, and analysis, though some additional analysis would strengthen comparisons with past work.

Reviewer #1 (Public Review):

Summary:<br /> The manuscript describes a series of experiments using human intracranial neural recordings designed to evaluate the processing of self-generated speech in the setting of feedback delays. Specifically, the authors aim to address the question about the relationship between speech-induced suppression and feedback sensitivity in the auditory cortex, whose relationship has been conflicting in the literature. They found a correlation between speech suppression and feedback delay sensitivity, suggesting a common process. Additional controls were done for possible forward suppression/adaptation, as well as controlling for other confounds due to amplification, etc.

Strengths:<br /> The primary strength of the manuscript is the use of human intracranial recording, which is a valuable resource and gives better spatial and temporal resolution than many other approaches. The use of delayed auditory feedback is also novel and has seen less attention than other forms of shifted feedback during vocalization. Analyses are robust, and include demonstrating a scaling of neural activity with the degree of feedback delay, and more robust evidence for error encoding than simply using a single feedback perturbation.

Weaknesses:<br /> Some of the analyses performed differ from those used in past work, which limits the ability to directly compare the results. Notably, past work has compared feedback effects between production and listening, which was not done here. There were also some unusual effects in the data, such as increased activity with no feedback delay when wearing headphones, that the authors attempted to control for with additional experiments, but remain unclear. Confounds by behavioral results of delayed feedback are also unclear.

Overall the work is well done and clearly explained. The manuscript addresses an area of some controversy and does so in a rigorous fashion, namely the correlation between speech-induced suppression and feedback sensitivity (or lack thereof). While the data presented overlaps that collected and used for a previous paper, this is expected given the rare commodity these neural recordings represent. Contrasting these results to previous ones using pitch-shifted feedback should spawn additional discussion and research, including verification of the previous finding, looking at how the brain encodes feedback during speech over multiple acoustic dimensions, and how this information can be used in speech motor control.

Reviewer #2 (Public Review):

Summary:<br /> In "Speech-induced suppression and vocal feedback sensitivity in human cortex", Ozker and colleagues use intracranial EEG to understand audiomotor feedback during speech production using a speech production and delayed auditory feedback task. The purpose of the paper is to understand where and how speaker-induced suppression occurs, and whether this suppression might be related to feedback monitoring. First, they identified sites that showed auditory suppression during speech production using a single-word auditory repetition task and a visual reading task, then observed whether and how these electrodes show sensitivity to auditory feedback using a DAF paradigm. The stimuli were single words played auditorily or shown visually and repeated or read aloud by the participant. Neural data were recorded from regular- and high-density grids from the left and right hemispheres. The main findings were:<br /> • Speaker-induced suppression is strongest in the STG and MTG, and enhancement is generally seen in frontal/motor areas except for small regions of interest in the dorsal sensorimotor cortex and IFG, which can also show suppression.<br /> • Delayed auditory feedback, even when simultaneous, induces larger response amplitudes compared to the typical auditory word repetition and visual reading tasks. The authors presume this may be due to the effort and attention required to perform the DAF task.<br /> • The degree of speaker-induced suppression is correlated with sensitivity to delayed auditory feedback.<br /> • pSTG (behind TTS) is more strongly modulated by DAF than mid-anterior STG

Strengths:<br /> Overall, I found the manuscript to be clear, the methodology and statistics to be solid, and the findings mostly quite robust. The large number of participants with high-density coverage over both the left and right lateral hemispheres allows for a greater dissection of the topography of speaker-induced suppression and changes due to audiomotor feedback. The tasks were well-designed and controlled for repetition suppression and other potential caveats.

Weaknesses:<br /> (1) In Figure 1D, it would make more sense to align the results to the onset of articulation rather than the onset of the auditory or visual cue, since the point is to show that the responses during articulation are relatively similar. In this form, the more obvious difference is that there is an auditory response to the auditory stimulus, and none to the visual, which is expected, but not what I think the authors want to convey.<br /> (2) The DAF paradigm includes playing auditory feedback at 0, 50, 100, and 200 ms lag, and it is expected that some of these lags are more likely to induce dysfluencies than others. It would be helpful to include some analysis of whether the degree of suppression or enhancement varies by performance on the task, since some participants may find some lags more interfering than others.<br /> (3) Figure 3 shows data from only two electrodes from one patient. An analysis of how amplitude changes as a function of the lag across all of the participants who performed this task would be helpful to see how replicable these patterns of activity are across patients. Is sensitivity to DAF always seen as a change in amplitude, or are there ever changes in latency as well? The analysis in Figure 4 gets at which electrodes are sensitive to DAF but does not give a sense of whether the temporal profile is similar to those shown in Figure 3.<br /> (4) While the sensitivity index helps to show whether increasing amounts of feedback delay are correlated with increased response enhancement, it is not sensitive to nonlinear changes as a function of feedback delay, and it is not clear from Figure 3 or 4 whether such relationships exist. A deeper investigation into the response types observed during DAF would help to clarify whether this is truly a linear relationship, dependent on behavioral errors, or something else.

eLife assessment

This fundamental study substantially advances our understanding of the role of different-sized soil invertebrates in shaping the rates of leaf litter decomposition, using an experiment across seasons along an aridity gradient. The authors provide compelling evidence that the summed effects of all invertebrates (with large-sized invertebrates being more active in summer and small-sized invertebrates in winter) on decomposition rates result in similar levels of leaf litter decomposition across seasons. The work will be of broad interest to ecosystem ecologists interested in soil food webs, and researchers interested in modeling carbon cycles to understand global warming.

Reviewer #1 (Public Review):

Summary:<br /> I really enjoyed this manuscript from Torsekar et al on "Contrasting responses to aridity by different-sized decomposers cause similar decomposition rates across a precipitation gradient". The authors aimed to examine how climate interacts with decomposers of different size categories to influence litter decomposition. They proposed a new hypothesis: "The opposing climatic dependencies of macrofauna and that of microorganisms and mesofauna should lead to similar overall decomposition rates across precipitation gradients".

This study emphasizes the importance as well as the contribution of different groups of organisms (micro, meso, macro, and whole community) across different seasons (summer with the following characteristics: hot with no precipitation, and winter with the following characteristics: cooler and wetter winter) along a precipitation gradient. The authors made use of 1050 litter baskets with different mesh sizes to capture decomposers contribution. They proposed a new hypothesis that was aiming to understand the "dryland decomposition conundrum". They combined their decomposition experiment with the sampling of decomposers by using pittfall traps across both experiment seasons. This study was carried out in Israel and based on a single litter species that is native to all seven sites. The authors found that microorganism contribution dominated in winter while macrofauna decomposition dominated the overall decomposition in summer. These seasonality differences combined with the differences in different decomposers groups fluctuation along precipitation resulted in similar overall decomposition rates across sites.<br /> I believe this manuscript has a potential to advance our knowledge on litter decomposition.

Strengths:<br /> Well design study with combination of different approaches (methods) and consideration of seasonality to generalize pattern.<br /> The study expands to current understanding of litter decomposition and interaction between factors affecting the process (here climate and decomposers).

Weaknesses:<br /> The study was only based on a single litter species.

Reviewer #2 (Public Review):

Summary: Torsekar et al. use a leaf litter decomposition experiment across seasons, and in an aridity gradient, to provide a careful test of the role of different-sized soil invertebrates in shaping the rates of leaf litter decomposition. The authors found that large-sized invertebrates are more active in the summer and small-sized invertebrates in the winter. The summed effects of all invets then translated into similar levels of decomposition across seasons. The system breaks down in hyper-arid sites.

Strengths: This is a well-written manuscript that provides a complete statistical analysis of a nice dataset. The authors provide a complete discussion of their results in the current literature.

Weaknesses: I have only three minor comments. Please standardize the color across ALL figures (use the same color always for the same thing, and be friendly to color-blind people). Fig 1 may benefit from separating the orange line (micro and meso) into two lines that reflect your experimental setup and results. I would mention the dryland decomposition conundrum earlier in the Introduction. And the manuscript is full of minor grammatical errors. Some careful reading and fixing of all these minor mistakes here and there would be needed.

eLife assessment

This important work substantially advances our understanding of episodic memory in individuals with aphantasia, and sheds light on the neural underpinnings of episodic memory and mental imagery. The evidence supporting the conclusions is convincing, including evidence from a well-established interview paradigm complemented with fMRI to assess neural activation during memory recall. The work will be of broad interest to memory researchers and mental imagery researchers alike.

Reviewer #1 (Public Review):

Summary:<br /> In this article, the authors investigate whether the connectivity of the hippocampus is altered in individuals with aphantasia ¬- people who have reduced mental imagery abilities and where some describe having no imagery, and others describe having vague and dim imagery. The study investigated this question using a fMRI paradigm, where 14 people with aphantasia and 14 controls were tested, and the researchers were particularly interested in the key regions of the hippocampus and the visual-perceptual cortices. Participants were interviewed using the Autobiographical Interview regarding their autobiographical memories (AMs), and internal and external details were scored. In addition, participants were queried on their perceived difficulty in recalling memories, imagining, and spatial navigation, and their confidence regarding autobiographical memories was also measured. Results showed that participants with aphantasia reported significantly fewer internal details (but not external details) compared to controls; that they had lower confidence in their AMs; and that they reported finding remembering and imagining in general more difficult than controls. Results from the fMRI section showed that people with aphantasia displayed decreased hippocampal and increased visual-perceptual cortex activation during AM retrieval compared to controls. In contrast, controls showed strong negative functional connectivity between the hippocampus and the visual cortex. Moreover, resting state connectivity between the hippocampus and visual cortex predicted better visualisation skills. The authors conclude that their study provides evidence for the important role of visual imagery in detail-rich vivid AM, and that this function is supported by the connectivity between the hippocampus and visual cortex. This study extends previous findings of reduced episodic memory details in people with aphantasia, and enables us to start theorising about the neural underpinnings of this finding.

The data provided good support for the conclusion that the authors draw, namely that there is a 'tight link between visual imagery and our ability to retrieve vivid and detail-rich personal past events'. However, as the authors also point out, the exact nature of this relationship is difficult to infer from this study alone, as the slow temporal resolution of fMRI cannot establish the directionality between the hippocampus and the visual-perceptual cortex. This is an exciting future avenue to explore.

Weaknesses:<br /> A weakness of the study is that some of the questions used are a bit vague, and no objective measure is used, which could have been more informative. For example, the spatial navigation question (reported as 'How difficult is it typically for you to orient you spatially?' - a question which is ungrammatical, but potentially reflects a typo in the manuscript) could have been more nuanced to tap into whether participants relied mostly on cognitive maps (likely supported by the hippocampus) or landmarks. It would also have been interesting to conduct a spatial navigation task, as participants do not necessarily have insight into their spatial navigation abilities (they could have been overconfident or underconfident in their abilities). Secondly, the question 'how difficult is it typically for you to use your imagination?' could also be more nuanced, as imagination is used in a variety of ways, and we only have reason to hypothesise that people with aphantasia might have difficulties in some cases (i.e. sensory imagination involving perceptual details). It is unlikely that people with aphantasia would have more difficulty than controls in using their imagination to imagine counterfactual situations and engage in counterfactual thought (de Brigard et al., 2013, https://doi.org/10.1016%2Fj.neuropsychologia.2013.01.015) due to its non-sensory nature, but the question used does not distinguish between these types of imagination. Again, this is a ripe area for future research. The general phrasing of 'how difficult is [x]' could also potentially bias participants towards more negative answers, something which ought to be controlled for in future research.

Strengths:<br /> A great strength of this study is that it introduces a fMRI paradigm in addition to the autobiographical interview, paralleling work done on episodic memory in cognitive science (e.g. Addis and Schacter, 2007, https://doi.org/10.1016%2Fj.neuropsychologia.2006.10.016 ), which has examined episodic and semantic memory in relation to imagination (future simulation) in non-aphantasic participants as well as clinical populations. Future work could build on this study, and for example use the recombination paradigm (Addis et al. 2009, 10.1016/j.neuropsychologia.2008.10.026 ), which would shed further light on the ability of people with aphantasia to both remember and imagine events. Future work could also build on the interesting findings regarding spatial navigation, which together with previous findings in aphantasia (e.g. Bainbridge et al., 2021, https://doi.org/10.1016/j.cortex.2020.11.014 ) strongly suggests that spatial abilities in people with aphantasia are unaffected. This can shed further light on the different neural pathways of spatial and object memory in general. In general, this study opens up a multitude of new avenues to explore and is likely to have a great impact on the field of aphantasia research.

Reviewer #2 (Public Review):

Summary:<br /> This study investigates to what extent neural processing of autobiographical memory retrieval is altered in people who are unable to generate mental images ('aphantasia'). Self-report as well as objective measures were used to establish that the aphantasia group indeed had lower imagery vividness than the control group. The aphantasia group also reported fewer sensory and emotional details of autobiographical memories. In terms of brain activity, compared to controls, aphantasics had a reduction in activity in the hippocampus and an increase in activity in the visual cortex during autobiographical memory retrieval. For controls, these two regions were also functionally connected during autobiographical memory retrieval, which did not seem to be the case for aphantasics. Finally, resting-state connectivity between the visual cortex and hippocampus was positively related to autobiographical vividness in the control group but negatively in the aphantasia group. The results are in line with the idea that aphantasia is caused by an increase in noise within the visual system combined with a decrease in top-down communication from the hippocampus.

Recent years have seen a lot of interest in the influence of aphantasia on other cognitive functions and one of the most consistent findings is deficits in autobiographical memory. This is one of the first studies to investigate the neural correlates underlying this difference, thereby substantially increasing our understanding of aphantasia and the relationship between mental imagery and autobiographical memory.

Strengths:<br /> One of the major strengths of this study is the use of both self-report as well as objective measures to quantify imagery ability. Furthermore, the fMRI analyses are hypothesis-driven and reveal unambiguous results, with alterations in hippocampal and visual cortex processing seeming to underlie the deficits in autobiographical memory.

Weaknesses:<br /> In terms of weaknesses, the control task, doing mathematical sums, also differs from the autobiographical memory task in aspects that are unrelated to imagery or memory, such as self-relevance and emotional salience, which makes it hard to conclude that the differences in activity are reflecting only the cognitive processes under investigation.

Overall, I believe that this is a timely and important contribution to the field and will inspire novel avenues for further investigation.

eLife assessment

Amacrine cells are a heterogeneous and understudied set of retinal interneurons. This study presents valuable new insights into the structure, function, and circuit connectivity of a particular subset of wide field amacrine cells (WACs). The authors use an impressive set of techniques to study structural and functional properties of these cells and to establish their postsynaptic circuit partners. Evidence for the central conclusions is solid, although some of the most interesting results could be pursued more completely.

Reviewer #1 (Public Review):

Summary:

This manuscript from Park et al examines the molecular, anatomical and functional properties of a subset of wide-field amacrine cell (WAC) types in mouse retina. More than 60 mouse amacrine cell types have been identified by single-cell transcriptomic studies (Yan et al., 2020, PMID: 32457074), but the functions of most of these are unknown and WACs are particularly understudied. The authors use intersectional genetics to target a subset of mouse WACs that co-express Bhlhe22 and the kappa opioid receptor (referred to as B/K WACs). They used electrophysiological and anatomical approaches to determine how WACs contribute to neural computations in the retina.

Strengths:

Overall, the paper presents a technically impressive set of experiments that build strong evidence for the presence of at least 3 discrete WAC types in the B/K transgenic line. These cells vary with respect to their morphology, dendritic stratification, response polarity (On vs Off) and resting membrane potentials. All types have long, monostratified dendrites and appear to lack axons. Electrophysiological recordings establish that these WACs are non-spiking, while calcium imaging revealed orientation selectivity in dendritic segments with tuning that correlates strongly with dendritic orientation. The authors go on to use optogenetics to show that WACs provide strong GABA-A receptor mediated inhibitory input to OFF and ON alpha sustained RGCs. This connectivity is further substantiated, at least for the OFF sustained alpha RGCs, by connectomic analyses from serial block face EM volumes. The use of the APEX2 reporter system to label the B/K cells in one of the EM volumes is particularly nice, making identification of the B/K WACs unambiguous. The conclusions are largely well supported by the experimental data. The study provides novel insights into the structure and function of specific WACs that will provide a foundation for further studies investigating the role of these amacrine cells in retinal circuits.

Weaknesses:

A limitation of the study is that the B/K WAC types described here could not be aligned to specific transcriptomic identities. The authors show more than 15 GABA expressing ACs express Bhlhe22 in the transcriptomic dataset, but it is unclear which of these also express the kappa opioid receptor (Opkr1).

The optogenetic evidence suggests that WACs provide GABA-A receptor mediated inhibitory input to both the sustained OFF and ON alpha RGCs. However, at least in the examples shown, there appears to be a dramatic difference in the timecourse of the rising phase of the inhibitory inputs to these two cell types, with the inputs to the ON sustained alpha RGCs appearing slower than those in the OFF sustained and OFF transient alpha RGCs. This apparent temporal difference was accompanied by a relatively lower sensitivity to light stimulation for the ON sustained cells. The slow timecourse seems unexpected for a direct GABA-A mediated synaptic connections between the WACs and ON alpha sustained cells. Moreover, since the connectomic analyses do not examine inputs to ON RGC types, the direct synaptic connection between B/K WACs and On alpha RGC is less well substantiated.

Reviewer #2 (Public Review):

Summary:

An important frontier in research on the mammalian retina is to understand the role of inhibitory amacrine cells in visual processing. These cell types have been found to play roles in tuning the output of the retina to specific visual features like motion and orientation. These cell types are understudied for two main reasons. First, there are many types of them-over 60 types in the mouse--, and second, they are quite unconventional as far as neurons go, as they have dendrites but often lack axons. The manuscript "Molecular identification of wide-field amacrine cells in mouse retina that encode stimulus orientation" by Park et al. provides a characterization of two (or possibly more) cell types within the amacrine cell class. Specifically, they characterize types of widefield amacrine cells (WACs), which they have gained genetic access to using an intersectional transgenic mouse strategy (Bhlhe22 x KOR). The authors used a broad range of experiments to characterize these WACs' anatomical properties, their stimulus tuning, and their wiring within the retina to their postsynaptic partners. These experiments include anatomy, electrophysiology, calcium imaging, and electron microscopy.

Strengths:

Overall, the manuscript presents strong evidence that the Bhlhe22 x KOR WACs represent multiple WAC types in the retina and that these cell types are orientation tuned. The most exciting finding is that their orientation tuning is correlated with the physical orientations of the dendrites, which suggests that this anatomical feature supports the tuning in these cells.

Weaknesses:

(1) The one common thought about widefield amacrine cells (WACs) is that these are spiking cells, which allows them to transmit signals along their long dendrites. The authors state that "none of the recorded cells fired conventional action potentials (spikes)." (p.7) This is a surprising finding, which leads to an interesting question: how do these cells integrate information from their presynaptic partners to generate the orientation tuning observed without the ability to conduct over long distances? However, the authors have not fully ruled out that the cells do spike.<br /> For instance, one possibility is that spiking requires a specific stimulus and the authors did not play that stimulus during their recordings. Most somatic recordings did not result in very large depolarizations, and the cell could still be below threshold. Depolarizing the cell to attempt to evoke spikes directly could be used to explore this possibility. A second possibility is that the dendrites spike, but these spikes are attenuated at the soma. Direct injections of current into the cells to evoke such spikes could be used to observe whether dendritic spiking occurs. A third possibility is that some important machinery for spiking is being washed out by the whole cell recordings. Cell attached recordings could be used to assess whether spiking occurs in an intact cell. The authors may wish to address these possibilities experimentally, but at least should qualify their statement about spiking in these cells and discuss these possibilities.

(2) It was unclear in this paper how many cell types are present in the intersectional cross. I think the paper would be stronger if they clarified that. For instance, in Fig. 1B: the authors show Bhlhe22 expression in amacrine cells from a previous study. They should also show the expression of the other gene they used in their intersectional strategy, the Kappa Opioid receptor (Oprk1), which is available in the same dataset. Another piece of analysis that could help would be clearer quantification of the anatomical features of the cells. For instance, the cells shown in Fig. 2 A2 vs. B2 have clear differences in number of dendrites and the relative angles of the dendrites. The On cells appear to have more dendrites evenly spread around the soma, while the Off cells appear to have more clumping along a line. Is this the case for all the cells recorded, or just these examples? The authors should present some population-level quantification.

(3) In Fig. 4E, the preferred orientation of calcium responses and physical orientation of the dendrites appears to clump around specific orientations. The Methods don't mention if the retinas were aligned to the body axis during the dissection. Is this clumping real, or is this an artifact of the analysis? If there are specific preferred orientations to these WAC cell types, that would be important to discuss in the paper - for instance how this relates to the preferred direction in the direction selectivity system or how it might relate to the function of these cells for behavior.

Reviewer #3 (Public Review):

Summary:

Amacrine cells are a heterogeneous collection of retinal interneurons. Most are inhibitory, and like inhibitory neurons in other neural circuits, strongly shape retinal function. With a few exceptions, the role of amacrine cells in retinal signaling is poorly understood. This paper introduces an approach to study a set of wide-field amacrine cells that extend processes over large regions of the retina.

Strengths:

A substantial strength of the paper is the combination of genetic manipulations, electrophysiology, optogenetics and electron microscopy used to study these cells. As a result of that broad set of techniques, the results cover many properties of how the cells work and provide a nice overview. The paper is also (with a few exceptions below) clearly presented and the experiments look to be carefully executed with clean results.

Weaknesses:

My largest concern with the paper is that overall the results provided an initial view of an interesting set of issues about the function of these cells, but the interesting initial results are not pursued in more depth.

Spatial spread of signals in neurites<br /> An immediate question about axonless WACs is the extent of spread of signals along their processes, and hence whether they act as a collection of independent or semi-independent elements. This bears directly on interpretation of the responses to oriented stimuli for example. Did you do any experiments that might provide additional information about this issue? For example, if you stimulate one of the WACs peripherally do you see a strong modulation of the somatic voltage? Or in the imaging experiments, if you mask a region of the processes so that it is not receiving a stimulus, do you see responses "leak" into that occluded region from surrounding stimulated regions?

Orientation tuning and connectivity<br /> The most developed functional results in the paper relate to the sensitivity of the WAC processes to oriented stimuli. Interpretation of these results depends on a few factors. First is the spread of signals in the WAC processes - as noted above. Second is connectivity. The paper shows that the B/K WAC activity increases inhibitory input to Off-delta and On-alpha ganglion cells. These cells, as noted in the paper, are not orientation tuned. But the orientation tuned ganglion cells stratify in a similar location within the IPL, and hence are situated in an appropriate place to receive input from the B/K WACs. Did you focus exclusively on connections to the Off-delta and On-alpha cells (along with the Off-alpha) or did you look at any other ganglion cell types? This should at least get discussed in more detail.

In several places it is unclear whether the paper intends to be a methods paper or a basic research paper. One example is the last sentence of the abstract. If it is intended to be a basic research paper (which is my overall impression) then I suggest shifting the emphasis in some of those key locations towards results and away from methods.

eLife assessment

This important study describes patterns of anatomical connectivity between the cortex and the thalamus using magnetic resonance imaging data in humans and non-human primates. The measures are related to numerous other modalities to develop a robust understanding of the organisation of the system. The authors provide solid evidence that there is a difference between sensory and association cortices in terms of their connectivity with the thalamus, which may have downstream effects on brain function. This work will be of interest to neuroscientists interested in the organization and dynamics of cortico-thalamic circuits.

Reviewer #1 (Public Review):

Summary:<br /> The thalamus is a central subcortical structure that receives anatomical connections from various cortical areas, each displaying a unique pattern. Previous studies have suggested that certain cortical areas may establish more extensive connections within the thalamus, influencing distributed information flow. Despite these suggestions, a quantitative understanding of cortical areas' anatomical connectivity patterns within the thalamus is lacking. In this study, the researchers addressed this gap by employing diffusion magnetic resonance imaging (dMRI) on a large cohort of healthy adults from the Human Connectome Project. Using brain-wide probabilistic tractography, a framework was developed to measure the spatial extent of anatomical connections within the thalamus for each cortical area. Additionally, the researchers integrated resting-state functional MRI, cortical myelin, and human neural gene expression data to investigate potential variations in anatomical connections along the cortical hierarchy. The results unveiled two distinct cortico-thalamic tractography motifs: 1) a sensorimotor cortical motif featuring focused thalamic connections to the posterolateral thalamus, facilitating fast, feed-forward information flow; and 2) an associative cortical motif characterized by diffuse thalamic connections targeting the anteromedial thalamus, associated with slower, feed-back information flow. These motifs exhibited consistency across human subjects and were corroborated in macaques, underscoring cross-species generalizability. In summary, the study illuminates differences in the spatial extent of anatomical connections within the thalamus for sensorimotor and association cortical areas, potentially contributing to functionally distinct cortico-thalamic information flow.

Strengths:<br /> * Quantitative Approach: The study employs diffusion magnetic resonance imaging (dMRI) and probabilistic tractography on a substantial sample size of 828 healthy adults, providing a robust quantitative analysis of anatomical connectivity patterns within the thalamus.

* Multi-Modal Integration: By incorporating resting-state functional MRI, cortical myelin, and human neural gene expression data, the study offers a comprehensive approach to understanding anatomical connections, enriching the interpretation of findings and enhancing the study's overall validity.

* Cross-Species Generalizability: The identification of consistent cortico-thalamic tractography motifs in both human subjects and macaques demonstrates the robustness and cross-species generalizability of the findings, strengthening the significance and broader applicability of the study.

* Supplementary Analyses: There are numerous, excellent examples of clear surrogates used to test the major claims of the paper. This is exemplary work.

Weaknesses:<br /> * Indirect Estimates of White Matter Connections: While dMRI is a valuable tool, it inherently provides indirect and inferred information about neural pathways. The accuracy and specificity of tractography can be influenced by various factors, including fiber crossing, partial volume effects, and algorithmic assumptions. A potential limitation in the accuracy of indirect estimates might affect the precision of spatial extent measurements, introducing uncertainty in the interpretation of cortico-thalamic connectivity patterns. Addressing the methodological limitations associated with indirect estimates and considering complementary approaches could strengthen the overall robustness of the findings.

Reviewer #2 (Public Review):

Summary:<br /> This paper by Howell and colleagues focuses on describing macro patterns of anatomical connections between cortical areas and the thalamus in the human brain. This research topic poses significant challenges due to the inability to apply the gold standard of mapping anatomical connections, and viral tracing, to humans. Moreover, when applied to animal models, viral tracing often has limited scope and resolution. As a result, the field has thus far lacked a comprehensive and validated description of thalamocortical anatomical connectivity in humans.

The paper focuses on an intriguing question: whether anatomical connections from the cortex to the thalamus exhibit a diffuse pattern, targeting multiple thalamic sub-regions, or a more focal pattern, selectively targeting specific thalamic subregions. This novel and significant question holds substantial implications for our understanding of thalamocortical information processing. The authors have developed a sophisticated and innovative quantitative metric to address this question. The study revealed two main patterns: a focal pattern originating from sensorimotor cortical regions to the posterior thalamus and a more diffuse pattern from associative cortical regions to the anterior-medial thalamus. These findings are then framed within the context of thalamocortical motifs involved in feedforward versus feedback processing.

While this paper has several strengths, including its significance and methodological sophistication, its extension to non-human primates, and other forms of data for testing hierarchy, there are important limitations. These limitations, discussed in more detail below, primarily concern tracking accuracy and the known limitations of using diffusion data to track thalamocortical connections in humans. These limitations may potentially introduce systematic biases into the results, weakening their support. Addressing these limitations through better validation is crucial, though some may remain unresolved due to the fundamental constraints of diffusion imaging.

Strengths:<br /> This research holds significant basic, clinical, and translational importance as it contributes to our understanding of how thalamocortical anatomical connectivity is organized. Its relevance spans cognitive, systems, and clinical neuroscientists in various subfields.

The central question addressed in this study, concerning whether cortico-thalamic projections are focal or diffuse, is both novel and previously unexplored to the best of my knowledge. It offers valuable insights into the potential capabilities of the thalamocortical system in terms of parallel or integrative processing.

The development of quantitative metrics to analyze anatomical connectivity is highly innovative and suitable for addressing the research questions at hand.

The findings are not only interesting but also robust, aligning with data from other sources that suggest a hierarchical organization in the brain.

Using PCA to integrate results across a range of thresholds is innovative.

The study's sophisticated integration of a diverse range of data and methods provides strong, converging support for its main findings, enhancing the overall credibility of the research.

Weaknesses:<br /> Structural thalamocortical connectivity was estimated from diffusion imaging data obtained from the HCP dataset. Consequently, the robustness and accuracy of the results depend on the suitability of this data for such a purpose. Conducting tractography on the cortical-thalamic system is recognized as a challenging endeavor for several reasons. First, diffusion directions lose their clearly defined principal orientations once they reach the deep thalamic nuclei, rendering the tracking of structures on the medial side, such as the medial dorsal (MD) and pulvinar nuclei difficult. Somewhat concerning is those are regions that authors found to show diffuse connectivity patterns. Second, the thalamic radiata diverge into several directions, and routes to the lateral surface often lack the clarity necessary for successful tracking. It is unclear if all cortical regions have similar levels of accuracy, and some of the lateral associative regions might have less accurate tracking, making them appear to be more diffuse, biasing the results.

While the methodology employed by the authors appears to be state-of-the-art, there exists uncertainty regarding its appropriateness for validation, given the well-documented issues of false positives and false negatives in probabilistic diffusion tractography, as discussed by Thomas et al. 2014 PNAS. Although replicating the results in both humans and non-human primates strengthens the study, a more compelling validation approach would involve demonstrating the method's ability to accurately trace known tracts from established tracing studies or, even better, employing phantom track data. Many of the control analyses the authors presented, such as track density, do not speak to accuracy.

Because the authors included data from all thresholds, it seems likely that false positive tracks were included in the results. The methodology described seems to unavoidably include anatomically implausible pathways in the spatial extent analyses.

If tracking the medial thalamus is indeed less accurate, characterized by higher false positives and false negatives, it could potentially lead to increased variability among individual subjects. In cases where results are averaged across subjects, as the authors have apparently done, this could inadvertently contribute to the emergence of the "diffuse" motif, as described in the context of the associative cortex. This presents a critical issue that requires a more thorough control analysis and validation process to ensure that the main results are not artifacts resulting from limitations in tractography.

The thresholding approach taken in the manuscript aimed to control for inter-areal differences in anatomical connection strength that could confound the ED estimates. Here I am not quite clear why inter-areal differences in anatomical connection strength have to be controlled. A global threshold applied on all thalamic voxels might kill some connections that are weak but do exist. Those weak pathways are less likely to survive at high thresholds. In the meantime, the mean ED is weighted, with more conservative thresholds having higher weights. That being said, isn't it possible that more robust pathways might contribute more to the mean ED than weaker pathways?

Reviewer #3 (Public Review):

Summary:<br /> In the current work, Howell et al studied the connectivity between the cortex and thalamus using DTI tractorgraphy per parcel to all voxels in the thalamus. Following they performed various dimensional reduction techniques to uncover how differences in connectivity to the thalamus vary across cortical parcels. Following they explore the spatial correlation of these variations with cortical myelin and functional organization, thalamic nuclei, gene expression derived core-matrix cell differentiation, and extend the model towards macaques. Overall, the authors find a differentiation between sensory and association areas in terms of the association with the thalamus, which reflects differences in cortical microstructure and function, and links to core-matrix differences and can be replicated in macaques.

Strengths:<br /> A clear strength of the current work is the combination of different models and approaches to study the link between the cortex and the thalamus. This approach nicely bridges different approaches to describe the role of the thalamus in cortical organisation using a diffusion-based approach. Especially the extension of the model to the macaque is quite nice.

Weaknesses:<br /> Potential weaknesses of the study are that it seems to largely integrate aspects of the thalamus that have been already described before. The differentiation between sensory and association systems across thalamic subregions is something that has been described before (see: Oldham and Ball, 2023; Zheng et al., 2023; Yang et al., 2020 Mueller, 2020; Behrens, 2003).

Appraisal:<br /> However, the aim of the study: 'to investigate the spatial extent of anatomical connectivity patterns within the thalamus in both humans and non-human primates and determine if such patterns differ between sensorimotor and association cortical areas' has been met.

Discussion:<br /> Overall, I think the study is a nice addition to the growing literature studying the anatomical connectivity between the thalamus and cortex and its functional implications.

eLife assessment

This important study introduces a new cortical circuit model for predictive processing. Simulations effectively illustrate that, with appropriate synaptic plasticity, a canonical layer 2/3 cortical circuit - comprising two classes of interneurons providing subtractive and divisive inhibition - can generate uncertainty-modulated prediction errors by pyramidal neurons. The model's effectiveness is supported by solid numerical analysis. Although the model is convincing and offers testable predictions, it currently lacks direct comparison to experimental data, and the presentation clarity could be improved. Nonetheless, the model is expected to be of great interest to those involved in cortical and predictive processing research.

Reviewer #1 (Public Review):

Summary:<br /> Wilmes and colleagues present a computational model of a cortical circuit for predictive processing which tackles the issue of how to learn predictions when different levels of uncertainty are present for the predicted sensory stimulus. When a predicted sensory outcome is highly variable, deviations from the average expected stimulus should evoke prediction errors that have less impact on updating the prediction of the mean stimulus. In the presented model, layer 2/3 pyramidal neurons represent either positive or negative prediction errors, SST neurons mediate the subtractive comparison between prediction and sensory input, and PV neurons represent the expected variance of sensory outcomes. PVs therefore can control the learning rate by divisively inhibiting prediction error neurons such that they are activated less, and exert less influence on updating predictions, under conditions of high uncertainty.

Strengths:<br /> The presented model is a very nice solution to altering the learning rate in a modality and context-specific way according to expected uncertainty and, importantly, the model makes clear, experimentally testable predictions for interneuron and pyramidal neuron activity. This is therefore an important piece of modelling work for those working on cortical and/or predictive processing and learning. The model is largely well-grounded in what we know of the cortical circuit.

Weaknesses:<br /> Currently, the model has not been challenged with experimental data, presumably because data from an adequate paradigm is not yet available. I therefore only have minor comments regarding the biological plausibility of the model:

Beyond the fact that some papers show SSTs mediate subtractive inhibition and PVs mediate divisive inhibition, the selection of interneuron types for the different roles could be argued further, given existing knowledge of their properties. For instance, is a high PV baseline firing rate, or broad sensory tuning that is often interpreted as a 'pooling' of pyramidal inputs, compatible with or predicted by the model?

On a related note, SSTs are thought to primarily target the apical dendrite, while PVs mediate perisomatic inhibition, so the different roles of the interneurons in the model make sense, particularly for negative PE neurons, where a top-down excitatory predicted mean is first subtractively compared with the sensory input, s, prior to division by the variance. However, sensory input is typically thought of as arising 'bottom-up', via layer 4, so the model may match the circuit anatomy less in the case of positive PE neurons, where the diagram shows 's' arising in a top-down manner. Do the authors have a justification for this choice?

In cortical circuits, assuming a 2:8 ratio of inhibitory to excitatory neurons, there are at least 10 pyramidal neurons to each SST and PV neuron. Pyramidal neurons are also typically much more selective about the type of sensory stimuli they respond to compared to these interneuron classes (e.g., Kerlin et al., 2012, Neuron). A nice feature of the proposed model is that the same interneurons can provide predictions of the mean and variance of the stimulus in a predictor-dependent manner. However, in a scenario where you have two types of sensory stimulus to predict (e.g., two different whiskers stimulated), with pyramidal neurons selective for prediction errors in one or the other, what does the model predict? Would you need specific SST and PV circuits for each type of predicted stimulus?

Reviewer #2 (Public Review):

Summary:<br /> This computational modeling study addresses the observation that variable observations are interpreted differently depending on how much uncertainty an agent expects from its environment. That is, the same mismatch between a stimulus and an expected stimulus would be less significant, and specifically would represent a smaller prediction error, in an environment with a high degree of variability than in one where observations have historically been similar to each other. The authors show that if two different classes of inhibitory interneurons, the PV and SST cells, (1) encode different aspects of a stimulus distribution and (2) act in different (divisive vs. subtractive) ways, and if (3) synaptic weights evolve in a way that causes the impact of certain inputs to balance the firing rates of the targets of those inputs, then pyramidal neurons in layer 2/3 of canonical cortical circuits can indeed encode uncertainty-modulated prediction errors. To achieve this result, SST neurons learn to represent the mean of a stimulus distribution and PV neurons its variance.

The impact of uncertainty on prediction errors is an understudied topic, and this study provides an intriguing and elegant new framework for how this impact could be achieved and what effects it could produce. The ideas here differ from past proposals about how neuronal firing represents uncertainty. The developed theory is accompanied by several predictions for future experimental testing, including the existence of different forms of coding by different subclasses of PV interneurons, which target different sets of SST interneurons (as well as pyramidal cells). The authors are able to point to some experimental observations that are at least consistent with their computational results. The simulations shown demonstrate that if we accept its assumptions, then the authors' theory works very well: SSTs learn to represent the mean of a stimulus distribution, PVs learn to estimate its variance, firing rates of other model neurons scale as they should, and the level of uncertainty automatically tunes the learning rate, so that variable observations are less impactful in a high uncertainty setting.

Strengths:<br /> The ideas in this work are novel and elegant, and they are instantiated in a progression of simulations that demonstrate the behavior of the circuit. The framework used by the authors is biologically plausible and matches some known biological data. The results attained, as well as the assumptions that go into the theory, provide several predictions for future experimental testing.

Weaknesses:<br /> Overall, I found this manuscript to be frustrating to read and to try to understand in detail, especially the Results section from the UPE/Figure 4 part to the end and parts of the Methods section. I don't think the main ideas are so complicated, and it should be possible to provide a much clearer presentation.

For me, one source of confusion is the comparison across Figure 1EF, Figure 2A, Figure 3A, Figure 4AB, and Figure 5A. All of these are meant to be schematics of the same circuit (although with an extra neuron in Figure 5), yet other than Figures 1EF and 4AB, no two are the same! There should be a clear, consistent schematic used, with identical labeling of input sources, neuron types, etc. across all of these panels.

The flow of the Results section overall is clear until the ``Calculation of the UPE in Layer 2/3 error neurons' and Figure 4, where I find that things become significantly more confusing. The mention of NMDA and calcium spikes comes out of the blue, and it's not clear to me how this fits into the authors' theory. Moreover: Why would this property of pyramidal cells cause the PV firing rate to increase as stated? The authors refer to one set of weights (from SSTs to UPE) needing to match two targets (weights from s to UPE and weights from mean representation to UPE); how can one set of weights match two targets? Why do the authors mention ``out-of-distribution detection' here when that property is not explored later in the paper? (see also below for other comments on Figure 4)

Coming back to one of the points in the previous paragraph: How realistic is this exact matching of weights, as well as the weight matching that the theory requires in terms of the weights from the SSTs to the PVs and the weights from the stimuli to the PVs? This point should receive significant elaboration in the discussion, with biological evidence provided. I would not advocate for the authors' uncertainty prediction theory, despite its elegant aspects, without some evidence that this weight matching occurs in the brain. Also, the authors point out on page 3 that unlike their theory, "...SSTs can also have divisive effects, and PVs can have subtractive effects, dependent on circuit and postsynaptic properties". This should be revisited in the Discussion, and the authors should explain why these effects are not problematic for their theory. In a similar vein, this work assumes the existence of two different populations of SST neurons with distinct UPE (pyramidal) targets. The Discussion doesn't say much about any evidence for this assumption, which should be more thoroughly discussed and justified.

Finally, I think this is a paper that would have been clearer if the equations had been interspersed within the results. Within the given format, I think the authors should include many more references to the Methods section, with specific equation numbers, where they are relevant throughout the Results section. The lack of clarity is certainly made worse by the current state of the Methods section, where there is far too much repetition and poor ordering of material throughout.

Reviewer #3 (Public Review):

Summary:<br /> The authors proposed a normative principle for how the brain's internal estimate of an observed sensory variable should be updated during each individual observation. In particular, they propose that the update size should be inversely proportional to the variance of the variable. They then proposed a microcircuit model of how such an update can be implemented, in particularly incorporating two types of interneurons and their subtractive and divisive inhibition onto pyramidal neurons. One type should represent the estimated mean while another represents the estimated variance. The authors used simulations to show that the model works as expected.

Strengths:<br /> The paper addresses two important issues: how uncertainty is represented and used in the brain, and the role of inhibitory neurons in neural computation. The proposed circuit and learning rules are simple enough to be plausible. They also work well for the designated purposes. The paper is also well-written and easy to follow.

Weaknesses:<br /> I have concerns with two aspects of this work.

(1) The optimality analysis leading to Eq (1) appears simplistic. The learning setting the authors describe (estimating the mean of a stationary Gaussian variable from a stream of observations) is a very basic problem in online learning/streaming algorithm literature. In this setting, the real "optimal" estimate is simply the arithmetic average of all samples seen so far. This can be implemented in an online manner with \hat{\mu}_{t} = \hat{\mu}_{t-1} +(s_t-\hat{\mu}_{t-1})/t. This is optimal in the sense that the estimator is always the maximum likelihood estimator given the samples seen up to time t. On the other hand, doing gradient descent only converges towards the MLE estimator after a large number of updates. Another critique is that while Eq (1) assumes an estimator of the mean (\hat{mu}), it assumes that the variance is already known. However, in the actual model, the variance also needs to be estimated, and a more sophisticated analysis thus needs to take into account the uncertainty of the variance estimate and so on. Finally, the idea that the update should be inverse to the variance is connected to the well-established idea in neuroscience that more evidence should be integrated over when uncertainty is high. For example, in models of two-alternative forced choices it is known to be optimal to have a longer reaction time when the evidence is noisier.

(2) While the incorporation of different inhibitory cell types into the model is appreciated, it appears to me that the computation performed by the circuit is not novel. Essentially the model implements a running average of the mean and a running average of the variance, and gates updates to the mean with the inverse variance estimate. I am not sure about how much new insight the proposed model adds to our understanding of cortical microcircuits.

eLife assessment

This study presents important findings on the differential activity of noradrenergic and dopaminergic input to dorsal hippocampus CA1 in head-fixed mice traversing a runway in a virtual environment that is familiar or novel. While the data appear to be rigorously analysed, and the observed divergence in the dynamics of activity in the dopaminergic and noradrenergic axons is solid, there are some methodological concerns that mean the strength of evidence is currently incomplete.

Reviewer #1 (Public Review):

Summary:

Heer and Sheffield used 2 photon imaging to dissect the functional contributions of convergent dopamine and noradrenaline inputs to the dorsal hippocampus CA1 in head-restrained mice running down a virtual linear path. Mice were trained to collect water rewards at the end of the track and on test days, calcium activity was recorded from dopamine (DA) axons originating in the ventral tegmental area (VTA, n=7) and noradrenaline axons from the locus coeruleus (LC, n=87) under several conditions. When mice ran laps in a familiar environment, VTA DA axons exhibited ramping activity along the track that correlated with distance to reward and velocity to some extent, while LC input activity remained constant across the track, but correlated invariantly with velocity and time to motion onset. A subset of recordings taken when the reward was removed showed diminished ramping activity in VTA DA axons, but no changes in the LC axons, confirming that DA axon activity is locked to reward availability. When mice were subsequently introduced to a new environment, the ramping to reward activity in the DA axons disappeared, while LC axons showed a dramatic increase in activity lasting 90 s (6 laps) following the environment switch. In the final analysis, the authors sought to disentangle LC axon activity induced by novelty vs. behavioral changes induced by novelty by removing periods in which animals were immobile and established that the activity observed in the first 2 laps reflected novelty-induced signal in LC axons.

Strengths:

The results presented in this manuscript provide insights into the specific contributions of catecholaminergic input to the dorsal hippocampus CA1 during spatial navigation in a rewarded virtual environment, offering a detailed analysis of the resolution of single axons. The data analysis is thorough and possible confounding variables and data interpretation are carefully considered.

Weaknesses:

Aspects of the methodology, data analysis, and interpretation diminish the overall significance of the findings, as detailed below.

The LC axonal recordings are well-powered, but the DA axonal recordings are severely underpowered, with recordings taken from a mere 7 axons (compared to 87 LC axons). Additionally, 2 different calcium indicators with differential kinetics and sensitivity to calcium changes (GCaMP6S and GCaMP7b) were used (n=3, n=4 respectively) and the data pooled. This makes it very challenging to draw any valid conclusions from the data, particularly in the novelty experiment. The surprising lack of novelty-induced DA axon activity may be a false negative. Indeed, at least 1 axon (axon 2) appears to be showing a novelty-induced rise in activity in Figure 3C. Changes in activity in 4/7 axons are also referred to as a 'majority' occurrence in the manuscript, which again is not an accurate representation of the observed data.

The authors conducted analysis on recording data exclusively from periods of running in the novelty experiment to isolate the effects of novelty from novelty-induced changes in behavior. However, if the goal is to distinguish between changes in locus coeruleus (LC) axon activity induced by novelty and those induced by motion, analyzing LC axon activity during periods of immobility would enhance the robustness of the results.

The authors attribute the ramping activity of the DA axons to the encoding of the animals' position relative to reward. However, given the extensive data implicating the dorsal CA1 in timing, and the remarkable periodicity of the behavior, the fact that DA axons could be signalling temporal information should be considered.

The authors should explain and justify the use of a longer linear track (3m, as opposed to 2m in the DAT-cre mice) in the LC axon recording experiments.

Reviewer #2 (Public Review):

Summary:

The authors used 2-photon Ca2+-imaging to study the activity of ventral tegmental area (VTA) and locus coeruleus (LC) axons in the CA1 region of the dorsal hippocampus in head-fixed male mice moving on linear paths in virtual reality (VR) environments.

The main findings were as follows:

- In a familiar environment, the activity of both VTA axons and LC axons increased with the mice's running speed on the Styrofoam wheel, with which they could move along a linear track through a VR environment.<br /> - VTA, but not LC, axons showed marked reward position-related activity, showing a ramping-up of activity when mice approached a learned reward position.<br /> - In contrast, the activity of LC axons ramped up before the initiation of movement on the Styrofoam wheel.<br /> - In addition, exposure to a novel VR environment increased LC axon activity, but not VTA axon activity.

Overall, the study shows that the activity of catecholaminergic axons from VTA and LC to dorsal hippocampal CA1 can partly reflect distinct environmental, behavioral, and cognitive factors. Whereas both VTA and LC activity reflected running speed, VTA, but not LC axon activity reflected the approach of a learned reward, and LC, but not VTA, axon activity reflected initiation of running and novelty of the VR environment.

I have no specific expertise with respect to 2-photon imaging, so cannot evaluate the validity of the specific methods used to collect and analyse 2-photon calcium imaging data of axonal activity.

Strengths:

(1) Using a state-of-the-art approach to record separately the activity of VTA and LC axons with high temporal resolution in awake mice moving through virtual environments, the authors provide convincing evidence that the activity of VTA and LC axons projecting to dorsal CA1 reflect partly distinct environmental, behavioral and cognitive factors.

(2) The study will help a) to interpret previous findings on how hippocampal dopamine and norepinephrine or selective manipulations of hippocampal LC or VTA inputs modulate behavior and b) to generate specific hypotheses on the impact of selective manipulations of hippocampal LC or VTA inputs on behavior.

Weaknesses:

(1) The findings are correlational and do not allow strong conclusions on how VTA or LC inputs to dorsal CA1 affect cognition and behavior. However, as indicated above under Strengths, the findings will aid the interpretation of previous findings and help to generate new hypotheses as to how VTA or LC inputs to dorsal CA1 affect distinct cognitive and behavioral functions.

(2) Some aspects of the methodology would benefit from clarification.<br /> First, to help others to better scrutinize, evaluate, and potentially to reproduce the research, the authors may wish to check if their reporting follows the ARRIVE (Animal Research: Reporting of In Vivo Experiments) guidelines for the full and transparent reporting of research involving animals (https://arriveguidelines.org/). For example, I think it would be important to include a sample size justification (e.g., based on previous studies, considerations of statistical power, practical considerations, or a combination of these factors). The authors should also include the provenance of the mice. Moreover, although I am not an expert in 2-photon imaging, I think it would be useful to provide a clearer description of exclusion criteria for imaging data.<br /> Second, why were different linear tracks used for studies of VTA and LC axon activity (from line 362)? Could this potentially contribute to the partly distinct activity correlates that were found for VTA and LC axons?<br /> Third, the authors seem to have used two different criteria for defining immobility. Immobility was defined as moving at <5 cm/s for the behavioral analysis in Figure 3a, but as <0.2 cm/s for the imaging data analysis in Figure 4 (see legends to these figures and also see Methods, from line 447, line 469, line 498)? I do not understand why, and it would be good if the authors explained this.

(3) In the Results section (from line 182) the authors convincingly addressed the possibility that less time spent immobile in the novel environment may have contributed to the novelty-induced increase of LC axon activity in dorsal CA1 (Figure 4). In addition, initially (for the first 2-4 laps), the mice also ran more slowly in the novel environment (Figure 3aIII, top panel). Given that LC and VTA axon activity were both increasing with velocity (Figure 1F), reduced velocity in the novel environment may have reduced LC and VTA axon activity, but this possibility was not addressed. Reduced LC axon activity in the novel environment could have blunted the novelty-induced increase. More importantly, any potential novelty-induced increase in VTA axon activity could have been masked by decreases in VTA axon activity due to reduced velocity. The latter may help to explain the discrepancy between the present study and previous findings that VTA neuron firing was increased by novelty (see Discussion, from line 243). It may be useful for the authors to address these possibilities based on their data in the Results section, or to consider them in their Discussion.

(4) Sensory properties of the water reward, which the mice may be able to detect, could account for reward-related activity of VTA axons (instead of an expectation of reward). Do the authors have evidence that this is not the case? Occasional probe trials, intermixed with rewarded trials, could be used to test for this possibility.

Reviewer #3 (Public Review):

Summary:

Heer and Sheffield provide a well-written manuscript that clearly articulates the theoretical motivation to investigate specific catecholaminergic projections to dorsal CA1 of the hippocampus during a reward-based behavior. Using 2-photon calcium imaging in two groups of cre transgenic mice, the authors examine the activity of VTA-CA1 dopamine and LC-CA1 noradrenergic axons during reward seeking in a linear track virtual reality (VR) task. The authors provide a descriptive account of VTA and LC activities during walking, approach to reward, and environment change. Their results demonstrate LC-CA1 axons are activated by walking onset, modulated by walking velocity, and heighten their activity during environment change. In contrast, VTA-CA1 axons were most activated during the approach to reward locations. Together the authors provide a functional dissociation between these catecholamine projections to CA1. A major strength of their approach is the methodological rigor of 2-photon recording, data processing, and analysis approaches. These important systems neuroscience studies provide solid evidence that will contribute to the broader field of learning and memory. The conclusions of this manuscript are mostly well supported by the data, but some additional analysis and/or experiments may be required to fully support the author's conclusions.

Weaknesses:

(1) During teleportation between familiar to novel environments the authors report a decrease in the freezing ratio when combining the mice in the two experimental groups (Figure 3aiii). A major conclusion from the manuscript is the difference in VTA and LC activity following environment change, given VTA and LC activity were recorded in separate groups of mice, did the authors observe a similar significant reduction in freezing ratio when analyzing the behavior in LC and VTA groups separately?

(2) The authors satisfactorily apply control analyses to account for the unequal axon numbers recorded in the LC and VTA groups (e.g. Figure 1). However, given the heterogeneity of responses observed in Figures 3c, 4b and the relatively low number of VTA axons recorded (compared to LC), there are some possible limitations to the author's conclusions. A conclusion that LC-CA1 axons, as a general principle, heighten their activity during novel environment presentation, would require this activity profile to be observed in some of the axons recorded in most all LC-CA1 mice. Additionally, if the general conclusion is that VTA-CA1 axons ramp activity during the approach to reward, it would be expected that this activity profile was recorded in the axons of most all VTA-CA1 mice. Can the authors include an analysis to demonstrate that each LC-CA1 mouse contained axons that were activated during novel environments and that each VTA-CA1 mouse contained axons that ramped during the approach to reward?

(3) A primary claim is that LC axons projecting to CA1 become activated during novel VR environment presentation. However, the experimental design did not control for the presentation of a familiar environment. As I understand, the presentation order of environments was always familiar, then novel. For this reason, it is unknown whether LC axons are responding to novel environments or environmental change. Did the authors re-present the familiar environment after the novel environment while recording LC-CA1 activity?

eLife assessment

Alpha-synuclein is a synaptic vesicle associated protein that is linked to a number of neurodegenerative disorders. In this manuscript, the authors provide compelling evidence of alpha-synuclein's interaction with E-domain synapsins as the main culprit mediating the suppression of neurotransmitter release and synaptic vesicle recycling by alpha-synuclein. This important work provides molecular mechanisms underlying alpha-synuclein functions.

Reviewer #1 (Public Review):

This is a short but important study. Basically, the authors show that α-synuclein overexpression's negative impact on synaptic vesicle recycling is mediated by its interaction with E-domain containing synapsins. This finding is highly relevant for synuclein function as well as for the pathophysiology of synucleinopathies. The data is clear, functional analysis is highly adequate.

eLife assessment

This important study proposes a new method for tracking neurons recorded with Neuropixel electrodes across days. The methods and the strength of the evidence are convincing, but the authors do not address whether their approach can be generalized to other brain areas, species, behaviors, or tools. Overall, this method will be potentially of interest to many neuroscientists who want to study long-term activity changes of individual neurons in the brain.

Reviewer #1 (Public Review):

Neurons are not static-their activity patterns change as the result of learning, aging, and disease. Reliable tracking of activity from individual neurons across long time periods would enable studies of these important dynamics. For this reason, the authors' efforts to track electrophysiological activity across days without relying on matching neural receptive fields (which can change due to learning, aging, and disease) is very important.

By utilizing the tightly-spaced electrodes on Neuropixels probes, they are able to measure the physical distance and the waveform shape 'distance' between sorted units recorded on different days. To tune the matching algorithm and to validate the results, they used the visual receptive fields of neurons in the mouse visual cortex (which tend to change little over time) as ground truth. Their approach performs quite well, with a high proportion of neurons accurately matched across multiple weeks.

This suggests that the method may be useable in other cases where the receptive fields can't be used as ground truth to validate the tracking. This potential extendibility to tougher applications is where this approach holds the most promise. However, the study only looks at one brain area (visual cortex), in one species (mouse), using one type of spike sorter (Kilosort), and one type of behavioral prep (head-fixed). While the authors suggest methods to generalize their technique to other experimental conditions, no validation of those generalizations was done using data from different experimental conditions. Anyone using this method under different conditions would therefore need to perform such validation themselves.

Reviewer #2 (Public Review):

The manuscript presents a method for tracking neurons recorded with neuropixels across days, based on the matching of cells' spatial layouts and spike waveforms at the population level. The method is tested on neuropixel recordings of the visual cortex carried over 47 days, with the similarity in visual receptive fields used to verify the matches in cell identity.

This is an important tool as electrophysiological recordings have been notoriously limited in terms of tracking individual neuron's fate over time, unlike imaging approaches. The method is generally sound and properly tested but I think some clarifications would be helpful regarding the implementation of the method and some of the results.

(1) Page 6: I am not sure I understand the point of the imposed drift and how the value of 12µm is chosen.<br /> Is it that various values of imposed drift are tried, the EMDs computed to produce histograms as in Fig2c, values of rigid drifts estimated based on the histogram modes, and then the value associated with minimum cost selected? The corresponding manuscript section would need some clarification regarding this aspect.

(2) The EMD is based on the linear sum, with identical weight, of cell distance and waveform similarity measures. How performance is affected from using a different weighting of the 2 measures (for instance, using only cell distance and no waveform similarity)? It is common that spike waveforms associated to a given neuron appear different on different channels of silicon probes (i.e. the spike waveform changes depending the position of recording sites relative to the neuron), so I wonder if that feature is helping or potentially impeding the tracking.

(3) Fig.5: I assume the dots are representing time gaps for which cell tracking is estimated. The 3 different groups of colors correspond to the 3 mice used. For a given mouse, I would expect to always see 3 dots (for ref, putative and mixed) for a given tracking gap. However, for mouse AL036 for instance, at tracking duration of 8 days, a dot is visible for mixed but not for ref and putative. How come this is happening?

(4) Matched visual responses are measured by the sum of correlation of visual fingerprints, which are vectors of cells' average firing rate across visual stimuli, and correlation of PSTHs, which are implemented over all visual stimuli combined. I believe that some information is lost from combining all stimuli in the implementation of PSTHs (assuming that PSTHs show specificity to individual visual stimuli). The authors might consider, as alternative measure of matched visual responses, a correlation of the vector concatenations of all stimulus PSTHs. Such simpler measure would contain both visual fingerprint and PSTH information, and would not lose the information of PSTH specificity across visual stimuli.

2nd revision

(1) From reading the authors' response, I could understand several of the points I had previously missed. I still think that some part of the results are not straightforward to understand, the way it is written. Adding a few introductory sentences to the paragraphs (for instance the one related to my previous point #1) would really help the reader comprehend this important work.

(2) Following on my point #2, the w value used is 1500 and the recovery rate doesn't seems to reach a peak but rather a plateau for larger w values. From such large w value and the absence of a downward trend for increasing values, it would seem that only the 'waveform distance' matter and that the 'location distance' doesn't contribute much to the EMD distance. Is this correct?

eLife assessment

This manuscript details a new method and tool for examining TDP-43 loss of nuclear and gain of cytoplasmic function in neurons. This is a valuable resource that does not rely on artificial knockdown or overexpression. While the authors seek to use this new system to induce disease-associated TDP-43 pathology), their overall evaluation is incomplete and requires further characterization to enhance the applicability and utility of this new tool.

Reviewer #1 (Public Review):

Summary: Nuclear depletion and cytoplasmic mislocalization/aggregation of the DNA and RNA binding protein TDP-43 are pathological hallmarks of multiple neurodegenerative diseases. Prior work has demonstrated that depletion of TDP-43 from the nucleus leads to alterations in transcription and splicing. Conversely, cytoplasmic mislocalization/aggregation can contribute to toxicity by impairing mRNA transport and translation as well as miRNA dysregulation. However, to date, models of TDP-43 proteinopathy rely on artificial knockdown- or overexpression-based systems to evaluate either nuclear loss or cytoplasmic gain of function events independently. Few model systems authentically reproduce both nuclear depletion and cytoplasmic miscloalization/aggreagtion events. In this manuscript, the authors generate novel iPSC-based reagents to manipulate the localization of endogenous TDP-43. This is a valuable resource for the field to study pathological consequences of TDP-43 proteinopathy in a more endogenous and authentic setting. However, in the current manuscript, there are a number of weaknesses that should be addressed to further validate the ability of this model to replicate human disease pathology and demonstrate utility for future studies.

Strengths: The primary strength of this paper is the development of a novel in vitro tool.

Weaknesses: There are a number of weaknesses detailed below that should be addressed to thoroughly validate these new reagents as more authentic models of TDP-43 proteinopathy and demonstrate their utility for future investigations.

(1) The authors should include images of their engineered TDP-43-GFP iPSC line to demonstrate TDP-43 localization without the addition of any nanobodies (perhaps immediately prior to addition of nanobodies). Additionally, it is unclear whether simply adding a GFP tag to endogenous TDP-43 impact its normal function (nuclear-cytoplasmic shuttling, regulation of transcription and splicing, mRNA transport etc).

(2) Can the authors explain why there is a significant discrepancy in time points selected for nanobody transduction and immunostaining or cell lysis throughout Figure 1 and 2? This makes interpretation and overall assessment of the model challenging.

(3) The authors should further characterize their TDP-43 puncta. TDP-43 immunostaining is typically punctate so it is unclear if the puncta observed are physiologic or pathologic based on the analyses carried out in the current version of this manuscript. Additionally, do these puncta co-localize with stress granule markers or RNA transport granule markers? Are these puncta phosphorylated (which may be more reminiscent of end-stage pathologic observations in humans)?

(4) The authors should include multiple time points in their evaluation of TDP-43 loss of function events and aggregation. Does loss of function get worse over time? Is there a time course by which RNA misprocessing events emerge or does everything happen all at once? Does aggregation get worse over time? Do these neurons die at any point as a result of TDP-43 proteinopathy?

(5) Can the authors please comment on whether or not their model is "tunable"? In real human disease, not every neuron displays complete nuclear depletion of TDP-43. Instead there is often a gradient of neurons with differing magnitudes of nuclear TDP-43 loss. Additionally, very few neurons (5-10%) harbor cytoplasmic TDP-43 aggregates at end-stage disease. These are all important considerations when developing a novel authentic and endogenous model of TDP-43 proteinopathy which the current manuscript fails to address.

Reviewer #2 (Public Review):

Summary:<br /> TDP-43 mislocalization occurs in nearly all of ALS, roughly half of FTD, and as a co-pathology in roughly half of AD cases. Both gain-of-function and loss-of-function mechanisms associated with this mislocalization likely contribute to disease pathogeneisis.

Here, the authors describe a new method to induce TDP-43 mislocalization in cellular models. They endogenously-tagged TDP-43 with a C-terminal GFP tag in human iPSCs. They then expressed an intrabody - fused with a nuclear export signal (NES) - that targeted GFP to the cytosol. Expression of this intrabody-NES in human iPSC-derived neurons induced nuclear depletion of homozygous TDP-43-GFP, caused its mislocalization to the cytosol, and at least in some cells appeared to cause cytosolic aggregates. This mislocalization was accompanied by induction of cryptic exons in well characterized transcripts known to be regulated by TDP-43, a hallmark of functional TDP-43 loss and consistent with pathological nuclear TDP-43 depletion. Interestingly, in heterozygous TDP-43-GFP neurons, expression of intrabody-NES appeared to also induce the mislocalization of untagged TDP-43 in roughly half of the neurons, suggesting that this system can also be used to study effects on untagged endogenous TDP-43 as well as TDP-43-GFP fusion protein.

Strengths:<br /> A clearer understanding of how TDP-43 mislocalization alters cellular function, as well as pathways that mitigate clearance of TDP-43 aggregates, is critical. But modeling TDP-43 mislocalization in disease-relevant cellular systems has proven to be challenging. High levels of overexpression of TDP-43 lacking an NES can drive endogenous TDP-43 mislocalization, but such overexpression has direct and artificial consequences on certain cellular features (e.g. altered exon skipping) not seen in diseased patients. Toxic small molecules such as MG132 and arsenite can induce TDP-43 mislocalization, but co-induce myriad additional cellular dysfunctions unrelated to TDP-43 or ALS. TDP-43 binding oligonucleotides can cause cytosolic mislocalization as well. Each system has pros and cons, and additional ways to induce TDP-43 mislocalization would be useful for the field. The method described in this manuscript could provide researchers with a powerful way to study the combined biology of cytosolic TDP-43 mislocalization and nuclear TDP-43 depletion, with additional temporal control that is lacking in current method. Indeed, the authors see some evidence of differences in RNA splicing caused by pure TDP-43 depletion versus their induced mislocalization model. Finally, their method may be especially useful in determining how TDP-43 aggregates are cleared by cells, potentially revealing new biological pathways that could be therapeutically targeted.

Weaknesses:<br /> The method and supporting data have limitations in its current form, outlined below, and in its current form the findings are rather preliminary.

• Tagging of TDP-43 with a bulky GFP tag may alter its normal physiological functions, for example phase separation properties and functions within complex ribonucleoprotein complexes. In addition, alternative isoforms of TDP-43 (e.g. "short" TDP-43, would not be GFP tagged and therefore these species would not be directly manipulatable or visualizable with the tools currently employed in the manuscript.<br /> • The data regarding potential mislocalization of endogenous TDP-43 in the heterozygous TDP-43-GFP lines is especially intriguing and important, yet very little characterization was done. Does untagged TDP-43 co-aggregate with the tagged TDP-43? Is localization of TDP-43 immunostaining the same as the GFP signal in these cells?<br /> • The experiments in which dox was used to induce the nanobody-NES, then dox withdrawn to study potential longer-lasting or self-perpetuating inductions of aggregation is potentially interesting. However, the nanobody was only measured at the RNA level. We know that protein half lives can be very long in neurons, and therefore residual nanobody could be present at these delayed time points. The key measurement to make would be at the protein level of the nanobody if any conclusions are be made from this experiment.<br /> • Potential differences in splicing and microRNAs between TDP-43 knockdown and TDP-43 mislocalization are potentially interesting. However, different patterns of dysregulated RNA splicing can occur at different levels of TDP-knockdown, thus it is difficult to asses whether the changes observed in this paper are due to mislocalization per se, or rather just reflect differences in nuclear TDP-43 abundance.

eLife assessment

This important study explores numerous lines of evidence for the surprisingly diverse diets of a group of toothed birds that lived over 100 million years ago. The large amount of data the authors collected forms a solid dataset. The methods might in principle be extensible to other limbed vertebrates, although there are concerns regarding some of the details. The article will be of interest to colleagues studying ecological evolution in birds or dinosaurs more generally, as well as to anyone studying the impact of the mass extinction event 66 million years ago.

Author Response

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

We thank the reviewers for their valuable feedback which has improved this work greatly from its original form, and are elated to have such glowing reviews of the revised work published alongside the revised preprint. Reviewer 3 raises some final salient points, which deserve a brief address here.

Teeth: We thank the reviewer for clarifying their points. We do make the assumption that the ecological parameter space of toothed and beaked organisms will be comparable. Both are governed by the same set of physical principles and have the jaw bone as the most likely point of failure (teeth are harder than bone, and keratinous rhamphothecae are malleable and can be regrown with relative ease when deformed). Differences in stress/strain distribution between toothed and beaked organisms will occur but are already accounted for in our methods as we model both the teeth and rhamphotheca and will observe these different effects. We have added an explicit statement of this hypothesis to the Methods section of the manuscript.

Cranial kinesis: In our opinion, it is a safe assumption that the lower jaws of extant birds and enantiornithines are comparable. We do not see why the acquisition of kinesis in the upper jaw would generally affect the functional role of or constraints on the lower jaw. One possibility we discussed is that a quickly-moving kinetic premaxilla could let the lower jaw move a shorter distance during effective prey capture and lower the selection for speed (i.e. allow jaw-closing MA to remain higher). While we have added this possibility to our call for the investigation of cranial kinesis, we consider it too speculative to begin altering interpretations of fossil taxa. All raw measurement data remains available so that, if evidence is found for cranial kinesis having predictable effects on our measured parameters, future researchers can re-analyse our data and update any ecological predictions accordingly.

Organization: To our knowledge eLife format incorporates what one would think of as a Conclusions section into the Discussion. Our Discussion section currently contains 18 subheadings which should guide a reader to any specific topic of interest. The Discussion also progresses from a more narrow to broad focus which we and several colleagues find intuitive.

We thank all three reviewers once again for their feedback that has improved this work and their kind words throughout the process.

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

We thank all three reviewers for their detailed reviews, and generally agree with their feedback. To accompany the reviewed preprint of this manuscript, we wished to respond to comments from the reviewers so that they (and the public) will know what we are planning to incorporate in the revised manuscript we are currently preparing. If there are any comments on our plans in the meantime, please let us know.

• Reviewer 1, on concerns regarding identification of ontogenetic stage and comparison of taxa from different ontogenetic stages: It is fair to say that enantiornithine ontogeny is still poorly understood, though we believe all current evidence points to each specimen used in this study to being adequately mature for comparison to the extant birds used in the study. Stages of skeletal fusion are the standard method of assessing enantiornithine ontogeny (Hu and O'Connor 2017), and our comparison of histological work (Atterholt, Poust et al. 2021) to skeletal stages in Table S4 suggests a transition from juvenile to subadult in stage 0 or 1 and from subadult to adult within stage 3. Thus, the specimens we quantitatively examine in this study, all at stages 2 or 3 (Figure S10), are advanced subadults or adults. It is well-known that many living animals considered “adults” would be considered subadults or even juveniles to a palaeontologist (Hone, Farke et al. 2016). So, even if some individuals in this study are not fully skeletally mature, they should have obtained the morphology which they would possess for most of their lives and thus the morphology which undergoes selective pressure. We will add this context to the “Bohaiornithid Ontogeny” section and thank the reviewer for seeking more detail for this point.

• Reviewer 2, on need of a context figure: We have an artistic life reconstruction of a bohaiornithid in preparation, and can include that in the revised manuscript as a figure.

• Reviewer 2, on raptor claw categories: We explain these categories in-depth in a previous work (Miller, Pittman et al. 2023). However, we will now add a short summary of that explanation to this work so that this manuscript will become self-contained in this regard. In short, the “large raptor” category includes extant birds with records of regularly taking prey which cannot be encircled with the pes, while birds in the “small raptor” have no such records. As Reviewer 2 points out this does often follow phylogenetic lines, but not always. E.g. most owls specialise in taking small prey, but the great horned owl Bubo virginianus regularly takes mammals and birds larger than its pes (Artuso, Houston et al. 2020); and conversely we can only find reports of the common black hawk Buteogallus anthracinus taking prey samll enough for the pes to encircle (Schnell 2020) despite other accipiters frequently taking large prey. In both cases these taxa plot in PCA nearer to other large or small raptors (respectively) than to their phylogenetic relatives.

• Reviewer 3, on teeth vs beaks: We are not aware of any foods which are exclusive to toothed or beaked animals. There are some aspects of extant bird biology that may affect the way a certain diet may need to be adapted to which we do comment on, e.g. discussion of alternatives to the crop and ventriculus for processing plant matter in the Bohaiornithid Ecology and Evolution section. For functional studies, e.g. FEA, we have included the rhamphotheca in toothless models which serves the same role as teeth, to be a feeding surface. It should not matter, in theory, if the feeding surface is hard or soft as mechanical failure occurs in high stress/strain states regardless of the medium. If having teeth necessarily increases or decreses overall stress/strain relative to a beak (and from our work this does not appear to be the case), this would in turn necessarily limit dietary options. So, all models in our work should be directly comparable.

As an additional note on this topic, we address tooth shape in bohaiornithids at the end of the Bohaiornithid Ecology and Evolution section. We specifically note that their tooth shape is likley controlled by phylogeny in the current version, though we will add a note in the upcoming version that the morphospace of bohaiorntihid teeth overlaps that of many other clades with purportedly diverse diets, which is consistent with a hypothesis of diverse diets within the clade.

• Reviewer 3, on cranial kinesis: Our FE models should be unaffected by cranial kinesis, as these are two-dimensional and model the akinetic lower jaw only. Some mediolateral kinesis may be relevant in the mandible in the form of “wishboning” in different taxa, but its prevalence in extant birds is currently unknown. The preservation of enantiornithines (two-dimensionally and typically in lateral view) limits the ability to capture any mediolateral function regardless.

Our models of mechanical advantage do not account for any cranial kinesis. This is a necessary simplifcation. The nature of cranial kinesis in extant birds, and the role that it plays in feeding, is poorly understood. Cranial kinesis will increase gape, but we don’t yet know how/if it affects jaw closing force and speed (moreover, given the variation in quadrate and hinge morphology present in extant birds, this is also something that is likely to be highly diverse). We have therefore modelled the extant birds’ jaw closing systems as having one, akinetic out lever (the jaw joint to the bite point), to match the situation in our fossil taxa. This is a common simplification that has been used previously with success (Corbin, Lowenberger et al. 2015, Olsen 2017). However, we acknowledge that this simplification may introduce some error. Unfortunately, until the mechanics of cranial kinesis – and the variation in the anatomy and performance of kinetic structures in extant birds – are better understood, we cannot determine exactly what that error looks like. We therefore have greater confidence in the inter-species comparability this conservative, akinetic approach (in other words, we may not be making assumptions that are 100% accurate, but we are at least making the same assumption across all taxa, so it should be comparable in its error). We will add a section in the Mechanical Advantage and Functional Indices discussion calling for further research into the mechanics of cranial kinesis so future mechanical advantage work in birds can take this matter into account.

• Reviewer 3, on skull reconstruction: This issue is partly addressed in the Bohaiornithid Skull Reconstruction section, though we agree that adding more mentions of it in the MA and FEA Discussion sections and the Bohaiornithid Ecology and Evolution sections will benefit the manuscript. Most notably Shenqiornis and Sulcavis have similar ecological interpretations, but much of the Shenqiornis skull reconstruction uses Sulcavis bones. Longusunguis is the only other taxon which takes more than two bones from a different taxon, and in this case all but the quadrate are not used in any quanitative measurements. We have ensured that the skull reconstructions presented in Figure 2 show what portions of the skull come from what specimen so that as new material is discovered and phylogenetic relationships are updated it will be clear to future readers which parts of reconstructions will need to be updated.

• Reviewer 3, on data availability: All data including FEA models and raw measurement data are included in the same repository as the scripts, which we will make clear in the manuscript. Good catch on the data link being dead, we will publish it now.

As a final note, it was brought to our attention by another colleague that the original manuscript’s ancestral state reconstrction lacked an outgroup. An updated reconstruction using Sapeornis as an outgroup will be included in the revised manuscript. The addition of the outgroup does not change any conclusions of the manuscript.

We once again thank our reviewers for their valuable feedback and will submit a revised version of this manuscript for publication shortly. Please let us know if you have any additional comments after reading our response that we can take onboard in our revision.

References

Artuso, C., C. S. Houston, D. G. Smith and C. Rohner (2020). Great Horned Owl (Bubo virginianus), version 1.0. Birds of the World. A. F. Poole. Ithaca, NY, USA, Cornell Lab of Ornithology.

Atterholt, J., A. W. Poust, G. M. Erickson and J. K. O'Connor (2021). "Intraskeletal osteohistovariability reveals complex growth strategies in a Late Cretaceous enantiornithine." Frontiers in Earth Science 9: 640220.

Corbin, C. E., L. K. Lowenberger and B. L. Gray (2015). "Linkage and trade‐off in trophic morphology and behavioural performance of birds." Functional ecology 29(6): 808-815.

Hone, D. W. E., A. A. Farke and M. J. Wedel (2016). "Ontogeny and the fossil record: what, if anything, is an adult dinosaur?" Biology letters 12(2): 20150947.

Hu, H. and J. K. O'Connor (2017). "First species of Enantiornithes from Sihedang elucidates skeletal development in Early Cretaceous enantiornithines." Journal of Systematic Palaeontology 15(11): 909-926.

Miller, C. V., M. Pittman, X. Wang, X. Zheng and J. A. Bright (2023). "Quantitative investigation of Mesozoic toothed birds (Pengornithidae) diet reveals earliest evidence of macrocarnivory in birds." iScience 26(3): 106211.

Olsen, A. M. (2017). "Feeding ecology is the primary driver of beak shape diversification in waterfowl." Functional Ecology 31(10): 1985-1995.

Schnell, J. H. (2020). Common Black Hawk (Buteogallus anthracinus), version 1.0. Birds of the World. A. F. Poole and F. B. Gill. Ithaca, NY, USA, Cornell Lab of Ornithology.

Reviewer #1 (Public Review):

Understanding the ecology including the dietary ecology of enantiornithines is challenging by all means. This work explores the possible trophic diversity of the "opposite-bird" enantiornithines by referring to the body mass, jaw mechanical advantage, finite element analysis of the jaw bones, and morphometrics of the claws and skull of both fossil and extant avian species. By incorporation the dietary information of longipterygids and pengornithinds, the authors predicted a wide variety of foods for enantiornithine ancestors. This indicates the evolutionary successes of enantiornitine during Cretaceous is very likely to have been driven by the wide range of recipes. I believe this work represented the most comprehensive analysis of enantiornithines' diet and trophic diversity by far and the first systematic dietary analysis of bohaiornithids, though the analysis themselves are largely based on the indirect evidence including jaw bone morphologies and claw and skull morphometrics. Anyway, I believe the authors did most the paleontologists could do, and I do not know whether the conclusions could be further supported by incorporating some geochemical data, as most of the specimens the authors analyzed were recovered from a small geographic area. The results also indicate that the developmental trajectories of enantiornithines, at least for jaw bones, might also have been diverse to some extent in response to the diverse ecological niches they adapted. My only concern regarding the analysis is to what extent the conclusions are convincing by comparing specimens representing various ontogenetic stages. This concern has been addressed in the revised manuscript. I believe the authors have almost exhausted all available methods, and I congratulate the authors for the detailed study they conducted.

Reviewer #2 (Public Review):

Miller et al. take a variety of measurements and analytical techniques to assess the ecology of various species of the enantiornithine clade Bohaiornithidae. From this they suggest that the ancestral enantiornithine was a generalist and that the descendant clades occupied a breadth of niches similar to that of the radiation of derived birds after the K-Pg extinction.

Overall, I find the idea that enantiornithines had occupied a similar niche breadth to post-K-Pg derived birds to be a curious, thought-provoking proposal.

I am satisfied with the edits made by the authors and approve the revised version of the manuscript.

Reviewer #3 (Public Review):

Summary:<br /> The authors use several quantitative approaches to characterize the feeding ecologies of bohaiornithid enantiornithines, including allometric data, mechanical advantage and finite element analyses of the jaw, and morphometric analyses of the claws. The authors combine their results with data for other enantiornithines collected from the literature to shed new insight on the ecological evolution of Enantiornithes as a clade.

Although the authors have taken steps to improve their paper, I generally find improvements unsatisfying, especially regarding my comments.

My remaining concerns:

Teeth: My concern here is not whether having teeth limits available niche space compared to having a keratinous beak. Rather, my concern regards how exploitation of the same niche space might be differently reflected in parameter space between birds with teeth and birds with beaks. Can we reliably expect two species that both eat seeds to occupy the same parameter space if, for example, distribution of stress/strain is across a series of teeth vs. across a more uniform beak? In this manuscript, the authors are clearly making this assumption, but that assumption is not made explicit, let alone justified. The authors should discuss this.

Cranial kinesis: As with teeth, my concern here regards our ability to compare data between birds with and without a flexible beak to mitigate forces when foraging. I appreciate that the functional complexity of the kinetic neognath skull precludes our ability to account for it in analyses such as these, but when comparisons are made using these analyses *specifically among neognaths*, we can reliably assume that we are comparing like to like - that is, we can assume that both have kinetic skulls, and so kinesis is reflected similarly in the data for each bird. Similarly, even if a comparison between two neognaths focuses exclusively on the mandible - in which cranial kinesis is not directly reflected - we can assume that those mandibles serve as comparisons between functionally similar systems. However, we cannot necessarily make those same assumptions when comparing the kinetic skull of a neognath to the akinetic skull of an enantiornithine. Indeed, even when focusing just on the mandible, can we reliably assume that data collected from an akinetic enantiornithine reflect the same comparative context as data collected from kinetic neognaths? I appreciate that the authors added a call for better functional understanding of bird cranial kinesis - a call I enthusiastically endorse - but the authors should still discuss how that current lack of understanding impacts interpretations of the comparisons they draw.

Finally, I still find the discussion to be overly long and lacking clear focus and organization. I again urge the authors to minimally consider adding subheadings to better allow the reader to follow the flow of ideas, and I second Reviewer #1's suggestion to add a "Conclusions" section.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The authors compared four types of hiPSCs and four types of hESCs at the proteome level to elucidate the differences between hiPSCs and hESCs. Semi-quantitative calculations of protein copy numbers revealed increased protein content in iPSCs. Particularly in iPSCs, proteins related to mitochondrial and cytoplasmic were suggested to reflect the state of the original differentiated cells to some extent. However, the most important result of this study is the calculation of the protein copy numbers per cell, and the validity of this result is problematic. In addition, several experiments need to be improved, such as using cells of different genders (iPSC: female, ESC: male) in mitochondrial metabolism experiments.

Strengths:

The focus on the number of copies of proteins is exciting and appreciated if the estimated calculation result is correct and biologically reproducible.

Weaknesses:

The proteome results in this study were likely obtained by simply looking at differences between clones, and the proteome data need to be validated. First, there were only a few clones for comparison, and the gender and number of cells did not match between ESCs and iPSCs. Second, no data show the accuracy of the protein copy number per cell obtained by the proteome data.

We agree with the reviewer in their assessment that more independent stem cell clones and an equal gender balance would be preferable. We will mention these considerations as limitations of our study and encourage a larger-scale follow-up.

Regarding the estimated copy numbers, we would like to highlight that they have been extensively in the field, with direct validation of the differences in copy numbers with orthogonal methods like FACS2-4,7,10. Furthermore, the original paper directly compared the copy numbers estimated using the “proteomic ruler” to spike-in protein epitope signature tags and found remarkable concordance. This was performed with a much older generation mass spectrometer with reduced peptide coverage, and the author predicted that higher coverage would increase the quantitative performance.

Reviewer #2 (Public Review):

Summary:

Pluripotent stem cells are powerful tools for understanding development, differentiation, and disease modeling. The capacity of stem cells to differentiate into various cell types holds great promise for therapeutic applications. However, ethical concerns restrict the use of human embryonic stem cells (hESCs). Consequently, induced human pluripotent stem cells (ihPSCs) offer an attractive alternative for modeling rare diseases, drug screening, and regenerative medicine.

A comprehensive understanding of ihPSCs is crucial to establish their similarities and differences compared to hESCs.

This work demonstrates systematic differences in the reprogramming of nuclear and non-nuclear proteomes in ihPSCs.

We thank the reviewer for the positive assessment.

Strengths:

The authors employed quantitative mass spectrometry to compare protein expression differences between independently derived ihPSC and hESC cell lines. Qualitatively, protein expression profiles in ihPSC and hESC were found to be very similar. However, when comparing protein concentration at a cellular level, it became evident that ihPSCs express higher levels of proteins in the cytoplasm, mitochondria, and plasma membrane, while the expression of nuclear proteins is similar between ihPSCs and hESCs. A higher expression of proteins in ihPSCs was verified by an independent approach, and flow cytometry confirmed that ihPSCs had larger cell sizes than hESCs. The differences in protein expression were reflected in functional distinctions. For instance, the higher expression of mitochondrial metabolic enzymes, glutamine transporters, and lipid biosynthesis enzymes in ihPSCs was associated with enhanced mitochondrial potential, increased ability to uptake glutamine, and increased ability to form lipid droplets.

Weaknesses:

While this finding is intriguing and interesting, the study falls short of explaining the mechanistic reasons for the observed quantitative proteome differences. It remains unclear whether the increased expression of proteins in ihPSCs is due to enhanced transcription of the genes encoding this group of proteins or due to other reasons, for example, differences in mRNA translation efficiency. Another unresolved question pertains to how the cell type origin influences ihPSC proteomes. For instance, whether ihPSCs derived from fibroblasts, lymphocytes, and other cell types all exhibit differences in their cell size and increased expression of cytoplasmic and mitochondrial proteins. Analyzing ihPSCs derived from different cell types and by different investigators would be necessary to address these questions.

We agree with the Reviewer that our study does not provide a mechanistic reason for the quantitative differences between the two cell types. However, we will include an expanded section in the discussion where we discuss the potential causes.<br /> We also agree studying hiPSCs reprogrammed from different cell types, such as blood lymphocytes, would be of great interest and will include a section about this within the discussion to encourage further research into the area.

Reviewer #3 (Public Review):

Summary:

In this study, Brenes and colleagues carried out proteomic analysis of several human induced pluripotent (hiPSC) and human embryonic stem cell (hESC) lines. The authors found quantitative differences in the expression of several groups of cytoplasmic and mitochondrial proteins. Overall, hiPSC expressed higher levels of proteins such as glutamine transporters, mitochondrial metabolism proteins, and proteins related to lipid synthesis. Based on the protein expression differences, the authors propose that hiPSC lines differ from hESC in their growth and metabolism.

Strengths:

The number of generated hiPSC and hESC lines continues to grow, but potential differences between hiPSC and hESC lines remain to be quantified and explained. This study is a promising step forward in understanding of the differences between different hiPSC and hESC lines.

Weaknesses:

It is unclear whether changes in protein levels relate to any phenotypic features of cell lines used. For example, the authors highlight that increased protein expression in hiPSC lines is consistent with the requirement to sustain high growth rates, but there is no data to demonstrate whether hiPSC lines used indeed have higher growth rates.

We respectfully disagree with the reviewer on this point. Our data shows that hESCs and hiPSCs show significant differences in protein mass and cell size, validated by the EZQ assay and FACS, while having no significant differences in their cell cycle profiles. Thus increased size and protein content would require higher growth rates to sustain the increased mass, which is what we show.

The authors claim that the cell cycle of the lines is unchanged. However, no details of the method for assessing the cell cycle were included so it is difficult to appreciate if this assessment was appropriately carried out and controlled for.<br /> We apologise for this omission; the details will be included in the revised version of the document.

Details and characterisation of iPSC and ESC lines used in this study were overall lacking. The lines used are merely listed in methods, but no references are included for published lines, how lines were obtained, what passage they were used at, their karyotype status, etc. For details of basic characterisation, the authors should refer to the ISSC Standards for the use of human stem cells in research. In particular, the authors should consider whether any of the changes they see may be attributed to copy number variants in different lines.

We agree with the reviewer on this. The hiPSC lines were generated by the HipSci consortium in the Wellcome Sanger Centre as described in the flagship HipSci paper13. We cite the flagship paper which specifies in great detail the reprogramming protocols and quality control measures, including looking at copy number variations13. However, we agree that we did not make this information easily accessible for readers. We also believe it is relevant to also explicitly include this information on our manuscript instead of expecting readers to look at the flagship paper. These details will be added to the revised version.

The expression data for markers of undifferentiated state in Figure 1a would ideally be shown by immunocytochemistry or flow cytometry as it is impossible to tell whether cultures are heterogeneous for marker expression.

We agree with the reviewer on this. FACS is indeed much more quantitative and a better method to study heterogeneity. However, we did not have protocols to study these markers using FACS.

TEM analysis should ideally be quantified.

We agree with the reviewer that it would be nice to have a quantitative measure.

All figure legends should explicitly state what graphs are representing (e.g. average/mean; how many replicates (biological or technical), which lines)? Some data is included in Methods (e.g. glutamine uptake), but not for all of the data (e.g. TEM).

We agree with the reviewer completely. These points will be remediated in the revised version of the manuscript.

Validation experiments were performed typically on one or two cell lines, but the lines used were not consistent (e.g. wibj_2 versus H1 for respirometry and wibj_2, oaqd_3 versus SA121 and SA181 for glutamine uptake). Can the authors explain how the lines were chosen?

We will include these details within the updated manuscript.

The authors should acknowledge the need for further functional validation of the results related to immunosuppressive proteins.

We agree with the reviewer and will add a clear sentence in the discussion making this point explicitly.

Differences in H1 histone abundance were highlighted. Can the authors speculate as to the meaning of these differences?

Regarding H1 histones, our study of the literature as well as interaction with chromatin and histone experts both within our institute and externally have not shed light into what the differences could imply. We think this is an interesting result that merits further study, but we don’t have a clear hypothesis on the consequences.

In summary, we thank the reviewers for their comments and will prepare a revised version that addresses their suggestions.

eLife assessment

Pluripotent stem cells can be obtained from embryos (embryonic stem cells, ESCs) or through induction by transfection (induced pluripotent stem cells, iPSCs). This valuable study uses semi-quantitative proteomics to compare both types of cells, finding interesting differences. The value of the study lies in demonstrating that ESCs and iPSCs cannot be used interchangeably. The conclusions are backed by solid data even if a greater number and diversity in ESC and iPSC clones would help in generalizing the observations.

Reviewer #1 (Public Review):

Summary:<br /> The authors compared four types of hiPSCs and four types of hESCs at the proteome level to elucidate the differences between hiPSCs and hESCs. Semi-quantitative calculations of protein copy numbers revealed increased protein content in iPSCs. Particularly in iPSCs, proteins related to mitochondrial and cytoplasmic were suggested to reflect the state of the original differentiated cells to some extent. However, the most important result of this study is the calculation of the protein copy numbers per cell, and the validity of this result is problematic. In addition, several experiments need to be improved, such as using cells of different genders (iPSC: female, ESC: male) in mitochondrial metabolism experiments.

Strengths:<br /> The focus on the number of copies of proteins is exciting and appreciated if the estimated calculation result is correct and biologically reproducible.

Weaknesses:<br /> The proteome results in this study were likely obtained by simply looking at differences between clones, and the proteome data need to be validated. First, there were only a few clones for comparison, and the gender and number of cells did not match between ESCs and iPSCs. Second, no data show the accuracy of the protein copy number per cell obtained by the proteome data.

Reviewer #2 (Public Review):

Summary:<br /> Pluripotent stem cells are powerful tools for understanding development, differentiation, and disease modeling. The capacity of stem cells to differentiate into various cell types holds great promise for therapeutic applications. However, ethical concerns restrict the use of human embryonic stem cells (hESCs). Consequently, induced human pluripotent stem cells (ihPSCs) offer an attractive alternative for modeling rare diseases, drug screening, and regenerative medicine. A comprehensive understanding of ihPSCs is crucial to establish their similarities and differences compared to hESCs. This work demonstrates systematic differences in the reprogramming of nuclear and non-nuclear proteomes in ihPSCs.

Strengths:<br /> The authors employed quantitative mass spectrometry to compare protein expression differences between independently derived ihPSC and hESC cell lines. Qualitatively, protein expression profiles in ihPSC and hESC were found to be very similar. However, when comparing protein concentration at a cellular level, it became evident that ihPSCs express higher levels of proteins in the cytoplasm, mitochondria, and plasma membrane, while the expression of nuclear proteins is similar between ihPSCs and hESCs. A higher expression of proteins in ihPSCs was verified by an independent approach, and flow cytometry confirmed that ihPSCs had larger cell sizes than hESCs. The differences in protein expression were reflected in functional distinctions. For instance, the higher expression of mitochondrial metabolic enzymes, glutamine transporters, and lipid biosynthesis enzymes in ihPSCs was associated with enhanced mitochondrial potential, increased ability to uptake glutamine, and increased ability to form lipid droplets.

Weaknesses:<br /> While this finding is intriguing and interesting, the study falls short of explaining the mechanistic reasons for the observed quantitative proteome differences. It remains unclear whether the increased expression of proteins in ihPSCs is due to enhanced transcription of the genes encoding this group of proteins or due to other reasons, for example, differences in mRNA translation efficiency. Another unresolved question pertains to how the cell type origin influences ihPSC proteomes. For instance, whether ihPSCs derived from fibroblasts, lymphocytes, and other cell types all exhibit differences in their cell size and increased expression of cytoplasmic and mitochondrial proteins. Analyzing ihPSCs derived from different cell types and by different investigators would be necessary to address these questions.

Reviewer #3 (Public Review):

Summary:<br /> In this study, Brenes and colleagues carried out proteomic analysis of several human induced pluripotent (hiPSC) and human embryonic stem cell (hESC) lines. The authors found quantitative differences in the expression of several groups of cytoplasmic and mitochondrial proteins. Overall, hiPSC expressed higher levels of proteins such as glutamine transporters, mitochondrial metabolism proteins, and proteins related to lipid synthesis. Based on the protein expression differences, the authors propose that hiPSC lines differ from hESC in their growth and metabolism.

Strengths:<br /> The number of generated hiPSC and hESC lines continues to grow, but potential differences between hiPSC and hESC lines remain to be quantified and explained. This study is a promising step forward in understanding of the differences between different hiPSC and hESC lines.

Weaknesses:<br /> It is unclear whether changes in protein levels relate to any phenotypic features of cell lines used. For example, the authors highlight that increased protein expression in hiPSC lines is consistent with the requirement to sustain high growth rates, but there is no data to demonstrate whether hiPSC lines used indeed have higher growth rates.

The authors claim that the cell cycle of the lines is unchanged. However, no details of the method for assessing the cell cycle were included so it is difficult to appreciate if this assessment was appropriately carried out and controlled for.

Details and characterisation of iPSC and ESC lines used in this study were overall lacking. The lines used are merely listed in methods, but no references are included for published lines, how lines were obtained, what passage they were used at, their karyotype status, etc. For details of basic characterisation, the authors should refer to the ISSC Standards for the use of human stem cells in research. In particular, the authors should consider whether any of the changes they see may be attributed to copy number variants in different lines.

The expression data for markers of undifferentiated state in Figure 1a would ideally be shown by immunocytochemistry or flow cytometry as it is impossible to tell whether cultures are heterogeneous for marker expression.

TEM analysis should ideally be quantified.

All figure legends should explicitly state what graphs are representing (e.g. average/mean; how many replicates (biological or technical), which lines)? Some data is included in Methods (e.g. glutamine uptake), but not for all of the data (e.g. TEM).

Validation experiments were performed typically on one or two cell lines, but the lines used were not consistent (e.g. wibj_2 versus H1 for respirometry and wibj_2, oaqd_3 versus SA121 and SA181 for glutamine uptake). Can the authors explain how the lines were chosen?

The authors should acknowledge the need for further functional validation of the results related to immunosuppressive proteins.

Differences in H1 histone abundance were highlighted. Can the authors speculate as to the meaning of these differences?

Author Response

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

Reviewer #1 (Public Review):

Assessment:

The manuscript titled 'Rab7 dependent regulation of goblet cell protein CLCA1 modulates gastrointestinal 1 homeostasis' by Gaur et al discusses the role of Rab7 in the development of ulcerative colitis by regulating the lysosomal degradation of Clca1, a mucin protease. The manuscript presents interesting data and provides a potential molecular mechanism for the pathological alterations observed in ulcerative colitis. Gaur et al demonstrate that Rab7 levels are lowered in UC and CD. However, a similar analysis of Rab7 levels in ulcerative colitis (UC) and Crohn's disease (CD) patient samples was conducted recently (Du et al, Dev Cell, 2020) which showed that Rab7 levels are found to be elevated under these conditions. While Gaur et al have briefly mentioned Du et al's paper in passing in the discussion, they need to discuss these contradictory results in their paper and clarify these differences. Additionally, Du et al are not included in the list of references.

Strengths:

The manuscript used a multi-pronged approach and compares patient samples, mouse models of DSS, and protocols that allow differentiation of goblet cells. They also use a nanogel-based delivery system for siRNAs, which is ideal for the knockdown of specific genes in the gut.

Weaknesses:

(1) Du et al, Dev Cell 2020 (https://doi.org/10.1016/j.devcel.2020.03.002) have previously shown that Rab7 levels are elevated in a similar set of colonic samples (age group, number etc.) from UC and CD patients. Gaur et al have not discussed this paper or its findings in detail, which directly contradicts their results. Clarification regarding this should be provided.

We thank and appreciate the reviewer for bringing this point.

The results shown by Du et al, Dev Cell, 2020 depict elevated expression of Rab7 in UC and CD patients compared to controls. In first occurrence, these results appear contradictory, but there may be a few possible explanations for this.

Firstly, Rab7 expression levels may fluctuate in the tissue depending on the degree of the gut inflammation. This can be concluded from our observations in DSS-mice dynamics model and the human patient samples with mild and moderate UC. Furthermore, Du et al provide no information of the severity of the condition among the patients employed in the study. Our motive, in the current work, was to emphasize this aspect. This point was mentioned in the discussion section of the manuscript. However, in view of the reviewer’s concern, we have now added a detailed comment on this in the main text of the revised version of the manuscript.

Secondly, the control biopsies in our investigation were acquired from non-IBD patients, and not what was done by Du et al., wherein biopsies from the normal para-carcinoma region of the colorectal cancer patients were used. One cannot overlook the fact that physiological and molecular changes are apparent even in non-inflamed regions in the gut of an IBD or CRC patient. It is possible that the observed discrepancy arises due to the differences in the sample type used for comparing the Rab7 expression.

Finally, the main sub-tissue region showing a decrease in Rab7 expression in UC samples, appeared to be the Goblet cells which was not covered by Du et al.

Keeping these points in mind we do not think that there is a contradiction in our findings with that of Du et al., 2020. In the revised submission some of these explanations are incorporated (Lines 106-109).

This was an oversight from our side. We have actually mentioned Du et al., 2020 in the discussion (line number 345) but somehow the reference was missing in the main list. We have ensured that the reference is included in the revised version and that their findings are included both in main text and in the discussion.

Reviewer #2 (Public Review):

Summary:

In this work, the authors report a role for the well-studied GTPase Rab7 in gut homeostasis. The study combines cell culture experiments with mouse models and human ulcerative colitis patient tissues to propose a model where, Rab7 by delivering a key mucous component CLCA1 to lysosomes, regulates its secretion in the goblet cells. This is important for the maintenance of mucous permeability and gut microbiota composition. In the absence of Rab7, CLCA1 protein levels are higher in tissues as well as the mucus layer, corroborating with the anticorrelation of Rab7 (reduced) and CLCA1 (increased) from ulcerative colitis patients. The authors conclude that Rab7 maintains CLCA1 level by controlling its lysosomal degradation, thereby playing a vital role in mucous composition, colon integrity, and gut homeostasis.

Strengths:

The biggest strength of this manuscript is the combination of cell culture, mouse model, and human tissues. The experiments are largely well done and, in most cases, the results support their conclusions. The authors go to substantial lengths to find a link, such as alteration in microbiota, or mucus proteomics.

Weaknesses:

(1) There are also some weaknesses that need to be addressed. The association of Rab7 with UC in both mice and humans is clear, however, claims on the underlying mechanisms are less clear. Does Rab7 regulate specifically CLCA1 delivery to lysosomes, or is it an outcome of a generic trafficking defect?

We thank the reviewer for the insightful comment. We would like to bring forth the following explanation for each these concerns:

Our immunofluorescence imaging experiments revealed co-localization of Rab7 protein with CLCA1 and the lysosomes (Fig 7I). In addition, the absence of Rab7 affects the transport of CLCA1 to lysosomes (Fig 7J). This demonstrates that Rab7 may be involved in regulation of CLCA1 transport (presumably along with other cargo), to lysosomes selectively. However, we do recognize that the point raised by the reviewer about possible effect of a generic trafficking defect is valid.

(2) CLCA1 is a secretory protein, how does it get routed to lysosomes, i.e., through Golgi-derived vesicles, or by endocytosis of mucous components? Mechanistic details on how CLCA1 is routed to lysosomes will add substantial value.

As mentioned in the manuscript, the trafficking of CLCA1 protein or CLCA1-containing vesicles within the goblet cell is unknown, with no information on the proteins involved in its mobility. The switching of CLCA1 containing vesicles from the secretory route to lysosomes needs extensive investigation involving overall trafficking of the protein. Taken together, the complete answer to both these important questions will need a series of experiments and those may be interesting avenues for future research.

(3) Why does the level of Rab7 fluctuate during DSS treatment (Fig 1B)?

This is a very thoughtful point from the reviewer. We detected a distinct pattern of Rab7 expression fluctuation in intestinal epithelial cells after DSS-dynamics treatment in mice. Perhaps, these changes are the result of complex cellular signaling in response to the DSS treatment. Rab7, being a fundamental protein involved in protein sorting pathway, is expected to undergo alteration based on cells requirement. Presently there are no reports suggesting the regulatory mechanisms that govern Rab7 levels in the gut.

(4) Does the reduction seen in Rab7 levels (by WB) also reflect in reduced Rab7 endosome numbers?

We observed reduction in Rab7 expression both at RNA and protein levels. To confirm whether this alteration will lead to reduced Rab7 positive endosome numbers may require detailed investigations.

(5) Are other late endosomal (and lysosomal) populations also reduced upon DSS treatment and UC? Is there a general defect in lysosomal function?

There are no direct evidences showing reduction in the late endosomal and lysosomal population during gut inflammation, but few studies link lysosomal dysfunction with risk for colitis (doi: 10.1016/j.immuni.2016.05.007).

(6) The evidence for lysosomal delivery of CLCA1 (Fig 7 I, J) is weak. Although used sometimes in combination with antibodies, lysotracker red is not well compatible with permeabilization and immunofluorescence staining. The authors can substantiate this result further using lysosomal antibodies such as Lamp1 and Lamp2. For Fig 7J, it will be good to see a reduction in Rab7 levels upon KD in the same cell.

We used Lysotracker red in live cells followed by fixation. So, permeabilization issues were resolved. Lamp1, as suggested by the reviewer, is definitely a better marker for lysosomes in immunofluorescence studies, but is also shown to mark late endosomes (doi: 10.1083/jcb.132.4.565). As Rab7 protein also marks the late endosomes, using Lamp1 may leave the ambiguity of CLCA1 in Rab7 positive late endosomes versus lysosomes. Nevertheless, we have carried out this experiment, as suggested by the reviewer, by staining the cells with LAMP1 (author response image 1). As demonstrated in our previous data, the colocalization of CLCA1 with LAMP1 positive vesicles decreased upon Rab7 knockdown. Also, we observed a decrease in the intensity of LAMP1 staining in cells with Rab7 knockdown. Additionally, we noted a reduction in the LAMP1 staining intensity in cells where Rab7 was knocked down. This observation can be attributed to the decrease in the presence of Rab7-positive vesicles or late endosomes which also exhibit LAMP1 staining.

Author response image 1.

(A) Representative confocal images of HT29-MTX-E12 cells transfected with either scrambled siRNA (control) or Rab7 siRNA (Rab7Knockdown). Cells are stained with CLCA1 (green) using antiCLCA1 antibody and lysosomes with LAMP1. (B) Graph shows quantitation of colocalization between CLCA1 and LAMP1 from images (n=20) using Mander’s overlap coefficient. Inset shows zoomed areas of the image with colocalization puncta (yellow) marked with arrows.

(7) In this connection, Fig S3D is somewhat confusing. While it is clear that the pattern of Muc2 in WT and Rab7-/- cells are different, how this corroborates with the in vivo data on alterations in mucus layer permeability -- as claimed -- is not clear.

The data in Fig. S3D suggest the involvement of Rab7 in packaging of Muc2. The whole idea for doing this experiment was to support our observation in the Rab7KD-mice model where mucus layer was seen to be loose and more permeable in Rab7 deficient mice.

(8) Overall, the work shows a role for a well-studied GTPase, Rab7, in gut homeostasis. This is an important finding and could provide scope and testable hypotheses for future studies aimed at understanding in detail the mechanisms involved.

We thank the reviewer for this comment.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Specific questions to the authors:

(1) Why is the dotted line in Fig. 1c at -7.5? What does this signify?

Response: The dotted line was intended to represent the baseline; in the revised manuscript it is corrected and placed at y=0.

(2) Du et al should be cited. Fig 6 K-Q from Du et al should be discussed and reasons for contradictory findings should be given in greater detail, rather than a single sentence in the discussion.

Response: The reference for Du et al is included in the list and the possible reasons the findings of the current work are discussed in the main text (Line 106-109).

(3) Fig1. Why are Rab7 levels low even in remission patient samples? Can DSS be withdrawn to induce remission followed by analysis of colonic samples?

Response: A possible explanation for this observation could be that the restoration of Rab7 levels may not immediately follow the resolution of clinical symptoms in remission patients. After the remission initiation, the normalization of cellular processes, including the regulation of Rab7 expression, might exhibit a time lag. A thorough investigation of Rab7 levels and the allied pathways at different time points during the remission phase could provide deeper insights into the gradual dynamics of recovery. As suggested by the reviewer, DSS withdrawal induced recovery model can be utilized for understanding the same and could be a good approach for future investigations.

(4) Fig. 2: Single-channel fluorescence should be shown.

Response: The single channel fluorescence images are incorporated in Fig. S2.

(5) Line 456 should be modified. 'Blind pathologist' does not read well!

Response: The line has been modified with ‘Blinded pathologist’.

(6) Other inflammatory markers, cytokine levels should be looked at in addition to TNF alpha.

Response: TNF-α is a crucial mediator in intestinal inflammation, actively contributing to the development of IBD. Elevated levels of TNF-α are observed in patients of IBD (Billmeier U. et al, World J Gastroenterol. 2016). In the current work, while probing for TNF-α our primary objective was to examine this significant indicator of colitis following Rab7 knockdown in mice, aiming to gain insights into heightened gut inflammation.

(7) Quantitation of S3D should be provided.

Response: The dispersed expression of Muc2 was observed in n=20 cells per sample and it was a qualitative observation. The aim was to identify any changes in Muc2 packaging under Rab7 knockout conditions.

(8) Microbiota analysis should include Rab7KD+DSS mice.

Response: We understand the importance of this point, however, in the current work our primary objective was to specifically investigate changes in microbial diversity and abundance in Rab7KD mice compared to both DSS+CScr and CScr mice. Rab7KD+DSS mice is expected to show higher dysbiosis in comparison to DSS+CScr.

(9) Fig 6 H and I, G. How do Clca1 levels reduce in Rab7kd +DSS relative to Scr+DSS while they are higher in Rab7kd compared to Scr. Comment.

Response: The decreased expression of CLCA1 in the mucus of DSS+Rab7KD mice can be attributed to a consequence of significant reduction in goblet cell numbers in these mice, as evidenced by the observed loss of these cells (Fig.S3 B and Fig. S3C). CLCA1 is exclusively secreted by goblet cells, so a decline in their numbers directly affects CLCA1 levels.

(10) How are Rab7 levels downregulated? What is the predicted mechanism?

Response: While our current study didn't explore this aspect, it's worth noting that Rab7 protein levels undergo regulation through various mechanisms, including post-translational modifications such as Ubiquitination and SUMOylation. These modifications are known to regulate Rab7 stability, transport and recycling. Specific experiments conducted during this study (work not included in the manuscript) indicated the participation of SENP7, a deSUMOylase, in controlling the stability of Rab7 protein, particularly in the context of colitis. Additionally, goblet cell specific mechanisms are also likely to be controlling the Rab7 in the gut.

(11) What is the explanation for opposite changes in CLCa1 RNA (down) and protein (up).

Response: The reduction in CLCA1 at the RNA level could be associated with the decrease in goblet cell numbers during colitis. Our investigation indicates that Rab7 predominantly influences CLCA1 at the protein level by impacting its degradation pathway. It is important to acknowledge that not all the alterations in CLCA1 observed during colitis can be solely attributed to Rab7, but our study has identified a connection between Rab7 and CLCA1.

(12) In light of Du et al, it would be interesting to see how the number of peroxisomes changes upon alteration of Rab7 levels.

Response: The suggestion by the reviewer is noteworthy. Since, being an altogether different domain, it deviates from the primary objectives of current work. Here, our goal was specifically on exploring the role of Rab7 in goblet cell functioning. Thus is an attractive theme for future investigations.

(13) While Gaur et al suggest in their discussion that Du et al may have observed an upregulation in Rab7 levels in different cell types of the intestine, this is not apparent from the data provided. Tissue sections should be carefully analysed to provide data supporting this observation. Differences in reagents used (antibodies) should also be considered. As far as the human patient data is concerned, it does not appear that the sample stages are very different across the two manuscripts (based on age, inclusion criteria etc.).

Response: This has been explained in detail in our public comments.

Reviewer #2 (Recommendations For The Authors):

(1) In general, image-based measurements could be done better (for example, object-based statistics than pixel-based overlaps) and represented differently. It is difficult to appreciate the reduction in Rab7 levels in goblet cells in Fig 2 A, C. It might be good to show the channels separately, and perhaps use an intensity gradient LUT for the Rab7 channel.

Response: The single channel fluorescence images are incorporated in Fig. S2.

(2) The EM images, and particularly Fig 2F are not convincing, with an oddly square-shaped vesicle. I'm not sure what value they are adding to the interpretation.

Response: The observed square-shaped vesicle in Fig. 2F could be attributed to the dynamic nature of vesicles within a cell. This dynamicity allows them to adopt various shapes depending on their state and function within the cell. The presence of Rab7 near vacuoles of goblet cells signify its probable involvement in the regulation of secretory function of these cells which is the key aspect being covered in this work.

(3) A general method question concerns the definition of the distal colon. How is this decided, particularly when colon lengths are reduced upon DSS treatment?

Response: The murine colon is divided into proximal and distal colon of mouse and has a visual difference of inner folds which are quite prominent in proximal colon. Additionally, the portion towards the rectum (predominantly distal colon) was majorly utilized for the experiments. In each case the various experimental groups were matched for the respective areas.

(4) The use of an in vivo intestine-specific Rab7 silencing model is good. Why does Rab7 KD itself not capitulate aspects of DSS treatment, rather it seems to exacerbate it.

Response: Our objective was to determine whether the downregulation of Rab7 during colitis was the cause or consequence of gut inflammation. Interestingly, our investigation using the murine Rab7 knockdown model revealed that the reduction of Rab7 expression in the intestine exacerbates inflammation. Subsequent analysis demonstrated that the absence of Rab7 disrupts goblet cell secretory function, consequently contributing to heightened inflammation. Our findings overall suggest that Rab7 downregulation is not merely a consequence but plays a contributory role in aggravating inflammation in the context of colitis.

(5) The axes labels in Fig 5 are not readable. It is unclear how Rab7 KD is more similar in gut microbiota phenotypes to DSS than to CScr.

Response: The microbial analysis revealed an abnormal composition of gut microbiota in Rab7KD mice compared to CScr. Interestingly, this composition exhibited some similarity to the inflamed gut microbiota observed in DSSScr mice. The analysis further demonstrated a shift in microbial diversity in Rab7KD mice, showcasing characteristics akin to those observed in inflamed mice. This similarity in gut microbiota phenotypes between Rab7KD and DSSScr suggests a potential link or influence of Rab7 downregulation on the microbiota, contributing to the observed similarities with DSS-induced inflammation.

(6) The use of mucous proteomics to identify mechanisms of Rab7-mediated phenotype is a good approach. The replicates in the proteomics dataset (Fig 6F) do not seem to match. Detailing of methodology used for analysis will help to overcome these doubts.

Response: The identified proteins in different samples of mucus proteomics were subjected to label free quantification. Subsequently, the significantly altered proteins were subjected to analysis with the False Discovery Rate (FDR) to control for potential false positives and ascertain the validity of the findings.

(7) It will be good to see the immunoblots showing the negative correlation between Rab7 and CLCL1 in Fig 7D.

Response: Fig. 7C shows western blot for protein expression of CLCA1of the same control and UC samples which were used in Fig. 1F to show Rab7 expression. Fig. 7D is the quantitative correlation plot for Fig. 1F (Rab7 expression) and Fig. 7C (CLCA1 expression).

(8) Why is UC different from the DSS model for Rab7 gene expression but not protein levels? Endosomal counts could help address this.

Response: We encountered challenges in accurately counting the individual puncta of Rab7 expression in immunofluorescence images due to the nature of tissue samples. Locating endosomes within a single cell proved to be challenging, and the proximity of many puncta made it difficult to delineate them individually. Despite these technical difficulties, the intriguing prospect of correlating Rab7 expression with endosomal counts remains a compelling aspect that may well be area for future investigations.

eLife assessment

This is an important study for understanding the pathogenesis of ulcerative colitis. It convincingly demonstrates reduced levels of the vesicular trafficking protein Rab7 in ulcerative colitis and Crohn's disease, leading to altered levels of calcium-activated chloride channel regulator 1 (CLCA1) and subsequent mucin dysregulation, highlighting Rab7's significance in gut homeostasis maintenance. The manuscript advances the field as it provides insights into a novel regulatory pathway implicated in ulcerative colitis, potentially paving the way for the development of targeted therapeutic interventions.

Reviewer #1 (Public Review):

Assessment:

The manuscript titled 'Rab7 dependent regulation of goblet cell protein CLCA1 modulates gastrointestinal 1 homeostasis' by Gaur et al discusses the role of Rab7 in the development of ulcerative colitis by regulating the lysosomal degradation of Clca1, a mucin protease. The manuscript presents interesting data, and provides a potential molecular mechanism for the pathological alterations observed in ulcerative colitis.

Strengths:

The manuscript used a multi-pronged approach and compares patient samples, mouse models of DSS and protocols that allow differentiation of goblet cells. They also use a nanogel-based delivery system for siRNAs, which is ideal for knockdown of specific genes in the gut.

Weaknesses:

The manuscript should also mention the limitations of the study.

Reviewer #2 (Public Review):

Summary:

In this work, the authors report a role for the well-studied GTPase Rab7 in gut homeostasis. The study combines cell culture experiments with mouse models and human ulcerative colitis patient tissues to propose a model where, Rab7 by delivering a key mucous component CLCA1 to lysosomes, regulates its secretion in the goblet cells. This is important for the maintenance of mucous permeability and gut microbiota composition. In the absence of Rab7, CLCA1 protein levels are higher in tissues as well as the mucus layer, corroborating with the anti-correlation of Rab7 (reduced) and CLCA1 (increased) from ulcerative colitis patients. The authors conclude that Rab7 maintains CLCA1 level by controlling its lysosomal degradation, thereby playing a vital role in mucous composition, colon integrity, and gut homeostasis.

Strengths:

The biggest strength of this manuscript is the combination of cell culture, mouse model, and human tissues. The experiments are largely well done and in most cases, the results support their conclusions. The authors go to substantial lengths to find a link, such as alteration in microbiota, or mucus proteomics.

Weaknesses:

There are also some weaknesses that need to be addressed. The association of Rab7 with UC in both mice and humans is clear, however, claims on the underlying mechanisms are less clear. Does Rab7 regulate specifically CLCA1 delivery to lysosomes, or is it an outcome of a generic trafficking defect? CLCA1 is a secretory protein, how does it get routed to lysosomes, i.e. through Golgi-derived vesicles, or by endocytosis of mucous components? Mechanistic details on how CLCA1 is routed to lysosomes will add substantial value.

Why does the level of Rab7 fluctuate during DSS treatment (Fig 1B)? Does the reduction seen in Rab7 levels (by WB) also reflect in reduced Rab7 endosome numbers? Are other late endosomal (and lysosomal) populations also reduced upon DSS treatment and UC? Is there a general defect in lysosomal function?

While it is clear that the pattern of Muc2 in WT and Rab7-/- cells are different, how this corroborates with the in vivo data on alterations in mucus layer permeability - as claimed - is not clear.

The use of an in vivo intestine-specific Rab7 silencing model is good. Why does Rab7 KD itself not capitulate aspects of DSS treatment, rather it seems to exacerbate it.

The use of mucous proteomics to identify mechanisms of Rab7-mediated phenotype is a good approach. The replicates in the proteomics dataset (Fig 6F) do not seem to match. Detailing of methodology used for analysis will help to overcome these doubts.

The work shows a role for a well-studied GTPase, Rab7, in gut homeostasis. This is an important finding and could provide scope and testable hypotheses for future studies aimed at understanding in detail the mechanisms involved.

Author Response

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

eLife assessment

This study uses a multi-pronged empirical and theoretical approach to advance our understanding of how differences in learning relate to differences in the ways that male versus female animals cope with urban environments, and more generally how reversal learning may benefit animals in urban habitats. The work makes an important contribution and parts of the data and analyses are solid, although several of the main claims are only partially supported or overstated and require additional support.

Public Reviews:

We thank the Editor and both Reviewers for their time and for their constructive evaluation of our manuscript. We worked to address each comment and suggestion offered by the Reviewers in our revision—please see our point-by-point responses below.

Reviewer #1 (Public Review):

Summary:

In this highly ambitious paper, Breen and Deffner used a multi-pronged approach to generate novel insights on how differences between male and female birds in their learning strategies might relate to patterns of invasion and spread into new geographic and urban areas.

The empirical results, drawn from data available in online archives, showed that while males and females are similar in their initial efficiency of learning a standard color-food association (e.g., color X = food; color Y = no food) scenario when the associations are switched (now, color Y = food, X= no food), males are more efficient than females at adjusting to the new situation (i.e., faster at 'reversal learning'). Clearly, if animals live in an unstable world, where associations between cues (e.g., color) and what is good versus bad might change unpredictably, it is important to be good at reversal learning. In these grackles, males tend to disperse into new areas before females. It is thus fascinating that males appear to be better than females at reversal learning. Importantly, to gain a better understanding of underlying learning mechanisms, the authors use a Bayesian learning model to assess the relative role of two mechanisms (each governed by a single parameter) that might contribute to differences in learning. They find that what they term 'risk sensitive' learning is the key to explaining the differences in reversal learning. Males tend to exhibit higher risk sensitivity which explains their faster reversal learning. The authors then tested the validity of their empirical results by running agent-based simulations where 10,000 computersimulated 'birds' were asked to make feeding choices using the learning parameters estimated from real birds. Perhaps not surprisingly, the computer birds exhibited learning patterns that were strikingly similar to the real birds. Finally, the authors ran evolutionary algorithms that simulate evolution by natural selection where the key traits that can evolve are the two learning parameters. They find that under conditions that might be common in urban environments, high-risk sensitivity is indeed favored.

Strengths:

The paper addresses a critically important issue in the modern world. Clearly, some organisms (some species, some individuals) are adjusting well and thriving in the modern, human-altered world, while others are doing poorly. Understanding how organisms cope with human-induced environmental change, and why some are particularly good at adjusting to change is thus an important question.

The comparison of male versus female reversal learning across three populations that differ in years since they were first invaded by grackles is one of few, perhaps the first in any species, to address this important issue experimentally.

Using a combination of experimental results, statistical simulations, and evolutionary modeling is a powerful method for elucidating novel insights.

Thank you—we are delighted to receive this positive feedback, especially regarding the inferential power of our analytical approach.

Weaknesses:

The match between the broader conceptual background involving range expansion, urbanization, and sex-biased dispersal and learning, and the actual comparison of three urban populations along a range expansion gradient was somewhat confusing. The fact that three populations were compared along a range expansion gradient implies an expectation that they might differ because they are at very different points in a range expansion. Indeed, the predicted differences between males and females are largely couched in terms of population differences based on their 'location' along the rangeexpansion gradient. However, the fact that they are all urban areas suggests that one might not expect the populations to differ. In addition, the evolutionary model suggests that all animals, male or female, living in urban environments (that the authors suggest are stable but unpredictable) should exhibit high-risk sensitivity. Given that all grackles, male and female, in all populations, are both living in urban environments and likely come from an urban background, should males and females differ in their learning behavior? Clarification would be useful.

Thank you for highlighting a gap in clarity in our conceptual framework. To answer the Reviewer’s question—yes, even with this shared urban ‘history’, it seems plausible that males and females could differ in their learning. For example, irrespective of population membership, such sex differences could come about via differential reliance on learning strategies mediated by an interaction between grackles’ polygynous mating system and malebiased dispersal system, as we discuss in L254–265 (now L295–306). Population membership might, in turn, differentially moderate the magnitude of any such sex-effect since an edge population, even though urban, could still pose novel challenges—for example, by requiring grackles to learn novel daily temporal foraging patterns such as when and where garbage is collected (grackles appear to track this food resource: Rodrigo et al. 2021 [DOI: 10.1101/2021.06.14.448443]). We now introduce this important conceptual information— please see L89–96.

Reinforcement learning mechanisms:

Although the authors' title, abstract, and conclusions emphasize the importance of variation in 'risk sensitivity', most readers in this field will very possibly misunderstand what this means biologically. Both the authors' use of the term 'risk sensitivity' and their statistical methods for measuring this concept have potential problems.

Please see our below responses concerning our risk-sensitivity term.

First, most behavioral ecologists think of risk as predation risk which is not considered in this paper. Secondarily, some might think of risk as uncertainty. Here, as discussed in more detail below, the 'risk sensitivity' parameter basically influences how strongly an option's attractiveness affects the animal's choice of that option. They say that this is in line with foraging theory (Stephens and Krebs 2019) where sensitivity means seeking higher expected payoffs based on prior experience. To me, this sounds like 'reward sensitivity', but not what most think of as 'risk sensitivity'. This problem can be easily fixed by changing the name of the term.

We apologise for not clearly introducing the field of risk-sensitive foraging, which focuses on how animals evaluate and choose between distinct food options, and how such foraging decisions are influenced by pay-off variance i.e., risk associated with alternative foraging options (seminal reviews: Bateson 2002 [DOI: 10.1079/PNS2002181]; Kacelnik & Bateson 1996 [DOI: 10.1093/ICB/36.4.402]). We have added this information to our manuscript in L494–497. We further apologise for not clearly explaining how our lambda parameter estimates such risk-sensitive foraging. To do so here, we need to consider our Bayesian reinforcement learning model in full. This model uses observed choice-behaviour during reinforcement learning to infer our phi (information-updating) and lambda (risksensitivity) learning parameters. Thus, payoffs incurred through choice simultaneously influence estimation of each learning parameter—that is, in a sense, they are both sensitive to rewards. But phi and lambda differentially direct any reward sensitivity back on choicebehaviour due to their distinct definitions. Glossing over the mathematics, for phi, stronger reward sensitivity (bigger phi values) means faster internal updating about stimulus-reward pairings, which translates behaviourally into faster learning about ‘what to choose’. For lambda, stronger reward sensitivity (bigger lambda values) means stronger internal determinism about seeking the non-risk foraging option (i.e., the one with the higher expected payoffs based on prior experience), which translates behaviourally into less choice-option switching i.e., ‘playing it safe’. We hope this information, which we have incorporated into our revised manuscript (please see L153–161), clarifies the rationale and mechanics of our reinforcement learning model, and why lamba measures risk-sensitivity.

In addition, however, the parameter does not measure sensitivity to rewards per se - rewards are not in equation 2. As noted above, instead, equation 2 addresses the sensitivity of choice to the attraction score which can be sensitive to rewards, though in complex ways depending on the updating parameter. Second, equations 1 and 2 involve one specific assumption about how sensitivity to rewards vs. to attraction influences the probability of choosing an option. In essence, the authors split the translation from rewards to behavioral choices into 2 steps. Step 1 is how strongly rewards influence an option's attractiveness and step 2 is how strongly attractiveness influences the actual choice to use that option. The equation for step 1 is linear whereas the equation for step 2 has an exponential component. Whether a relationship is linear or exponential can clearly have a major effect on how parameter values influence outcomes. Is there a justification for the form of these equations? The analyses suggest that the exponential component provides a better explanation than the linear component for the difference between males and females in the sequence of choices made by birds, but translating that to the concepts of information updating versus reward sensitivity is unclear. As noted above, the authors' equation for reward sensitivity does not actually include rewards explicitly, but instead only responds to rewards if the rewards influence attraction scores. The more strongly recent rewards drive an update of attraction scores, the more strongly they also influence food choices. While this is intuitively reasonable, I am skeptical about the authors' biological/cognitive conclusions that are couched in terms of words (updating rate and risk sensitivity) that readers will likely interpret as concepts that, in my view, do not actually concur with what the models and analyses address.

To answer the Reviewer’s question—yes, these equations are very much standard and the canonical way of analysing individual reinforcement learning (see: Ch. 15.2 in Computational Modeling of Cognition and Behavior by Farrell & Lewandowsky 2018 [DOI: 10.1017/CBO9781316272503]; McElreath et al. 2008 [DOI: 10.1098/rstb/2008/0131]; Reinforcement Learning by Sutton & Barto 2018). To provide a “justification for the form of these equations'', equation 1 describes a convex combination of previous values and recent payoffs. Latent values are updated as a linear combination of both factors, there is no simple linear mapping between payoffs and behaviour as suggested by the reviewer. Equation 2 describes the standard softmax link function. It converts a vector of real numbers (here latent values) into a simplex vector (i.e., a vector summing to 1) which represents the probabilities of different outcomes. Similar to the logit link in logistic regression, the softmax simply maps the model space of latent values onto the outcome space of choice probabilities which enter the categorial likelihood distribution. We can appreciate how we did not make this clear in our manuscript by not highlighting the standard nature of our analytical approach—we now do so in our revised manuscript (please see L148–149). As far as what our reinforcement learning model measures, and how it relates cognition and behaviour, please see our previous response.

To emphasize, while the authors imply that their analyses separate the updating rate from 'risk sensitivity', both the 'updating parameter' and the 'risk sensitivity' parameter influence both the strength of updating and the sensitivity to reward payoffs in the sense of altering the tendency to prefer an option based on recent experience with payoffs. As noted in the previous paragraph, the main difference between the two parameters is whether they relate to behaviour linearly versus with an exponential component.

Please see our two earlier responses on the mechanics of our reinforcement learning model.

Overall, while the statistical analyses based on equations (1) and (2) seem to have identified something interesting about two steps underlying learning patterns, to maximize the valuable conceptual impact that these analyses have for the field, more thinking is required to better understand the biological meaning of how these two parameters relate to observed behaviours, and the 'risk sensitivity' parameter needs to be re-named.

Please see our earlier response to these suggestions.

Agent-based simulations:

The authors estimated two learning parameters based on the behaviour of real birds, and then ran simulations to see whether computer 'birds' that base their choices on those learning parameters return behaviours that, on average, mirror the behaviour of the real birds. This exercise is clearly circular. In old-style, statistical terms, I suppose this means that the R-square of the statistical model is good. A more insightful use of the simulations would be to identify situations where the simulation does not do as well in mirroring behaviour that it is designed to mirror.

Based on the Reviewer’s summary of agent-based forward simulation, we can see we did a poor job explaining the inferential value of this method—we apologise. Agent-based forward simulations are posterior predictions, and they provide insight into the implied model dynamics and overall usefulness of our reinforcement learning model. R-squared calculations are retrodictive, and they say nothing about the causal dynamics of a model. Specifically, agent-based forward simulation allows us to ask—what would a ‘new’ grackle ‘do’, given our reinforcement learning model parameter estimates? It is important to ask this question because, in parameterising our model, we may have overlooked a critical contributing mechanism to grackles’ reinforcement learning. Such an omission is invisible in the raw parameter estimates; it is only betrayed by the parameters in actu. Agent-based forward simulation is ‘designed’ to facilitate this call to action—not to mirror behavioural results. The simulation has no apriori ‘opinion’ about computer ‘birds’ behavioural outcomes; rather, it simply assigns these agents random phi and lambda draws (whilst maintaining their correlation structure), and tracks their reinforcement learning. The exercise only appears circular if no critical contributing mechanism(s) went overlooked—in this case computer ‘birds’ should behave similar to real birds. A disparate mapping between computer ‘birds’ and real birds, however, would mean more work is needed with respect to model parameterisation that captures the causal, mechanistic dynamics behind real birds’ reinforcement learning (for an example of this happening in the human reinforcement learning literature, see Deffner et al. 2020 [DOI: 10.1098/rsos.200734]). In sum, agent-based forward simulation does not access goodness-of-fit—we assessed the fit of our model apriori in our preregistration (https://osf.io/v3wxb)—but it does assess whether one did a comprehensive job of uncovering the mechanistic basis of target behaviour(s). We have worked to make the above points on the method and the insight afforded by agent-based forward simulation explicitly clear in our revision—please see L192–207 and L534–537.

Reviewer #2 (Public Review):

Summary:

The study is titled "Leading an urban invasion: risk-sensitive learning is a winning strategy", and consists of three different parts. First, the authors analyse data on initial and reversal learning in Grackles confronted with a foraging task, derived from three populations labeled as "core", "middle" and "edge" in relation to the invasion front. The suggested difference between study populations does not surface, but the authors do find moderate support for a difference between male and female individuals. Secondly, the authors confirm that the proposed mechanism can actually generate patterns such as those observed in the Grackle data. In the third part, the authors present an evolutionary model, in which they show that learning strategies as observed in male Grackles do evolve in what they regard as conditions present in urban environments.

Strengths:

The manuscript's strength is that it combines real learning data collected across different populations of the Great-tailed grackle (Quiscalus mexicanus) with theoretical approaches to better understand the processes with which grackles learn and how such learning processes might be advantageous during range expansion. Furthermore, the authors also take sex into account revealing that males, the dispersing sex, show moderately better reversal learning through higher reward-payoff sensitivity. I also find it refreshing to see that the authors took the time to preregister their study to improve transparency, especially regarding data analysis.

Thank you—we are pleased to receive this positive evaluation, particularly concerning our efforts to improve scientific transparency via our study’s preregistration (https://osf.io/v3wxb).

Weaknesses:

One major weakness of this manuscript is the fact that the authors are working with quite low sample sizes when we look at the different populations of edge (11 males & 8 females), middle (4 males & 4 females), and core (17 males & 5 females) expansion range. Although I think that when all populations are pooled together, the sample size is sufficient to answer the questions regarding sex differences in learning performance and which learning processes might be used by grackles but insufficient when taking the different populations into account.

In Bayesian statistics, there is no strict lower limit of required sample size as the inferences do not rely on asymptotic assumptions. With inferences remaining valid in principle, low sample size will of course be reflected in rather uncertain posterior estimates. We note all of our multilevel models use partial pooling on individuals (the random-effects structure), which is a regularisation technique that generally reduces the inference constraint imposed by a low sample size (see Ch. 13 in Statistical Rethinking by Richard McElreath [PDF: https://bit.ly/3RXCy8c]). We further note that, in our study preregistration (https://osf.io/v3wxb), we formally tested our reinforcement learning model for different effect sizes of sex on learning for both target parameters (phi and lambda) across populations, using a similarly modest N (edge: 10 M, 5 F; middle: 22 M, 5 F ; core: 3 M, 4 F) to our actual final N, that we anticipated to be our final N at that time. This apriori analysis shows our reinforcement learning model: (i) detects sex differences in phi values >= 0.03 and lambda values >= 1; and (ii) infers a null effect for phi values < 0.03 and lambda values < 1 i.e., very weak simulated sex differences (see Figure 4 in https://osf.io/v3wxb). Thus, both of these points together highlight how our reinforcement learning model allows us to say that across-population null results are not just due to small sample size. Nevertheless the Reviewer is not wrong to wonder whether a bigger N might change our population-level results (it might; so might muchneeded population replicates—see L310), but our Bayesian models still allow us to learn a lot from our current data. We now explain this in our revised manuscript—please see L452–457.

Another weakness of this manuscript is that it does not set up the background well in the introduction. Firstly, are grackles urban dwellers in their natural range and expand by colonising urban habitats because they are adapted to it? The introduction also fails to mention why urban habitats are special and why we expect them to be more challenging for animals to inhabit. If we consider that one of their main questions is related to how learning processes might help individuals deal with a challenging urban habitat, then this should be properly introduced.

In L74–75 (previously L53–56) we introduce that the estimated historical niche of grackles is urban environments, and that shifts in habitat breadth—e.g., moving into more arid, agricultural environments—is the estimated driver of their rapid North American colonisation. We hope this included information sufficiently answers the Reviewer’s question. We have worked towards flushing out how urban-imposed challenges faced by grackles, such as the wildlife management efforts introduced in L64–65 (now L85–86), may apply to animals inhabiting urban environments more broadly; for example, we now include an entire paragraph in our Introduction detailing how urban environments may be characterised differently to nonurban environments, and thus why they are perhaps more challenging for animals to inhabit— please see L56–71.

Also, the authors provide a single example of how learning can differ between populations from more urban and more natural habitats. The authors also label the urban dwellers as the invaders, which might be the case for grackles but is not necessarily true for other species, such as the Indian rock agama in the example which are native to the area of study. Also, the authors need to be aware that only male lizards were tested in this study. I suggest being a bit more clear about what has been found across different studies looking at: (1) differences across individuals from invasive and native populations of invasive species and (2) differences across individuals from natural and urban populations.

We apologise for not including more examples of such learning differences. We now include three examples (please see L43–49), and we are careful to call attention to the fact that these data cover both resident urban and non-urban species as well as urban invasive species (please see L49–50). We also revised our labelling of the lizard species (please see L44). We are aware only male lizards were tested but this information is not relevant to substantiating our use of this study; that is, to highlight that learning can differ between urbandwelling and non-urban counterparts. We hope the changes we did make to our manuscript satisfy the Reviewer’s general suggestion to add biological clarity.

Finally, the introduction is very much written with regard to the interaction between learning and dispersal, i.e. the 'invasion front' theme. The authors lay out four predictions, the most important of which is No. 4: "Such sex-mediated differences in learning to be more pronounced in grackles living at the edge, rather than the intermediate and/or core region of their range." The authors, however, never return to this prediction, at least not in a transparent way that clearly pronounces this pattern not being found. The model looking at the evolution of risk-sensitive learning in urban environments is based on the assumption that urban and natural environments "differ along two key ecological axes: environmental stability 𝑢 (How often does optimal behaviour change?) and environmental stochasticity 𝑠 (How often does optimal behaviour fail to pay off?). Urban environments are generally characterised as both stable (lower 𝑢) and stochastic (higher 𝑠)". Even though it is generally assumed that urban environments differ from natural environments the authors' assumption is just one way of looking at the differences which have generally not been confirmed and are highly debated. Additionally, it is not clear how this result relates to the rest of the paper: The three populations are distinguished according to their relation to the invasion front, not with respect to a gradient of urbanization, and further do not show a meaningful difference in learning behaviour possibly due to low sample sizes as mentioned above.

Thank you for highlighting a gap in our reporting clarity. We now take care to transparently report our null result regarding our fourth prediction; more specifically, that we did not detect credible population-level differences in grackles’ learning (please see L130). Regarding our evolutionary model, we agree with the Reviewer that this analysis is only one way of looking at the interaction between learning phenotype and apparent urban environmental characteristics. Indeed, in L282–288 (now L325–329) we state: “Admittedly, our evolutionary model is not a complete representation of urban ecology dynamics. Relevant factors—e.g., spatial dynamics and realistic life histories—are missed out. These omissions are tactical ones. Our evolutionary model solely focuses on the response of reinforcement learning parameters to two core urban-like (or not) environmental statistics, providing a baseline for future study to build on”. But we can see now that ‘core’ is too strong a word, and instead ‘supposed’, ‘purported’ or ‘theorised’ would be more accurate—we have revised our wording throughout our manuscript to say as much (please see, for example, L24; L56; L328). We also further highlight the preliminary nature of our evolutionary model, in terms of allowing a narrow but useful first-look at urban eco-evolutionary dynamics—please see L228–232. Finally, we now detail the theorised characteristics of urban environments in our Introduction (rather than in our Results; please see L56–71), and we hope that by doing so, how our evolutionary results relate to the rest of our paper is now better set up and clear.

In conclusion, the manuscript was well written and for the most part easy to follow. The format of eLife having the results before the methods makes it a bit harder to follow because the reader is not fully aware of the methods at the time the results are presented. It would, therefore, be important to more clearly delineate the different parts and purposes. Is this article about the interaction between urban invasion, dispersal, and learning? Or about the correct identification of learning mechanisms? Or about how learning mechanisms evolve in urban and natural environments? Maybe this article can harbor all three, but the borders need to be clear. The authors need to be transparent about what has and especially what has not been found, and be careful to not overstate their case.

Thank you, we are pleased to read that the Reviewer found our manuscript to be generally digestible. We have worked to add further clarity, and to tempter our tone (please see our above and below responses).

Reviewer #1 (Recommendations For The Authors):

Several of the results are based on CIs that overlap zero. Tone these down somewhat.

We apologise for overstating our results, which we have worked to tone down in our revision. For instance, in L185–186 we now differentiate between estimates that did or did not overlap zero (please also see our response to Reviewer 2 on this tonal change). We note we do not report confidence intervals (i.e., the range of values expected to contain the true estimate if one redoes the study/analysis many times). Rather, we report 89% highest posterior density intervals (i.e., the most likely values of our parameters over this range). We have added this definition in L459, to improve clarity.

The literature review suggesting that urban environments are more unpredictable is not convincing. Yes, they have more noise and light pollution and more cars and planes, but does this actually relate to the unpredictability of getting a food reward when you choose an option that usually yields rewards?

To answer the Reviewer’s question—yes. But we can see that by not including empirical examples from the literature, we did a poor job of arguing such links. In L43–49 we now give three empirical examples; more specifically, we state: “[...] experimental data show the more variable are traffic noise and pedestrian presence, the more negative are such human-driven effects on birds' sleep (Grunst et al., 2021), mating (Blickley et al., 2012), and foraging behaviour (Fernández-Juricic, 2000).” We note we now detail such apparently stable but stochastic urban environmental characteristics in our Introduction rather than our Results section, to hopefully improve the clarity of our manuscript (please see L56–71). We further note that we cite three literature reviews—not one—suggesting urban environments are stable in certain characteristics and more unpredictable in others (please see L59–60). Finally, we appreciate such characterisation is not certain, and so in our revision we have qualified all writing about this potential dynamic with words such as “apparent”, “supposed”, “theorised”, “hypothesised” etc.

It would be interesting to see if other individual traits besides sex affect their learning/reversal learning ability and/or their learning parameters. Do you have data on age, size, condition, or personality? Or, the habitat where they were captured?

We do not have these data. But we agree with the Reviewer that examining the potential influence of such covariates on grackles’ reinforcement learning would be interesting in future study, especially habitat characteristics (please see L306–309).

For most levels of environmental noise, there appears to be an intermediate maximum for the relationship between environmental stability and the risk sensitivity parameter. What does this mean?

There is indeed an intermediate maximum for certain values of environmental stochasticity (although the differences are rather small). The most plausible reason for this is that for very stable environments, simulated birds essentially always “know” the rewarded solution and never need to “relearn” behaviour. In this case, differences in latent values will tend to be large (because they consistently get rewarded for the same option), and different lambda values (in the upper range) will produce the same choice behaviour, which results in very weak selection. While in very unstable environments, optimal choice behaviour should be more exploratory, allowing learners to track frequently-changing environments. We now note this pattern in L240–248.

Reviewer #2 (Recommendations For The Authors):

L2: I'd encourage the authors to reconsider the term "risk-sensitive learning", at least in the title. It's not apparent to me how 'risk' relates to the investigated foraging behaviour. Elsewhere, risk-reward sensitivity is used which may be a better term.

We apologise for not clearly introducing the field of risk-sensitive foraging, which focuses on how animals evaluate and choose between distinct food options, and how such foraging decisions are influenced by pay-off variance i.e., risk associated with alternative foraging options (seminal reviews: Bateson 2002 [DOI: 10.1079/PNS2002181]; Kacelnik & Bateson 1996 [DOI: 10.1093/ICB/36.4.402]). We have added this information to our manuscript in L494–497. In explaining our reinforcement model, we also now detail how risk relates to foraging behaviour. Specifically, in L153–161 we now state: “Both learning parameters capture individual-level internal response to incurred reward-payoffs, but they differentially direct any reward sensitivity back on choice-behaviour due to their distinct definitions (full mathematical details in Materials and methods). For 𝜙, stronger reward sensitivity (bigger values) means faster internal updating about stimulus-reward pairings, which translates behaviourally into faster learning about ‘what to choose’. For 𝜆, stronger reward sensitivity (bigger values) means stronger internal determinism about seeking the nonrisk foraging option (i.e., the one with the higher expected payoffs based on prior experience), which translates behaviourally into less choice-option switching i.e., ‘playing it safe’.” We hope this information clarifies why lamba measures risk-sensitivity, and why we continue to use this term.

L1-3: The title is a bit misleading with regard to the empirical data. From the data, all that can be said is that male grackles relearn faster than females. Any difference between populations actually runs the other way, with the core population exhibiting a larger difference between males and females than the mid and edge populations.

It is customary for a manuscript title to describe the full scope of the study. In our study, we have empirical data, cognitive modelling, and evolutionary simulations of the background theory all together. And together these analytical approaches show: (1) across three populations, male grackles—the dispersing sex in this historically urban-dwelling and currently urban-invading species—outperform female counterparts in reversal learning; (2) they do this via risk-sensitive learning, so they’re more sensitive to relative differences in reward payoffs and choose to stick with the ‘safe’ i.e., rewarding option, rather than continuing to ‘gamble’ on an alternative option; and (3) risk-sensitive learning should be favoured in statistical environments characterised by purported urban dynamics. So, we do not feel our title “Leading an urban invasion: risk-sensitive learning is a winning strategy” is misleading with regard to our empirical data; it just doesn’t summarise only our empirical data. Finally, as we now state in L312–313, we caution against speculating about any between-population variation, as we did not infer any meaningful behavioural or mechanistic population-level differences.

L13: "Assayed", is that correctly put, given that the authors did not collect the data?

Merrian-Webster defines assay as “to analyse” or “examination or determination as to characteristics”, and so to answer the Reviewer’s question—yes, we feel this is correctly put. We note we explicitly introduce in L102–103 that we did not collect the data, and we have an explicit “Data provenance” section in our methods (please see L342–347).

L42-46: The authors provide a single example of how learning can differ between populations from more urban and more natural habitats. I would like to point out that many of these studies do not directly confirm that the ability in question has indeed led to the success of the species tested (e.g. show fitness consequences). Then the authors could combine these insights to form a solid prediction for the grackles. As of now, this looks like cherry-picking supportive literature without considering negative results.

Here are some references that might be helpful in identifying relevant literature to cite:

Szabo, B., Damas-Moreira, I., & Whiting, M. J. (2020). Can cognitive ability give invasive species the means to succeed? A review of the evidence. Frontiers in Ecology and Evolution, 8, 187.

Griffin AS, Tebbich S, Bugnyar T, 2017. Animal cognition in a human-dominated world. Anim Cogn 20(1):1-6.

Kark, S., Iwaniuk, A., Schalimtzek, A., & Banker, E. (2007). Living in the city: Can anyone become an "urban exploiter"? Journal of Biogeography, 34(4), 638-651.

We apologise for not including more examples of such learning differences. We now include three examples (please see L43–49). We are aware that direct evidence of fitness consequences is entirely lacking in the scientific literature on cognition and successful urban invasion; hence why such data is not present in our paper. But we now explicitly point out a role for likely fitness-affecting anthropogenic disturbances on sleep, mate, and foraging behaviour on animals inhabiting urban environments (please see L63–68). We hope these new data bolster our predictions for our grackles. Finally, the Reviewer paints a (in our view) inaccurate picture of our use of available literature. Nevertheless, to address their comment, we now highlight a recent meta-analysis advocating for further research to confirm apparent ‘positive’ trends between animal ‘smarts’ and successful ‘city living’ (please see L43).

L64: Is their niche historically urban, or have they recently moved into urban areas?

In L74–75 (previously L53–56) we introduce that the estimated historical niche of grackles is urban environments, and that shifts in habitat breadth—e.g., moving into more arid, agricultural environments—is the estimated driver of their rapid North American colonisation. We hope this included information sufficiently answers the Reviewer’s question.

L66-67: This is an important point that is however altogether missing from the discussion.

We thank the Reviewer for highlighting a gap in our discussion regarding populationlevel differences in grackles’ reinforcement learning. In L310–312 we now state: “The lack of spatial replicates in the existing data set used herein inherently poses limitations on inference. Nevertheless, the currently available data do not show meaningful population-level behavioural or mechanistic differences in grackles’ reinforcement learning, and we should thus be cautious about speculating on between-population variation”.

L68-71: The paper focuses on cognitive ability. The whole paragraph sets up the prediction of why male grackles should be better learners due to their dispersal behaviour. This example, however, focuses on aggression, not cognition. Here is a study showing differences in learning in male and female mynas that might be better suited:

Federspiel IG, Garland A, Guez D, Bugnyar T, Healy SD, Güntürkün O, Griffin AS, 2017. Adjusting foraging strategies: a comparison of rural and urban common mynas (Acridotheres tristis). Anim Cogn 20(1):65-74.

We thank the Reviewer for suggesting this paper. We feel it is better suited to substantiating our point in the Discussion about reversal learning not being indicative of cognitive ability—please see L276–277.

L73: Generally, I suggest not writing "for the first time" as this is not a valid argument for why a study should be conducted. Furthermore, except for replication studies, most studies investigate questions that are novel and have not been investigated before.

The Reviewer makes a fair point—we have removed this statement.

L80-81: Here again, this is left undiscussed later on.

By ‘this’ we assume the Reviewer is referring to our hypothesis, which is that sex differences in dispersal are related to sex differences in learning in an urban invader— grackles. At the beginning of our Discussion, we state how we found support for this hypothesis (please see L250–261); and in our ‘Ideas and speculation’ section, we discuss how these hypothesis-supporting data fit into the literature more broadly (please see L294–331). We feel this is therefore sufficiently discussed.

L77-81: This sentence is very long and therefore hard to read. I suggest trying to split it into at least 2 separate sentences which would improve readability.

Per the Reviewer’s useful suggestion, we have split this sentence into two separate sentences—please see L97–115.

L83: Please explain choice-option switches. I am not aware of what that is and it should be explained at first mention.

We apologise for this operational oversight. We now include a working definition of speed and choice-option switches at first mention. Specifically, in L107–108 we state: “[...] we expect male and female grackles to differ across at least two reinforcement learning behaviours: speed (trials to criterion) and choice-option switches (times alternating between available stimuli)”.

L83-87: Again, a very long sentence. Please split.

We thank the Reviewer for their suggestion. In this case we feel it is important to not change our sentence structure because we want our prediction statements to match between our manuscript and our preregistration.

L96-97: Important to not overstate this. It merely demonstrates the potential of the proposed (not detected) mechanism to generate the observed data.

As in any empirical analysis, our drawn conclusions depend on causal assumptions about the mechanisms generating behaviour (Pearl, J. (2009). Causality). Therefore, we “detected” specific learning mechanisms assuming a certain generative model, namely reinforcement learning. As there is overwhelming evidence for the widespread importance of value-based decision making and Rescorla-Wagner updating rules across numerous different animals (Sutton & Barto (2018) Reinforcement Learning), we would argue that this assumed model is highly plausible in our case. Still, we changed the text to “inferred” instead of “detected” learning mechanisms to account for this concern—please see L123–124.

L99: "urban-like settings" again a bit confusing. The authors talk about invasion fronts, but now also about an urbanisation gradient. Is the main difference between the size and the date of establishment, or is there additionally a gradient in urbanisation to be considered?

We now include a paragraph in our Introduction detailing apparent urban environmental characteristics (please see 56–71), and we now refer to this dynamic specifically when we define urban-like settings (please see L126–127). To answer the Reviewer’s question—we consider both differences. Specifically, we consider the time since population establishment in our paper (with respect to our behavioural and mechanistic modelling), as well as how statistical environments that vary in how similar they are to apparently characteristically urban-like environments, might favour particular learning phenotypes (with respect to our evolutionary modelling). We hope the edits to our Introduction as a whole now make both of the aims clear.

L11-112: Above the authors talk about a comparable number of switches (10.5/15=0.7), and here of fewer number of switches (25/35=0.71), even though the magnitude of the difference is almost identical and actually runs the other way. The authors are probably misled by their conservative priors, which makes the difference appear greater in the second case than in the first. Using flat priors would avoid this particular issue.

Mathematically, the number of trials-to-finish and the number of choice-optionswitches are both a Poisson distributed outcome with rate λ (we note lambda here is not our risk-sensitivity parameter; just standard notation). As such, our Poisson models infer the rate of these outcomes by sex and phase—not the ratio of these outcomes by sex and phase. So comparing the magnitude of divided medians of choice-option-switches between the sexes by phase is not a meaningful metric with respect to the distribution of our data, as the Reviewer does above. For perspective, 1 vs. 2 switches provides much less information about the difference in rates of a Poisson distribution than 50 vs 100 (for the former, no difference would be inferred; for the latter, it would), but both exhibit a 1:2 ratio. To hopefully prevent any such further confusion, and to focus on the fact that our Poisson models estimate the expected value i.e., the mean, we now report and graph (please see Fig. 2) mean and not median trialsto-finish and total-switch-counts. Finally, we can see that our use of the word “conservative” to describe our weakly informative priors is confusing, because conservative could mean either strong priors with respect to expected effect size (not our parameterisation) or weak priors with respect to such assumptions (our parameterisation). To address this lack of clarity, we now state that we use “weakly informative priors” in L457–458.

L126: It is not clear what risk sensitivity means in the context of these experiments.

Thank you for pointing out our lack of clarity. In L153–161 we now state: “Both learning parameters capture individual-level internal response to incurred reward-payoffs, but they differentially direct any reward sensitivity back on choice-behaviour due to their distinct definitions (full mathematical details in Materials and methods). For 𝜙, stronger reward sensitivity (bigger values) means faster internal updating about stimulus-reward pairings, which translates behaviourally into faster learning about ‘what to choose’. For 𝜆, stronger reward sensitivity (bigger values) means stronger internal determinism about seeking the nonrisk foraging option (i.e., the one with the higher expected payoffs based on prior experience), which translates behaviourally into less choice-option switching i.e., ‘playing it safe’.” We hope this information clarifies what risk sensitivity means and measures, with respect to our behavioural experiments.

L128-129: I find this statement too strong. A plethora of other mechanisms could produce similar patterns, and you cannot exclude these by way of your method. All you can show is whether the mechanism is capable of producing broadly similar outcomes as observed

In describing the inferential value of our reinforcement learning model, we now qualify that the insight provided is of course conditional on the model, which is tonally accurate. Please see L161.

L144: As I have already mentioned above, here is the first time we hear about unpredictability related to urban environments. I suggest clearly explaining in the introduction how urban and natural environments are assumed to be different which leads to animals needing different cognitive abilities to survive in them which should explain why some species thrive and some species die out in urbanised habitats.

Thank you for this suggestion. We now include a paragraph in our Introduction detailing as much—please see L56–71.

L162: "almost entirely above zero" again, this is worded too strongly.

In reporting our lambda across-population 89% HPDI contrasts in L185–186, we now state: “[...] across-population contrasts that lie mostly above zero in initial learning, and entirely above zero in reversal learning”. Our previous wording stated: ““[...] across-population contrasts that lie almost entirely above zero”. The Reviewer was correct to point out that this previous wording was too strong if we considered the contrasts together, as, indeed, we find the range of the contrast in initial learning does minimally overlap zero (L: -0.77; U: 5.61), while the range of the contrast in reversal learning does not (L: 0.14; U: 4.26). This rephrasing is thus tonally accurate.

L178-179: I think it should be said instead that the model accounts well for the observed data.

We have rephrased in line with the Reviewer’s suggestion, now stating in L217–218 that “Such quantitative replication confirms our reinforcement learning model results sufficiently explain our behavioural sex-difference data.”

L188-190: I am not convinced this is a general pattern. It is quite a bold claim that I don't find to be supported by the citations. Why should biotic and abiotic factors differ in how they affect behavioural outcomes? Also, events in urban environments such as weekend/weekday could lead to highly regular optimal behaviour changes.

Please see our response to Reviewer 1 on this point. We note we now touch on such regular events in L94–96.

L209-211: The first sentence is misleading. The authors have found that males and females differ in 'risk sensitivity', that their learning model can fit the data rather well, and that under certain, not necessarily realistic assumptions, the male learning type is favoured by natural selection in urban environments. A difference between core, middle, and edge habitats however is barely found, and in fact seems to run the other way than expected.

In our study, we found: (1) across three populations, male grackles—the dispersing sex in this historically urban-dwelling and currently urban-invading species—outperform female counterparts in reversal learning; (2) they do this via risk-sensitive learning, so they’re more sensitive to relative differences in reward payoffs and choose to stick with the ‘safe’ i.e., rewarding option, rather than continuing to ‘gamble’ on an alternative option; (3) we are sufficiently certain risk-sensitive learning generates our sex-difference data, as our agentbased forward simulations replicate our behavioural results (not because our model ‘fits’ the data, but because we inferred meaningful mechanistic differences—see our response to Reviewer 1 on this point); and (4) under theorised dynamics of urban environments, natural selection should favour risk-sensitive learning. We therefore do not feel it is misleading to say that we mapped a full pathway from behaviour to mechanisms through to selection and adaptation. Again, as we now state in L311–313, we caution against speculating about any between-population variation, as we did not infer any meaningful behavioural or mechanistic population-level differences. And we note the Reviewer is wrong to assume an interaction between learning, dispersal, and sex requires population-level differences on the outcome scale—please see our discussion on phenotypic plasticity and inherent species trait(s) in L313–324.

L216: "indeed explain" again worded too strongly.

We have tempered our wording. Specifically, we now state in L218: “sufficiently explain”. This wording is tonally accurate with respect to the inferential value of agent-based forward simulations—please see L192–207 on this point.

L234: "reward-payoff sensitivity" might be a better term than risk-sensitivity?

Please see our earlier response to this suggestion. We note we have changed this text to state “risk-sensitive learning” rather than “reward-payoff sensitivity”, to hopefully prevent the reader from concluding only our lambda term is sensitive to rewards—a point we now include in L153–154.

L234-237: I think these points may be valuable, but come too much out of the blue. Many readers will not have a detailed knowledge of the experimental assays. It therefore also does not become clear how they measure the wrong thing, what this study does to demonstrate this, or whether a better alternative is presented herein. It almost seems like this should be a separate paper by itself.

We apologise for this lack of context. We now explicitly state in L275 that we are discussing reversal learning assays, to give all readers this knowledge. In doing so, we hope the logic of our argument is now clear: reversal learning assays do not measure behavioural flexibility, whatever that even is. The Reviewer’s suggestion of a separate paper focused on what reversal learning assays actually measure, in terms of mechanism(s), is an interesting one, and we would welcome this discussion. But any such paper should build on the points we make here.

L270-288: Somewhere here the authors have to explain how they have not found differences between populations, or that in so far as they found them, they run against the originally stated hypothesis.

We thank the Reviewer for these suggestions. In L310—313 we now state: “The lack of spatial replicates in the existing data set used herein inherently poses limitations on inference. Nevertheless, the currently available data do not show meaningful population-level behavioural or mechanistic differences in grackles’ reinforcement learning, and we should thus be cautious about speculating on between-population variation”.

L284: should be "missing" not "missed out"

We have made this change.

L290-291: It is unclear what "robust interactive links" were found. A pattern of sexbiased learning was found, which can potentially be attributed to evolutionary pressures in urban environments. An interaction e.g. between learning, dispersal, and sex can only be tentatively suggested (no differences between populations). Also "fully replicable" is a bit misleading. The analysis may be replicable, but the more relevant question of whether the findings are replicable we cannot presently answer.

We apologise for our lack of clarity. By “robust” we mean “across population”, which we now state in L333. We again note the Reviewer is wrong to assume an interaction between learning, dispersal, and sex requires population-level differences on the outcome scale— please see our discussion on phenotypic plasticity and inherent species trait(s) in L313–324. Finally, the Reviewer makes a good point about our analyses but not our findings being replicable. In L334 we now make this distinction by stating “analytically replicable”.

L306-315: I think you have a bit of a sample size issue not so much when populations are pooled but when separated. This might also factor in the fact that you do not really find differences across the populations in your analysis. When we look at the results presented in Figure 2 (and table d), we can see a trend towards males having better risk sensitivity in core (HPDI above 0) and middle populations (HPDI barely crossing 0) but the difference is very small. Especially the results on females are based on the performance of only 8 and 4 females respectively. I suggest making this clear in the manuscript.

In Bayesian statistics, there is no strict lower limit of required sample size as the inferences do not rely on asymptotic assumptions. With inferences remaining valid in principle, low sample size will of course be reflected in rather uncertain posterior estimates. We note all of our multilevel models use partial pooling on individuals (the random-effects structure), which is a regularisation technique that generally reduces the inference constraint imposed by a low sample size (see Ch. 13 in Statistical Rethinking by Richard McElreath [PDF: https://bit.ly/3RXCy8c]). We further note that, in our study preregistration (https://osf.io/v3wxb), we formally tested our reinforcement learning model for different effect sizes of sex on learning for both target parameters (phi and lambda) across populations, using a similarly modest N (edge: 10 M, 5 F; middle: 22 M, 5 F ; core: 3 M, 4 F) to our actual final N, that we anticipated to be our final N at that time. This apriori analysis shows our reinforcement learning model: (i) detects sex differences in phi values >= 0.03 and lambda values >= 1; and (ii) infers a null effect for phi values < 0.03 and lambda values < 1 i.e., very weak simulated sex differences (see Figure 4 in https://osf.io/v3wxb). Thus, both of these points together highlight how our reinforcement learning model allows us to say that across-population null results are not just due to small sample size. Nevertheless the Reviewer is not wrong to wonder whether a bigger N might change our population-level results; it might; so might muchneeded population replicates—see L310. But our Bayesian models still allow us to learn a lot from our current data, and, at present, we infer no meaningful population-level behavioural or mechanistic differences in grackles’ behaviour. To make clear the inferential sufficiency of our analytical approach, we now include some of the above points in our Statistical analyses section in L452–457. Finally, we caution against speculating on any between-population variation, as we now highlight in L311—313 of our Discussion.

Figure 2: I think the authors should rethink their usage of colour in this graph. It is not colour-blind friendly or well-readable when printed in black and white.

We used the yellow (hex code: #fde725) and green (hex code: #5ec962) colours from the viridis package. As outlined in the viridis package vignette (https://cran.rproject.org/web/packages/viridis/index.html), this colour package is “designed to improve graph readability for readers with common forms of color blindness and/or color vision deficiency. The color maps are also perceptually-uniform, both in regular form and also when converted to black-and-white for printing”.

Figure 3B: Could the authors turn around the x-axis and the colour code? It would be easier to read this way.

We appreciate that aesthetic preferences may vary. In this case, we prefer to have the numbers on the x-axis run the standard way i.e., from small to large. We note we did remove the word ‘Key’ from this Figure, in line with the Reviewer’s point about these characteristics not being totally certain.

I also had a look at the preregistration. I do think that there are parts in the preregistration that would be worth adding to the manuscript:

L36-40: This is much easier to read here than in the manuscript.

We changed this text generally in the Introduction in our revision, so we hope the Reviewer will again find this easier to read.

L49-56: This is important information that I would also like to see in the manuscript.

We no longer have confidence in these findings, as our cleaning of only one part of these data revealed considerable experimenter oversight (see ‘Learning criterion’).

L176: Why did you remove the random effect study site from the model? It is not part of the model in the manuscript anymore.

The population variable is part of the RL_Comp_Full.stan model that we used in our manuscript to assess population differences in grackles’ reinforcement learning, the estimates from which we report in Table C and D (please note we never coded this variable as “study cite”). But rather than being specified as a random effect, in our RL_Comp_Full.stan model we index phi and lambda by population as a predictor variable, to explicitly model population-level effects. Please see our code:

https://github.com/alexisbreen/Sex-differences-in-grackles- learning/blob/main/Models/Reinforcement%20learning/RL_Comp_Full.stan

L190-228: I am wondering if the model validation should also be part of the manuscript as well, rather than just being in the preregistration?

We are not sure how the files were presented to the Reviewer for review, but our study preregistration, which includes our model validation, should be part of our manuscript as a supplementary file.

eLife assessment

This important study uses a multi-pronged empirical and theoretical approach to advance our understanding of animal cognition. It presents convincing data on how differences in learning relate to differences in the ways that male versus female animals cope with urban environments, and more generally how reversal learning may benefit animals in urban habitats.

Reviewer #2 (Public Review):

Summary: The study is titled "Leading an urban invasion: risk-sensitive learning is a winning strategy", and consists of three different parts. First, the authors analyse data on initial and reversal learning in Grackles confronted with a foraging task, derived from three populations labeled as "core", "middle" and "edge" in relation to the invasion front. The suggested difference between study populations does not surface, but the authors do find support for a difference between male and female individuals. Secondly, the authors confirm that the proposed mechanism can actually generate patterns such as observed in the Grackle data through agent-based forward simulations. In the third part, the authors present an evolutionary model, in which they show that learning strategies, as observed in male Grackles, do evolve in simplified urban conditions including different levels of environmental stability and environmental stochasticity.

Strengths: The manuscript's strength is that it combines real learning data collected across different populations of the Great-tailed grackle (Quiscalus mexicanus) with theoretical approaches to better understand the processes with which grackles learn and how such learning processes might be advantageous during range expansion and invasion. Furthermore, the authors also take sex into account revealing that males, the dispersing sex, show better reversal learning through higher reward-payoff sensitivity. I also find it refreshing to see that the authors took the time to preregister their study to improve transparency especially regarding data analysis.

Weakness: The small sample size of grackles across populations increases uncertainty as to parameter estimates and the conclusions drawn from these estimates.

After revision, the introduction is appropriate, and in the methods, the authors take great care in explaining the rational behind decisions as to the selection of analysis methods and parameters. I very much appreciate that the authors took such care in revising their paper, the quality of which has now greatly improved.

Author Response

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

eLife assessment

This fundamental study evaluates the evolutionary significance of variations in the accuracy of the intron-splicing process across vertebrates and insects. Using a powerful combination of comparative and population genomics approaches, the authors present convincing evidence that species with lower effective population size tend to exhibit higher rates of alternative splicing, a key prediction of the drift-barrier hypothesis. The analysis is carefully conducted and all observations fit with this hypothesis, but focusing on a greater diversity of metazoan lineages would make these results even more broadly relevant. This study will strongly appeal to anyone interested in the evolution of genome architecture and the optimisation of genetic systems.

Public Reviews):

Reviewer #1 (Public Review:

Summary:

Functionally important alternative isoforms are gold nuggets found in a swamp of errors produced by the splicing machinery.

The architecture of eukaryotic genomes, when compared with prokaryotes, is characterised by a preponderance of introns. These elements, which are still present within transcripts, are rapidly removed during the splicing of messenger RNA (mRNA), thus not contributing to the final protein. The extreme rarity of introns in prokaryotes, and the elimination of these introns from mRNAs before translation into protein, raises questions about the function of introns in genomes. One explanation comes from functional biology: introns are thought to be involved in post-transcriptional regulation and in the production of translational variants. The latter function is possible when the positions of the edges of the spliced intron vary. While some light has been shed on specific examples of the functional role of alternative splicing, to what extent are they representative of all introns in metazoans?

In this study, the hypothesis of a functional role for alternative splicing, and therefore to a certain extent for introns, is evaluated against another explanation coming from evolutionary biology: isoforms are above all errors of imprecision by the molecular machinery at work during splicing. This hypothesis is based on a principle established by Motoo Kimura, which has become central to population genetics, explaining that the evolutionary trajectory of a mutation with a given effect is intimately linked to the effective population size (Ne) where this mutation emerges. Thus, the probability of fixation of a weakly deleterious mutation increases when Ne decreases, and the probability of fixation of a weakly advantageous mutation increases when Ne increases. The genomes of populations with low Ne are therefore expected to accumulate more weakly deleterious mutations and fewer weakly advantageous mutations than populations with high Ne. In this framework, if splicing errors have only small effects on the fitness of individuals, then natural selection cannot increase the precision of the splicing machinery, allowing tolerance for the production of alternative isoforms.

In the past, the debate opposed one-off observations of effectively functional isoforms on the one hand, to global genomic quantities describing patterns without the possibility of interpreting them in detail. The authors here propose an elegant quantitative approach in line with the expected continuous variation in the effectiveness of selection, both between species and within genomes. The result describing the inter-specific pattern on a large scale confirms what was already known (there is a negative relationship between effective size and average alternative splicing rate). The essential novelty of this study lies in 1) the quantification, for each intron studied, of the relative abundance of each isoform, and 2) the analysis of a relationship between this abundance and the evolutionary constraints acting on these isoforms.

What is striking is the light shed on the general very low abundance of alternative isoforms. Depending on the species, 60% to 96% of cases of alternatively spliced introns lead to an isoform whose abundance is less than 5% of the total variants for a given intron.

In addition to the fact that 60 %-96% of the total isoforms are more than 20 times less abundant than their majority form, this large proportion of alternative isoforms exhibit coding-phase shift at rates similar to what would be expected by chance, i.e. for a third of them, which reinforces the idea that there is no particular constraint on these isoforms.

The remaining 4%-40% of isoforms see their coding-phase shift rate decrease as their relative abundance increases. This result represents a major step forward in our understanding of alternative splicing and makes it possible to establish a quantitative model directly linking the relative abundance of an isoform with a putative functional role concerning only those isoforms produced in abundance. Only the (rare) isoforms which are abundantly produced are thought to be involved in a biological function.

Within the same genome, the authors show that only highly expressed genes, i.e. those that tend to be more constrained on average, are also the genes with the lowest alternative splicing rates on average.

The comparison between species in this study reveals that the smaller the effective size of a species, the more its genome produces isoforms that are low in abundance and low in constraint. Conversely, species with a large effective size relatively reduce rare isoforms, and increase stress on abundant isoforms. To sum up:

• the higher the effective size of a species, the fewer introns are spliced.

• highly expressed genes are spliced less.

• when splicing occurs, it is mainly to produce low-abundance isoforms.

• low-abundance isoforms are also less constrained.

Taken together, these results reinforce a quantitative view of the evolution of alternative splicing as being mainly the product of imprecision in the splicing machinery, generating a great deal of molecular noise. Then, out of all this noise, a few functional gold nuggets can sometimes emerge. From the point of view of the reviewer, the evolutionary dynamics of genomes are depressing. The small effective population sizes are responsible for the accumulation of multiple slightly deleterious introns. Admittedly, metazoan genomes try to get rid of these introns during RNA maturation, but this mechanism is itself rendered imprecise by population sizes.

Strengths:

• The authors simultaneously study the effects of effective population size, isoform abundance, and gene expression levels on the evolutionary constraints acting on isoforms. Within this framework, they clearly show that an isoform becomes functionally important only under certain rare conditions.

• The authors rule out an effect putatively linked to variations in expression between different organs which could have biased comparisons between different species.

Weaknesses:

• While the longevity of organisms as a measure of effective size seems to work overall, it may not be relevant for discriminating within a clade. For example, within Hymenoptera, we might expect them to have the same overall longevity, but that effective size would be influenced more by the degree of sociality: solitary bees/ants/wasps versus eusocial. I am therefore certain that the relationship shown in Figure 4D is currently not significant because the measure of effective size is not relevant for Hymenoptera. The article would have been even more convincing by contrasting the rates of alternative splicing between solitary versus social hymenopterans.

As suggested by the reviewer, we investigated the degree of sociality for the 18 hymenopterans included in our study. We observed that the average dN/dS of the 12 eusocial species (4 bees, 6 ants, 2 wasps) is significantly higher than that of the 6 solitary species (p=2.1x10-3; Fig. R1A), consistent with a lower effective population size in eusocial species compared to solitary ones.

However, the AS rate does not differ significantly between these two groups, neither for the full set of major-isoform introns (Fig. R1B), nor for the subsets of low-AS or high-AS major-isoform introns (Fig. R1C,D). Given the limited sample size (12 eusocial species, 6 solitary species), it is possible that some uncontrolled variables affecting the AS rate hide the impact of Ne.

Author response image 1.

Comparison of solitary (N=6) and eusocial hymenopterans (N=12). A: dN/dS ratio. B: AS rate (all major-isoform introns). C: AS rate (low-AS major-isoform introns). D: AS rate (high-AS major-isoform introns). The means of the two group were compared with a Wilcoxon test.

<ahref="https://imgur.com/NiBIJde">

• When functionalist biologists emphasise the role of the complexity of living things, I'm not sure they're thinking of the comparison between "drosophila" and "homo sapiens", but rather of a broader evolutionary scale. Which gives the impression of an exaggeration of the debate in the introduction.

We disagree with the referee: in fact, all the debate regarding the paradox of the absence of relationship between the number of genes and organismal complexity arose from the comparative analysis of gene repertoires across metazoans. This debate started in the early 2000’s, when the sequencing of the human genome revealed that it contains only ~20,000 protein-coding genes (far less than the ~100,000 genes that were expected at that time). This came as a big surprise because it showed that the gene repertoire of mammals is not larger than that of invertebrates such as Caenorhabditis elegans (19,000 genes) or Drosophila melanogaster (14,000 genes) . We cite below several articles that illustrate how this paradox has been perceived by the scientific community:

Graveley BR 2001 Alternative splicing: increasing diversity in the proteomic world. Trends in Genetics 17 : 100–107. https://doi.org/10.1016/S0168-9525(00)02176-4

“ How can the genome of Drosophila melanogaster contain fewer genes than the undoubtedly simpler organism Caenorhabditis elegans? ”

Ewing B and Green P 2000 Analysis of expressed sequence tags indicates 35,000 human genes. Nature Genetics 25 : 232–234. https://doi.org/10.1038/76115

“ the invertebrates Caenorhabditis elegans and Drosophila melanogaster having 19,000 and 13,600 genes, respectively. Here we estimate the number of human genes […] approximately 35,000 genes, substantially lower than most previous estimates. Evolution of the increased physiological complexity of vertebrates may therefore have depended more on the combinatorial diversification of regulatory networks or alternative splicing than on a substantial increase in gene number. ”

Kim E, Magen A and Ast G 2007 Different levels of alternative splicing among eukaryotes. Nucleic Acids Research 35 : 125–131. https://doi.org/10.1093/nar/gkl924

“we reveal that the percentage of genes and exons undergoing alternative splicing is higher in vertebrates compared with invertebrates. […] The difference in the level of alternative splicing suggests that alternative splicing may contribute greatly to the mammal higher level of phenotypic complexity,”

Nilsen TW and Graveley BR 2010 Expansion of the eukaryotic proteome by alternative splicing. Nature 463 : 457–463. https://doi.org/10.1038/nature08909

“ It is noteworthy that Caenorhabditis elegans, D. melanogaster and mammals have about 20,000 (ref. 68), 14,000 (ref. 69) and 20,000 (ref. 70) genes, respectively, but mammals are clearly much more complex than nematodes or flies.”

Reviewer #2 (Public Review):

Summary:

Two hypotheses could explain the observation that genes of more complex organisms tend to undergo more alternative splicing. On one hand, alternative splicing could be adaptive since it provides the functional diversity required for complexity. On the other hand, increased rates of alternative splicing could result through nonadaptive processes since more complex organisms tend to have smaller effective population sizes and are thus more prone to deleterious mutations resulting in more spurious splicing events (drift-barrier hypothesis). To evaluate the latter, Bénitière et al. analyzed transcriptome sequencing data across 53 metazoan species. They show that proxies for effective population size and alternative splicing rates are negatively correlated. Furthermore, the authors find that rare, nonfunctional (and likely erroneous) isoforms occur more frequently in more complex species. Additionally, they show evidence that the strength of selection on splice sites increases with increasing effective population size and that the abundance of rare splice variants decreases with increased gene expression. All of these findings are consistent with the drift-barrier hypothesis.

This study conducts a comprehensive set of separate analyses that all converge on the same overall result and the manuscript is well organized. Furthermore, this study is useful in that it provides a modified null hypothesis that can be used for future tests of adaptive explanations for variation in alternative splicing.

Strengths:

The major strength of this study lies in its complementary approach combining comparative and population genomics. Comparing evolutionary trends across phylogenetic diversity is a powerful way to test hypotheses about the origins of genome complexity. This approach alone reveals several convincing lines of evidence in support of the drift-barrier hypothesis. However, the authors also provide evidence from a population genetics perspective (using resequencing data for humans and fruit flies), making results even more convincing.

The authors are forward about the study's limitations and explain them in detail. They elaborate on possible confounding factors as well as the issues with data quality (e.g. proxies for Ne, inadequacies of short reads, heterogeneity in RNA-sequencing data).

Weaknesses:

The authors primarily consider insects and mammals in their study. This only represents a small fraction of metazoan diversity. Sampling from a greater diversity of metazoan lineages would make these results and their relevance to broader metazoans substantially more convincing. Although the authors are careful about their tone, it is challenging to reconcile these results with trends across greater metazoans when the underlying dataset exhibits ascertainment bias and represents samples from only a few phylogenetic groups. Relatedly, some trends (such as Figure 1B-C) seem to be driven primarily by non-insect species, raising the question of whether some results may be primarily explained by specific phylogenetic groups ( although the authors do correct for phylogeny in their statistics). How might results look if insects and mammals (or vertebrates) are considered independently?

Following the referee’s suggestion, we investigated the relationship between AS rate and proxies of Ne, separately for insects and vertebrates (Supplementary Fig. 11) . We observed that the relationship was consistent in vertebrates and insects: linear regressions show a positive correlation, significant (p<0.05) in all cases, except for body length in vertebrates. We added a sentence (line 166) to mention this point.

Note that for these analyses we have smaller sample sizes, so we have a weaker power to detect signal. We therefore prefer to present the combined analyses, using PGLS to account for phylogenetic inertia.

Throughout the manuscript, the authors refer to infrequently spliced ( mode <5%) introns as "minor introns" and frequently spliced (mode >95%) as "major introns". This is extremely confusing since "minor introns" typically represent introns spliced by the U12 spliceosome, whereas "major introns" are those spliced by the U2 spliceosome.

To avoid any confusion, we modified the terminology: we now refer to infrequently spliced introns as " minor-isoform introns" and frequently spliced as "major -isoform introns" (see line 135-137) . The entire manuscript (including the figures) has been modified accordingly.

Furthermore, it remains unclear whether the study only considers major introns or both major and minor introns. Minor introns typically have AT-AC splice sites whereas major introns usually have GT/GC-AG splice sites, although in rare cases the U2 can recognize AT-AC (see Wu and Krainer 1997 for example).

We modified the text (line 148-150) to clearly state that we studied all introns, both U2-type and U12-type.

The authors also note that some introns show noncanonical AT-AC splice sites while these are actually canonical splice sites for minor introns.

This is corrected (line 148).

Recommendations for the authors:

Reviewer #2 (Recommendations For The Authors):

Figures 1, 3, and 4: I suggest that authors add regression lines.

We added the regression lines with the “pgls” function from the R package “caper” (in Fig. 1, 3 and 4, and also in all other figures where we present correlations).

Figure 2: As previously mentioned, the terms "minor introns" and "major introns" are extremely confusing. I strongly suggest the authors use different naming conventions.

We changed the terminology:

minor introns -> minor-isoform introns

major introns -> major-isoform introns

Figure 5: Intron-exon boundaries and splice site annotations are shown at the bottom of B, C, and D but not A. I suggest removing the annotation beneath B for consistency and since A+C and B+D are aligned on the x-axis.

Corrected, it was a mistake.

Figure 7: The yellow dotted line is very challenging to see in A.

Corrected, the line has been widened.

Reviewer #1 (Public Review):

Summary:<br /> Functionally important alternative isoforms are gold nuggets found in a swamp of errors produced by the splicing machinery.

The architecture of eukaryotic genomes, when compared with prokaryotes, is characterised by a preponderance of introns. These elements, which are still present within transcripts, are rapidly removed during the splicing of messenger RNA (mRNA), thus not contributing to the final protein. The extreme rarity of introns in prokaryotes, and the elimination of these introns from mRNAs before translation into protein, raises questions about the function of introns in genomes. One explanation comes from functional biology: introns are thought to be involved in post-transcriptional regulation and in the production of translational variants. The latter function is possible when the positions of the edges of the spliced intron vary. While some light has been shed on specific examples of the functional role of alternative splicing, to what extent are they representative of all introns in metazoans?

In this study, the hypothesis of a functional role for alternative splicing, and therefore to a certain extent for introns, is evaluated against another explanation coming from evolutionary biology: isoforms are above all errors of imprecision by the molecular machinery at work during splicing. This hypothesis is based on a principle established by Motoo Mikura, which has become central to population genetics, explaining that the evolutionary trajectory of a mutation with a given effect is intimately linked to the effective population size (Ne) where this mutation emerges. Thus, the probability of fixation of a weakly deleterious mutation increases when Ne decreases, and the probability of fixation of a weakly advantageous mutation increases when Ne increases. The genomes of populations with low Ne are therefore expected to accumulate more weakly deleterious mutations and fewer weakly advantageous mutations than populations with high Ne. In this framework, if splicing errors have only small effects on the fitness of individuals, then natural selection cannot increase the precision of the splicing machinery, allowing tolerance for the production of alternative isoforms.

In the past, the debate opposed one-off observations of effectively functional isoforms on the one hand, to global genomic quantities describing patterns without the possibility of interpreting them in detail. The authors here propose an elegant quantitative approach in line with the expected continuous variation in the effectiveness of selection, both between species and within genomes. The result describing the inter-specific pattern on a large scale confirms what was already known (there is a negative relationship between effective size and average alternative splicing rate). The essential novelty of this study lies in 1) the quantification, for each intron studied, of the relative abundance of each isoform, and 2) the analysis of a relationship between this abundance and the evolutionary constraints acting on these isoforms.

What is striking is the light shed on the general very low abundance of alternative isoforms. Depending on the species, 60% to 96% of cases of alternatively spliced introns lead to an isoform whose abundance is less than 5% of the total variants for a given intron.

In addition to the fact that 60%-96% of the total isoforms are more than 20 times less abundant than their majority form, this large proportion of alternative isoforms exhibit coding-phase shift at rates similar to what would be expected by chance, i.e. for a third of them, which reinforces the idea that there is no particular constraint on these isoforms.

The remaining 4%-40% of isoforms see their coding-phase shift rate decrease as their relative abundance increases. This result represents a major step forward in our understanding of alternative splicing and makes it possible to establish a quantitative model directly linking the relative abundance of an isoform with a putative functional role concerning only those isoforms produced in abundance. Only the (rare) isoforms which are abundantly produced are thought to be involved in a biological function.

Within the same genome, the authors show that only highly expressed genes, i.e. those that tend to be more constrained on average, are also the genes with the lowest alternative splicing rates on average.

The comparison between species in this study reveals that the smaller the effective size of a species, the more its genome produces isoforms that are low in abundance and low in constraint. Conversely, species with a large effective size relatively reduce rare isoforms, and increase stress on abundant isoforms.

To sum up:<br /> • the higher the effective size of a species, the fewer introns are spliced.<br /> • highly expressed genes are spliced less.<br /> • when splicing occurs, it is mainly to produce low-abundance isoforms.<br /> • low-abundance isoforms are also less constrained.

Taken together, these results reinforce a quantitative view of the evolution of alternative splicing as being mainly the product of imprecision in the splicing machinery, generating a great deal of molecular noise. Then, out of all this noise, a few functional gold nuggets can sometimes emerge. From the point of view of the reviewer, the evolutionary dynamics of genomes are depressing. The small effective population sizes are responsible for the accumulation of multiple slightly deleterious introns. Admittedly, metazoan genomes try to get rid of these introns during RNA maturation, but this mechanism is itself rendered imprecise by population sizes.

Strengths:<br /> • The authors simultaneously study the effects of effective population size, isoform abundance, and gene expression levels on the evolutionary constraints acting on isoforms. Within this framework, they clearly show that an isoform becomes functionally important only under certain rare conditions.<br /> • The authors rule out an effect putatively linked to variations in expression between different organs which could have biased comparisons between different species.

Weaknesses:<br /> • While the longevity of organisms as a measure of effective size seems to work overall, it may not be relevant for discriminating within a clade. For example, within Hymenoptera, we might expect them to have the same overall longevity, but that effective size would be influenced more by the degree of sociality: solitary bees/ants/wasps versus eusocial. I am therefore certain that the relationship shown in Figure 4D is currently not significant because the measure of effective size is not relevant for Hymenoptera. The article would have been even more convincing by contrasting the rates of alternative splicing between solitary versus social hymenopterans.<br /> • When functionalist biologists emphasise the role of the complexity of living things, I'm not sure they're thinking of the comparison between "drosophila" and "homo sapiens", but rather of a broader evolutionary scale. Which gives the impression of an exaggeration of the debate in the introduction.

eLife assessment

This fundamental study evaluates the evolutionary significance of variations in the accuracy of the intron-splicing process across vertebrates and insects. Using a powerful combination of comparative and population genomics approaches, the authors present convincing evidence that higher rates of alternative splicing tend to be observed in species with lower effective population size, a key prediction of the drift-barrier hypothesis. The analysis is carefully conducted and has broad implications beyond the studied species. As such, it will strongly appeal to anyone interested in the evolution of genome architecture and the optimisation of genetic systems.

Reviewer #2 (Public Review):

Summary:<br /> Two hypotheses could explain the observation that genes of more complex organisms tend to undergo more alternative splicing. On one hand, alternative splicing could be adaptive since it provides the functional diversity required for complexity. On the other hand, increased rates of alternative splicing could result through nonadaptive processes since more complex organisms tend to have smaller effective population sizes and are thus more prone to deleterious mutations resulting in more spurious splicing events (drift-barrier hypothesis). To evaluate the latter, B́enitiere et al. analyzed transcriptome sequencing data across 53 metazoan species. They show that proxies for effective population size and alternative splicing rates are negatively correlated. Furthermore, the authors find that rare, nonfunctional (and likely erroneous) isoforms occur more frequently in more complex species. Additionally, they show evidence that the strength of selection on splice sites increases with increasing effective population size and that the abundance of rare splice variants decreases with increased gene expression. All of these findings are consistent with the drift-barrier hypothesis.

This study conducts a comprehensive set of separate analyses that all converge on the same overall result and the manuscript is well organized. Furthermore, this study is useful in that it provides a modified null hypothesis that can be used for future tests of adaptive explanations for variation in alternative splicing.

Strengths:<br /> The major strength of this study lies in its complementary approach combining comparative and population genomics. Comparing evolutionary trends across phylogenetic diversity is a powerful way to test hypotheses about the origins of genome complexity. This approach alone reveals several convincing lines of evidence in support of the drift-barrier hypothesis. However, the authors also provide evidence from a population genetics perspective (using resequencing data for humans and fruit flies), making results even more convincing.

The authors are forward about the study's limitations and explain them in detail. They elaborate on possible confounding factors as well as the issues with data quality (e.g. proxies for Ne, inadequacies of short reads, heterogeneity in RNA-sequencing data).

Weaknesses:<br /> The authors primarily consider insects and mammals in their study. This only represents a small fraction of metazoan diversity. Sampling from a greater diversity of metazoan lineages would make these results and their relevance to broader metazoans substantially more convincing. Although the authors are careful about their tone, it is challenging to reconcile these results with trends across greater metazoans when the underlying dataset exhibits ascertainment bias and represents samples from only a few phylogenetic groups. Relatedly, some trends (such as Figure 1B-C) seem to be driven primarily by non-insect species, raising the question of whether some results may be primarily explained by specific phylogenetic groups (although the authors do correct for phylogeny in their statistics). How might results look if insects and mammals (or vertebrates) are considered independently?

Throughout the manuscript, the authors refer to infrequently spliced (mode <5%) introns as "minor introns" and frequently spliced (mode >95%) as "major introns". This is extremely confusing since "minor introns" typically represent introns spliced by the U12 spliceosome, whereas "major introns" are those spliced by the U2 spliceosome. Furthermore, it remains unclear whether the study only considers major introns or both major and minor introns. Minor introns typically have AT-AC splice sites whereas major introns usually have GT/GC-AG splice sites, although in rare cases the U2 can recognize AT-AC (see Wu and Krainer 1997 for example). The authors also note that some introns show noncanonical AT-AC splice sites while these are actually canonical splice sites for minor introns.

eLife assessment

This study presents an important finding on the splicing regulatory function of RBM7 and its functional impact in breast cancer metastasis. The evidence supporting the claims of the authors is solid, although the inclusion of more delineation of how RBM7 regulates NF-kB and coordinates splicing would have strengthened the study. The work will be of interest to scientists working on breast cancer.

Reviewer #1 (Public Review):

Summary:<br /> Fang Huang et al found that RBM7 deficiency promotes metastasis by coordinating MFGE8 splicing switch and NF-kB pathway in breast cancer by utilizing clinical samples as well as cell and tail vein injection models.

Strengths:<br /> This study uncovers a previously uncharacterized role of MFGE8 splicing alteration in breast cancer metastasis, and provides evidence supporting RBM7 function in splicing regulation. These findings facilitate the mechanistic understanding of how splicing dysregulation contributes to metastasis in cancer, a direction that has increasingly drawn attention recently, and provides a potentially new prognostic and therapeutic target for breast cancer.

Weaknesses:<br /> This study can be strengthened in several aspects by additional experiments or at least by further discussions. First, how RBM7 regulates NF-kB, and how it coordinates splicing and canonical function as a component of NEXT complex should be clarified. Second, although the roles of MFGE8 splicing isoforms in cell migration and invasion have been demonstrated in transwell and wound healing assays, it would be more convincing to explore their roles in vivo such as the tail vein injection model. Third, the clinical significance would be considerably improved, if the therapeutic value of targeting MFGE8 splicing could be demonstrated.

Reviewer #2 (Public Review):

Summary:<br /> In this manuscript, the authors reported the biological role of RBM7 deficiency in promoting metastasis of breast cancer. They further used a combination of genomic and molecular biology approaches to discover a novel role of RBM7 in controlling alternative splicing of many genes in cell migration and invasion, which is responsible for the RBM7 activity in suppressing metastasis. They conducted an in-depth mechanistic study on one of the main targets of RBM7, MFGE8, and established a regulatory pathway between RBM7, MFGE8-L/MFGE8-S splicing switch, and NF-κB signaling cascade. This link between RBM7 and cancer pathology was further supported by analysis of clinical data.

Strengths:<br /> Overall, this is a very comprehensive study with lots of data, and the evidence is consistent and convincing. Their main conclusion was supported by many lines of evidence, and the results in animal models are pretty impressive.

Weaknesses:<br /> However, there are some controls missing, and the data presentation needs to be improved. The writing of the manuscript needs some grammatical improvements because some of the wording might be confusing.

Specific comments:<br /> (1) Figure 2. The figure legend is missing for Figure 2C, which caused many mislabels in the rest of the panels. The labels in the main text are correct, but the authors should check the figure legend more carefully. Also in Figure 2C, it is not clear why the authors choose to examine the expression of this subset of genes. The authors only refer to them as "a series of metastasis-related genes", but it is not clear what criteria they used to select these genes for expression analysis.

(2) Line 218-220. The comparison of PSI changes in different types of AS events is misleading. Because these AS events are regulated in different mechanisms, they cannot draw the conclusion that "the presence of RBM7 may promote the usage of alternative splice sites". For example, the regulators of SE and IR may even be opposite, and thus they should discuss this in different contexts. If they want to conclude this point, they should specifically discuss the SE and A5SS rather than draw an overall conclusion.

(3) In the section starting at line 243, they first referred to the gene and isoforms as "EFG-E8" or "EFG-E8-L", but later used "EFGE8" and "EFGE8-L". Please be consistent here. In addition, it will be more informative if the authors add a diagram of the difference between two EFGE8 isoforms in terms of protein structure or domain configuration.

(4) Figure 7B and 7C. The figures need quantification of the inclusion of MFGE exon7 (PSI value) in addition to the RT-PCR gel. The difference seems to be small for some patients.

Minor comments:<br /> The writing in many places is a little odd or somewhat confusing, I am listing some examples, but the authors need to polish the whole manuscript more to improve the writing.

(1) Line 169-170, "...followed by profiling high-throughput transcriptome by RNA sequencing", should be "followed by high-throughput transcriptome profiling with RNA sequencing".

(2) Line 170, "displayed a wide of RBM7-regulated genes were enriched...", they should add a "that" after the "displayed" as the sentence is very long.

(3) Line 213, "PSI (percent splicing inclusion)" is not correct, PSI stands for "percent spliced in".

(4) Line 216-217, the sentence is long and fragmented, they should break it into two sentences.

(5) Line 224, the "tethering" should be changed to "recognizing". There is a subtle difference in the mechanistic implication between these two words.

(6) Line 250, should be changed to "..in the ratio of two MFGE8 isoforms".

eLife assessment

This useful study utilizes proteomics analysis across a large panel of 51 cancer cell lines to elucidate mechanisms underlying the sensitivity of cancer cells to high-dose vitamin C (Ascorbate). While the associations between specific molecular pathways and sensitivity to ascorbate are interesting, a major limitation is that the study is largely descriptive and incomplete, lacking evidence on the molecular underpinnings of cancer cells' sensitivity to high-dose vitamin C.

Reviewer #1 (Public Review):

Summary:<br /> The study by Valles-Marti et al. was aimed at elucidating mechanisms of high-dose vitamin C (Ascorbate) sensitivity using proteomics of a large panel of cancer cell lines. The study is primarily based on correlating protein expression to vitamin C sensitivity based on IC50 from cell viability studies. As expected, cancer type-specific proteome patterns emerge and the authors conclude that some pan-cancer pathways, such as proliferation correlate with high sensitivity to VitC. In a subset of PDAC cells proteomics and phospho proteomics were also carried out following vitamin C treatment, albeit those studies did not identify significant changes in response to treatment.

Strengths:<br /> The premise for the work is of interest as high dose vitamin C is in clinical trials and thus studies investigating mechanisms of sensitivity and potential resistance mechanisms to this therapy are of interest to the field. The authors have collected large proteomic datasets on some of the most common cancer cells used and these data may be a useful resource for others when made publicly available. Although this is not necessarily novel, since proteomics data sets for some of the included cell lines are already available.

Weaknesses:<br /> The title suggests that the proteomics data presented "underscores high-dose vitamin C as a potent anti-cancer agent" However, while the proteomic data are extensive, it is my assessment that without further validation there are no clear pathways identified by the presented proteomics data that conclusively determine vitamin C sensitivity.

A major question arising from this work is how specific the proteomics data reflect sensitivity to vitamin C over general sensitivity to other cytotoxic agents. It would be of interest to compare the correlation of proteomic data and ascorbate sensitivity to the sensitivity of cell lines to other cytotoxic agents. (e.g. comparison to NCI-60 growth inhibition data). In other words, do the proteomic data that correlate with ascorbate sensitivity simply reflect susceptibility to other cytotoxic agents? The comments that vitamin C toxicity is not dependent on underlying histological or genetic subtypes of cancers ("one size fits all") suggest this.

The genetic backgrounds of tumor cells have not been taken into consideration in the analysis and how this may influence VitC susceptibility. An example that comes to mind is KEAP1/Nrf2 aberrations in lung cancer.

The study would be significantly strengthened if some of the proteins identified were further validated in eliciting low or high sensitivity to Vitamin C. Of particular interest are proteins that have functions related to known mechanisms of action of Vitamin C toxicity, such as iron homeostasis. Some of the metabolic-related protein changes are also of interest. For example, HCCS expression is mentioned several times as being associated with lower sensitivity to ascorbate. Providing experimental evidence that this protein is of significance to Vitamin C sensitivity and if this is due to its effects on iron and subsequent generation of ROS in response to VitC would be of significance.

Similarly, an interesting aspect of the findings is the authors' conclusion that proliferation is associated with Vitamin C sensitivity. The authors propose in their discussion that Vitamin C may be an attractive alternative to treat heavily pretreated and chemoresistant cancers. Thus it would be important to know which of the highly proliferative cell lines tested have a chemoresistance phenotype and are also more susceptible to Vitamin C toxicity. Perhaps partitioning the cells further into chemoresistant and sensitive cell lines to standard chemotherapy and then assessing which protein signatures are associated with Vitamin C sensitivity will allow for better elucidation of sensitivity mechanisms that are more relevant to using Vitamin C as an alternate therapy for chemoresistant tumors.

Following on from this, there is an interesting mechanistic question as to why more proliferative cells are more sensitive to vitamin C, and whether this is related to changes in metabolism and underlying changes in their steady-state levels of ROS. Further investigating this mechanistically based on the identified proteomic signatures could make the findings more significant.

Vitamin C can also generate H2O2 extracellularly in the presence of iron. Thus, Vitamin C toxicity could be affected by different abilities of the tumor cells to scavenge extracellular H2O2, such as different expression levels of extracellular antioxidant enzymes. Judging from the methods section, it does not appear that proteomic data include secreted proteins. Can the authors comment on how this may be a potential caveat?

In light of this, the strong effects of exogenous catalase addition on cell viability suggest that H2O2 may be produced by ascorbate in the media.

Similarly, can the authors comment on the cell culture conditions used to compare IC50s between cell lines, specifically if different media and FBS batches were used, as these have the potential to vary in metal/iron concentrations that might influence the pro-oxidant generation by high dose ascorbate in media. Specifically, have the authors looked into the iron content and how these different conditions may be contributing to intracellular H2O2 and extracellular H2O2 (AmplexRed) production in response to Vitamin C.

Other comments relate to methods:

How was ascorbate prepared? There is no mention of degassing of H2O and ensuring that H2O does not have mental impurities, which can lead to auto-oxidation.

The OxiSelect probe is based on DCFDA, which is an oxidant-sensitive probe that has been described to be fraught with artifacts. Thus it is advised to mention the caveats associated with the use of this probe (as outlined in PMCID: PMC3911769) and consider backing up these experiments with additional Oxidant probes.

Reviewer #2 (Public Review):

Summary:<br /> The authors generated proteome profiles of 51 cancer cell lines treated with pharmacologic ascorbate. The idea was to identify players responsible for the sensitivity or relative resistance to ascorbate to delineate mechanisms of action of this potentially transformative new treatment.

Strengths:<br /> The proteomic profiles themselves. The identification of MAPK and mTOR as overrepresented proteomic elements and close correlations between proliferation, cell cycle mediators, and sensitivity to ascorbate indicate that rapidly proliferating cancer may be more sensitive to ascorbate. Also, the finding that sensitivity to ascorbate is correlated to different pathways in different types of cancer is interesting. For instance, in some pancreatic and lung cancers sensitivity seemed to be related to iron handling while in breast DNA damage/repair seemed to be most involved.

Weaknesses:<br /> The study is quite descriptive. Although the proteomes indicate what pathways are more or less represented after ascorbate challenge there is little mechanistic information about their relevance to the sensitivity to ascorbate. Since activity is not assessed, proteins may be present in higher or lower abundance but not necessarily at the peak of their activity. Also, many statements are made as "known facts" but no references are provided.

Reviewer #3 (Public Review):

Summary:<br /> In the manuscript titled "Large pan-cancer cell screen coupled to (phospho-)proteomics underscores high-dose vitamin C (VitC) as a potent anti-cancer agent," the authors use a combination of proteomics and cell viability assays to understand the effect of Vitamin C on different solid tumor models in 51 different cancer cell lines. They found that many cancer cell lines are sensitive to high-dose Vitamin C, with IC50 values in the micromolar to millimolar range. Given that Vitamin C, when administered intravenously, can reach 20mM, this suggests that Vitamin C could provide some benefits to patients. The authors also generate and analyze bulk proteomic data for all 51 cell lines. They perform statistical analysis of these data to identify proteins that are up or downregulated in sensitive vs resistant cell lines in the same tumor and commonly across tumors. They then focus on PDAC cell lines and measure bulk and phosphoproteomics of PDAC cell lines 2, 4 and 24 hours after Vitamin C treatment.

Strengths: The strengths of the study are the rather large datasets accumulated on bulk proteomics of 51 different cancer cell lines. The IC50 values of these cell lines in response to Vitamin C is also useful.

Weaknesses:<br /> Though identifying targets to sensitize cancer cells to Vitamin C treatment is interesting, I felt the manuscript delved too much into listing off genes they found, with speculation on why the particular protein would be enriched in sensitive or resistant cell lines without testing any key claims experimentally.

Major Issues

(1) The overall premise of the study is that proteins that are enriched in Vitamin C-sensitive cell lines point to mechanisms of sensitivity and those enriched in Vitamin C resistant lines underlie mechanisms of resistance. Yet this is never directly tested. To show that the authors would need to knockdown/knockout a gene enriched in resistant lines and show this sensitizes cells to Vitamin C treatment or overexpress a protein associated with resistance and show that this leads to resistance in an otherwise sensitive cell line.

(2) One of the key strengths of this study is the large datasets generated, namely the proteomics data for 51 different cell lines. Yet the data is not included as a supplement or uploaded to a public repository.

eLife assessment

This useful study proposes a role of lysosomal Ca2+ release in inflammasome signaling and metabolic inflammation. While the proposed model would be of considerable interest to the field of immunology if validated, the experimental approaches to study calcium dynamics are problematic, with one of several concerns being the transfection efficiency. The major claims of the paper are thus only incompletely supported.

Reviewer #1 (Public Review):

This manuscript proposes a complex incoherent model involving Ca2+ signaling in inflammasome activation. The experimental approaches used to study the calcium dynamics are highly problematic and the results shown are of very poor quality.

Major concerns:

(1) The analysis of lysosomal Ca2+release is being carried out after many hours of treatment. Such evidence is not meaningful to claim that PA activates Ca2+ efflux from lysosome and even if this phenomenon was robust, it is not doubtful that such kinetics are meaningful for the regulation of inflammasome activation. Furthermore, the evidence for lysosomal Ca2+ release is indirect and relies on a convoluted process that doesn't make any conceptual sense to me. In addition to these major shortcomings, the indirect evidence of perilysosomal Ca2+ elevation is also of very poor quality and from the standpoint of my expertise in calcium signaling, the data are incredulous. The use of GCaMP3-ML1, *transiently transfected* into BMDMs is highly problematic. The efficiency of transfection in BMDMs is always extremely low and overexpression of the sensor in a few rare cells can lead to erroneous observations. The overexpression also results in gross mislocalization of such membrane-bound sensors. The accumulation of GCaMP3-ML1 in the ER of these cells would prevent any credible measurements of perilysosomal Ca2+ signals. A meaningful investigation of this process in primary macrophages requires the generation of a mouse line wherein the sensor is expressed at low levels in myeloid cells, and shown to be localized almost exclusively in the lysosomal membrane. The mechanistic framework built around these major conceptual and technical flaws is not especially meaningful and since these are foundational results, I cannot take the main claims of this study seriously.

A few transfected cells may overexpress the protein through a strong promoter but this is not ideal. For reliable Ca2+ measurements, one needs low expression of the sensor in a substantially high percentage of cells. This can only be demonstrated by showing the time lapse of Ca2+ responses in the macrophages. More generally, I have nearly 2 decades of experience working with primary BMDMs and it is widely known that primary BMDMs are incredibly difficult to transfect - it is the nature of these cells. The claim that they get high efficiency of transfection is frankly too incredulous to take seriously.

(2) The cytosolic Ca2+ imaging shown in figure 1C doesn't make any sense. It looks like a snapshot of basal Ca2+ many hours after PA treatment - calcium elevations are highly dynamic. Snapshot measurements are not helpful and analyses of Calcium dynamics requires a recording over a certain timespan. Unfortunately, this technical approach has been used throughout the manuscript. Also, BAPTA-AM abrogates IL-1b secretion because IL-1b transcription is Ca2+ dependent - the result shown in figure 1D does not shed light on anything to do with inflammasome activation and it is misleading to suggest that.

(3) Trpm2-/- macrophages are known to be hyporesponsive to inflammatory stimuli - the reduced secretion of IL-1b by these macrophages is not novel. From a mechanistic perspective, this study does not add much to that observation and the proposed role of TRPM2 as a lysosomal Ca2+ release channel is not substantiated by good quality Ca2+ imaging data (see point 3 above). Furthermore, the study assumes that TRPM2 is a lysosomal ion channel. One paper reported TRPM2 in the lysosomes but this is a controversial claim, with no replication or further development in the last 14 years. This core assumption can be highly misleading to readers unfamiliar with TRPM2 biology and it is necessary to present credible evidence that TRPM2 is functional in the lysosomal membrane of macrophages. Ideally, this line of investigation should rest on robust demonstration of TRPM2 currents in patch-clamp electrophysiology of lysosomes. If this is not technically feasible for the authors, they should at least investigate TRPM2 localization on lysosomal membranes of macrophages.

In the revised manuscript, authors showed TRPM2 localization but these results are problematic. The authors provide no information on what TRPM2 antibody they used for this study and whether it has been validated by use of knockouts. The staining shows very high amounts of TRPM2 all across the cell - even more than LAMP2. In reality, TRPM2 expression in macrophages is very low. Are the authors overexpressing TRPM2? These data only add to my concerns about this manuscript.

(4) Apigenin and Quercetin are highly non-specific and their effects cannot be attributed to CD38 inhibition alone. Such conclusions need strong loss of function studies using genetic knockouts of CD38 - or at least siRNA knockdown. Importantly, if indeed TRPM2 is being activated downstream of CD38, this should be easily evident in whole cell patch clamp electrophysiology. TRPM2 currents can be resolved using this technique and authors have Trpm2-/- cells for proper controls. Authors attempted these experiments but the results are of very poor quality. If the TRPM2 current is being activated through ADPR generated by CD38 (in response to PA stimulation), then it is very odd that authors need to include 200 uM cADPR to see TRPM2 current (Fig. 3A). Oddly, even these data cast great doubt on the technical quality of the electrophysiology experiments. Even with such high concentrations of cADPr, the TRPM2 current is tiny and Trpm2-/- controls are missing. The current-voltage relationship is not shown, and I feel that the results are merely reporting leak currents seen in measurements with substandard seals. Also 20 uM ACA is not a selective inhibitor of TRPM2 - relying on ACA as the conclusive diagnostic is problematic.

(5) TRPM2 is expressed in many different cell lines. The broad metabolic differences observed by the authors in the Trpm2-/- mice cannot be attributed to macrophage-mediated inflammation. Such a conclusion requires the study of mice wherein Trpm2 is deleted selectively in macrophages or at least in the cells of the myeloid lineage.

(6) The ER-Lysosome Ca2+ refilling experiments rely on transient transfection of organelle-targeted sensors into BMDMs. See point #1 to understand why I find this approach to be highly problematic. Furthermore the data procured are also not convincing and lack critical controls (localization of sensors has not been demonstrated and their response to acute mobilization of Ca2+ has not been shown inspire any confidence in these results).

(7) Authors claim that SCOE is coupled to K+ efflux. But there is no credible evidence that SOCE is activated in PA stimulated macrophages. The data shown in Fig 4 supp 1 do not investigate SOCE in a reliable manner - the conclusion is again based on snapshot measurements and crude non-selective inhibitors. The correct way to evaluate SOCE is to record cytosolic Ca2+ elevations over a period of time in absence and presence of extracellular Ca2+. However, even such recordings can be unreliable since the phenomenon is being investigated hours after PA stimulation. So, the only definitive way to demonstrate that Orai channels are indeed active during this process is through patch clamp electrophysiology of PA stimulated cells.

Authors failed to respond to these concerns in a credible manner and simply tried to obfuscate the matters with extraneous arguments and wild claims. The revised manuscript was not a significant improvement. I have major concerns with this manuscript and let it be on record that this is very poor-quality science.

Reviewer #2 (Public Review):

In this manuscript by Kang et. al., the authors investigated the mechanisms of K+-efflux-coupled SOCE in NLRP3 inflammasome activation by LP（LPS+PA, and identified an essential role of TRPM2-mediated lysosomal Ca2+ release and subsequent IP3Rs-mediated ER Ca2+ release and store depletion in the process. K+ efflux is shown to be mediated by a Ca2+-activated K+ channel (KCa3.1). LP-induced cytosolic Ca2+ elevation also induced a delayed activation of ASK1 and JNK, leading to ASC oligomerization and NLRP3 inflammasome activation. Overall, this is an interesting and comprehensive study that has identified several novel molecular players in metabolic inflammation. The manuscript can benefit if the following concerns could be addressed.

(1) The expression of TRPM2 in the lysosomes of macrophages needs to more definitively established. For instance, the cADPR-induced TRPM2 currents should be abolished in the TRPM2 KO macrophages. Can you show the lysosomal expression of TRPM2, either with an antibody if available or with a fluorescently-tagged TRPM2 overexpression construct?

In the revised manuscript, the authors did not perform the KO control experiment to support that cADPR-induced currents were indeed mediated by TRPM2. Additonally, the co-localization analyses failed to convincingly establish the lysosomal perimeter membrane residence of TRPM2.

(2) Can you use your TRPM2 inhibitor ACA to pharmacologically phenocopy some results, e.g., about [Ca2+]ER, [Ca2+]LY, and [Ca2+]i from the TRPM2 knockout?

In the revised manuscript, most suggested experiments were not performed. In the only experiment that was conducted, Figure 3-figure supplement 1A, the effect of ACA was marginal.

(3) In Fig. S4A, bathing the cells in zero Ca2+ for three hours might not be ideal. Can you use a SOCE inhibitor, e.g, YM-58483, to make the point?

The specific suggested experiment was not performed.

(4) In Fig. 1A, you need a positive control, e.g., ionomycin, to show that the GPN response was selectively reduced upon LP treatment.

Results in a previous study cannot be used to substitute the missing control experiments in the current study.

eLife assessment

This important study contributes to the current knowledge in the field of acute and chronic infarction. It is a significant study because the results provide convincing evidence for the need to incorporate additional risk factors for assessing patients after myocardial infarction.

Reviewer #1 (Public Review):

Summary:<br /> In this study by Zhou, Wang, and colleagues, the authors utilize biventricular electromechanical simulations to illustrate how different degrees of ionic remodeling can contribute to different ECG morphologies that are observed in either acute or chronic post-myocardial infarction (MI) patients. Interestingly, the simulations show that abnormal ECG phenotypes - associated with a higher risk of sudden cardiac death - are predicted to have almost no correspondence with left ventricular ejection fraction, which is conventionally used as a risk factor for arrhythmia.

Strengths:<br /> The numerical simulations are state-of-the-art, integrating detailed electrophysiology and mechanical contraction predictions, which are often modeled separately. The simulation provides mechanistic interpretation, down to the level of single-cell ionic current remodeling, for different types of ECG morphologies observed in post-MI patients. Collectively, these results demonstrate compelling and significant evidence for the need to incorporate additional risk factors for assessing post-MI patients.

Weaknesses:<br /> The study is rigorous and well-performed. However, some aspects of the methodology could be clearer, and the authors could also address some aspects of the robustness of the results. Specifically, does variability in ionic currents inherent in different patients, or the location/size of the infarct and surrounding remodeled tissue impact the presentation of these ECG morphologies?

Reviewer #2 (Public Review):

Summary:<br /> The authors constructed multi-scale modeling and simulation methods to investigate the electrical and mechanical properties of acute and chronic myocardial infarction (MI). They simulated three acute MI conditions and two chronic MI conditions. They showed that these conditions gave rise to distinct ECG characteristics that have been seen in clinical settings. They showed that the post-MI remodeling reduced ejection fraction up to 10% due to weaker calcium current or SR calcium uptake, but the reduction of ejection fraction is not sensitive to remodeling of the repolarization heterogeneities.

Strengths:<br /> The major strength of this study is the construction of computer modeling that simulates both electrical behavior and mechanical behavior for post-MI remodeling. The links of different heterogeneities due to MI remodeling to different ECG characteristics provide some useful information for understanding complex clinical problems.

Weaknesses:<br /> The rationale (e.g., physiological or medical bases) for choosing the 3 acute MI and 2 chronic MI settings is not clear. Although the authors presented a huge number of simulation data, in particular in the supplemental materials, it is not clearly stated what novel findings or mechanistic insights this study gained beyond the current understanding of the problem.

eLife assessment

The present study provides valuable evidence on the neurochemical mechanisms underlying working memory in obesity. The authors' approach considering specific working memory operations (maintenance, updating) and putative dopaminergic genes is solid, though the inclusion of a more direct measure of dopamine signaling and further theoretical analysis and interpretation of findings would have strengthened the work.

Reviewer #1 (Public Review):

Herzog and colleagues investigated the interactions between working memory (WM) task condition (updating, maintenance) and BMI (body-mass-index), while considering selected dopaminergic genes (COMT, Taq1A, C957T, DARPP-32). Emerging evidence suggests that there might be a specific negative association with BMI in the updating but not maintenance condition, with potential bearings to reversal reward learning in obesity. The inclusion of multiple dopaminergic genes is a strength in the present study, considering the complexity of the interactions between tonic and phasic dopamine across the brain that may distinctly associate with the component processes of WM. Here, the finding was that BMI was negatively associated with WM performance regardless of the condition (updating, maintenance), but in models including moderation by either Taq1A or DARPP-32 (but not by COMT and C957T) an interaction by task condition was observed. Furthermore, a two-way interaction effect between BMI and genotype was observed exclusively in the updating condition. These findings are in line with the accounts by which striatal dopamine as reflected by Taq1A and DARPP-32 play an important role in working memory updating, while cortical dopamine as reflected by COMT is mainly associated with maintenance. The authors conclude that the genetic moderation reflects a compound negative effect of having high BMI and a risk allele in Taq1A or DARPP-32 to working memory updating specifically.

These data increment the accumulating evidence that the dopamine system may play an important role in obesity, but some of the claims in the present work are not entirely supported by the data and analysis presented. In particular, theoretical analysis of the extant evidence and formulation of the hypothesis remains elusive in terms of the potential mechanisms of updating/maintaining balance in obesity, and as such the interpretation of the present findings in the light of dopaminergic moderation warrants some caution. The result that Taq1A and DARPP-32 moderated the interaction between WM condition and BMI requires intricate post hoc analysis to understand the bearings to update. The authors found that Taq1A or DARPP-32 genotype moderated the negative association between BMI and WM exclusively in the update condition (significant two-way interaction effect), suggesting that the BMI-WM associations in other conditions were similar across genotypes. Importantly, visual inspection of the relationship between WM and BMI (Fig 4 & 5) suggests more prevalent positive effects of the putatively advantageous Taq1A-A1 and DARPP-32-AA genotypes to the overall negative relationship between WM and BMI in updating, but not in the other conditions. Given that an overall negative relationship was statistically supported across all conditions (model 1), a plausible interpretation would be that the updating condition stands out in terms of a positive moderation by putative advantageous genotypes, rather than compound negative consequences of BMI and genotype in updating. Critically, this interpretation stands in stark contrast with the interpretation put forth by the authors suggesting a specifically negative association between BMI and WM updating.

In conclusion, in its current form the title of the present work is ambivalent in terms of 1) the use of the term "impaired" in the context of cognitively normal individuals, 2) a BMI group difference specifically in the updating condition, and 3) the dopaminergic mechanisms based on observational data.

Reviewer #2 (Public Review):

Summary:<br /> The authors investigated if obesity is associated with elevated working memory deficits. Prior theorizing would suggest that individuals with a higher BMI would be worse at working memory updating, potentially due to impaired dopaminergic signaling in the striatum. However, the authors find that higher BMI was associated with worse working memory performance, irrespective of having to ignore or update new information. To further explore the putative dopaminergic mechanisms, participants are stratified according to genetic polymorphisms in COMT, Taq1A, DARPP, and C957T and the ratio of the amino acids phenylalanine and tyrosine, all implicated in dopamine-signaling. They find that especially for working memory updating, carriers of a risk allele of Taq1A and DARPP, but not of COMT and C957T, performed worse with increasing BMI. The detrimental effects of these polymorphisms on updating only surfaced for individuals with high but not low BMI.

Although the authors allude to potential imbalances in the striatal go/no-go dopamine pathways to explain these findings, the dopaminergic mechanisms of the effects remain speculative.

Strengths:<br /> Differentiating between working memory maintenance (ignoring) and updating is a powerful way to get a deeper insight into specific working memory deficits in individuals with obesity. This way of assessing working memory could potentially be applied to various populations at risk for cognitive or working memory deficits.

By pooling data from three studies, the authors reached a relatively large sample of 320 participants, which enables the assessment of more subtle effects on working memory, including the differentiation between updating and ignoring.

Working memory gating has long implicated striatal dopamine signaling. This paper shows that specific combinations of risk factors, a high BMI and carrying a risk allele, can contribute to very selective working memory impairments. More insight into how these risk factors interact can ultimately lead to more tailor-made treatments.

Weaknesses:<br /> The majority of participants seem to fall within the normal BMI range, whereas the interaction between BMI and genetic variations or amino acid ratio particularly surfaces at higher BMI. As genetic variations are usually associated with small effect sizes, the effective sample size, although large for a behavioral analysis only, might have been too small to detect meaningful effects of risk alleles of COMT and C957T.

The relationships between genetic variations, BMI, and specific disturbances in dopamine signaling are complex, as compensating mechanisms might be at play to mitigate any detrimental effects. The results would therefore benefit from more direct measures or manipulations of dopaminergic processes.

The introduction could benefit from a more elaborate description of the predicted effects: into which direction (better or worse updating) would the authors predict each effect to go and why? This is clearly explained for COMT, but not for e.g. DARPP-32.

Author Response

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

We would like to extend our sincere thanks to the editors and reviewers for their time and effort in reviewing our manuscript and offering insightful feedback. We have now completed the revisions, and the following is a summary of the key changes made.

(1) Analysis of myrf-1 and myrf-2 Mutations

A major concern raised was the characterization of the myrf-1(ju1121 G274R) mutation as a loss-of-function and myrf-1(syb1313, 1-700, gfp) as a gain-of-function mutation used in our study. These analyses have been previously detailed in our published papers (Meng, Dev Cell. 2017; Xia, Elife. 2021). In the revised manuscript, we have included a thorough explanation of this information in the introduction and added diagrams (Figure 1D, E) to illustrate the mutants used in this study. A more detailed description is also available in the provisional letter I sent following the receipt of the decision letter.

We have incorporated new analyses of the endogenous lin-4 expression reporter in the myrf-1(ybq6, indel null), myrf-2(ybq42, indel null), and myrf-1(ybq6); myrf-2(ybq42) double mutants (Figure 2C). The results demonstrate complete inactivation of lin-4 expression in the double mutants. The data suggest that myrf-1 predominantly drives lin4 expression, while myrf-2 plays a minor role. This aligns with their roles in synaptic rewiring and is consistent with the observed lack of lin-4 expression in myrf-1(ju1121).

Furthermore, we have included analyses using pan-1(gk142) deletion mutants. PAN-1 is critical for MYRF trafficking to the cell membrane (Xia, Elife, 2021). In the absence of PAN-1, MYRF is trapped in the ER and subsequently degraded. The pan-1 mutants exhibit impaired synaptic rewiring, similar to myrf-1; myrf-2 double mutants, but somehow show larval arrest significantly later than myrf-1 mutants. Notably, lin-4 expression is not activated in pan-1 mutants. (references on the larval arrest phenotypes in pan-1 mutants: Gao G, Dev Biol. 2012 PMID: 22342905; Gissendanner CR, BMC Dev Biol. 2013. PMID: 23682709.)

Overall, these findings provide substantial evidence that MYRF is crucial for activating lin-4 during larval development.

(2) Regarding the Use of maIs134 as a lin-4 Expression Reporter

In response to the concerns raised about the use of the 2.4 kb Plin-4-gfp reporter (maIs134) as an indicator of lin-4 transcription, as detailed in the provisional letter, there is no evidence suggesting that maIs134 is an unsuitable reporter for lin-4 transcription. Recently, Kinney et al. (Dev Cell 2023, PMID: 37643611) showed that the pulse control element (PCE), located approximately 2.8 kb upstream, is not essential for lin-4 expression. Their findings also imply that a 2.4 kb region, encompassing what is referred to as the "short" regulatory region in their paper, contains essential elements required for driving the expression of lin-4. Nevertheless, I acknowledge that using an endogenously tagged reporter would be more ideal. It's important to note that we employed the endogenous expression reporter in our analyses of the myrf-1; myrf-2 double mutants, pan-1 mutants, and in the gain-of-function analysis of myrf-1. The outcomes from these studies corroborate our principal conclusions, reinforcing the validity of using maIs134 in our research context.

(3) Direct Binding of MYRF to the lin-4 Promoter

The technical challenges of MYRF ChIP (Chromatin Immunoprecipitation) have proven to be significant. Consequently, we have decided not to postpone the manuscript revision while awaiting additional results. We have included a section titled 'Limitations of the Study' to acknowledge our current lack of direct evidence for MYRF-1 binding to the endogenous lin-4 promoter. If it aligns better with eLife's format policy, we are open to relocating this paragraph to the discussion section.

(4) Specific Issues

We have provided responses to each specific question following the respective inquiries (see below).

Reviewer #1 (Public Review):

In this work, the authors set out to ask whether the MYRF family of transcription factors, represented by myrf-1 and myrf-2 in C. elegans, have a role in the temporally controlled expression of the miRNA lin-4. The precisely timed onset of lin-4 expression in the late L1 stage is known to be a critical step in the developmental timing ("heterochronic") pathway, allowing worms to move from the L1 to the L2 stage of development. Despite the importance of this step of the pathway, the mechanisms that control the onset of lin-4 expression are not well understood.

Overall, the paper provides convincing evidence that MYRF factors have a role in the regulation of lin-4 expression. However, some of the details of this role remain speculative, and some of the authors' conclusions are not fully supported by the studies shown. These limitations arise from three concerns. First, the authors rely heavily on a transcriptional reporter (maIs134) that is known not to contain all of the regulatory elements relevant for lin-4 expression. Second, the authors use mutant alleles with unusual properties that have not been completely characterized, making a definitive interpretation of the results difficult. Third, some conclusions are drawn from circumstantial or indirect evidence that does not use field-standard methods.

The authors convincingly demonstrate that the cytoplasmic-to-nuclear translocation of MYRF-1 coincides with the activation of lin-4 expression, making MYRF-1 a good candidate for mediating this activation. However, the evidence that MYRF-1 is required for the activation of lin-4 is somewhat incomplete. The authors provide convincing evidence that lin-4 activation fails in animals carrying the unusual mutation myrf1(ju1121), which the authors describe as disrupting both myrf-1 and myrf-2 activity. The concern here is that it is difficult to rule out that ju1121 is not also disrupting the activity of other factors, and it does not disentangle the roles of myrf-1 and myrf-2. Partially alleviating this issue, they also find that expression from the maIs134 reporter is disrupted in putative myrf-1 null alleles, but making inferences from maIs134 about the regulation of endogenous lin-4 is problematic. Helpfully, an endogenous Crisprgenerated lin-4 reporter allele is used in some studies, but only using the ju1121 allele. Together, these findings provide solid evidence that MYRF factors probably do have a role in lin-4 activation, but the exact roles of myrf-1 and myrf-2 remain unclear because of limitations of the unusual ju1121 allele and the use of the maIs134 reporter. The creative use of a conditional myrf-1 alleles (floxed and using the AID system) partially overcomes these concerns, providing strong evidence that myrf-1 acts cellautonomously to regulate lin-4, though again, these key experiments are only carried out with the maIs134 transgene.

A second important question asked by the authors is whether MYRF activity is sufficient to activate lin-4 expression. The authors provide evidence that supports this idea, but this support is somewhat incomplete, because the authors rely partially on the maIs104 array and, more importantly, on mutant alleles of MYRF-1 that they propose are constitutively active but are not completely characterized here.

The authors also approach the question of whether MYRF-1 regulates lin-4 via direct interaction with its promoter. The evidence presented here is consistent with this idea, but it relies on indirect evidence involving genetic interactions between myrf-1 and the presence of multiple copies of the lin-4 promoter, as well as the detection of nuclear foci of MYRF-1::GFP in the presence of multiple copies of the lin-4 promoter. This is not the field-standard approach for testing this kind of hypothesis, and the positive control presented (using the TetR/TetO interaction) is unconvincing. Thus, the evidence here is consistent with the authors' hypothesis, but the studies shown are incomplete and do not represent a rigorous test of this possibility.

Finally, the authors ask whether MYRF factors have a role in the regulation of other miRNAs. The evidence provided (RNAseq experiments, validated by several reporter transgenes) solidly supports this idea, with the provision that it is not completely clear that ju1121 is disrupting only the activity of myrf-1 and myrf-2.

Reviewer #2 (Public Review):

In this manuscript, the authors attempt to examine how the temporal expression of the lin-4 microRNA is transcriptionally regulated. However, the experimental support for some claims is incomplete. The authors repeatedly use the ju1121(G247R) mutation of myrf-1, but more information is required to evaluate their claim that this mutation "abolishes its DNA binding capability but also negatively interferes with its close paralogue MYRF-2". Additionally, in the lin-4 scarlet endogenous transcriptional reporter, the lin-4 sequence is removed. Since lin-4 has been reported to autoregulate, it seems possible that the removal of lin-4 coding sequence could influence reporter expression. Further, concrete evidence for direct lin-4 regulation by MYRF-1 is lacking, as the approaches used are indirect and not standard in the field. Overall, while the aims of the work are mostly achieved, data regarding the direct regulation of lin-4 by MYRF-1 and placing the work into the context of previous related reports is lacking. Because of its very specific focus, this paper reports useful findings on how a single transcription factor family might control the expression of a microRNA.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

(1) p.4 Authors should be cautious about this statement: "Once produced, lin-4 can selfenhance its own transcription by directly interacting with cis-elements in the promoter region[26, 27]." As reference 27 shows, this autoregulation is apparently an artifact of the reporter transgene; lin-4 does not appear to have the same role at the endogenous locus.

The discussion of “auto regulation” has been removed from the introduction.

(2) p.4 please provide a reference: "It is worth noting that external food signals are insufficient to drive lin-4 expression as lin-4 doesn't promptly turn on when animals encounter food."

This statement is derived from a combination of our unpublished observations and personal deductions. Within the sequence of events occurring during L1 development, the initiation of lin-4 expression happens relatively late. Therefore, the original sentence has been revised to “Given that lin-4 expression initiates in late L1, it is reasonable to deduce that merely providing food is inadequate to induce lin-4 expression.”

(3) Please provide more detail about myrf-1(syb1468) and myrf-1(syb1491) - are they likely null alleles? Are the phenotypes recessive? Please show the specific locations of deletions.

Please also refer to our response for the main issues raised. The two alleles under discussion are documented in Xia et al. (eLife, 2021). Both of these alleles are recessive and functionally equivalent to null mutations. Interestingly, all the myrf-1 alleles we have analyzed show recessive characteristics in various phenotypic aspects, including growth, synaptic rewiring, and M-cell division. The precise location of these genetic alterations is visually represented in Figure 1D and E.

(4) Fig 2C: are these animals heterozygous for the lin-4 Crispr reporter? If not, this is a lin-4 null. If lin-4 is required for the maintenance of its own expression, this result might be misleading about the role of myrf-1.

The lin-4 gene is located at Chr II: -0.86, and myrf-1 is positioned at Chr II: +2.98. Both of these alleles are balanced by mIn1. As a result, homozygotes for myrf-1 are also homozygotes for umn84. Regarding the role of lin-4 microRNA in its own transcription, research from Frank Slack’s lab has concluded that lin-4 microRNA does not affect the transcription of the lin-4 gene.

(5) p.8: please provide evidence/citation: "however, this experiment used a short promoter of dpy-7, which is not activated in seam cells..."

The dpy-7 promoter has been extensively utilized for transgene experiments in both Andrew Chisholm’s lab and our own. For reference, the original publication has been cited (PMID: 9121480).

(6) Fig 4B. Do the hypodermal knockout animals arrest at L1/L2?

This specific dual allele does not display arrest at the L1/L2 stages. A comprehensive description of the phenotype related to myrf-1LoxP(ybq98); Pdpy-7-Cre(tmIs1028) has been incorporated into the main text, and corresponding new data have been integrated into Figure 4.

(7) p. 11-13. I suggest that the authors consider making the section "MYRF-1 interacts with lin-4 promoter directly" much more succinct. The unsuccessful gelshift experiments can be explained in 1-2 sentences. The backstory about the weak Daf-C phenotype of the floxed allele is likely to confuse readers who are not experts in the field.

We have omitted the description of the gel shift experiments. However, we chose to retain the explanation of the Daf-C phenotype, simplifying the narrative for clarity. The Daf-C phenotype, which we have thoroughly analyzed, is considered significant. Our current research is exploring how nutrients facilitate the cleavage of myrf-1 on the cell membrane."

(8) Fig. 6G. I see no obvious change in the localization of TetR-RFP with or without the presence of TetO DNA, even though the authors use this as a positive control and claim that it validates the use of this approach to study MYRF-1/lin-4p interaction. What tissue is being imaged here? Hypodermis?

The intensity of fluorescent foci varies across transgenic F1 individuals. To assist with visualization, white arrows have been included in Figure 6G. These arrows highlight the formation of puncta in TetR::tagRFP(ybqSi233) due to the presence of a 7xTetO sequence-containing DNA array (indicated by white arrows), while simultaneously showing the lack of aggregation in GFP::MYRF-1. MYRF-1 is expressed across a broad range of tissues, and our analysis did not focus on any specific tissue type. The nuclei shown in the images are derived from a variety of tissues, including the intestine, epidermis, neurons, and the somatic cells of the reproductive system, as inferred from their morphology.

(9) Fig. 7A. Please consider using a different color scheme for the wt vs mutant data. These colors are too similar to those used for the expression-level heatmap. (Also, it's unclear how the fold-change data are normalized - i.e, fold-change compared to what?)

The color scheme in the clustering heatmap has been revised for enhanced contrast. This heatmap does not simply display raw read counts (TPM) or log2 values, though log2 transformation is part of the math process. If displayed directly, variables with low values can overshadow those with high values in the color representation. Instead, the read count data have undergone a series of transformations, including rlog transformation, size factor normalization, and gene-wise scaling, which leads to a more visually informative display of expression changes. Initially, we utilized a web-based tool (https://www.bioinformatics.com.cn/en) for creating the heatmap. However, due to the lack of detailed documentation on this site, we opted to reanalyze the data using functions from the DESeq2 package in R. This reanalysis enabled us to update the graph along with a revised figure legend, aiming to enhance clarity and comprehension.

(10) p. 14: "Remarkably, 6 out of the 7 up-regulated microRNAs are clustered on one phylogenetic branch" - does this mean upregulated in the mutant compared to WT?

The sentence has been revised to “Notably, 6 of the 7 microRNAs showing increased expression in myrf-1(ju1121) compared to wild type are clustered on a single phylogenetic branch,..”

(11) Fig. 7C: Authors might comment on which tissues show expression of these miRNAs.

A sentence has been added: “The reporter for mir-48 is primarily detected in the pharynx, mir-73 is present in both the pharynx and seam cells, whereas mir-230 is detected in seam cells.”

(12) p. 16: "Our report includes the partial dauer-constitutive phenotype caused by the interaction between the lin- 4 promoter DNA and MYRF-1." - consider rewording this; according to the author's model, it's not the interaction per se that causes the Daf-C phenotype, but rather the sequestration of MYRF-1 (or -2?) by excess lin-4p.

The sentence has been revised to “Our observations suggest that the tandem array of lin-4 promoter DNA may sequester a certain amount of MYRF protein. This sequestration could limit the availability of MYRF, potentially leading to a partial dauerconstitutive phenotype.”

Reviewer #2 (Recommendations For The Authors):

(1) The use of L1 (and not even defining what L1 means) in the abstract is very C. elegans-field specific. Make the writing more accessible to a general audience

This sentence has been revised.

(2) Instead of writing in the context of upregulation and downregulation - I advise using activation/induction and repression instead. e.g. MYFR-1 is necessary for lin-14 induction in late stage L1.

The wording of “upregulation” and “downregulation” has been changed.

(3) We find that lin-4 transcription reporter fails to be upregulated in myrf-1(ju1121) at any viable stages that can be analyzed - should this just say 'fails to be expressed'?

“at any viable stages that can be analyzed” has been removed.

(4) The section starting with this sentence is strange as in the previous section the authors showed that MYRF-1 expressed in muscle or epidermis IS sufficient to drive lin4 expression - 'The next question was whether MYRF-1 is sufficient to drive the upregulation of lin-4.'

The sentence has been updated to reflect our research focus: “Given that both the induction of lin-4 and the cleavage of MYRF at the cell membrane happen within a specific time window, we investigated whether a gain of function in MYRF-1 alone is adequate to modify the onset timing of lin-4.”

(5) This sentence needs modifying "A series of MYRF-1 variants were expressed in HEK cells by transfection, and cell lysis was tested for their binding with 498 bp DNA of the lin-4 promoter." This sentence suggests that cell lysis tests the binding of the protein to DNA which is obviously incorrect.

We have chosen to omit the description of these experiments from our text due to their inconclusive results.

(6) Typographical changes/suggestions to aid clarity:

Introduction: lin-4 and lin-14 are the two that have been studied in details - change to lin4 and lin-14 are the two that have been studied in detail

Results: Write NAA solution in full the first time it is mentioned.

Remove ', a collagen,' when describing the dpy-7 promoter. The authors don't describe what myo-3 encodes so keep this consistent.

Page 14 'itself was upregulated in the mutants.' be more specific. Which mutants?

All four identified places have been appropriately corrected or revised.

eLife assessment

The microRNA lin-4, originally discovered in C. elegans, has a key role in developmental timing across species, but how its expression is developmentally controlled is poorly understood. Here, the authors provide convincing evidence that two MYRF transcription factors are essential positive regulators of lin-4 during early C. elegans larval development. These results provide important insight into the molecular nature of developmental timing that could have significant implications for understanding these processes in more complex systems.

Reviewer #1 (Public Review):

In this work, the authors set out to ask whether the MYRF family of transcription factors, represented by myrf-1 and myrf-2 in C. elegans, have a role in the temporally controlled expression of the miRNA lin-4. The precisely timed onset of lin-4 expression in the late L1 stage is known to be a critical step in the developmental timing ("heterochronic") pathway, allowing worms to move from the L1 to the L2 stage of development. Despite the importance of this step of the pathway, the mechanisms that control the onset of lin-4 expression are not well understood.

Overall, the paper provides convincing evidence that MYRF factors have a role in the regulation of lin-4 expression. Using state-of-the-art techniques (knock-in reporters and conditional alleles), the authors show that MYRF factors are essential for lin-4 activation and act cell-autonomously. While there are some minor concerns about the use of unusual gain-of-function alleles, these are mitigated by consistent results using other approaches. The authors also provide evidence that MYRF factors activate lin-4 by directly activating its promoter. While their results are certainly consistent with this possibility, they rely on indirect measurements and are therefore not definitive. Further experiments will be necessary to determine whether this model is accurate.

Some details about the relative roles of the two C. elegans MYRF factors, myrf-1 and myrf-2, remain unclear. myrf-1 clearly seems to play the more important role lin-4 activation and the regulation of developmentally timed processes. However, there are numerous hints that myrf-2 may act in the opposite direction, either by inhibiting myrf-1 itself or its ability to activate its targets. Further work will be necessary to understand the genetic and mechanistic relationships between these two genes.

Overall, the findings in this paper are convincing, and the results will be of interest to a wide range of developmental biologists.

Reviewer #2 (Public Review):

Summary:<br /> In this manuscript, the authors examine how temporal expression of the lin-4 microRNA is transcriptionally regulated.

In the revised manuscript, the authors have suitably addressed my original concerns.

Aims achieved: The aims of the work are now achieved.

Impact: This study shows that a single transcription factor (MYRF-1) is important for the regulation of multiple microRNAs that are expressed early in development to control developmental timing.

Author Response

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

Reviewer #1 (Public Review):

(1.1) This work introduces a new method of imaging the reaction forces generated by small crawling organisms and applies this method to understanding locomotion of Drosophila larva, an important model organism. The force and displacement data generated by this method are a qualitative improvement on what was previously available for studying the larva, improving simultaneously the spatial, temporal, and force resolution, in many cases by an order of magnitude. The resulting images and movies are quite impressive.

We thank the reviewer for their recognition of the achievements our work presents and for their feedback with regard to what they consider our most important findings and the points raised in their review. We will address these points individually below.

(1.2) As it shows the novel application of recent technological innovations, the work would benefit from more detail in the explanation of the new technologies, of the rationales underlying the choice of technology and certain idiosyncratic experimental details, and of the limitations of the various techniques. In the methods, the authors need to be sure to provide sufficient detail that the work can be understood and replicated. The description of the results and the theory of motion developed here focus only on forces generated when the larva pushes against the substrate and ignores the equally strong adhesive forces pulling the larva onto the substrate.

As the reviewer correctly points out, our present work adapts a recently developed set of methods (namely, ERISM and WARP) for use with small soft-bodied animals. The foundational methods have been described in detail in previous publications (refs, 23 and 26). However, upon reflection, we agree that more information can be provided to ensure our work is more accessible and reproducible. We also agree that some additional clarifying information on our approach could be helpful. We have addressed this in the following ways:

(1) We have included a detailed Key Resources table in the methods section to allow for maximum transparency on equipment and reagent sourcing. This can now be found on Pages 16-19.

(2) We have modified the ‘Freely behaving animals force imaging’ section of the Materials and Methods section to include more detailed information on practical aspects of conducting experiments. These changes can be found on page 23-24 (lines 566–567, 571-577).

(3) We have re-ordered the Materials and Methods section, such that microcavity fabrication and microcavity characterisation occur prior to the description of ERISM and WARP experiments - this change should hopefully aid replication. Details regarding the application of a silicone well to the surface of microcavities have also been added (lines 472-474).

(4) We have added additional text in the Introduction and Results (Pages 3-4 and 7, lines 56-86, and 152-153) to explain our rationale for using ERISM/WARP and additional text in the discussion that discusses the potential role(s) of adhesive forces in larval locomotion (Page 12, lines 301307).

(1.3) The substrate applies upward, downward, and horizontal forces on the larva, but only upward and downward forces are measured, and only upward forces are considered in the discussions of "Ground Reactive Forces." An apparent weakness of the WARP technique for the study of locomotion is that it only measures forces perpendicular to the substrate surface ("vertical forces" in Meek et al.), while locomotion requires the generation of forces parallel to the substrate ("horizontal forces"). It should be clarified that only vertical forces are studied and that no direct information is provided about the forces that actually move the larva forward (or about the forces which impede this motion and are also generated by the substrate). Along with this clarification, it would be helpful to include a discussion of other techniques, especially micropillar arrays and traction force microscopy, that directly measure horizontal forces and of why these techniques are inappropriate for the motions studied here.

We attempted to provide a streamlined Introduction in our initial submission and then compared ERISM/WARP to other methods in our discussion. We are happy to provide a brief overview of substrate force measurement methods in the introduction to help set the stage for readers. The Introduction section of our revised manuscript now contains the following comparison of different mechanobiological imaging techniques on pages 3-4 lines 56-86:

‘However, in the field of cellular mechanobiology, many new force measuring techniques have been developed which allow measurement of comparatively small forces from soft structures exhibiting low inertia (15–17) often with relatively high spatial-resolution. Early methods such as atomic force microscopy required the use of laser-entrained silicon probes to make contact with a cell of interest (15). This approach is problematic for studying animal behaviour due to the risk of the laser and probe influencing behaviour. Subsequently, techniques have been developed which allow indirect measurement of substrate interactions. One such approach is Traction Force Microscopy (TFM) in which the displacement of fluorescent markers suspended in a material with known mechanical properties relative to a zero-force reference allows for indirect measurement of horizontally aligned traction forces (17–19). This technique allows for probe-free measurement of forces, but the need to obtain a precise zero-force reference would make time-lapse measurements on behaving animals challenging; further, depending on the version used, it has insufficient temporal resolution for the measurement of forces produced by many behaving animals, despite recent improvements (20). A second approach revolves around the use of micropillar arrays; in this technique, horizontally-aligned traction forces are measured by observing the deflection of pillars made of an elastic material with known mechanical properties. This approach can be limited in spatial resolution and introduces a non-physiological substrate that may influence animal behavior (21,22).

Recently we have introduced a technique named Elastic Resonator Interference Stress Microscopy (ERISM) which allows for the optical mapping of vertically aligned GRFs in the pico and nanonewton ranges with micrometre spatial resolution by monitoring local changes in optical resonances of soft and deformable microcavities. This technique allows reference-free mapping of substrate deformations and calculation of vertically directed GRFs; it has been used to study a range of questions related to exertion of cellular forces (23–25). Until recently, this technique was limited by its low temporal resolution (~10s), making it unsuitable for recording substrate interaction during fast animal movements, but a further development of ERISM known as wavelength alternating resonance pressure microscopy (WARP), has been demonstrated to achieve down to 10 ms temporal resolution (26). Given ERISM/WARP allows for probe-free measurement of vertical ground reaction forces with high spatial and temporal resolution, it becomes an attractive method for animal-scale mechanobiology.’

(1.4) The larvae studied are about 1 mm long and 0.1 mm in cross-section. Their volumes are therefore on order 0.01 microliter, their masses about 0.01 mg, and their weights in the range of 0.1 micronewton. This contrasts with the force reported for a single protpodium of 1 - 7 micronewtons. This is not to say that the force measurements are incorrect. Larvae crawl easily on an inverted surface, showing gravitational forces are smaller than other forces binding the larva to the substrate. The forces measured in this work are also of the same magnitude as the horizontal forces reported by Khare et al. (ref 32) using micropillar arrays.

I suspect that the forces adhering the larva to the substrate are due to the surface tension of a water layer. This would be consistent with the ring of upward stress around the perimeter of the larva visible in S4D, E and in video SV3. The authors remark that upward deflection of the substrate may be due to the Poisson's ratio of the elastomer, but the calibration figure S5 shows that these upward deflections and forces are much smaller than the applied downward force. In any case, there must be a downward force on the larva to balance the measured upward forces and this force must be due to interaction with the substrate. It should be verified that the sum of downward minus upward forces on the gel equals the larva's weight (given the weight is neglible compared to the forces involved, this implies that the upward and downward forces should sum to 0).

We have carefully calculated the forces exerted by protopodia and are confident in the accuracy of our measurements as reported. We further agree with the reviewer’s suggestion that gravitational forces can be largely neglected.

As the reviewer points out, one would expect forces due to upward and downward deflections to cancel when considering the entire system. However, we see indications that the counteracting / balancing force often acts over a much larger area than the acting force, e.g. a sharp indentation by a protopodium might be counteracted by an upward deflection over a 10-20 fold larger radius and hence 100 to 400-fold larger area, thereby reducing the absolute value of the upward deflection at any given pixel surrounding the indentation. This in turn increases error in determining the integrated upward deformation, making it difficult to perform an absolute comparison of acting and counteracting force. Further, recording the entire counteracting force induced deformation would require acquiring data with a prohibitively large field of view.

We agree that in some situations, water surface tension may be adhering animals to the substrate. Importantly, this is a challenge that the animal faces outside the lab in its natural environment of moist rotting fruit and yeast. The intricate force patterns seen in our study in the presence of water surface tension are therefore ecologically relevant. In other situations (e.g. preparing for pupation), larvae are able to stick to dry surfaces, suggesting that other adhesive forces such as mucoid adhesion can also come into play in certain behavioural contexts. A full characterization of the effects of water tension and mucoid adhesion are beyond the scope of this study. However, we have now added a sentence on pages 8 and 12 commenting on these other biomechanical forces at play:

‘We also observed that the animals travel surrounded by a relatively large water droplet (lines 189-190).’

‘We observed that larvae travel surrounded by moisture from a water droplet, which produces a relatively large upwardly directed force in a ring around the animal. The surface tension produced by such a water droplet likely serves a role in adhering the animal to the substrate. However, during forward waves, we found that protopodia detached completely during SwP, suggesting this surface tensionrelated adhesion force can be easily overcome by the behaving animal. (lines 301-307) .’

(1.5) Much of the discussion and the model imply that the sites where the larva exerts downward force on the gel are the sites where horizontal propulsion is generated. This assumption should be justified. Can the authors rule out that the larva 'pulls' itself forward using surface tension instead of 'pushing' itself forward using protopodia?

Determining the exact ‘sites’ where horizontal propulsion is generated is challenging. In our conceptual model, movement is not initiated by protopodia per se, but rather by a constellation of muscle contractions, which act upon the hydrostatic skeleton, which in turn causes visceral pistoning that heaves larvae forward. This is based on previous findings in Ref 31. While there are indeed downward protopodial ‘vaulting’ forces prior to initiation of swing, we propose that the main function of protopodia is not to push the larvae forward, but rather to provide anchoring to counteract opposing forces generated by muscles. We agree that water surface tension could also be sculpting biomechanical interactions; however, a full characterization of how water surface tension shapes larval locomotion is beyond the scope of this study.

Since we have observed larvae move over dry terrain (e.g. glass) without an encasing water bubble, we do not believe that an encasing water bubble is strictly required for locomotion. We have also seen no obvious locomotion related modulations in the pulling forces created by water bubbles encasing larva, which would be expected if animals were somehow using water tension to pull themselves forward. Overall, the most likely explanation is that larvae use a mixture of biomechanical tactics to suit the moment in a given environment. This represents a challenge but also an opportunity for future research.

We have now added additional text in the ‘Functional subdivisions within protopodia’ subsection to discuss these nuances (page 14, lines 382-387):

‘This increased force transmitted into the substrate is unexpected as the forces generated for the initiation of movement should arise from the contraction of the somatic muscles. We propose that the contraction of the musculature responsible for sequestration acts to move haemolymph into the protopodia thus exerting an increased pressure onto the substrate while the contact area decreases as a consequence of the initiation of sequestration.’

and (page 15, lines 398-399):

‘Water surface films appear to facilitate larval locomotion in general but the biomechanical mechanisms by which they do this remain unclear.’

(1.6) More detail should be provided about the methods, their limitations, and the rationale behind certain experimental choices.

We thank the reviewer for this comment. As this significantly overlaps with a point raised earlier, we kindly direct them to our answer to comment #1.2 above.

(1.7) Three techniques are introduced here to study how a crawling larva interacts with the substrate: standard brightfield microscopy of a larva crawling in an agarose capillary, ERISM imaging of an immobilized larva, and WARP imaging of a crawling larva. The authors should make clear why each technique was chosen for a particular study - e.g. could the measurements using brightfield microscopy also be accomplished using WARP? They should also clarify how these techniques relate to and possibly improve on existing techniques for measuring forces organisms exert on a substrate, particularly micropillar arrays and Traction Force Microscopy.

Indeed, each of the three methods used has a specific merit. The brightfield microscopy was selected to track features on the animal’s body and to provide a basic control for the later measurements. However, this technique cannot directly measure the substrate interaction, it only allows inferences to be made from tracked features at the substrate interface. ERISM provides high resolution maps of the indentation induced by the larva; it is also extensively validated for mapping cell forces and the data analysis is robust against defects on the substrate (refs 23, 24 and 25). However, as we explain in the manuscript, ERISM lacks the temporal resolution needed to monitor mechanical activity of behaving larva. Its use was therefore limited to the study of anaesthetised animals. For mapping forces exerted by behaving larva, we used WARP which is a further development of ERISM that offers higher frame rates but at the cost of requiring more extensive calibration (Supplementary Figure S4). The streamlined introduction of the different methods in our original manuscript originates from our attempt to be as concise as possible. However, as state in response to comment #1.2, we agree that additional explanation and discussion will be helpful for readers and that it will helpful to briefly refer to other methods for force mapping. We have now added references to a variety of techniques in the Introduction (Page 3-4, lines 56-86) as stated in a prior response.

(1.8) As written, "(ERISM) (19) and a variant, Wavelength Alternating Resonance Pressure microscopy (WARP) (20) enable optical mapping of GRFs in the nanonewton range with micrometre and millisecond precision..." (lines 53-55) may generate confusion. ERISM as described in this work has a much lower temporal resolution (requires the animal to be still for 5 seconds - lines 474-5); In this work, WARP does not appear to have nanonewton precision (judging by noise on calibration figures) and it is not clear that it has millisecond precision (the camera used and its frame rate should be specified in the methods).

Previous studies have demonstrated the capabilities and limitations of ERISM and WARP. Upon reflection, we agree that our wording here could be more precise. To clarify our claim, we now separate the statements on ERISM and WARP in the introduction as follows (page 4, lines 78-83):

“Until recently, this technique was limited by its low temporal resolution (~10s) making it unsuitable for use in recording substrate interaction during fast animal movements, but a further development of ERISM known as wavelength alternating resonance pressure microscopy (WARP), has been demonstrated to achieve down to 10 ms temporal resolution (26)”

While WARP can achieve comparable force resolution as ERISM when used in a cellular context (c.f. Ref 26), we agree that for the present study, the resolution was in the 10s of nanonewton range, due to the need to use stiffer substrates and larger fields of view.

The camera used in our work was specified in the appropriate subsection of the Materials and Methods (“All WARP and ERISM images were acquired using an Andor Zyla 4.2 sCMOS camera (Andor Technology, Belfast, UK)”). We apologise that the exact frame rate used in our current work was not mentioned in our original manuscript; this has now been added to the ‘Freely behaving animals force imaging’ section of the Materials and Methods (page 23, lines 574-577).

(1.9) It would be helpful to have a discussion of the limits of the techniques presented and tradeoffs that might be involved in overcoming them. For instance, what is the field of view of the WARP microscope, and could it be increased by choosing a lower power objective? What would be required to allow WARP microscopy to measure horizontal forces? Can a crawling larva be imaged over many strides by recentering it in the field of view, or are there only particular regions of the elastomer where a measurement may be made?

We agree with the reviewer that some discussion of the limitations of our technique will allow readers to have a more informed appreciation of what we are capable of measuring using WARP. However, as this is the first work to ever demonstrate such measurements, the limitations and tradeoffs cannot all be known with certainty at the present stage.

To answer your individual questions:

(1) There is a trade-off between numerical aperture and the ability to resolve individual interference fringes. Since our approach to calculate displacement from reflection maps relies upon counting of individual fringe transitions, going to a lower powered objective risks having these fringes blend and thus the identification of the individual transitions becoming impossible. The minimum numerical aperture of the objective will therefore generally depend on the steepness of indentations produced by the animals; the steeper an indentation, the closer the neighbouring fringes and thus the higher the required magnification to resolve them.

(2) From WARP and ERISM data, one can make inferences about horizontal forces, as is described in detail in our earlier publications about ERISM (ref, 23). However, quantitation of horizontal forces at sufficient temporal resolution to allow the investigation of behaving Drosophila larva is currently not possible.

(3) Many strides can indeed be imaged using our technique, however, this comes with additional technical challenges. Whether or not the animal itself can be recentred is an ongoing challenge. We have found that the animals are amenable to recentring themselves within the field of view if chasing an attractive odorant. However, manual recentering using a paintbrush risks destroying the top surface of the soft elastic resonator and recentering the microscope stage would require real-time object tracking which has been outside the scope of this original work, given the other challenging requirements on hardware and optics for obtaining high quality force maps.

To provide more information on limitations of our technique, we have added the following text into the discussion (pages 13-14, lines 356-370).

‘Despite the substantial advances they have provided, the use of WARP and ERISM also brings challenges and has several technical limitations. For example, fabrication of resonators is much more challenging than preparation of the agarose substrates conventionally used for studying locomotion of Drosophila. This problem is compounded by the fragility of the devices owing to the fragility of the thin gold top mirror. This becomes problematic when placing animals onto the microcavities, as often the area local to the initial placement of the animal is damaged by the paintbrush used to move the animals. Further, as a result of the combining of the two wavelengths, the effective framerate of the resultant displacement and stress maps is equal to half of the recorded framerate of the interference maps. To be able to monitor fast movements, recording at very high framerates is therefore necessary which, depending on hardware, might require imaging at reduced image size, but this in turn reduces the number of peristaltic waves that can be recorded before the animal escapes the field of view. A further limitation is that WARP and ERISM are sensitive mainly to forces in the vertical direction; this is complementary to TFM, which is sensitive to forces in horizontal directions. Using WARP in conjunction with high speed TFM (possibly using the tuneable elastomers presented here) could provide a fully integrated picture of underlying vertical and horizontal traction forces during larval locomotion.’ And further on page 13, lines 337-341:

‘More detailed characterisation of this behaviour remains a challenge owing to the changing position of the mouth hooks. Due to their rigid structure and the relatively large forces produced in planting, mouth hooks produce substrate interaction patterns which our technique struggles to map accurately due to overlapping interference fringes ambiguating the fringe transitions.’

We trust that the above discussion and our modifications to our manuscript resulting from these will address the reviewer’s concerns.

Reviewer #2 (Public Review):

(2.1) With a much higher spatiotemporal resolution of ground dynamics than any previous study, the authors uncover new "rules" of locomotory motor sequences during peristalsis and turning behaviors. These new motor sequences will interest the broad neuroscience community that is interested in the mechanisms of locomotion in this highly tractable model. The authors uncover new and intricate patterns of denticle movements and planting that seem to solve the problem of net motion under conditions of force-balance. Simply put, the denticulated "feet" or tail of the Drosophila larva are able to form transient and dynamic anchors that allow other movements to occur.

We thank the reviewer for their feedback and the information regarding which of our results is likely to resonate most impactfully with readers from a biological background.

The biology and dynamics are well-described. The physics is elementary and becomes distracting when occasionally overblown. For example, one doesn't need to invoke Newton's third law, per se, to understand why anchors are needed so that peristalsis can generate forward displacements. This is intuitively obvious.

We are sorry to hear that the reviewer found some of the physics details distracting. To address this concern, we have simplified some of the language while still attempting to keep the core arguments intact. For context and analogy, we still believe that including a brief reference to the laws of motion is helpful for some readers to explain some of our results and highlight their general implications, especially with regard to anchoring against reaction forces.

One of our objectives is to make this article accessible and interesting for biologists and physicists at all levels. We feel it is important to reach out to both communities and try to be inclusive as possible in our writing. Newton’s 3rd law is clearly relevant for our study and it is a common point of reference for anyone with a highschool education, and so we feel it is appropriate to mention it as a way to help readers across disciplines understand the biophysical challenges faced by the animals we study.

(2.2) Another distracting allusion to "physics" is correlating deformation areas with displaced volume, finding that "volume is a consequence of mass in a 2nd order polynomial relationship". I have no idea what this "physics" means or what relevance this relationship has to the biology of locomotion.

Upon reflection, we agree that this language may be overly complex and distracts from what is, at its core, a simple, but important principle governing how Drosophila larvae interact with their substrates. The point we are trying to make is that our data show that forces exerted by an animal are proportional in a non-linear way to contact area. This suggests that to increase force exerted on the substrate, an animal must increase contact area. We do not observe contact area remaining constant while force increases, or vice versa. To make this result more clear, we have made several changes in our revised manuscript. Figure 5B no longer shows the relationship between the protopodial contact area and the displaced volume of the elastic resonator, but instead now shows the protopodial contact area and recorded force transmitted into the substrate. This then shows that in order to increase force transmitted into the substrate, these animals must increase their contact area. We have made changes to the figure legend of Figure 5 and the statements in the Results section accordingly (Page 9, lines 220-222).

2.3 The ERISM and WARP methods are state-of-the-art, but aside from generally estimating force magnitudes, the detailed force maps are not used. The most important new information is the highly accurate and detailed maps of displacement itself, not their estimates of applied force using finite element calculations. In fact, comparing displacements to stress maps, they are pretty similar (e.g., Fig 4), suggesting that all experiments are performed in a largely linear regime. It should also be noted that the stress maps are assumed to be normal stresses (perpendicular to the plane), not the horizontal stresses that are the ones that actually balance forces in the plane of animal locomotion.

We largely agree with the statement made by the reviewer here. However, we have found that in many contexts, audiences appreciate having the absolute number of the forces and stresses involved reported. Therefore, where possible, we have used stress maps, rather than displacement maps. We also observe that while stress and displacement maps show similar patterns, features sometimes appear sharper in the stress map, which is a result of the finite element algorithm being able to attribute a broad indentation to a somewhat more localised downward force. We have thus opted to keep to original stress maps. We have been more explicit about WARP and ERISM being more tuned to recording vertically directed forces throughout the revised manuscript (lines 75, 78, 86, 162, 301, 305, 336).

We have also modified our Discussion section to encourage further investigation of our proposed model using a technique more tuned to horizontal stresses (pages 12-13, lines 324-328):

‘However, WARP microscopy is best suited to measurements of forces in the vertical direction, and though we can make inferences such as this as they are a consequence of fundamental laws of physics, we present this conclusion as a testable prediction which could be confirmed using a force measurement technique more tuned to horizontally directed forces relative to the substrate.’

(2.4) But none of this matters. The real achievements are the new locomotory dynamics uncovered with these amazing displacement measurements. I'm only asking the authors to be precise and down-to-earth about the nature of their measurements.

We thank the reviewer for their perceptiveness in finding that though the forces are interesting, the interactions themselves are the most noteworthy result here. We trust that with the changes made in our revised manuscript, the description is now more “down-to-earth”, more concise where appropriate, and accurate as to which results are particularly important and novel.

(2.5) It would be good to highlight the strength of the paper -- the discovery of new locomotion dynamics with high-resolution microscopy -- by describing it in simple qualitative language. One key discovery is the broad but shallow anchoring of the posterior body when the anterior body undertakes a "head sweep". Another discovery is the tripod indentation at the tail at the beginning of peristalsis cycles.

We thank the reviewer for this recommendation. We agree that including a more explicit statement of some of our findings, especially with regards to these new posterior tripod structures and the whole-abdomen preparatory anchoring prior to head sweeps, would make the paper more impactful. As a result, we have modified the discussion section to include a statement for each new result and have also amended our abstract as a result (lines 407-416):

“Here we have provided new insights into the behaviour of Drosophila larval locomotion. We have provided new quantitative details regarding the GRFs produced by locomoting larvae with high spatiotemporal resolution. This mapping allowed the first detailed observations of how these animals mitigate friction at the substrate interface and thus provide new rules by which locomotion is achieved. Further, we have ascribed new locomotor function to appendages not previously implicated in locomotion in the form of tripod papillae, providing a new working hypothesis of how these animals initiate movement. These new principles underlying the locomotion outlined here may serve as useful biomechanical constraints as called for by the wider modelling community (39).”

(2.6) As far as I know, these anchoring behaviors are new. It is intuitively obvious that anchoring has to occur, but this paper describes the detailed dynamics of anchoring for the first time. Anchoring behavior now has to be included in the motor sequence for Drosophila larva locomotion in any comprehensive biomechanical or neural model.

We agree with the reviewer on this. We think it is best to let our colleagues reflect on our findings and then decide how best to include them in future models.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Please be sure to describe in a figure caption or in the methods the details of the optical setup, especially the focal lengths of all the lenses, including the objective, and part numbers of the LEDs and filters. It would be helpful to have a figure in the main paper explaining the principles of ERISM/WARP microscopy along with the calibration measurements and computational pipeline (this would mainly combine elements already in the supplement). Such a figure should also include details of the setup that are alluded to in the methods but not fully explained (for instance, a "silicone well" is referred to in the methods but never described). The calibration of elastomer stiffness that now appears in the main text could be made a supplementary figure, unless there is some new art in the fabrication of the elastomers that should be highlighted as an advance in the main text.

We appreciate the importance of explaining our methods to readers.

In response to the public comments, we have added further details in our methods section to clarify practical aspects and ensure that readers will be able to reproduce our work.

In Supplemental Figure 2, we show the full optical light path for ERISM and WARP along with named components. In addition, the principles of ERISM and WARP microscopy have already been extensively described in previous publications (See Refs 23-26). In light of this, we feel that the best approach in this paper is to direct readers to those publications.

We feel that it is appropriate to present the calibration of elastomer stiffness in the main text because this is indeed a new innovation that is not just about making the elastomers but making force sensors based on these different materials. This is really important because it shows how researchers can tune the stiffness of an ERISM/WARP elastomer to match the type of tissue or organism under study. This is really the key technical advance that enables whole animal biomechanics across a range of animal sizes, so we think it is appropriate to keep it in the main text.

We want to make sure that we do not oversell this point, and we feel that we make it sufficiently clear in the main text of our manuscript that making elastomer based force sensors of appropriate stiffness is important, when we state

“First, we developed optical microcavities with mechanical stiffnesses in the range found in hydrogel substrates commonly used for studying Drosophila larval behaviour, i.e. Young’s modulus (E) of 10-30kPa (36–38).” (p. 5, ll. 124) and later

“Here we used Drosophila larvae as a test case, but our methods now allow elastic optical resonators to be tuned to a wide range of animal sizes and thus create new possibilities for studying principles of neuro-biomechanics across an array of animals.” (p. 12, ll. 337)

I would appreciate a description of the "why" behind some experimental choices, as understanding the motivation would be helpful for other researchers looking to adopt these techniques.

We have now added additional text in the introduction and discussion that explains the rationale behind our experimental choices. in more detail. Please see our response to Reviewer 1’s public comments on the same point.

(1) The WARP and ERISM experiments were conducted on a collagen coated gold surface rather than agarose. Why? EG does agarose not adhere to the gold, or would its thickness interfere with the measurement?

The gold layer is applied above the elastomer and the collagen on top of the gold layer makes the gold a more natural biological surface for the animals. Agarose is unsuitable as an elastomer because it would dry during the vacuum based deposition of the gold. It is also unsuitable as a surface coating on top of the gold as the coating on the gold needs to very thin to preserve the spatial and mechanical resolution of our sensors. Further, processing of agarose generally requires temperatures of 60°C and higher which we find can damage the elastomer / gold films.

(2) The ERISM measurements are made on a cold anesthetized animal right as it starts to wake up (visible mouth-hooks movement), which presents some difficulty. Why not start imaging while the animal is still completely immobile? Or why not use a dead larva?

This approach allowed us to get measurements of forces exerted by denticles that are physiologically and biomechanically accurate. In dead or fully anesthetized animals, one cannot be sure that the forces exerted by denticles and denticle bands are representative of the forces exerted by an animal with active hydrostatic control.

(3) In the ERISM setup the monochromator is spatially filtered by focusing through pinhole, while in the WARP setup, the LEDs are not.

Yes that’s correct. The LED light sources used in WARP have better spatial homogeneity than the tungsten filament used in ERISM and so a pinhole is not required in WARP.

(4) SV4 shows the interference image of a turning larva (presumably from one illumination wavelength) rather than a reconstruction of the displacement or stresses. Why?

We felt that in this particular case the interference images provided a clearer representation of the behavioural sequence, showing both the small indentations generated by individual denticles and the larger indentations of the animal overall.

Lines 49-50 "a lack of methods with sufficient spatiotemporal resolution for measuring GRFs in freely behaving animals has limited progress." This needs a discussion of what sufficient spatial and temporal resolutions would be and how existing methods fall short of these goals.

We have now rewritten the introduction to include an overview of other alternative approaches and of what we see as the requirements here. See our response to the public comments.

Figure caption 1B (line 789) refers to "concave areas of naked cuticle (black line) which generally do not interact with the substrate" While I think this might be supported by later WARP images, it's not clear how the technique of figure 1 measures interaction, which could e.g. be mediated by surface tension of a transparent fluid.

The technique of Figure 1 provides qualitative information which as the reviewer points out is validated by WARP measurements later.

Lines 184-189 "However, unexpectedly, we observed an additional force on the substrate when protopodia leave the substrate (SI) and when they are replanted (ST). To investigate whether this force was due to an active behaviour or due to shifting body mass, we plotted integrated displacement (i.e. displaced volume) against the contact area for each protopodium, combining data from multiple forwards waves (Figure 5B). Area is correlated with displaced volume for most time points, indicating that volume is a consequence of mass in a 2nd order polynomial relationship." I couldn't follow this argument at all.

We have now reworded this section and explained our rationale. Also see our response to a similar critique in Reviewer 2’s public comments.

Generally the authors might reconsider their use of acronyms. e.g. (244-246) "SI latencies were much more strongly correlated with wave duration across most segments than ST latencies. SIs scale with SwP and this could be mediated by proprioceptor activity in the periphery" is made more difficult to parse by the abbreviations.

As we need to refer to these terms multiple times throughout the manuscript, we feel the use of acronyms is appropriate here.

The video captions are inadequate. Please expand on them to explain clearly what is shown, and also describe in the methods how the data were acquired and processed. For instance, it seems that in SV3 a motion correction algorithm is applied so that the larva appears stationary even as it crawls forward. I think "fourier filtered" means that the images were processed with a spatial high pass filter - this should be explained and the parameters noted.

We have revisited the video captions provided in the supplementary information document and conclude that these contain the important information. The mode of acquisition are described in the methods, e.g. Video 1 and 2 see section in Methods on “Denticle band kinematic imaging” and Videos 3 and 4 see section in Methods on WARP. Supplementary Video 3 does not make use of motion correction; indeed, one can see the larvae moving upwards/forwards in the field of view. We apologize for not explaining the Fourier filtering process for Video 3. We have now modified the video caption to read as follows:

Video SV3. WARP imaging during forwards peristalses.

Video showing high frame rate displacement maps produced by a freely behaving Drosophila larva. Displacement maps were Fourier filtered to make denticulated cuticle more readily visible and projected in 3D to show the effects of substrate interaction. Details of the Fourier filtering procedure were described elsewhere [Kronenberg et al, Nat Cell Biol 19, 864–872 (2017)].

What were the reflectances of the bottom (10 nm Au/Cr) and top (15nm Au) metal layers at the wavelengths used? I imagine the bottom layer should be less than 38%, the top layer higher, and the product of the square of the bottom transmission and the top reflectance coefficients equal to the bottom reflectance (to make the two paths of the interferometer contribute equal intensity), but none of this is stated.

The reflectance of the gold mirrors was studied in detail in prior work on ERISM. See Kronenberg et al, Nat Cell Biol 19, 864–872 (2017). We therefore refrained from adding a complete optical characterization of the ERISM sensors again here. In brief, we found that a reflectance >13% at each Au mirror is required for reliable ERISM measurements.

The description of the gold coated elastomer as a microcavity is confusing to me. Does the light really make multiple round trips between the plates before returning to the detector? The loss of light on each round trip would depend on the reflectance and parallelism of the top and bottom mirrors. From the WARP calculation it's appears that there is only one round trip - a pi/2 phase shift results from the calculation for one round trip: 2pi*2nL 5nm/(630nm)^2, with n = 1.4 and L = 8 microns - if there were two round trips, the phase shift would be pi etc. Would this better be described as a mostly common path interferometer?

The physics of our devices is best described within the framework of thin film interference and (weak) microcavity optics. Indeed, light can make multiple roundtrips, though it gets attenuated with each reflection. The complete calculation of the multiple roundtrips is only required to obtain quantitative information on the amount of light that is reflected. The spectral position of minima in reflectance can also be obtained from assuming one roundtrip which is what is done in the description of the WARP calculations.

Figure 2 e,f: the line fits appear to be dominated by the data points at 2 s. If these are removed, do the fits change? To support the argument that 2e shows a correlation and 2f does not, some kind of statistical test, ideally a hierarchical bootstrap, should be conducted to compare between the two measurements.

If we remove the data points at 2 s, then R^2’s for swing initiation latencies change as follows: A2: 0.35 to 0.005; A4: 0.78 to 0.31; A6: 0.61 to 0.01. The data in 2e,f are the averages from 3 waves in each animal and so the data points at 2 s are not simply the result of single ‘rogue’ waves but rather averages of several trials. Further, if all individual waves are plotted, we can see that the overall trends are still visible.

We don’t think it is appropriate to remove the data at 2 s from our analysis, but we take the point regarding statements about presence or absence of correlation in a formal sense. We have therefore changed the wording in the description of 2e,f to refer simply to the fact that wave duration can ‘largely determine' latencies in some instances, but is less able to in other instances, as is suggested by the R^2 (coefficient of determination) data. In discussion, we have also adjusted our wording.

Figure 4 - please provide in the main figure or as a supplement the full images (i.e. not cropped to the assumed shape of the larva)

We do not feel that it is necessary or helpful to provide the full images given that the focus of the analysis is on dynamics of protopodia movements.

Figure 5e top: single data points around wave duration 0.6s appear to dominate fit lines. Does removing these points alter the fits? To support the argument that 5e top shows a correlation and 5e bottom does not, some kind of statistical test, ideally a hierarchical bootstrap, should be conducted to compare between the two measurements.

In Figure 5e, we are showing all waves analysed across animals. If we remove the datapoints at 0.6 s, A2 R^2 changes from 0.24 to 0.05, A4 R^2 changes from 0.48 to 0.11, A6 R^2 changes from 0.69 to 0.34; however we don’t feel it is appropriate to remove these data from our analysis. We take the point about needing to be cautious about making claims about correlation versus no correlation and have now reworded description of these results along same lines as Figure 4.

It appears from the methods (467-489) that animals were kept wet for warp imaging but not for ERISM imaging. Please confirm or explain further the presence or absence of a water layer in these two sets of measurements, as this could affect the adhesion forces.

In each case, the animals were transferred onto experimental substrates with a moistened paintbrush. We have added text explicitly stating this in the methods section.

Kim et al. Nature Methods 2017 (10.1038/nmeth.4429) describes recording two images separated by less than 60 microseconds using a scientific CMOS camera with a frame rate of 200 Hz. This is accomplished by triggering a pulsed LED once at the end of one frame's capture window and then a second time at the beginning of the next frame's window (see Supplementary Figure 10). I'm not sure if this trick is widely known, but it's worth considering if the authors are running into a problem with movement between the two wavelength exposures in their WARP setup.

Thank you for this tip. We will take this under consideration for future work.

Is the setup compatible with optogenetics? (EG is the red light dim enough that it wouldn't activate CsChrimson, or could a longer wavelength led be used for interferometry?) If so, activation of mooncrawler descending neuron (MDN) could be used to study backward crawling (or thermogenetic activation of MDN), e.g. to contrast the sites and order of "anchoring" between the two directions of crawling.

The set-up is potentially compatible with optogenetics. We are in the process of exploring this in current ongoing work.

Reviewer #2 (Recommendations For The Authors):

Simplify/reduce the commentary about force measurements, and highlight the clear, qualitative descriptions of the novel locomotion patterns that they have observed. The microscopy and movements seem to matter more than the ground force estimations.

We have addressed these issues in our responses to Reviewer 2’s public comments.

eLife assessment

This study reports important findings about new locomotory dynamics of crawling Drosophila larva based on imaging the reaction forces during larval crawling. The evidence with the new high-resolution microscopy method is compelling, as it significantly improves the spatial, temporal, and force resolution compared to previous methods for studying Drosophila larva and could be applied to other crawling organisms. The manuscript explains the new technology, WARP microscopy, and provides analysis of the data to characterize small animal behavior and discover new crawling-associated anatomical features and motor patterns. The work will be of interest to the broad neuroscience community interested in the mechanisms of locomotion in a highly tractable model.

Reviewer #1 (Public Review):

This work demonstrates a new technique to characterize the interaction between a crawling larva and the substrate on which it is crawling, at much higher temporal speed and spatial resolution than previously possible. While I have some questions about the interpretation of the data, both the demonstration of WARP microscopy to characterize small animal behavior and the discovery of new crawling-associated anatomical features and motor patterns make the paper worthy of attention.

I thank the authors for providing data underlying the figures. In these uncropped data sets, the deformation of the substrate due to the surface tension of an adhering water layer is visible. I would hope the authors would provide a subset of these images and some of the accompanying information (e.g. that the deformation of the gel due to the water layer cannot be accurately calculated due to too-rapid phase wrapping in the interferogram) as supplements to the text, to aid in interpretation and understanding of the data. It is also worth noting that in the data provided, under the larva, the integral of the stress on the gel is upward, despite the downward force exerted by the protopodia.

Future work using this exciting technique might address the role of surface tension and the balance of forces and might also produce direct evidence to show that the protopodia serve to "anchor" segments of the larva not in motion. Indeed, the most exciting aspect of this work is the number of new questions it both raises and provides a technological pathway towards resolving.

Reviewer #2 (Public Review):

The biology and dynamics is well-described. The ERISM and WARP methods are state-of-the-art. The most important new information is the highly accurate and detailed maps of displacement. The real achievements are the new locomotory dynamics uncovered with amazing displacement measurements. One key discovery is the broad but shallow anchoring of the posterior body when the anterior body undertakes a "head sweep". Another discovery is the tripod indentation at the tail at the beginning of peristalsis cycles. This paper describes the detailed dynamics of anchoring for the first time. Anchoring behavior now has to be included in the motor sequence for Drosophila larva locomotion in any comprehensive biomechanical or neural model.

eLife assessment

This study provides the fundamental insight that TGN46, a single-pass membrane protein, acts as a cargo receptor for proteins at the Trans-Golgi Network. The authors demonstrate that the luminal domain of TGN46 is crucial for the incorporation of the soluble secretory protein PAUF into CARTS, a class of vesicles mediating TGN to surface traffic. The data presented are compelling, yielding a clear model for the sorting of cargos destined for secretion.

Reviewer #1 (Public Review):

Drawing on insights from preceding studies, the researchers pinpointed mutations within the spag7 gene that correlate with metabolic aberrations in mice. The precise function of spag7 has not been fully described yet, thereby the primary objective of this investigation is to unravel its pivotal role in the development of obesity and metabolic disease in mice. First, they generated a mice model lacking spag7 and observed that KO mice exhibited diminished birth size, which subsequently progressed to manifest obesity and impaired glucose tolerance upon reaching adulthood. This behaviour was primarily attributed to a reduction in energy expenditure. In fact, KO animals demonstrated compromised exercise endurance and muscle functionality, stemming from a deterioration in mitochondrial activity. Intriguingly, none of these effects was observed when using a tamoxifen-induced KO mouse model, implying that Spag7's influence is predominantly confined to the embryonic developmental phase. Explorations within placental tissue unveiled that mice afflicted by Spag7 deficiency experienced placental insufficiency, likely due to aberrant development of the placental junctional zone, a phenomenon that could impede optimal nutrient conveyance to the developing fetus. Overall, the authors assert that Spag7 emerges as a crucial determinant orchestrating accurate embryogenesis and subsequent energy balance in the later stages of life.

The study boasts several noteworthy strengths. Notably, it employs a combination of animal models and a thorough analysis of metabolic and exercise parameters, underscoring a meticulous approach. Furthermore, the investigation encompasses a comprehensive evaluation of fetal loss across distinct pregnancy stages, alongside a transcriptomic analysis of skeletal muscle, thereby imparting substantial value. Upon addressing the previously mentioned aspects, the study is poised to exert a substantial influence on the field, its significance reverberating significantly. The methodologies and data presented undoubtedly hold the potential to facilitate the community's deeper understanding of the ramifications stemming from disruptions during pregnancy, shedding light on their enduring impact on the metabolic well-being of subsequent generations.

Author Response

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

eLife assessment

This important study combines a range of advanced ultrastructural imaging approaches to define the unusual endosomal system of African trypanosomes. Compelling images show that instead of a distinct set of compartments, the endosome of these protists comprises a continuous system of membranes with functionally distinct subdomains as defined by canonical markers of early, late and recycling endosomes. The findings suggest that the endocytic system of bloodstream stages has evolved to facilitate the extraordinarily high rates of membrane turnover needed to remove immune complexes and survive in the blood, which is of interest to anyone studying infectious diseases.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

Bloodstream stages of the parasitic protist, Trypanosoma brucei, exhibit very high rates of constitutive endocytosis, which is needed to recycle the surface coat of Variant Surface Glycoproteins (VSGs) and remove surface immune complexes. While many studies have shown that the endo-lysosomal systems of T. brucei BF stages contain canonical domains, as defined by classical Rab markers, it has remained unclear whether these protists have evolved additional adaptations/mechanisms for sustaining these very high rates of membrane transport and protein sorting. The authors have addressed this question by reconstructing the 3D ultrastructure and functional domains of the T. brucei BF endosome membrane system using advanced electron tomography and super-resolution microscopy approaches. Their studies reveal that, unusually, the BF endosome network comprises a continuous system of cisternae and tubules that contain overlapping functional subdomains. It is proposed that a continuous membrane system allows higher rates of protein cargo segregation, sorting and recycling than can otherwise occur when transport between compartments is mediated by membrane vesicles or other fusion events.

Strengths:

The study is a technical tour-de-force using a combination of electron tomography, super-resolution/expansion microscopy, immune-EM of cryo-sections to define the 3D structures and connectivity of different endocytic compartments. The images are very clear and generally support the central conclusion that functionally distinct endocytic domains occur within a dynamic and continuous endosome network in BF stages.

Weaknesses:

The authors suggest that this dynamic endocytic network may also fulfil many of the functions of the Golgi TGN and that the latter may be absent in these stages. Although plausible, this comment needs further experimental support. For example, have the authors attempted to localize canonical makers of the TGN (e.g. GRIP proteins) in T. brucei BF and/or shown that exocytic carriers bud directly from the endosomes?

We agree with the criticism and have shortened the discussion accordingly and clearly marked it as speculation. However, we do not want to completely abandon our hypothesis.

The paragraph now reads:

Lines 740 – 751:

“Interestingly, we did not find any structural evidence of vesicular retrograde transport to the Golgi. Instead, the endosomal ‘highways’ extended throughout the posterior volume of the trypanosomes approaching the trans-Golgi interface. It is highly plausible that this region represents the convergence point where endocytic and biosynthetic membrane trafficking pathways merge. A comparable merging of endocytic and biosynthetic functions has been described for the TGN in plants. Different marker proteins for early and recycling endosomes were shown to be associated and/ or partially colocalized with the TGN suggesting its function in both secretory and endocytic pathways (reviewed in Minamino and Ueda, 2019). As we could not find structural evidence for the existence of a TGN we tentatively propose that trypanosomes may have shifted the central orchestrating function of the TGN as a sorting hub at the crossroads of biosynthetic and recycling pathways to the endosome. Although this is a speculative scenario, it is experimentally testable.”

Furthermore, we removed the lines 51 - 52, which included the suggestion of the TGN as a master regulator, from the abstract.

Reviewer #2 (Public Review):

The authors suggest that the African trypanosome endomembrane system has unusual organisation, in that the entire system is a single reticulated structure. It is not clear if this is thought to extend to the lysosome or MVB. There is also a suggestion that this unusual morphology serves as a trans-(post)Golgi network rather than the more canonical arrangement.

The work is based around very high-quality light and electron microscopy, as well as utilising several marker proteins, Rab5A, 11 and 7. These are deemed as markers for early endosomes, recycling endosomes and late or pre-lysosomes. The images are mostly of high quality but some inconsistencies in the interpretation, appearance of structures and some rather sweeping assumptions make this less easy to accept. Two perhaps major issues are claims to label the entire endosomal apparatus with a single marker protein, which is hard to accept as certainly this reviewer does not really even know where the limits to the endosomal network reside and where these interface with other structures. There are several additional compartments that have been defined by Rob proteins as well, and which are not even mentioned. Overall I am unconvinced that the authors have demonstrated the main things they claim.<br /> The endomembrane system in bloodstream form T. brucei is clearly delimited. Compared to mammalian cells it is tidy and confined to the posterior part of the spindleshaped cell. The endoplasmic reticulum is linked to one side of the longitudinal cell axis, marked by the attached flagellum, while the mitochondrion locates to the opposite side. Glycosomes are easily identifiable as spheres, as are acidocalcisomes, which are smaller than glycosomes and – in electron micrographs – are characterized by high electron density. All these organelles extend beyond the nucleus, which is not the case for the endosomal compartment, the lysosome and the Golgi. The vesicles found in the posterior half of the trypanosome cell are quantitatively identifiable as COP1, CCVI or CCVII vesicles, or exocytic carriers. The lysosome has a higher degree of morphological plasticity, but this is not topic of the present work. Thus, the endomembrane system in T. brucei is comparatively well structured and delimited, which is why we have chosen trypanosomes as cell biological model.

We have published EP1::GFP as marker for the endosome system and flagellar pocket back in 2004. We have defined the fluid phase volume of the trypanosome endosome in papers published between 2002 and 2007. This work was not intended to represent the entirety of RAB proteins. We were only interested in 3 canonical markers for endosome subtypes. We do not claim anything that is not experimentally tested, we have clearly labelled our hypotheses as such, and we do not make sweeping assumptions.

The approaches taken are state-of-the-art but not novel, and because of the difficulty in fully addressing the central tenet, I am not sure how much of an impact this will have beyond the trypanosome field. For certain this is limited to workers in the direct area and is not a generalisable finding.

To the best of our knowledge, there is no published research that has employed 3D Tokuyasu or expansion microscopy (ExM) to label endosomes. The key takeaway from our study, which is the concept that "endosomes are continuous in trypanosomes" certainly is novel. We are not aware of any other report that has demonstrated this aspect.

The doubts formulated by the reviewer regarding the impact of our work beyond the field of trypanosomes are not timely. Indeed, our results, and those of others, show that the conclusions drawn from work with just a few model organisms is not generalisable. We are finally on the verge of a new cell biology that considers the plethora of evolutionary solutions beyond ophistokonts. We believe that this message should be widely acknowledged and considered. And we are certainly not the only ones who are convinced that the term "general relevance" is unscientific and should no longer be used in biology.

Reviewer #3 (Public Review):

Summary:

As clearly highlighted by the authors, a key plank in the ability of trypanosomes to evade the mammalian host’s immune system is its high rate of endocytosis. This rapid turnover of its surface enables the trypanosome to ‘clean’ its surface removing antibodies and other immune effectors that are subsequently degraded. The high rate of endocytosis is likely reflected in the organisati’n and layout of the endosomal system in these parasites. Here, Link et al., sought to address this question using a range of light and three-dimensional electron microscopy approaches to define the endosomal organisation in this parasite.

Before this study, the vast majority of our information about the make-up of the trypanosome endosomal system was from thin-section electron microscopy and immunofluorescence studies, which did not provide the necessary resolution and 3D information to address this issue. Therefore, it was not known how the different structures observed by EM were related. Link et al., have taken advantage of the advances in technology and used an impressive combination of approaches at the LM and EM level to study the endosomal system in these parasites. This innovative combination has now shown the interconnected-ness of this network and demonstrated that there are no ‘classical’ compartments within the endosomal system, with instead different regions of the network enriched in different protein markers (Rab5a, Rab7, Rab11).

Strengths:

This is a generally well-written and clear manuscript, with the data well-presented supporting the majority of the conclusions of the authors. The authors use an impressive range of approaches to address the organisation of the endosomal system and the development of these methods for use in trypanosomes will be of use to the wider parasitology community.

I appreciate their inclusion of how they used a range of different light microscopy approaches even though for instance the dSTORM approach did not turn out to be as effective as hoped. The authors have clearly demonstrated that trypanosomes have a large interconnected endosomal network, without defined compartments and instead show enrichment for specific Rabs within this network.

Weaknesses:

My concerns are:

i) There is no evidence for functional compartmentalisation. The classical markers of different endosomal compartments do not fully overlap but there is no evidence to show a region enriched in one or other of these proteins has that specific function. The authors should temper their conclusions about this point.

The reviewer is right in stating that Rab-presence does not necessarily mean Rabfunction. However, this assumption is as old as the Rab literature. That is why we have focused on the 3 most prominent endosomal marker proteins. We report that for endosome function you do not necessarily need separate membrane compartments. This is backed by our experiments.

ii) The quality of the electron microscopy work is very high but there is a general lack of numbers. For example, how many tomograms were examined? How often were fenestrated sheets seen? Can the authors provide more information about how frequent these observations were?

The fenestrated sheets can be seen in the majority of the 37 tomograms recorded of the posterior volume of the parasites. Furthermore, we have randomly generated several hundred tiled (= very large) electron micrographs of bloodstream form trypanosomes for unbiased analyses of endomembranes. In these 2D-datasets the “footprint” of the fenestrated flat and circular cisternae is frequently detectable in the posterior cell area.

We now have included the corresponding numbers in all EM figure legends.

iii) The EM work always focussed on cells which had been processed before fixing. Now, I understand this was important to enable tracers to be used. However, given the dynamic nature of the system these processing steps and feeding experiments may have affected the endosomal organisation. Given their knowledge of the system now, the authors should fix some cells directly in culture to observe whether the organisation of the endosome aligns with their conclusions here.

This is a valid criticism; however, it is the cell culture that provides an artificial environment. As for a possible effect of cell harvesting by centrifugation on the integrity and functionality of the endosome system, we consider this very unlikely for one simple reason. The mechanical forces acting in and on the parasites as they circulate in the extremely crowded and confined environment of the mammalian bloodstream are obviously much higher than the centrifugal forces involved in cell preparation. This becomes particularly clear when one considers that the mass of the particle to be centrifuged determines the actual force exerted by the g-forces. Nevertheless, the proposed experiment is a good control, although much more complex than proposed, since tomography is a challenging technique. We have performed the suggested experiment and acquired tomograms of unprocessed cells. The corresponding data is now included as supplementary movie 2, 3 and 4. We refer to it in lines 202 – 206: To investigate potential impacts of processing steps (cargo uptake, centrifugation, washing) on endosomal organization, we directly fixed cells in the cell culture flask, embedded them in Epon, and conducted tomography. The resulting tomograms revealed endosomal organization consistent with that observed in cells fixed after processing (see Supplementary movie 2, 3, and 4).

We furthermore thank the reviewer for the experiment suggestion in the acknowledgments.

iv) The discussion needs to be revamped. At the moment it is just another run through of the results and does not take an overview of the results presenting an integrated view. Moreover, it contains reference to data that was not presented in the results.

We have improved the discussion accordingly.

Recommendations for the authors:

The reviewers concurred about the high calibre of the work and the importance of the findings.

They raised some issues and made some suggestions to improve the paper without additional experiments - key issues include

(1) Better referencing of the trypanosome endocytosis/ lysosomal trafficking literature.

The literature, especially the experimental and quantitative work, is very limited. We now provide a more complete set of references. However, we would like to mention that we had cited a recent review that critically references the trypanosome literature with emphasis on the extensive work done with mammalian cells and yeast.

(2) Moving the dSTORM data that detracts from otherwise strong data in a supplementary figure.

We have done this.

(3) Removal of the conclusion that the continuous endosome fulfils the functions of TGN, without further evidence.

As stated above, this was not a conclusion in our paper, but rather a speculation, which we have now more clearly marked as such. Lines 740 to 751 now read:

“Interestingly, we did not find any structural evidence of vesicular retrograde transport to the Golgi. Instead, the endosomal ‘highways’ extended throughout the posterior volume of the trypanosomes approaching the trans-Golgi interface. It is highly plausible that this region represents the convergence point where endocytic and biosynthetic membrane trafficking pathways merge. A comparable merging of endocytic and biosynthetic functions was already described for the TGN in plants. Different marker proteins for early and recycling endosomes were shown to be associated and/ or partially colocalized with the TGN suggesting its function in both secretory and endocytic pathways (reviewed in Minamino and Ueda, 2019). As we could not find structural evidence for the existence of a TGN we tentatively propose that trypanosomes may have shifted the central orchestrating function of the TGN as a sorting hub at the crossroads of biosynthetic and recycling pathways to the endosome. Although this is a speculative scenario, it is experimentally testable.”

(4) Broader discussion linking their findings to other examples of organelle maturation in eukaryotes (e.g cisternal maturation of the Golgi)

We have improved the discussion accordingly.

Reviewer #1 (Recommendations For The Authors):

What are the multi-vesicular vesicles that surround the marked endosomal compartments in Fig 1. Do they become labelled with fluid phase markers with longer incubations (e.g late endosome/ lysosomal)?

The function of MVBs in trypanosomes is still far from being clear. They are filled with fluid phase cargo, especially ferritin, but are devoid of VSG. Hence it is likely that MVBs are part of the lysosomal compartment. In fact, this part of the endomembrane system is highly dynamic. MVBs can be physically connected to the lysosome or can form elongated structures. The surprising dynamics of the trypanosome lysosome will be published elsewhere.

Figure 2. The compartments labelled with EP1::Halo are very poorly defined due to the low levels of expression of the reporter protein and/or sensitivity of detection of the Halo tag. Based on these images, it would be hard to conclude whether the endosome network is continuous or not. In this respect, it is unclear why the authors didn't use EP1-GFP for these analyses? Given the other data that provides more compelling evidence for a single continuous compartment, I would suggest removing Fig 2A.

We have used EP1::GFP to label the entire endosome system (Engstler and Boshart, 2004). Unfortunately, GFP is not suited for dSTORM imaging. By creating the EP1::Halo cell line, we were able to utilize the most prominent dSTORM fluorescent dye, Alexa 647. This was not primarily done to generate super resolution images, but rather to measure the dynamics of the GPI-anchored, luminal protein EP with single molecule precision. The results from this study will be published separately. But we agree with the reviewer and have relocated the dSTORM data to the supplementary material.

The observation that Rab5a/7 can be detected in the lumen of lysosome is interesting. Mechanistically, this presumably occurs by invagination of the limiting membrane of the lysosome. Is there any evidence that similar invagination of cytoplasmic markers occurs throughout or in subdomains of the endocytic network (possibly indicative of a 'late endosome' domain)?

So far, we have not observed this. The structure of the lysosome and the membrane influx from the endosome are currently being investigated.

The authors note that continuity of functionally distinct membrane compartments in the secretory/endocytic pathways has been reported in other protists (e.g T. cruzi). A particular example that could be noted is the endo-lysosomal system of Dictyostelium discoideum which mediates the continuous degradation and eventual expulsion of undigested material.

We tried to include this in the discussion but ultimately decided against it because the Dictyostelium system cannot be easily compared to the trypanosome endosome.

Reviewer #2 (Recommendations For The Authors):

Abstract

Not sure that 'common' is the correct term here. Frequent, near-universal..... it would be true that endocytosis is common across most eukaryotes.

We have changed the sentence to “common process observed in most eukaryotes” (line 33).

Immune evasion - the parasite does not escape the immune system, but does successfully avoid its impact, at least at the population level.

We have replaced the word “escape” with “evasion” (line 35).

The third sentence needs to follow on correctly from the second. Also, more than Igs are internalised and potentially part of immune evasion, such as C3, Factor H, ApoL1 etcetera.

We believe that there may be a misunderstanding here. The process of endocytic uptake and lysosomal degradation has so far only been demonstrated in the context of VSGbound antibodies, which is why we only refer to this. Of course, the immune system comprises a wide range of proteins and effector molecules, all of which could be involved in immune evasion.

I do not follow the logic that the high flux through the endocytic system in trypanosomes precludes distinct compartmentalisation - one could imagine a system where a lot of steps become optimised for example. This idea needs expanding on if it is correct.

Membrane transport by vesicle transfer between several separate membrane compartments would be slower than the measured rate of membrane flux.

Again I am not sure 'efficient' on line 40. It is fast, but how do you measure efficiency? Speed and efficiency are not the same thing.

We have replaced the word “efficient” with “fast” (line 42).

The basis for suggesting endosomes as a TGN is unclear. Given that there are AP complexes, retromer, exocyst and other factors that are part of the TGN or at least post-G differentiation of pathways in canonical systems, this seems a step too far. There really is no evidence in the rest of the MS that seems to support this.

Yes, we agree and have clarified the discussion accordingly. We have not completely removed the discussion on the TGN but have labelled it more clearly as speculation.

I am aware I am being pedantic here, but overall the abstract seems to provide an impression of greater novelty than may be the case and makes several very bold claims that I cannot see as fully valid.

We are not aware of any claim in the summary that we have not substantiated with experiments, or any hypothesis that we have not explained.

Moreover, the concept of fused or multifunctional endosomes (or even other endomembrane compartments) is old, and has been demonstrated in metazoan cells and yeast. The concept of rigid (in terms of composition) compartments really has been rejected by most folks with maturation, recycling and domain structures already well-established models and concepts.

We agree that the (transient) presence of multiple Rab proteins decorating endosomes has been demonstrated in various cell types. This finding formed the basis for the endosomal maturation model in mammals and yeast, which has replaced the previous rigid compartment model.

However, we do not appreciate attempts to question the originality of our study by claiming that similar observations have been made in metazoans or yeast. This is simply wrong. There are no reports of a functionally structured, continuous, single and large endosome in any other system. The only membrane system that might be similar was described in the American parasite Trypanosoma cruzi, however, without the use of endosome markers or any functional analysis. We refer to this study in the discussion.

In summary, the maturation model falls short in explaining the intricacies of the membrane system we have uncovered in trypanosomes. Therefore, one plausible interpretation of our data is that the overall architecture of the trypanosome endosomes represents an adaptation that enables the remarkable speed of plasma membrane recycling observed in these parasites. In our view, both our findings and their interpretation are novel and worth reporting. Again, modern cell biology should recognize that evolution has developed many solutions for similar processes in cells, about whose diversity we have learned almost nothing because of our reductionist view. A remarkable example of this are the Picozoa, tiny bipartite eukaryotes that pack the entire nutritional apparatus into one pouch and the main organelles with the locomotor system into the other. Another one is the “extreme” cell biology of many protozoan parasites such as Giardia, Toxpoplasma or Trypanosoma.

Higher plants have been well characterised, especially at the level of Rab/Arf proteins and adaptins.

We now mention plant endosomes in our brief discussion of the trypanosome TGN. Lines 744 – 747:

“A comparable merging of endocytic and biosynthetic functions was already described for the TGN in plants. Different marker proteins for early and recycling endosomes were shown to be associated and/ or partially colocalized with the TGN suggesting its function in both secretory and endocytic pathways (reviewed in Minamino and Ueda, 2019).”

The level of self-citing in the introduction is irritating and unscholarly. I have no qualms with crediting the authors with their own excellent contributions, but work from Dacks, Bangs, Field and others seems to be selectively ignored, with an awkward use of the authors' own publications. Diversity between organisms for example has been a mainstay of the Dacks lab output, Rab proteins and others from Field and work on exocytosis and late endosomal systems from Bangs. These efforts and contributions surely deserve some recognition?

This is an original article and not a review. For a comprehensive overview the reviewer might read our recent overview article on exo- and endocytic pathways in trypanosomes, in which we have extensively cited the work of Mark Field, Jay Bangs and Joel Dacks. In the present manuscript, we have cited all papers that touch on our results or are otherwise important for a thorough understanding of our hypotheses. We do not believe that this approach is unscientific, but rather improves the readability of the manuscript. Nevertheless, we have now cited additional work.

For the uninitiated, the posterior/anterior axis of the trypanosome cell as well as any other specific features should be defined.

In lines 102 - 110 we wrote:

“This process of antibody clearance is driven by hydrodynamic drag forces resulting from the continuous directional movement of trypanosomes (Engstler et al., 2007). The VSG-antibody complexes on the cell surface are dragged against the swimming direction of the parasite and accumulate at the posterior pole of the cell. This region harbours an invagination in the plasma membrane known as the flagellar pocket (FP) (Gull, 2003; Overath et al., 1997). The FP, which marks the origin of the single attached flagellum, is the exclusive site for endo- and exocytosis in trypanosomes (Gull, 2003; Overath et al., 1997). Consequently, the accumulation of VSG-antibody complexes occurs precisely in the area of bulk membrane uptake.”

We think this sufficiently introduces the cell body axes.

I don't understand the comment concerning microtubule association. In mammalian cells, such association is well established, but compartments still do not display precise positioning. This likely then has nothing to do with the microtubule association differences.

We have clarified this in the text (lines 192 – 199). There is no report of cytoplasmic microtubules in trypanosomes. All microtubules appear to be either subpellicular or within the flagellum. To maintain the structure and position of the endosomal apparatus, they should be associated either with subpellicular microtubules, as is the case with the endoplasmic reticulum, or with the more enigmatic actomyosin system of the parasites. We have been working on the latter possibility and intend to publish a follow-up paper to the present manuscript.

The inability to move past the nucleus is a poor explanation. These compartments are dynamic. Even the nucleus does interesting things in trypanosomes and squeezes past structures during development in the tsetse fly.

The distance between the nucleus and the microtubule cytoskeleton remains relatively constant even in parasites that squeeze through microfluidic channels. This is not unexpected as the nucleus can be highly deformed. A structure the size of the endosome will not be able to physically pass behind the nucleus without losing its integrity. In fact, the recycling apparatus is never found in the anterior part of the trypanosome, most probably because the flagellar pocket is located at the posterior cell pole.

L253 What is the evidence that EP1 labels the entire FP and endosomes? This may be extensive, but this claim requires rather more evidence. This is again suggested at l263. Again, please forgive me for being pedantic, but this is an overstatement unless supported by evidence that would be incredibly difficult to obtain. This is even sort of acknowledged on l271 in the context of non-uniform labelling. This comes again in l336.

The evidence that EP1 labels the entire FP and endosomes is presented here: Engstler and Boshart, 2004; 10.1101/gad.323404).

Perhaps I should refrain from comments on the dangers of expansion microscopy, or asking what has actually been gained here. Oddly, the conclusion on l290 is a fair statement that I am happy with.

An in-depth discussion regarding the advantages and disadvantages of expansion microscopy is beyond the manuscript's intended scope. Our approach involved utilizing various imaging techniques to confirm the validity of our findings. We appreciate that our concluding sentence is pleasing.

F2 - The data in panel A seem quite poor to me. I also do not really understand why the DAPI stain in the first and second columns fails to coincide or why the kinetoplast is so diffuse in the second row. The labelling for EP1 presents as very small puncta, and hence is not evidence for a continuum. What is the arrow in A IV top? The data in panel B are certainly more in line with prior art, albeit that there is considerable heterogeneity in the labelling and of the FP for example. Again, I cannot really see this as evidence for continuity. There are gaps.... Albeit I accept that labelling of such structures is unlikely to ever be homogenous.

We agree that the dSTORM data represents the least robust aspect of the findings we have presented, and we concur with relocating it to the supplementary material.

F3 - Rather apparent, and specifically for Rab7, that there is differential representation - for example, Cell 4 presents a single Rab7 structure while the remaining examples demonstrate more extensive labelling. Again, I am content that these are highly dynamic strictures but this needs to be addressed at some level and commented upon. If the claim is for continuity, the dynamics observed here suggest the usual; some level of obvious overlap of organellar markers, but the representation in F3 is clever but not sure what I am looking at. Moreover, the title of the figure is nothing new. What is also a bit odd is that the extent of the Rab7 signal, and to some extent the other two Rabs used, is rather variable, which makes this unclear to me as to what is being detected. Given that the Rab proteins may be defining microdomains or regions, I would also expect a region of unique straining as well as the common areas. This needs to at least be discussed.

The differences in the representation result from the dynamics of the labelled structures. Therefore, we have selected different cells to provide examples of what the labelling can look like. We now mention this in the results section.

The overlap of the different Rab signals was perhaps to be expected, but we now have demonstrated it experimentally. Importantly, we performed a rigorous quantification by calculating the volume overlaps and the Pearson correlation coefficients.

In previous studies the data were presented as maximal intensity projections, which inherently lack the complete 3D information.

We found that Rab proteins define microdomains and that there are regions of unique staining as well as common areas, as shown in Figure 3. The volumes do not completely overlap. This is now more clearly stated in lines 315 – 319:

“These objects showed areas of unique staining as well as partially overlapping regions. The pairwise colocalization of different endosomal markers is shown in Figure 3 A, XI - XIII and 3 B. The different cells in Figure 3 B were selected to represent the dynamic nature of the labelled structures. Consequently, the selected cells provide a variety of examples of how the labelling can appear.”

This had already been stated in lines 331 – 336:

“In summary, the quantitative colocalization analyses revealed that on the one hand, the endosomal system features a high degree of connectivity, with considerable overlap of endosomal marker regions, and on the other hand, TbRab5A, TbRab7, and TbRab11 also demarcate separated regions in that system. These results can be interpreted as evidence of a continuous endosomal membrane system harbouring functional subdomains, with a limited amount of potentially separated early, late or recycling endosomes.”

F4-6 - Fabulous images. But a couple of issues here; first, as the authors point out, there is distance between the gold and the antigen. So, this of course also works in the z-plane as well as the x/y-planes and some of the gold may well be associated with membraneous figures that are out of the plane, which would indicate an absence of colinearity on one specific membrane. Secondly, in several instances, we have Rab7 essentially mixed with Rab11 or Rab5 positive membrane. While data are data and should be accepted, this is difficult to reconcile when, at least to some level, Rab7 is a marker for a late-endosomal structure and where the presence of degradative activity could reside. As division of function is, I assume, the major reason for intracellular compartmentalisation, such a level of admixture is hard to rationalise. A continuum is one thing but the data here seem to be suggesting something else, i.e. almost complete admixture.

We are grateful for the positive feedback regarding the image quality. It is true that the "linkage error," representing the distance between the gold and the antigen, also functions to some extent in the z-axis. However, it's important to note that the zdimension of the section in these Figures is 55 nm. Nevertheless, it's interesting to observe that membranes, which may not be visible within the section itself but likely the corresponding Rab antigen, is discernible in Figure 4C (indicated by arrows).

We have clarified this in lines 397 – 400:

“Consequently, gold particles located further away may represent cytoplasmic TbRab proteins or, as the “linkage error” can also occur in the z-plane, correspond to membranes that are not visible within the 55 nm thickness of the cryosection (Figure 4, panel C, arrows). “

The coexistence of different Rabs is most likely concentrated in regions where transitions between different functions are likely. Our focus was primarily on imaging membranes labelled with two markers. We wanted to show that the prevailing model of separate compartments in the trypanosome literature is not correct.

F7 - Not sure what this adds beyond what was published by Grunfelder.

First, this figure is an important control that links our results to published work (Grünfelder et al. (2003)). Second, we include double staining of cargo with Rab5, Rab7, and Rab11, whereas Grünfelder focused only on Rab11. Therefore, our data is original and of such high quality that it warrants a main figure.

F8 - and l583. This is odd as the claim is 'proof' which in science is a hard thing to claim (and this is definitely not at a six sigma level of certainty, as used by the physics community). However, I am seeing structures in the tomograms which are not contiguous - there are gaps here between the individual features (Green in the figure).

We have replaced the term "proof". It is important to note that the structures in individual tomograms cannot all be completely continuous because the sections are limited to a thickness of 250 nm. Therefore, it is likely that they have more connectivity above and below the imaged section. Nevertheless, we believe that the quality of the tomograms is satisfactory, considering that 3D Tokuyasu is a very demanding technique and the production of serial Tokuyasu tomograms is not feasible in practice.

Discussion - Too long and the self-citing of four papers from the corresponding author to the exclusion of much prior work is again noted, with concerns about this as described above. Moreover, at least four additional Rab proteins are known associated with the trypanosome endosomal system, 4, 5B, 21 and 28. These have been completely ignored.

We have outlined our position on referencing in original articles above. We also explained why we focused on the key marker proteins associated with early (Rab5), late (Rab7) and recycling endosomes (Rab11). We did not ignore the other Rabs, we just did not include them in the present study.

Overall this is disappointing. I had expected a more robust analysis, with a clearer discussion and placement in context. I am not fully convinced that what we have here is as extreme as claimed, or that we have a substantial advance. There is nothing here that is mechanistic or the identification of a new set of gene products, process or function.

We do not think that this is constructive feedback.

This MS suggests that the endosomal system of African trypanosomes is a continuum of membrane structures rather than representing a set of distinct compartments. A combination of light and electron microscopy methods are used in support. The basic contention is very challenging to prove, and I'm not convinced that this has been. Furthermore, I am also unclear as to the significance of such an organisation; this seems not really addressed.

We acknowledge and respect varying viewpoints, but we hold a differing perspective in this matter. We are convinced that the data decisively supports our interpretation. May future work support or refute our hypothesis.

Reviewer #3 (Recommendations For The Authors):

Line 81 - delete 's

Done.

Generally, the introduction was very well written and clearly summarised our current understanding but the paragraph beginning line 134 felt out of place and repeated some of the work mentioned earlier.

We have removed this paragraph.

For the EM analysis throughout quantification would be useful as highlighted in the public review. How many tomograms were examined, and how often were types of structures seen? I understand the sample size is often small but this would help the reader appreciate the diversity of structures seen.

We have included the numbers.

Following on from this how were the cells chosen for tomogram analysis? For example, the dividing cell in 1D has palisades associating with the new pocket - is this commonly seen? Does this reflect something happening in dividing cells. This point about endosomal division was picked up in the discussion but there was little about in the main results.

This issue is undoubtedly inherent to the method itself, and we have made efforts to mitigate it by generating a series of tomograms recorded randomly. We have refrained from delving deeper into the intricacies of the cell cycle in this manuscript, as we believe that it warrants a separate paper.

As the authors prosecute, the co-localisation analysis highlights the variable nature of the endosome and the overlap of different markers. When looking at the LM analysis, I was struck by the variability in the size and number of labelled structures in the different cells. For example, in 3A Rab7 is 2 blobs but in 3B Cell 1 it is 4/5 blobs. Is this just a reflection of the increase in the endosome during the cell cycle?

The variability in representation is a direct consequence of the dynamic nature of the labelled structures. For this reason, we deliberately selected different cells to represent examples of how the labelling can look like. We have decided not to mention the dynamics of the endosome during the cell cycle. This will be the subject of a further report.

Moreover, Rab 11 looks to be the marker covering the greatest volume of the endosomal system - is this true? I think there's more analysis of this data that could be done to try and get more information about the relative volumes etc of the different markers that haven't been drawn out. The focus here is on the co-localisation.

Precisely because we recognize the importance of this point, we intend to turn our attention to the cell cycle in a separate publication.

I appreciate that it is an awful lot of work to perform the immuno-EM and the data is of good quality but in the text, there could be a greater effort to tie this to the LM data. For example, from the Rab11 staining in LM you would expect this marker to be the most extensive across the networks - is this reflected in the EM?

For the immuno-EM there were no numbers, the authors had measured the position of the gold but what was the proportion of gold that was in/near membranes for each marker? This would help the reader understand both the number of particles seen and the enrichment of the different regions.

Our original intent was to perform a thorough quantification (using stereology) of the immuno-EM data. However, we later realized that the necessary random imaging approach is not suitable for Tokuyasu sections of trypanosomes. In short, the cells are too far apart, and the cell sections are only occasionally cut so that the endosomal membranes are sufficiently visible. Nevertheless, we continue to strive to generate more quantitative data using conventional immuno-EM.

The innovative combination of Tokuyasu tomograms with immuno-EM was great. I noted though that there was a lack of fenestration in these models. Does this reflect the angle of the model or the processing of these samples?

We are grateful to the referee, as we have asked ourselves the same question. However, we do not attribute the apparent lack of fenestration to the viewing angle, since we did not find fenestration in any of the Tokuyasu tomograms. Our suspicion is more directed towards a methodological problem. In the Tokuyasu workflow, all structures are mainly fixed with aldehydes. As a result, lipids are only effectively fixed through their association with membrane proteins. We suggest that the fenestration may not be visible because the corresponding lipids may have been lost due to incomplete fixation.

We now clearly state this in the lines 563 – 568.

“Interestingly, these tomograms did not exhibit the fenestration pattern identified in conventional electron tomography. We suspect that this is due to methodological reasons. The Tokuyasu procedure uses only aldehydes to fix all structures. Consequently, effective fixation of lipids occurs only through their association with membrane proteins. Thus, the lack of visible fenestration is likely due to possible loss of lipids during incomplete fixation.”

The discussion needs to be reworked. Throughout it contains references to results not in the main results section such as supplementary movie 2 (line 735). The explicit references to the data and figures felt odd and more suited to the results rather than the discussion. Currently, each result is discussed individually in turn and more effort needs to be made to integrate the results from this analysis here but also with previous work and the data from other organisms, which at the moment sits in a standalone section at the end of the discussion.

We have improved the discussion and removed the previous supplementary movies 2 and 3. Supplementary movie 1 is now mentioned in the results section.

Line 693 - There was an interesting point about dividing cells describing the maintenance of endosomes next to the old pocket. Does that mean there was no endosome by the new pocket and if so where is this data in the manuscript? This point relates back to my question about how cells were chosen for analysis - how many dividing cells were examined by tomography?

The fate of endosomes during the cell cycle is not the subject of this paper. In this manuscript we only show only one dividing cell using tomography. An in-depth analysis focusing on what happens during the cell cycle will be published separately.

Line 729 - I'm unclear how this represents a polarization of function in the flagellar pocket. The pocket I presume is included within the endosomal system for this analysis but there was no specific mention of it in the results and no marker of each position to help define any specialisation. From the results, I thought the focus was on endosomal co-localisation of the different markers. If the authors are thinking about specialisation of the pocket this paper from Mark Field shows there is evidence for the exocyst to be distributed over the entire surface of the pocket, which is relevant to the discussion here. Boehm, C.M. et al. (2017) The trypanosome exocyst: a conserved structure revealing a new role in endocytosis. PLoS Pathog. 13, e1006063

We have formulated our statement more cautiously. However, we are convinced that membrane exchange cannot physically work without functional polarization of the pocket. We know that Rab11, for example, is not evenly distributed on the pocket. By the way, in Boehm et al. (2017) the exocyst is not shown to cover the entire pocket (as shown in Supplementary Video 1).

We now refer to Boehm et al. (Lines 700 – 703):

“Boehm et al (2017) report that in the flagellar pocket endocytic and exocytic sites are in close proximity but do not overlap. We further suggest that the fusion of EXCs with the flagellar pocket membrane and clathrin-mediated endocytosis take place on different sites of the pocket. This disparity explains the lower colocalization between TbRab11 and TbRab5A.”

Line 735 - link to data not previously mentioned I think. When I looked at this data I couldn't find a key to explain what all the different colours related to.

We have removed the previous supplementary movies 2 and 3. We now reference supplementary movie 1 in the results section.

eLife assessment

This important study combines a range of advanced ultrastructural imaging approaches to define the unusual endosomal system of African trypanosomes. Compelling images reveal that, unlike a conventional set of compartments, the endosome in these protists forms a continuous membrane system with functionally distinct subdomains, as defined by canonical markers for early, late, and recycling endosomes. The findings compellingly support that the endocytic system in bloodstream stages has adapted to support remarkably high rates of membrane turnover necessary for immune complex removal and survival in the blood. This research is particularly relevant to those investigating infectious diseases

Reviewer #2 (Public Review):

The authors suggest that the African trypanosome endomembrane system has unusual organisation, in that the entire system is a single reticulated structure. It is not clear if this is thought to extend to the lysosome or MVB. There is also a suggestion that this unusual morphology serves as a trans-(post)Golgi network rather than the more canonical arrangement.

The updated manuscript is significantly improved. I remain at slight odds with the author's push for the lack of generality as important, and the new cell biology that we have been on the verge of for decades. However, that is a scholarly issue and is not grounds for any further revision of the present manuscript.

Reviewer #3 (Public Review):

Summary:<br /> A key element in the ability of trypanosomes to evade the mammalian host's immune system is its high rate of endocytosis. This rapid turnover of its surface enables the trypanosome to 'clean' its surface removing antibodies and other immune effectors that are subsequently degraded. The high rate of endocytosis is likely reflected in the organisation of the endosomal system in these parasites. Here, Link et al., sought to address this question using a range of light and three-dimensional electron microscopy approaches to define the endosomal organisation in this parasite.

Before this study, the vast majority of our information about the make-up of the trypanosome endosomal system was from thin section electron microscopy and immunofluorescence studies, which did not provide the necessary resolution and 3D information to address this issue. Therefore, it was not known how the different structures observed by EM were related. Link et al., have taken advantage of the advances in technology and used an impressive combination of approaches at the LM and EM level to study the endosomal system in these parasites. This innovative combination has now shown the interconnected-ness of this network and demonstrated that there are no 'classical' compartments within the endosomal system, with instead different regions of the network enriched in different protein markers (Rab5a, Rab7, Rab11). Overall, the authors have achieved their aims, with results supporting their conclusions.

This is a well written manuscript in which the authors use an impressive range of approaches to address the organisation of the endosomal system. The authors have clearly demonstrated that trypanosomes have a large interconnected endosomal network, without defined compartments and instead shows enrichment for specific Rabs within this network. I appreciate their inclusion of how they used a range of different light microscopy approaches even though for instance the dSTORM approach did not turn out to be as effective as hoped.

The methodological impact of this work has the potential to be large, as the authors have introduced a range of advanced EM techniques for the study of trypanosomes. Moreover, the study of fundamental biological processes such as endosomal trafficking in divergent eukaryotes is important to define the limits within which this process operates.

eLife assessment

The study addresses a central question in systems neuroscience (validation of active inference models of exploration) using a combination of behavior, neuroimaging, and modelling. The data provided are useful but incomplete, missing critical detail. Additionally, some of the conclusions require a comparison model, and proper consideration of alternative explanations.

Reviewer #1 (Public Review):

Summary:<br /> This paper presents a compelling and comprehensive study of decision-making under uncertainty. It addresses a fundamental distinction between belief-based (cognitive neuroscience) formulations of choice behaviour with reward-based (behavioural psychology) accounts. Specifically, it asks whether active inference provides a better account of planning and decision-making, relative to reinforcement learning. To do this, the authors use a simple but elegant paradigm that includes choices about whether to seek both information and rewards. They then assess the evidence for active inference and reinforcement learning models of choice behaviour, respectively. After demonstrating that active inference provides a better explanation of behavioural responses, the neuronal correlates of epistemic and instrumental value (under an optimised active inference model) are characterised using EEG. Significant neuronal correlates of both kinds of value were found in sensor and source space. The source space correlates are then discussed sensibly, in relation to the existing literature on the functional anatomy of perceptual and instrumental decision-making under uncertainty.

Strengths:<br /> The strengths of this work rest upon the theoretical underpinnings and careful deconstruction of the various determinants of choice behaviour using active inference. A particular strength here is that the experimental paradigm is designed carefully to elicit both information-seeking and reward-seeking behaviour; where the information-seeking is itself separated into resolving uncertainty about the context (i.e., latent states) and the contingencies (i.e., latent parameters), under which choices are made. In other words, the paradigm - and its subsequent modelling - addresses both inference and learning as necessary belief and knowledge-updating processes that underwrite decisions.

The authors were then able to model belief updating using active inference and then look for the neuronal correlates of the implicit planning or policy selection. This speaks to a further strength of this study; it provides some construct validity for the modelling of belief updating and decision-making; in terms of the functional anatomy as revealed by EEG. Empirically, the source space analysis of the neuronal correlates licences some discussion of functional specialisation and integration at various stages in the choices and decision-making.

In short, the strengths of this work rest upon a (first) principles account of decision-making under uncertainty in terms of belief updating that allows them to model or fit choice behaviour in terms of Bayesian belief updating - and then use relatively state-of-the-art source reconstruction to examine the neuronal correlates of the implicit cognitive processing.

Weaknesses:<br /> The main weaknesses of this report lies in the communication of the ideas and procedures. Although the language is generally excellent, there are some grammatical lapses that make the text difficult to read. More importantly, the authors are not consistent in their use of some terms; for example, uncertainty and information gain are sometimes conflated in a way that might confuse readers. Furthermore, the descriptions of the modelling and data analysis are incomplete. These shortcomings could be addressed in the following way.

First, it would be useful to unpack the various interpretations of information and goal-seeking offered in the (active inference) framework examined in this study. For example, it will be good to include the following paragraph:

"In contrast to behaviourist approaches to planning and decision-making, active inference formulates the requisite cognitive processing in terms of belief updating in which choices are made based upon their expected free energy. Expected free energy can be regarded as a universal objective function, specifying the relative likelihood of alternative choices. In brief, expected free energy can be regarded as the surprise expected following some action, where the expected surprise comes in two flavours. First, the expected surprise is uncertainty, which means that policies with a low expected free energy resolve uncertainty and promote information seeking. However, one can also minimise expected surprise by avoiding surprising, aversive outcomes. This leads to goal-seeking behaviour, where the goals can be regarded as prior preferences or rewarding outcomes.

Technically, expected free energy can be expressed in terms of risk plus ambiguity - or rearranged to be expressed in terms of expected information gain plus expected value, where value corresponds to (log) prior preferences. We will refer to both decompositions in what follows; noting that both decompositions accommodate information and goal-seeking imperatives. That is, resolving ambiguity and maximising information gain have epistemic value, while minimising risk or maximising expected value have pragmatic or instrumental value. These two kinds of values are sometimes referred to in terms of intrinsic and extrinsic value, respectively [1-4]."

The description of the modelling of choice behaviour needs to be unpacked and motivated more carefully. Perhaps along the following lines:

"To assess the evidence for active inference over reinforcement learning, we fit active inference and reinforcement learning models to the choice behaviour of each subject. Effectively, this involved optimising the free parameters of active inference and reinforcement learning models to maximise the likelihood of empirical choices. The resulting (marginal) likelihood was then used as the evidence for each model. The free parameters for the active inference model scaled the contribution of the three terms that constitute the expected free energy (in Equation 6). These coefficients can be regarded as precisions that characterise each subjects' prior beliefs about contingencies and rewards. For example, increasing the precision or the epistemic value associated with model parameters means the subject would update her beliefs about reward contingencies more quickly than a subject who has precise prior beliefs about reward distributions. Similarly, subjects with a high precision over prior preferences or extrinsic value can be read as having more precise beliefs that she will be rewarded. The free parameters for the reinforcement learning model included..."

In terms of the time-dependent correlations with expected free energy - and its constituent terms - I think the report would benefit from overviewing these analyses with something like the following:

"In the final analysis of the neuronal correlates of belief updating - as quantified by the epistemic and intrinsic values of expected free energy - we present a series of analyses in source space. These analyses tested for correlations between constituent terms in expected free energy and neuronal responses in source space. These correlations were over trials (and subjects). Because we were dealing with two-second timeseries, we were able to identify the periods of time during decision-making when the correlates were expressed.

In these analyses, we focused on the induced power of neuronal activity at each point in time, at each brain source. To illustrate the functional specialisation of these neuronal correlates, we present whole-brain maps of correlation coefficients and pick out the most significant correlation for reporting fluctuations in selected correlations over two-second periods. These analyses are presented in a descriptive fashion to highlight the nature and variety of the neuronal correlates, which we unpack in relation to the existing EEG literature in the discussion. Note that we did not attempt to correct for multiple comparisons; largely, because the correlations observed were sustained over considerable time periods, which would be almost impossible under the null hypothesis of no correlations."

There was a slight misdirection in the discussion of priors in the active inference framework. The notion that active inference requires a pre-specification of priors is a common misconception. Furthermore, it misses the point that the utility of Bayesian modelling is to identify the priors that each subject brings to the table. This could be easily addressed with something like the following in the discussion:

"It is a common misconception that Bayesian approaches to choice behaviour (including active inference) are limited by a particular choice of priors. As illustrated in our fitting of choice behaviour above, priors are a strength of Bayesian approaches in the following sense: under the complete class theorem [5, 6], any pair of choice behaviours and reward functions can be described in terms of ideal Bayesian decision-making with particular priors. In other words, there always exists a description of choice behaviour in terms of some priors. This means that one can, in principle, characterise any given behaviour in terms of the priors that explain that behaviour. In our example, these were effectively priors over the precision of various preferences or beliefs about contingencies that underwrite expected free energy."

(1) Oudeyer, P.-Y. and F. Kaplan, What is intrinsic motivation? a typology of computational approaches. Frontiers in Neurorobotics, 2007. 1: p. 6.<br /> (2) Schmidhuber, J., Formal Theory of Creativity, Fun, and Intrinsic Motivation (1990-2010). Ieee Transactions on Autonomous Mental Development, 2010. 2(3): p. 230-247.<br /> (3) Barto, A., M. Mirolli, and G. Baldassarre, Novelty or surprise? Front Psychol, 2013. 4: p. 907.<br /> (4) Schwartenbeck, P., et al., Computational mechanisms of curiosity and goal-directed exploration. Elife, 2019. 8: p. e41703.<br /> (5) Wald, A., An Essentially Complete Class of Admissible Decision Functions. Annals of Mathematical Statistics, 1947. 18(4): p. 549-555.<br /> (6) Brown, L.D., A Complete Class Theorem for Statistical Problems with Finite-Sample Spaces. Annals of Statistics, 1981. 9(6): p. 1289-1300.

Reviewer #2 (Public Review):

Summary:<br /> Zhang and colleagues use a combination of behavioral, neural, and computational analyses to test an active inference model of exploration in a novel reinforcement learning task.

Strengths:<br /> The paper addresses an important question (validation of active inference models of exploration). The combination of behavior, neuroimaging, and modeling is potentially powerful for answering this question.

Weaknesses:<br /> The paper does not discuss relevant work on contextual bandits by Schulz, Collins, and others. It also does not mention the neuroimaging study of Tomov et al. (2020) using a risky/safe bandit task.

The statistical reporting is inadequate. In most cases, only p-values are reported, not the relevant statistics, degrees of freedom, etc. It was also not clear if any corrections for multiple comparisons were applied. Many of the EEG results are described as "strong" or "robust" with significance levels of p<0.05; I am skeptical in the absence of more details, particularly given the fact that the corresponding plots do not seem particularly strong to me.

The authors compare their active inference model to a "model-free RL" model. This model is not described anywhere, as far as I can tell. Thus, I have no idea how it was fit, how many parameters it has, etc. The active inference model fitting is also not described anywhere. Moreover, you cannot compare models based on log-likelihood, unless you are talking about held-out data. You need to penalize for model complexity. Finally, even if active inference outperforms a model-free RL model (doubtful given the error bars in Fig. 4c), I don't see how this is strong evidence for active inference per se. I would want to see a much more extensive model comparison, including model-based RL algorithms which are not based on active inference, as well as model recovery analyses confirming that the models can actually be distinguished on the basis of the experimental data.

Another aspect of the behavioral modeling that's missing is a direct descriptive comparison between model and human behavior, beyond just plotting log-likelihoods (which are a very impoverished measure of what's going on).

The EEG results are intriguing, but it wasn't clear that these provide strong evidence specifically for the active inference model. No alternative models of the EEG data are evaluated.

Overall, the central claim in the Discussion ("we demonstrated that the active inference model framework effectively describes real-world decision-making") remains unvalidated in my opinion.

Reviewer #3 (Public Review):

Summary:<br /> This paper aims to investigate how the human brain represents different forms of value and uncertainty that participate in active inference within a free-energy framework, in a two-stage decision task involving contextual information sampling, and choices between safe and risky rewards, which promotes a shift from exploration to exploitation. They examine neural correlates by recording EEG and comparing activity in the first vs second half of trials and between trials in which subjects did and did not sample contextual information, and perform a regression with free-energy-related regressors against data "mapped to source space." Their results show effects in various regions, which they take to indicate that the brain does perform this task through the theorised active inference scheme.

Strengths:<br /> This is an interesting two-stage paradigm that incorporates several interesting processes of learning, exploration/exploitation, and information sampling. Although scalp/brain regions showing sensitivity to the active-inference-related quantities do not necessarily suggest what role they play, it can be illuminating and useful to search for such effects as candidates for further investigation. The aims are ambitious, and methodologically it is impressive to include extensive free-energy theory, behavioural modelling, and EEG source-level analysis in one paper.

Weaknesses:<br /> Though I could surmise the above general aims, I could not follow the important details of what quantities were being distinguished and sought in the EEG and why. Some of this is down to theoretical complexity - the dizzying array of constructs and terms with complex interrelationships, which may simply be part and parcel of free-energy-based theories of active inference - but much of it is down to missing or ambiguous details.

In general, an insufficient effort has been made to make the paper accessible to readers not steeped in the free energy principle and active inference. There are critical inconsistencies in key terminology; for example, the introduction states that aim 1 is to distinguish the EEG correlates of three different types of uncertainty: ambiguity, risk, and unexpected uncertainty. But the abstract instead highlights distinctions in EEG correlates between "uncertainty... and... risk" and between "expected free energy .. and ... uncertainty." There are also inconsistencies in mathematical labelling (e.g. in one place 'p(s|o)' and 'q(s)' swap their meanings from one sentence to the very next).

Some basic but important task information is missing, and makes a huge difference to how decision quantities can be decoded from EEG. For example:<br /> - How do the subjects press the left/right buttons - with different hands or different fingers on the same hand?<br /> - Was the presentation of the Stay/cue and safe/risky options on the left/right sides counterbalanced? If not, decisions can be formed well in advance especially once a policy is in place.<br /> - What were the actual reward distributions ("magnitude X with probability p, magnitude y with probability 1-p") in the risky option? 

The EEG analysis is not sufficiently detailed and motivated. For example,<br /> - why the high lower-filter cutoff of 1 Hz, and shouldn't it be acknowledged that this removes from the EEG any sustained, iteratively updated representation that evolves with learning across trials?<br /> - Since the EEG analysis was done using an array of free-energy-related variables in a regression, was multicollinearity checked between these variables?<br /> - In the initial comparison of the first/second half, why just 5 clusters of electrodes, and why these particular clusters? How many different variables are systematically different in the first vs second half, and how do you rule out less interesting time-on-task effects such as engagement or alertness? In what time windows are these amplitudes being measured? In the comparison of asked and not-asked trials, what trial stage and time window is being measured? Again, how many different variables, of the many estimated per trial in the active inference model, are different in the asked and not-asked trials, and how can you know which of these differences is the one reflected in the EEG effects? The authors choose to interpret that on not-asked trials the subjects are more uncertain because the cue doesn't give them the context, but you could equally argue that they don't ask because they are more certain of the possible hidden states.<br /> - The EEG regressors are not fully explained. For example, an "active learning" regressor is listed as one of the 4 at the beginning of section 3.3, but it is the first mention of this term in the paper and the term does not arise once in the methods.<br /> - In general, it is not clear how one can know that the EEG results reflect that the brain is purposefully encoding these very parameters while implementing this very mechanism, and not other, possibly simpler, factors that correlate with them since there is no engagement with such potential confounds or alternative models. For example, a model-free reinforcement learning model is fit to behaviour for comparison. Why not the EEG?

Author Response

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The current work by Kulich et al. examines the dynamic relocalization of NGR1 (LAZY2) a member of the LAZY protein family which is key for auxin redistribution during gravitropic responses. After gravistimulation of the triple mutant ngr123 (lazy234), the PIN3 activating kinase D6PK is not polarized in the columella cells.

Strengths:

The authors show a thorough characterization of NGR1 relocalization dynamics after gravistimulation.

Weaknesses:

Genetically the relocalization of D6PK depends on the LAZY protein family, but some essential details are missing in this study. On the one hand, NGR1-GFP does not associate with the BFA compartments and maintains its association with the PM and amyloplasts. On the other hand, D6PK relies on GNOM, via vesicle trafficking sensitive to BFA, suggesting that D6PK follows a different relocalization route than NGR1 which is BFA-insensitive. Based on these observations, D6PK relocalization requires the LAZY proteins, but D6PK and NGR1 relocalize through independent routes. How can this be interpreted or reconciled?

Response: Since we demonstrated that D6PK does not relocalize in the absence of NGR proteins, we conclude that NGR1 acts upstream of D6PK. The molecular mechanism driving this interaction is not fully understood; however, it is evident that NGR1 triggers the mobilization of D6PK. Despite previous investigations into D6PK mobility, the underlying mechanisms remain elusive. Notably, despite its sensitivity to BFA, D6PK does not localize to BFA bodies and does not undergo conventional endocytosis (https://doi.org/10.1016/j.devcel.2014.05.006). We fully acknowledge the importance and interest in gaining a better understanding of these processes, and it will be a focal point of our future research.

Two other works (now published) provide valuable and fundamental findings related to the mechanism examined in the current manuscript and display complementary and similar results to the ones shown in the current manuscript. Given the similarities in the examined mechanisms, these preprints should be referenced, recognized, and discussed in the manuscript under review. It is assumed that the three projects were independently developed, but the results of these previous works should be addressed and taken into account at least during the discussion and when drawing any conclusions. This does not mean that this work is less relevant. On the contrary, some of the observations that seem to be redundant are more solid, and firm conclusions can now be drawn from them.

Response: We have included and discussed these works in the revised discussion

Reviewer #2 (Public Review):

Summary:

This manuscript addresses what rapid molecular events underly the earliest responses after gravity-sensing via the sedimentation of starch-enriched amyloplasts in columella cells of the plant root cap. The LAZY or NEGATIVE GRAVITROPIC RESPONSE OF ROOTS (NGR) protein family is involved in this process and localizes to both the amyloplast and to the plasma membrane (PM) of columella cells.

The current manuscript complements and extends Nishimura et al., Science, 2023. Kulich and colleagues describe the role of the LZY2 protein, also called NGR1, during this process, imaging its fast relocation and addressing additional novel points such as molecular mechanisms underlying NGR1 plasma membrane association as well as revealing the requirement of NGR1/LZY2, 3,4 for the polar localization of the AGCVIII D6 protein kinase at the PM of columella cells, in which NGR1/LZY2 acts redundantly with LZY3 and LZY4.

The authors initially monitored relocalization of functional NGR1-GFP in columella cells of the ngr1 ngr2 ngr3 triple mutant after 180-degree reorientation of the roots. Within 10 -15 min NGR1-GFP signal disappeared from the upper PM after reorientation and reappeared at the lower PM of the reoriented cells in close proximity to the sedimented amyloplasts. Reorientation of NGR1-GFP occurred substantially faster than PIN3-GFP reorientation, at about the same time or slightly later than a rise in a calcium sensor (GCaMP3) just preceding a change in D2-Venus auxin sensor alterations. Reorientation of NGR1-GFP proved to be fast and not dependent on a brefeldin A-sensitive ARF GEF-mediated vesicle trafficking, unlike the trafficking of PIN proteins, like PIN3, or the AGCVIII D6 protein kinase. Strikingly, the PM association of NGR1-GFP was highly sensitive to pharmacological interference with sterol composition or concentration and phosphatidylinositol (4)kinase inhibition as well as dithiothreitol (DTT) treatment interfering with thioester bond formation e.g. during S-acylation. Indeed, combined mutation of a palmitoylation site and polybasic regions of NRG1 abolished its PM but not its amyloplast localization and rendered the protein non-functional during the gravitropic response, suggesting NRG1 PM localization is essential for the gravitropic response. Targeting the protein to the PM via an artificially introduced N-terminal myristoylation and an ROP2-derived polybasic region and geranylgeranylation site partially restored its functionality in the gravitropic response.

Strengths:

This timely work should be of broad interest to plant, cell and developmental biologists across the field as gravity sensing and signaling may well be of general interest. The point that NGR1 is rapidly responsive to gravistimulation, polarizes at the PM in the vicinity to amyloplast and that this is required for repolarization of D6 protein kinase, prior to PIN relocation is really compelling. The manuscript is generally well-written and accessible to a general readership. The figures are clear and of high quality, and the methods are sufficiently explained for reproduction of the experiments.

Weaknesses:

Statistical analysis has been performed for some figures but is lacking for most of the quantitative analyses in the figure legends.

Response: We added this information to the figure legends

The title claims a bit more than what is actually shown in the manuscript: While auxin response reporter alterations are monitored, "rapid redirection of auxin fluxes" are not really directly addressed and, while D6PK can activate PIN proteins in other contexts, it is not explicitly shown in the manuscript that PIN3 is a target in the context of columella cells in vivo. A title such as "Rapid redirection of D6 protein kinase during Arabidopsis root gravitropism relies on plasma membrane translocation of NGR proteins" would reflect the results better.

Response: We modified the title to Rapid translocation of NGR proteins driving polarization of PIN-activating D6 protein kinase during root gravitropism

Fig. 4: The point that D6PK is transcytosed cannot be made here based on the data of these authors. They should have used a photoswitchable version of NGR1 to show that the same molecules observed at the upper PM are translocated to the lower PM. Nishimura and colleagues actually did that for NGR4. However, this is a lot of work and maybe for NGR1 that fusion would have too low fluorescence intensity (as it was the case for NGR3). So, I think a rewording would be sufficient such as NGR-dependent reorientation of D6PK plasma membrane localization" as this does not say, from where it comes to the lower PM. Theoretically, the signal could also be amyloplast-derived or newly synthesized (or just folded) NGR1-GFP.

Response: We fully agree and rephrased the text using translocation instead of transcytosis

The authors make a model in which D6PK AGCVIII kinase-dependent on NGRs activates PIN3 to drive auxin fluxes. However, alterations in auxin responses are observed prior to PIN3 reorientation. They should explain this discrepancy better and clearly describe that this is a working hypothesis for the future rather than explicitly proven, yet.

Reviewer #3 (Public Review):

The mechanism controlling plant gravity sensing has fascinated researchers for centuries. It has been clear for at least the past decade that starch-filled plastids (termed statoliths) in specialised gravity-sensing columella cells sense changes in root orientation, triggering an asymmetric auxin gradient that alters root growth direction. Nevertheless, exactly how statolith movement triggers PIN auxin efflux carrier activation and auxin gradient formation has remained unclear until very recently. A series of new papers (in Science and Cell) and this manuscript report how LAZY proteins (also referred to as NEGATIVE GRAVITROPIC 50 RESPONSE OF ROOTS; NGR) play a pivotal role in regulating root gravitropism. In terms of their overall significance, their collective findings provide seminal insights into the very earliest steps for how plant roots sense gravity which are arguably the most important papers about root gravitropism in the past decade.

In the current manuscript, Kulich et al initially report (through creating a functional NGR1-GFP reporter) that "NGR1-GFP displayed a highly specific columella expression, which was most prominent at the PM and the statolith periphery." Is NGR1-GFP expressed in shoot tissues? If yes, is it in starch sheath (the gravity-sensing equivalent of root columella cells)? The authors also note "NGR1-GFP signal from the PM was not evenly distributed, but rather polarized to the lower side of the columella cells in the vicinity of the sedimented statoliths (Fig. 1A)." and (when overexpressing NGR-GFP) "chloroplasts in the vicinity of the PM strongly correlated with NGR1 accumulating at the PM nearby, similar to the scenario in columella" suggesting that NGR1 does not require additional tissue-specific factors (i.e. trafficking proteins or lipids) to assist in its intracellular movement from plastid to PM.

Response: Yes, NGR1, also called LAZY2 is expressed in the inner hypocotyl tissues, according to https://doi.org/10.1104/pp.17.00942. Unfortunately, we saw very little signal with our NGR-GFP construct, possibly due to NGR1-GFP weak signal and/or NGR1 being expressed only exclusively in the inner tissues.

Next, the authors study the spatiotemporal dynamics of NGR1-GFP re-localisation with other early gravitropic signals and/or components Calcium, auxin, and PIN3. The temporal data presented in Figure 1 illustrates how the GCaMP calcium reporter (in panel E) revealed "the first signaling event in the root gravitropic bending is the statolith removal from the top membrane, rather than its arrival at the bottom" It appeared that the auxin DII-VENUS reporter was also changing rapidly (panel G) - was this detectable BEFORE statolith re-sedimentation?

Response: In our data (Figure 1G), we observe that the increase in signal at the top side begins prior to starch sedimentation, in contrast to the bottom side, where the decrease starts only after starch grains land on the bottom membrane. While this observation aligns with our hypothesis and other data, we refrained from commenting on it due to the small differences between the first 2-3 timepoints, which are obscured by noise. This phenomenon arises because the DII response relies on protein degradation and is relatively slow. Hence, for rapid tracking of the auxin response, we utilized auxin-induced calcium as a proxy, with NPA treatment serving as a negative control.

Please can the authors explain their NPA result in Fig 1E? Why would treatment with the auxin transport inhibitor NPA block Ca signalling (unless the latter was dependent on the former)?

Response: Auxin induces rapid calcium transients (e.g., http://dx.doi.org/10.1016/j.cub.2015.10.025). Consequently, when auxin reaches the bottom elongation zone approximately 5-6 minutes after rotation, we observe an increased GCaMP signal at this location. Notably, when we inhibit PIN function using NPA, the GCaMP signal persists, but the difference between the top and bottom diminishes. This validates that the calcium transients at the bottom side can be interpreted as monitoring increase in auxin accumulation as a result of auxin transport.

They go on to note "This initial auxin asymmetry is mediated by PIN-dependent auxin transport, despite visible polarization of PIN3 can be detected only later" which suggests that PIN activity was being modified prior to PIN polarisation.

In contrast to other proteins involved in gravity response like RLDs and PINs, NGR1 localization and gravity-induced polarization does not undergo BFA-sensitive endocytic recycling by ARF-GEF GNOM. This makes sense given NGR1 is initially targeted to plastids, THEN the PM. Does NGR1 contain a cleavable plastid targeting signal? The authors go on to elegantly demonstrate that NGR1 PM targeting relies on palmitoylation through imaging and mutagenesis-based transgenic ngr rescue assays.

Response: Yes, there is weakly conserved plastid targeting signal on NGR1. Although we also started researching in this direction, we quickly realized, that two other groups showed very comprehensive data regarding NGR plastid localization.

Finally, the authors demonstrate that gravitropic-induced auxin gradient formation is initially dependent on PIN3 auxin efflux activation (prior to PIN3 re-localisation). This early PIN3 activation process is dependent on NGR1 re-targeting D6PK (a PIN3 activating kinase). This elegant molecular mechanism integrates all the regulatory components described in the paper into a comprehensive root gravity sensing model.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Minor comments:

Line 83: This construct fully rescued the agravitropic bending phenotype of the ngr1/2/3 triple mutant (see further).

What does it mean the see further in this context?

Response: It is a reference to the second part of the manuscript (Fig. 3, Supplementary Fig S3, Fig S4), where we extensively address the complementation with wild type and point mutated versions of NGR. There we show that the construct we are using is functional. This does not prove, but strongly imply that the GFP signal we obtain is relevant. We updated the text to point this out.

Line 101: Timing of events during the gravitropic response

When describing the equipment employed and the rotation applied to the samples, "the vertical stage microscope and minimized the time required for rotating the sample. 180{degree sign} rotation..."

The authors mentioned a travel time of 5 minutes first and later of 15 minutes for the relocalization of NGR1. Are these two different experiments? Were there two different rotation angles or degrees applied? Could the authors please rephrase this part of the description to answer these questions and help the reader understand how the assay performed?

Response: We added this explanation to the text.

Figure 1 E, F, and G.

Could the authors please provide pictures and/or videos for the PIN3 localization dynamics, intracellular calcium transients, and auxin reporter DII-Venus? In other words, show the complementing images for Figure 1E, 1F, and 1G as the authors did for Figure 2D where authors presented the pictures and the corresponding quantification plots.

Response: We wanted to avoid overcrowding the figure, but we would also love to show the videos. Therefore, we did additional supplementary movie 3, where we put all the additional observations.

Line 194: This implies the existence of posttranslational modifications such as S-acylation to associate with PM.

Why is this specific modification suggested/examined and no other modification? What is the criteria to select this kind of modification? Based on what premises? Could the authors elaborate on that? Could the authors please include references?

Response: Thank you for this comment. We of course first checked the prediction tools which have shown very strongly conserved S-acylation side. We now clarified this in the text and added other modifications as an example. Later on, we rule out myristoylation (that happens on the glycins) and prenylation (it happens only at the C-terminus CAAX box).

Line 255: NGR1 PM localization is synergistically mediated by polybasic regions and a palmitoylation site

Similarly to the previous commentary, How and why are these regions examined/analyzed? Likewise, why is the palmitoylation site selected? Please provide some background, criteria, and references.

Response: Here, we clearly state that the prediction of the palmitoylation site is made based on the GPS lipid prediction tool.

As for the polybasic region, these can be seen upon manual inspection of the primary protein sequence. We simply looked at the protein and saw it there. We rephrased the text so that it is more clear.

Reviewer #2 (Recommendations For The Authors):

Please, proofread the manuscript for style and minor language errors.

Statistical analysis has been performed for some figures but is lacking for most of the quantitative analyses in the figure legends. Where it has been performed it is not given what "n" number of roots, cells, or plasma membranes were analyzed NGR1-GFP and no information is given whether the data is derived from a representative experiment or several or pooled data from several experiments. This certainly requires revision in Fig. 1D-G, Fig. 2B-D, Fig. S2 B,E, Fig. 3B,D, F-H, Fig. S.3 B,D, Fig. S. 4 ,E-H, Fig. 4 D.

Response: Thank you, we added this information to the figure legends.

Reviewer #1 (Public Review):

Summary:

Plant roots grow following the gravity vector. Changes in the direction of gravity can be sensed in the root tip by specialized cells that hold starch granules. These starch granules act as levels. Movement and settling of the granules at the bottom of these specialized cells initiates an imbalanced distribution of auxin, a key hormone for plant development. Consequently, this leads to a reorientation of root growth towards the newly established gravity vector. This work provides new insights into granules' relocalization, the proteins associated with them, and the molecular processes triggered downstream.

Comments on revised submission:

In the previous review round, the reviewers noted that the authors had missed an opportunity to discuss the results presented in two recently published articles closely related to the topic of their manuscript. The authors have now referenced these articles in the current version of the manuscript, but the discussion remains rather brief. It would have been beneficial to summarize, identify, and highlight the similarities among these studies in a more comprehensive manner.

In Figure 1, it would have been more informative if the authors had provided specific information concerning the key time-points described in the graphs to visually illustrate the dynamics of PIN3 localization, intracellular calcium transients, and auxin reporter DII Venus. Including these images would have perfectly complemented panels E, F, and G.

The authors expressed concerns about overcrowding the figure. If the aesthetics of the figure were their primary concern, they could have included essential image frames for the data represented in the graphs in a supplementary figure. Alternatively, a detailed description of supplementary movie 3, highlighting the specific frames quantified in the graphs (Figure 1), could have sufficed.

eLife assessment

This fundamental study addresses the earliest events that enable plant roots to reorient growth in response to gravity. Compelling molecular and cell biological data establish that plasma membrane localization of the LAZY or NEGATIVE GRAVITROPIC RESPONSE OF ROOTS (NGR) protein family is required for rapid and polar redirection of D6 protein kinase, an activator of the PIN3 auxin transporter. This work complements and extends recent publications on the NGR family in gravity sensing (PMID: 37741279 and PMID: 37561884). Collectively these papers advance our understanding of rapid plant gravity sensing and response.

Reviewer #2 (Public Review):

Summary:

This manuscript addresses what rapid molecular events underly the earliest responses after gravity-sensing via the sedimentation of starch-enriched amyloplasts in columella cells of the plant root cap. The LAZY or NEGATIVE GRAVITROPIC RESPONSE OF ROOTS (NGR) protein family is involved in this process and localizes to both the amyloplast and to the plasma membrane (PM) of columella cells.

This manuscript complements and extends a very recent study, (Nishimura et al., Science, 2023, August 10, 2023) that reported that the LZY3 and LZY4 proteins translocate from amyloplasts to the PM and that this translocation is likely necessary for the root gravitropic response. Kulich and colleagues describe the role of the LZY2 protein, also called NGR1, during this process, imaging its fast relocation and addressing additional novel points such as molecular mechanisms underlying NGR1 plasma membrane association as well as revealing the requirement of NGR1/LZY2, 3,4 for the polar localization of the AGCVIII D6 protein kinase at the PM of columella cells, in which NGR1/LZY2 acts redundantly with LZY3 and LZY4.

The authors initially monitored relocalization of functional NGR1-GFP in columella cells of the ngr1 ngr2 ngr3 triple mutant after 180 degree reorientation of the roots. Within 10 -15 min NGR1-GFP signal disappeared from the upper PM after reorientation and reappeared at the lower PM of the reoriented cells in close proximity to the sedimented amyloplasts. Reorientation of NGR1-GFP occurred substantially faster than PIN3-GFP reorientation, at about the same time or slightly later than a rise in a calcium sensor (GCaMP3) just preceding a change in D2-Venus auxin sensor alterations. Reorientation of NGR1-GFP proved to be fast and not dependent on a brefeldin A-sensitive ARF GEF-mediated vesicle trafficking, unlike the trafficking of PIN proteins, like PIN3, or the AGCVIII D6 protein kinase. Strikingly, the PM association of NGR1-GFP was highly sensitive to pharmacological interference with sterol composition or concentration and phosphatidylinositol (4)kinase inhibition as well as dithiothreitol (DTT) treatment interfering with thioester bond formation e.g. during S-acylation. Indeed, combined mutation of a palmitoylation site and polybasic regions of NRG1 abolished its PM but not its amyloplast localization and rendered the protein non-functional during the gravitropic response, suggesting NRG1 PM localization is essential for the gravitropic response. Targeting the protein to the PM via an artificially introduced N-terminal myristoylation and a ROP2-derived polybasic region and geranylgeranylation site partially restored its functionality in the gravitropic response.

Strengths:

This timely work should be of broad interest to plant, cell and developmental biologists across the field as gravity sensing and signaling may well be of general interest. The point that NGR1 is rapidly responsive to gravistimulation, polarizes at the PM in the vicinity to amyloplast and that this is required for repolarization of D6 protein kinase, prior to PIN relocation is really compelling. The manuscript is generally well written and accessible to a general readership, except for very minor language errors. The figures are clear and of high quality, the methods are sufficiently explained for reproduction of the experiments.

Comments on revised submission:

The authors have addressed my comments to a large part, however, while they write they have updated the statistical analysis as requested, they only did this for the main figures, but NOT for the supplementary images (except for Fig. S2) and their legends. These issues need fixing in order to correctly describe the data and let the reader know, which distributions actually differed. Some specific examples of concerns are:

In Figs. 3F and D we now know that a one-way ANOVA test was performed and that letters designate the statistically significant difference between distributions with p smaller 0.0001, but we still do not know what "n" in the displayed distributions is e.g. how many PM/cytoplasm ratios were measured i.e. e.g 112? (from 112 cells?). It is said that 8-15 roots were quantified, but the data points in the distributions are not 8-15 .... . They are many more, so, "n" must be the number of cells derived from 8-15 roots but what is "n" in the displayed distributions and is that the same value that was used for the Anova test?

This must be clarified as it has very well been done for Fig. 2 and Fig. S2B, E in the legends and by inserting a lettering for significance differences in the figures.

Similar information is still lacking for Fig. S3D, no number "n" of cells from which the PM/cytoplasm ratios are analyzed is given, no lettering for differences, no p -value. This leaves one to guess which distributions differ from each other.

This also needs to be fixed for Figs. S4 E, F (for G and H one can see the differences where the SDs do not overlap and it is explained what they are derived from).

Author Response

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

Public Reviews:

Reviewer #1 (Public Review):

Summary

This fascinating paper by M. Alfatah et al. describes work to uncover novel genes affecting lifespan in the budding yeast S. cerevisiae, eventually identifying and further characterizing a gene, YBR238C, now named AAG1 by the authors. The authors began by considering published gene sets pulled from the Saccharomyces genome database that described increases or decreases in either chronological lifespan or replicative lifespan in yeast. They also began with gene sets known to be downregulated upon treatment with the lifespan-extending TOR inhibitor rapamycin.

YBR283C was unique in being largely uncharacterized, downregulated upon rapamycin treatment, and linked to both increased replicative lifespan and increased chronological lifespan upon deletion.

The authors show that YBR283C may act to negatively regulate mitochondrial function, in ways that are both dependent on and independent of the stressresponsive transcription factor Hap4, largely by looking at relative expression levels of relevant mitochondrial genes.

In a hard-to-fully interpret but well-documented series of experiments the authors note that the two paralogues YBR283C and RMD9 (which have ~66% similarity) (a) have opposite effects when acting alone, and (b) appear to interact in that some phenotypes of ybr283c are dependent on RMD9.

A particularly interesting finding in light of the current literature and of the authors' strategy in identifying YBR283C is that changes in electron transport chain genes upon rapamycin treatment appear to be affected via YBR283C.

Based on a series of experiments the authors move to conclude the existence of "a feedback loop between TORC1 and mitochondria (the TORC1-Mitochondria-TORC1 (TOMITO) signaling process) that regulates cellular aging processes."

Strengths

Overall, this study describes a great deal of new data from a large number of experiments, that shed light on the potential specific roles of YBR238C and its paralog RMD9 in aging in yeast, and also underscore the potential of an approach looking for "dark matter" such as uncharacterized genes when seining the increasing deluge of published datasets for new hypotheses to test. This work when revised will become a valuable addition to the field.

Weaknesses

A paralog of YBR283C, RMD9, also exists in the yeast genome. While the authors indicate that part of their interest in YBR283C lies in its uncharacterized nature, its paralogue, RMD9, is not uncharacterized but is named due to its phenotype of Required for Meiotic nuclear Division, which is not mentioned or discussed anywhere in the manuscript currently.

In the context of the current work, in addition to the cited Hillen, H.S et al. and Nouet C. et al, the authors might be very interested in the 2007 Genetics paper "Translation initiation in Saccharomyces cerevisiae mitochondria: functional interactions among mitochondrial ribosomal protein Rsm28p, initiation factor 2, methionyl-tRNAformyltransferase and novel protein Rmd9p" (PMID: 17194786), which does not appear to be cited or discussed in the current version of the manuscript.

Thank you for your thorough and insightful review of our manuscript. We value your positive feedback and recognition of the strengths in our study. Your constructive comments have been carefully considered, leading to the inclusion of RMD9, identified as 'Required for Meiotic Nuclear Division,' and the addition of the relevant reference (PMID: 12586695) in the revised manuscript. This information has been incorporated into the second paragraph of the "The YBR238C paralogue RMD9 deletion decreases the lifespan of cells" results section.

Furthermore, we appreciate the reviewer's suggestion to include the 2007 Genetics paper on translation initiation in Saccharomyces cerevisiae mitochondria (PMID: 17194786). This citation has been integrated into our revised manuscript.

We believe that these revisions significantly strengthen the manuscript and address the concerns raised by Reviewer #1. We thank the reviewer for their time and valuable input.

Reviewer #2 (Public Review):

The effectors of cellular aging in yeast have not been fully elucidated. To address this, the authors curated gene expression studies to link genes influenced by rapamycin - a well-known mediator of longevity across model systems - to genes known to affect chronological and replicative lifespan (RLS) in yeast. Through their analyses, they find one gene, ybr238c, whose deletion increases both CLS and RLS upon deletion and that is downregulated by rapamycin. Curiously, despite these selection criteria, the authors only use CLS as a proxy for cellular aging throughout their study and do not explore the effects of ybr238c deletion on RLS. This does not diminish their conclusions, but given the importance of this phenotype in their selection criteria, it is surprising that the authors did not choose to test both types of aging throughout their study.

Nonetheless, the authors demonstrate that deletion of ybr238c increases CLS across multiple yeast strains and through multiple assays. The authors also test the effects of YBR238C overexpression on lifespan and find the opposite effect, with overexpression yeast showing decreased survival relative to wild-type cells, consistent with "accelerated aging" as the authors propose. The authors also note that ybr238c has a paralog, rmd9, whose deletion decreases CLS and seems to be epistatic to ybr238c, as a double ybr238c/rmd9 mutant has decreased CLS relative to a wild-type strain.

Collectively, the data presented by the authors convincingly demonstrate that ybr238c influences lifespan in a manner that is distinct from (and likely opposite to) rmd9. However, the authors then link the increased CLS in Δybr238c yeast to mitochondrial function using only a handful of assays that do not directly test mitochondrial function. These include total cellular ATP levels, levels of reactive oxygen species, and the transcript levels of select nuclear-encoded mitochondrial genes. Yeast is well established to generate ATP through non-mitochondrial pathways such as glycolysis in fermentive conditions. While it is possible that the ATP levels assayed in the manuscript were tested in stationary phase, which would more likely reflect "mitochondrial function," the methods nor the figure legends contain these details, which are critical for the interpretation of these data. Similarly, ROS can be generated through non-mitochondrial pathways, and the transcription of nuclear-encoded mitochondrial genes is an indirect measure of mitochondrial function at best. Thus, the authors' proposed connection of ybr238c to mitochondrial function is correlative and should be substantiated with assays that more closely align with organellar function, such as respirometry or assaying the activity of oxidiative phosphorylation complexes. Finally, the authors attempt to tie the phenotypes of mitochondrial dysfunction caused by the deletion of ybr238c to TORC1 signaling, as the gene is influenced by rapamycin. However, the presentation of the data, such as reporting ATP levels as relative percentages or failing to perform appropriate statistical comparisons between conditions in which the authors derive conclusions, renders the data difficult to interpret. As such, this manuscript establishes that ybr238c is rapamycin responsive and influences CLS, but its influence on mitochondrial activity and ties to TORC1 signaling remain speculative.

We would like to express our gratitude to Reviewer #2 for the thoughtful feedback on our manuscript. We have carefully considered your comments and have made comprehensive revisions to address the concerns raised.

We appreciate the suggestion to investigate the role of YBR238C in replicative lifespan (RLS). However, we want to bring to your attention that four previous studies (references 7, 39, 40, and 41) have already identified the involvement of YBR238C in the RLS phenotype. Given the existing body of literature on this aspect, we chose not to duplicate these efforts in our study.

Instead, we focused our efforts on validating the role of YBR238C in chronological lifespan (CLS) phenotype, a finding reported in only one genome-wide study (reference 38). To enhance the comprehensiveness of our study, we performed analyses on different phenotypes, including mitochondria activity and oxidative stress, under both logarithmic-phase (condition for RLS) and stationary phase (condition for CLS). We now clearly indicate the logarithmic-phase/stationary phase conditions in the figure legends of the manuscript, specifying whether the conditions are relevant to RLS or CLS. Additional results of the new experiments have been included in the revised manuscript as supplementary figures (S3E-S3I).

To address concerns about the indirect nature of our mitochondrial function assays, we have performed relative mitochondria content (S3F), quantification of ROS levels from fermentative to stationary phase conditions (S3G), and assessment in respiratory glycerol medium (S3H), which provides a more direct insight into mitochondrial biology. Additionally, we have investigated the resistance of ybr238c∆ cells to H2O2 toxicity and found them to be more resistant compared to wild-type cells.

We believe these revisions strengthen the scientific rigor and clarity of our study. We sincerely appreciate the guidance from Reviewer #2, and we hope these modifications address the concerns raised effectively.

Reviewer #3 (Public Review):

Summary: The study by Alfatah et al. presented a role for YBR238C in mediating lifespan through improved mitochondrial function in a TOR1-dependent metabolic pathway. The authors used a dataset comparison approach to identify genes positively modulating yeast chronological (CLS) and Replicative (RLS) lifespan when deleted, and their expression is reduced under Rapamycin treatment condition. This approach revealed an unknown, mitochondria-localized yeast gene YBR238C, and through mechanistic studies, they identified its paralogous gene RMD9 regulating lifespan in an antagonistic effect.

Strengths:

Findings have valuable implications for understanding the YBR238C-mediated, mitochondrial-dependent yeast lifespan regulation, and the interplay between two paralogous genes in the regulation of mitochondrial function represents an inserting case for gene evolution.

Weaknesses:

Overall, the implication/findings of this study are restricted only to the yeast model since these two genes do not have any homology in higher eukaryotes. The primary methods must be carefully designed by considering two different metabolic states: respiration-associated with CLS and fermentation-associated with RLS in a single comparative approach. Yeast CLS and RLS are two completely different processes. It is already known that most gene-regulating CLS is not associated with RLS or vice versa. The method section is poorly written and missing important information. The experimental approaches are poorly designed, and variability across the datasets (e.g., media condition "YPD," "SC" etc.) and their experimental conditions are not well described/considered; thus, presented data are not conclusive, which decreases the overall rigor of the study.

We sincerely appreciate your thorough review of our manuscript and your insightful comments. We acknowledge the limitation of our study being yeast-specific due to the absence of homologous genes in higher eukaryotes. However, we would like to highlight the significance of our findings in revealing a feedback loop between mitochondrial function and TORC1 signaling (TORC1-Mitochondria-TORC1 or TOMITO signaling process) in cellular lifespan regulation.

Our interpretation of the experimental results is grounded in recent literature. Two studies (references 62 and 63) support our findings by demonstrating TORC1 activation after mitochondrial electron transport chain dysfunction and the delay in brain pathology progression upon TORC1 inhibition, respectively. These studies, discussed in our manuscript, reinforce the relevance of our work in a broader biological context.

We recognize the importance of carefully designing our primary methods to account for the different metabolic states associated with cellular processes, such as respiration in cellular lifespan (CLS) and fermentation in replicative lifespan (RLS). We want to bring to your attention that four previous studies (references 7, 39, 40, and 41) have already identified the involvement of YBR238C in the RLS phenotype. To avoid duplicating these efforts, we have chosen not to reiterate these findings in our study. However, we have clarified the logarithmic-phase/stationary phase conditions in the figure legends, specifying their metabolic states relevance to RLS or CLS. Additionally, we have included new supplementary figures (S3E-S3I) to provide further details on the new experiments conducted.

We appreciate your feedback regarding the clarity and completeness of our method section. In the revised manuscript, we have invested additional effort to enhance the clarity of the method section, providing a more detailed account of the experimental procedures, including the missing information you identified.

We believe these revisions strengthen the scientific rigor and clarity of our study. We sincerely appreciate the guidance from Reviewer #3, and we hope these modifications address the concerns raised effectively.

Reviewer #1 (Recommendations For The Authors):

Thank you for your detailed review and valuable recommendations. We have carefully addressed each of your comments in the revised manuscript. The specific changes made include:

(1) "TORC1 positively regulates aging, and its inhibition increases lifespan in various eukaryotic organisms including yeast and mammalian 13,26,27,29,30." Here I would suggest replacing "mammalian" with "mammals".

We have amended the sentence as recommended.

(2) "Next, we experimentally tested whether the transcriptome longevity signatures are associated with enhanced mitochondrial metabolism, whether the cellular energy level has gone up and cellular stress responses are induced with a switch to oxidative metabolism 47,48." Here I would replace "transcriptome longevity signatures is" with "transcriptome longevity signatures are".

We have amended the sentence as recommended.

(3) "Thus, HAP4-independent mechanism does exist through which YBR238C also affects cellular aging (Figure 3I)." I would replace "Thus, HAP4-independent" with "Thus, a HAP4-independent".

We have amended the sentence as recommended.

(4) "We examined other mitochondrial dysfunctional conditions to confirm that suppressive effect of rapamycin is not only specific to YBR238C-OE." I would change "that suppressive effect" to "that the suppressive effect".

We have amended the sentence as recommended.

(5) "Understanding the mechanism of aging will also require to understand the role of many genes of yet unknown function as YBR238C at the beginning of this work." I would switch "require to understand" to "require understanding".

We have amended the sentence as recommended.

(6) "The gene lists that modulate cellular lifespan in aging model organism yeast Saccharomyces cerevisiae were extracted from database SGD 22 and GenAge 23 (as of 8th November 2022)" "yeast" should not be italicized.

Corrected.

(7) Figure 1, panels C and D, ybr238c should be italicized.

Corrected.

(8) Figure 2B, top left-most (oxidative phosphorylation) network. I might consider repositioning some labels to make them more readable if possible.

Thank you for your feedback. The figure labels in Figure 2B are default from Metascape analysis, so repositioning isn't feasible. However, we have indicated in the figure legends that the full set of genes for functional enrichment analysis and the MCODE complex is available in Additional File 3.

(9) Figure 4E, rmd9, pet100, and cox6 should be italicized.

Corrected.

(10) Figure 5C, rmd9 and rmd9 ybr238c should be italicized. Corrected.

Reviewer #2 (Recommendations For The Authors):

Thank you for your detailed review and valuable recommendations. We have carefully addressed each of your comments in the revised manuscript. The specific changes made include:

(1) The presentation of data as heatmaps (Figures 1F, 3D, 4C, 4G, 5B, 5H, 5L, 6K) obfuscates the quantitative nature of the data. These data would be much stronger if presented as bar graphs with appropriate statistical analysis. If the authors prefer the visual of the heat map, there should be some statistical analysis performed to accompany these figures. This is particularly important for Figure 3D, in which the authors state "We found that HAP4 deletion significantly decrease the ETC complex I-V genes' expression" (bottom of page 8). As no statistical analyses were performed, the authors should refrain from using such language as it is unsupported by the data as analyzed.

Thank you for your insightful comments and suggestions regarding the presentation of our data. We appreciate the attention you have given to Figures 1F, 3D, 4C, 4G, 5B, 5H, 5L, and 6K.

In response to your feedback, we have carefully re-evaluated our approach. Considering the large volume of data associated with our lifespan analysis at different time points, we initially chose to visualize it using heatmaps to comprehensively capture the complexity of the results. However, we have now incorporated quantification information into the heatmaps.

For Figure 3D, which addresses the impact of HAP4 deletion on the expression of ETC complex I-V genes, we have replaced the heatmap with a bar graph. This modification allows for a clearer representation of the quantitative nature of the data. Moreover, we have conducted thorough statistical analyses comparing data between ybr238c∆ and ybr238c∆ hap4∆ to support the statements made in the text. The results of these analyses are now included in the revised figure. Moreover, we also replaced the Figure 6K heatmap with a bar graph.

We believe that these changes enhance the interpretability and robustness of our findings. We are grateful for your guidance, and we are confident that these adjustments will strengthen the overall quality of our manuscript.

(2) The presentation of ATP data, given its importance in supporting the core conclusions of this manuscript, is poor. The conditions under which yeast was collected are not reported, making these data impossible to interpret; total cellular ATP levels would be significantly altered and influenced by separate pathways in fermentive versus stationary phases. Minimally, the authors should describe the conditions of yeast growth (e.g., age, culture media) in which these measurements were made. The presentation of relative ATP percentages is problematic, particularly with measurements that deviate so far from wild-type ATP levels in conditions such as those in Figure 6A, in which the authors report that rapamycin induces a 1200% increase in cellular ATP. Previous papers have established that ATP levels in yeast hover around 4 mM and are stable through the cell cycle and across nutrient conditions (PMID: 30858198, 35438635). Given this, the reported ATP levels would be expected to be near 48 mM, which is strongly outside of the typically accepted values of 1-10 mM for this metabolite. Without understanding the contexts in which these measurements are made, as well as the absolute values for these measurements (which would be easily achievable through the use of a standard curve of ATP), these data are uninterpretable. Furthermore, it seems unlikely that yeast would be able to accommodate shifts of ATP levels that span an order of magnitude without dire cellular consequences, particularly during rapamycin treatment.

We appreciate the valuable feedback from the reviewer regarding the importance of providing detailed information on yeast growth conditions for interpreting ATP data. In response to this suggestion, we have enhanced the figure legends associated with the relevant figures to include a comprehensive description of the yeast growth conditions. This now specifies the age of the culture, culture media composition, and other pertinent parameters.

In addressing the concern raised about the rapamycin-induced ATP increase, we have carefully re-examined our experimental procedures. We performed additional experiments and confirmed the consistency of our findings in logarithmic-treated cultures. The results remain in alignment with our initial observations, reinforcing the reliability and reproducibility of our data.

(3) As stated above, the inference of mitochondrial function from cellular ATP levels, cellular ROS levels, and gene expression of a handful of nuclear-encoded genes is not sound. The authors should include further experimentation as evidence of mitochondrial functionality, such as respirometry or metabolic flux experiments.

Thank you for your constructive feedback on our manuscript. We appreciate your careful consideration of our work. In response to your concerns regarding the indirect nature of our mitochondrial function assays, we have implemented the following changes: We have incorporated additional assays to provide a more direct insight into mitochondrial biology. Specifically, we performed relative mitochondria content analysis (S3F) and quantified ROS levels under fermentative to stationary phase conditions (S3G). These assays offer a more direct and comprehensive assessment of mitochondrial function. Furthermore, we conducted experiments in respiratory glycerol medium (S3H) to complement our previous findings.

To further support our claims, we investigated the resistance of ybr238c∆ cells to H2O2 toxicity. Our results demonstrate that these cells exhibit increased resistance compared to wild-type cells. This additional evidence strengthens the link between mitochondrial function and cellular response to oxidative stress.

We believe these adjustments address your concerns and significantly enhance the robustness of our study. We hope you find these modifications satisfactory. We are grateful for your valuable input, which has undoubtedly improved the clarity and reliability of our findings.

(4) Multiple gene expression analyses are performed on n=2 measurements, and this should be bolstered by further replicates. Many bar graphs do not have accompanying statistics; these should be added. Some statistical tests are performed across inappropriate comparisons, such as Figure 3G, in which expression levels of mitochondrial genes in both deletion and overexpression strains should be compared to a wild-type control rather than to each other.

Thank you for your thorough review and constructive feedback on our manuscript. We appreciate your careful examination of our work. In response to your comments, we have made the following revisions to address your concerns: The multiple gene expression analysis in our study focused specifically on ETC genes. It is important to note that ETC genes themselves represent multiple replicates within the ybr238c deletion and overexpression cells, as illustrated in Figures 4D, 4G, and 6B.

We acknowledge and appreciate your observation regarding Figure 3G. To address this concern, we have revised the statistical comparisons. The expression levels of mitochondrial genes in the overexpression strain are now appropriately compared to a wild-type control. This correction has been applied in the figure that correctly corresponds to text in the manuscript.

(5) Figure 2B is uninterpretable as it stands, as most gene symbols are obscured.

We appreciate the reviewer's attention to Figure 2B and the feedback provided. Regarding the gene labels in Figure 2B, we would like to clarify that these labels are default outputs from the Metascape analysis, and unfortunately, repositioning them within the current figure layout isn't feasible without compromising the integrity of the information.

However, we have taken the reviewer's concern seriously and have made efforts to address the interpretability issue. To provide readers with access to the full set of genes for functional enrichment analysis and the MCODE complex, we have included this information in Additional File 3. The figure legends have been updated accordingly to guide readers to refer to Additional File 3 for a more detailed examination of the gene symbols and their annotations.

We hope that this solution addresses the concern raised by the reviewer.

(6) The conclusions to be drawn from Figure 3A are not clear, and this figure is cited only once in the text along with two other figures (page 8).

Thank you for your valuable feedback. We have carefully considered your comments and made revisions to improve the clarity of the conclusions drawn from Figure 3A.

(7) Figure 6K reports a range of 100-200% cell survival - how does a cell have 200% survival? Isn't survival binary (i.e., you survive or you are dead)? Perhaps this is meant to be relative to another condition; this should be more clearly stated in the figure, or the axis should be normalized to a maximum of 100% survival.

Thank you for your guidance and valuable feedback. Based on your recommendation, we have made significant changes to Figure 6K in the revised manuscript. Specifically, we replaced the heatmap with a bar graph to enhance clarity. Additionally, we would like to highlight that cell survival of combined treated cells is measured relative to the control treatment, which is considered 100% survival. This aims to provide a more accurate and comprehensible representation of the data. We believe these modifications contribute to a clearer presentation of our findings.

(8) The authors state that "TORC1 inhibition in yeast and human cells with mitochondrial dysfunction suppresses their accelerated aging." No studies of aging were done in human cells; survival in response to mitochondrial toxins does not reveal aging phenotypes. To state such is a substantial overstatement and should be amended to perhaps "cellular survival" rather than directly linked to aging.

We appreciate the careful review of our manuscript and the constructive feedback provided by the reviewer. In response to the concern raised regarding the statement about TORC1 inhibition and accelerated aging in human cells, we have revised the relevant passage as follows: "In turn, TORC1 inhibition in yeast and human cells with mitochondrial dysfunction enhances their cellular survival." We believe that this modification accurately reflects the outcomes of our experiments and addresses the concern raised by the reviewer. We would like to express our gratitude for the valuable feedback, which has contributed to the improvement of our manuscript. Thank you for your thoughtful consideration.

Reviewer #3 (Recommendations For The Authors):

Thank you for your detailed review and valuable recommendations. We have carefully addressed each of your comments in the revised manuscript. The specific changes made include:

The authors should have attempted to fully characterize the RLS and CLS phenotype of strains lacking the YBR238C and RMD9 gene, the single most important gene identified in this study. Before further characterization, its association with aging must be tested to replicate findings from the literature. Although Figure 3 shows partially characterized CLS in SC medium, different media conditions could be tested, and the full spectrum of CLS lifespan curves should be represented. RLS phenotypes of these cells were not analyzed throughout the study.

We appreciate the suggestion to investigate the role of YBR238C in both Replicative Lifespan (RLS) and Chronological Lifespan (CLS). However, it's essential to note that the involvement of YBR238C in the RLS phenotype has been previously documented in four studies (references 7, 39, 40, and 41). Considering the established literature on this matter, we chose not to duplicate these efforts in our study.

Our primary focus was on confirming the role of YBR238C in the chronological lifespan (CLS) phenotype, as indicated by a genome-wide study (reference 43). Accordingly, we also conducted an analysis of the role of RMD9 in CLS. The methods and figure legends explicitly state that CLS experiments for prototrophic CEN.PK113-7D strains were conducted in synthetic defined (SD) medium containing 6.7 g/L yeast nitrogen base with ammonium sulfate without amino acids and 2% glucose. For auxotrophic BY4743 strains, SD medium was supplemented with histidine (40 mg/L), leucine (160 mg/L), and uracil (40 mg/L).

It is important to clarify that SC medium was not used for CLS analysis. Instead, we employed SD medium, recommended for CLS analysis (reference 15; PMID: 22768836). The CLS experiments were conducted using three different methods, providing a comprehensive representation of the entire CLS lifespan (Figures 1C, 1D, 1E, and 1F).

While we did not present the Replicative Lifespan (RLS) phenotype explicitly, we performed experiments such as mitochondrial activity and ROS production under both CLS and RLS conditions. These additional analyses contribute valuable insights into the broader implications of YBR238C and RMD9 on cellular function.

We believe that these clarifications and the inclusion of additional experimental details enhance the robustness and validity of our findings. We hope these explanations address the concerns raised by the reviewer and contribute to the overall improvement of our manuscript.

In addition, authors include RNAseq data from Rapamycin-treated cells to identify differentially expressed genes. Notably, genes with decreased expression were used to compare KO strains' lifespan phenotype. Additional RNAseq analyses were performed on individual KO cells. The methodology section needs to be better written with information on which media and metabolic state that these cells are collected after treatment with rapamycin. If the cells are collected during logarithmic growth, the data can be compared with RLS aging gene sets only. A separate experiment has to be performed on stationary cells (respiratory) to collect RNAseq data after rapamycin treatment, then can be compared to the CLS aging gene set.

Thank you for your insightful comments and considerations regarding our methodology for obtaining Rapamycin response genes (RRGs). We appreciate the opportunity to address your concerns and provide further clarification on our experimental approach.

As mentioned in our manuscript, we obtained RRGs by treating logarithmic cells with 50 nM Rapamycin for 1 hour, and the details have been included in supplementary Figure S1C legends. Our primary objective was to compare these RRGs with agingassociated genes that modulate both Replicative Lifespan (RLS) and Chronological Lifespan (CLS). We acknowledge the significance of this comparison and believe that our approach, treating logarithmic cells, is suitable for achieving this goal.

It is important to note that the use of a higher concentration of Rapamycin for treatment renders the cells less efficient in terms of growth, resulting in a very low optical density (OD) at 72 hours, as illustrated in Figure 6H. Unfortunately, due to this limitation in growth efficiency, obtaining Rapamycin response genes at the stationary phase was not feasible in our experimental setup.

As the experimental conditions vary among the reports and the gene expression signature significantly changes under different metabolic conditions, the media condition that samples are collected for RNAseq analyses should match the media condition that the lifespans of those KO strains are tested. However, more information needs to be detailed on these methodologies. For example, the transcriptomic signature of the YBR238C KO strain should be done under both fermentative and respiratory conditions to understand the true gene expression signature associated with CLS and RLS. Throughout the manuscript, these two metabolic conditions and associated lifespan types (CLS vs. RLS) are not differentiated and treated as the same, probably causing the biggest confounding effect that resulted in the identification of a single yeast-specific gene.

We obtained the transcriptomic signature of the YBR238C KO strain from logarithmic phase cultures. This consistency was maintained to align with the Rapamycin Response Genes (RRGs) obtained from logarithmic cells treated with rapamycin. Detailed methodology and metabolic status information is provided in the method section and relevant figure legends.

To broaden the scope of our study, we conducted analyses on various phenotypes, including mitochondrial activity and oxidative stress, under both logarithmic phase (relevant to Replicative Lifespan, RLS) and stationary phase (relevant to Chronological Lifespan, CLS). We have now explicitly indicated the logarithmic phase/stationary phase conditions in the figure legends of the manuscript, specifying their relevance to RLS or CLS.

Results from these additional experiments have been incorporated into the revised manuscript as supplementary figures (S3E-S3I). We believe that these clarifications and the inclusion of additional experimental details enhance the robustness and validity of our findings. We trust that these explanations effectively address the concerns raised by the reviewer and contribute to the overall improvement of our manuscript.

YBR238C gene KO effect on mitochondrial function missing comprehensive characterization. Whether the improved mito function caused by increased mtDNA copy number and/or increased mitochondrial number could be easily tested by analyzing normalizing RNAseq reads from mtDNA genes to reads from nucDNA genes. Data could be further combined with western blot specific to mito membrane proteins to analyze mito copy number.

Thank you for your insightful comments and suggestions. Following your recommendation, we conducted an assessment of relative mitochondrial content (see Figure S3F) and observed significantly higher mtDNA content in the ybr238c∆ compared to the wild type (see Figure S3F). Additionally, we have incorporated the methodology for mitochondrial DNA copy number analysis in the methods section.

The two paralogous gene interaction is an interesting observation. However, in yeast, it is known that deletion of one of the paralogous genes causes copy number amplification of the certain chromosome that the other paralogous gene is located, causing aneuploid chromosome. Many of the observed phenotypes can be associated with increased chromosome copy number and should be carefully tested. However, the authors did not consider this important point. Simply, using RNA seq data normalized read/per chromosome could be plotted to analyze the karyotype of YBR238C and RMD9 KO cells.

We appreciate your thoughtful consideration of our work and the suggestion to investigate chromosome copy number variations. While we did not directly test the chromosome copy, we want to highlight that our study extensively explores the impact of YBR238C on cellular lifespan through an RMD9-dependent mechanism (Figure 5). Deletion of YBR238C increases, whereas overexpression of YBR238C decreases the expression of its paralog, RMD9 (Figure 5F). Furthermore, this phenotype is associated with the lifespan of YBR238C-deleted and overexpressed cells. In our study, we have thoroughly investigated this aspect.

Reviewer #3 (Public Review):

This reviewer appreciates the responses to previous notes. The authors attempted to address concerns mostly in writing, avoiding performing some of the experiments suggested in my previous review. Although some of the points were clarified, and the revised manuscript presents valuable insights into the implications of YBR238C and RMD9 on cellular function and yeast aging, my major concern still needs to be addressed. The gene expression signature significantly changes under different metabolic conditions. The media condition under which samples are collected for RNAseq analyses should match the media condition under which the lifespans of those KO strains are tested. This is the major confounding effect, and the conclusions are not informative based on the analysis done in this study.

To avoid experiments, the authors responded that yeast culture results in low optical density and does not reach the stationary phase under rapamycin treatment conditions; however, the simple solution is to grow the yeast cells until they reach the stationary phase and then rapamycin treatment can be done for certain hours - collect the cells for transcriptomics analysis then it can be compared to the CLS gene set.

Another example is chromosome copy number alteration, which can be easily analyzed using transcriptome data, and it is an important aspect to understand whether observed expression changes are also affected by this alteration in YBR238C KO cells. However, the authors ignore this important point as well.

After all, this is an interesting study "limited by subfield" and will be of general interest in the yeast aging field, again considering the lack of homology of the genes of interest in higher eukaryotes.

eLife assessment

This valuable study identifies an uncharacterized yeast gene regulating chronological lifespan in a mitochondrial-dependent pathway. The approach to identify and characterise this new gene is appealing, but the evidence in support of some of the major conclusions is incomplete. The paper focuses on chronological lifespan and mitochondrial function, and it will be of interest to yeast biologists working in metabolism and aging.

Reviewer #1 (Public Review):

Summary

This fascinating paper by M. Alfatah et al. describes work to uncover novel genes affecting lifespan in the budding yeast S. cerevisiae, eventually identifying and further characterizing a gene, YBR238C, now named AAG1 by the authors.<br /> The authors began by considering published gene sets pulled from the Saccharomyces genome database that described increases or decreases in either chronological lifespan or replicative lifespan in yeast. They also began with gene sets known to be downregulated upon treatment with the lifespan-extending TOR inhibitor rapamycin.

YBR283C was unique in being largely uncharacterized, downregulated upon rapamycin treatment and linked to both increased replicative lifespan and increased chronological lifespan upon deletion.

The authors show that YBR283C may act to negatively regulate mitochondrial function, in ways that are both dependent on and independent of the stress-responsive transcription factor Hap4, largely by looking at relative expression levels of relevant mitochondrial genes.

In a hard to fully interpret but well documented series of experiments the authors not that the two paralogues YBR283C and RMD9 (which have ~66% similarity) (a) have opposite effects when acting alone, and (b) appear to interact in that some phenotypes of ybr283c are dependent on RMD9.

A particularly interesting finding in light of the current literature and of the authors' strategy in identifying YBR283C is that changes in electron transport chain genes upon rapamycin treatment appear to be effected via YBR283C.<br /> Based on a series of experiments the authors move to conclude the existence of "a feedback loop between TORC1 and mitochondria (the TORC1-Mitochondria-TORC1 (TOMITO) signaling process) that regulates cellular aging processes."

Strengths

Overall, this study describes a great deal of new data from a large number of experiments, that shed light on the potential specific roles of YBR238C and its paralog RMD9 in aging in yeast, and also underscore the potential of an approach looking for "dark matter" such as uncharacterized genes when seining the increasing deluge of published datasets for new hypotheses to test. This work when revised will become a valuable addition to the field.

Weaknesses

A paralog of YBR283C, RMD9, also exists in the yeast genome. While the authors indicate that part of their interest in YBR283C lies in its uncharacterized nature, its paralogue, RMD9, is not uncharacterized but is named due to its phenotype of Required for Meiotic nuclear Division, which is not mentioned or discussed anywhere in the manuscript currently.

In the context of the current work, in addition to the cited Hillen, H.S et al. and Nouet C. et al, the authors might be very interested in the 2007 Genetics paper "Translation initiation in Saccharomyces cerevisiae mitochondria: functional interactions among mitochondrial ribosomal protein Rsm28p, initiation factor 2, methionyl-tRNA-formyltransferase and novel protein Rmd9p" (PMID: 17194786), which does not appear to be cited or discussed in the current version of the manuscript.

Reviewer #2 (Public Review):

The effectors of cellular aging in yeast have not been fully elucidated. To address this, the authors curated gene expression studies to link genes influenced by rapamycin - a well-known mediator of longevity across model systems - to genes known to affect chronological and replicative lifespan (RLS) in yeast. Through their analyses, they find one gene, ybr238c, whose deletion increases both CLS and RLS upon deletion and that is downregulated by rapamycin. The authors follow up their cellular aging studies using CLS as a model throughout their study, demonstrating that deletion of ybr238c increases CLS across multiple yeast strains and through multiple assays. The authors also test the effects of YBR238C overexpression on lifespan and find the opposite effect, with overexpression yeast showing decreased survival relative to wild type cells, consistent with accelerated aging as the authors propose. The authors also note that ybr238c has a paralog, rmd9, whose deletion decreases CLS and seems to be epistatic to ybr238c, as a double ybr238c/rmd9 mutant has decreased CLS relative to a wild-type strain.

Collectively, the data presented by the authors convincingly demonstrate that ybr238c influences lifespan in a manner that is distinct from (and likely opposite to) rmd9. The authors then link the increased CLS in Δybr238c yeast to HAP4, a transcription factor that promotes mitochondrial biogenesis and oxidative phosphorylation. Through genetic studies, the authors suggest a model in which YBR238C negatively regulates HAP4 activity, and thus loss of HAP4 repression in Δybr238c yeast leads to elevated mitochondrial function. Notably, while the authors use various methods to test mitochondrial function, including the quantification of transcripts associated with oxidative phosphorylation, cellular ATP levels, and mtDNA, none of these fully test mitochondrial function. Thus, while the trends of these proxies are consistent with the model proposed by the authors, including data such as respirometry or assaying the activity of oxidative phosphorylation complexes would have bolstered these conclusions.

Finally, the authors tie the phenotypes of mitochondrial dysfunction caused by deletion of ybr238c to TORC1 signaling, as the gene is influenced by rapamycin. However, the data assaying mitochondrial function in these experiments, such as profiling the transcriptional changes in oxidative phosphorylation complexes or monitoring cellular ATP levels, do not directly measure mitochondrial function. Furthermore, many of the studies performed by the authors rely on genetic or pharmacological rescue of lifespan to establish the influence of YBR238C on TORC1 signaling and mitochondrial function. While valuable, these assays leave questions as to the molecular mechanisms by which YBR238C functions. As such, this manuscript establishes that ybr238c is rapamycin responsive and influences CLS, but the molecular mechanisms by which it affects mitochondrial activity and TORC1 signaling remain to be elucidated.

Author Response

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

We appreciate the care and the detail shown by the Reviewers. Their comments have made our article more focused and more accessible to a general audience.

We would like to begin with a comment about the last sentence of the “eLife assessment”. The evolution of metamorphosis in insects was a major triumph in animal evolution that subsequently impacted almost every aspect of plant and animal evolution in the terrestrial and freshwater aquatic biospheres. Unlike the metamorphoses of most other groups, whose evolutions are lost in time, insect evolution arose relatively recently (~400 mya) and insect orders have branched off at various points in this evolution and have persisted to modern times. Although these “relic” groups also have undergone millions of years of evolution and specialization, they still provide us with windows into how this progression may have come about. The study of these groups provides a unique opportunity to explore the mechanisms that underlie major life history shifts and should be of interest to anyone interested in evolution – not just entomologists.

Reviewer #1 (Public Review):

Summary:

This paper provides strong evidence for the roles of JH in an ametabolous insect species. In particular, it demonstrates that:

• JH shifts embryogenesis from a growth mode to a differentiation mode and is responsible for terminal differentiation during embryogenesis. This, and other JH roles, are first suggested as correlations, based on the timing of JH peaks, but then experimentally demonstrated using JH antagonists and rescue thereof with JH mimic. This is a robust approach and the experimental results are very convincing.

• JH redirects ecdysone-induced molting to direct formation of a more mature cuticle

• Kr-h1 is downstream of JH in Thermobia, as it is in other insects, and is a likely mediator of many JH effects

• The results support the proposed model that an ancestral role of JH in promoting and maintaining differentiation was coopted during insect radiations to drive the evolution of metamorphosis. However, alternate evolutionary scenarios should also be considered.

Strengths:

Overall, this is a beautiful, in-depth student. The paper is well-written and clear. The background places the work in a broad context and shows its importance in understanding fundamental questions about insect biology. The researchers are leaders in the field, and a strength of this manuscript is their use of a variety of different approaches (enzymatic assays, gene expression, agonists & antagonists, analysis of morphology using different types of microscopy and detection, and more) to attack their research questions. The experimental data is clearly presented and carefully executed with appropriate controls and attention to detail. The 'multi-pronged' approach provides support for the conclusions from different angles, strengthening conclusions. In sum, the data presented are convincing and the conclusions about experimental outcomes are well-justified based on the results obtained.

Weaknesses:

This paper provides more detail than is likely needed for readers outside the field but also provides sufficient depth for those in the field. This is both a strength and a weakness. I would suggest the authors shorten some aspects of their text to make it more accessible to a broader audience. In particular, the discussion is very long and accompanied by two model figures. The discussion could be tightened up and much of the text used for a separate review article (perhaps along with Figure 11) that would bring more attention to the proposed evolution of JH roles.

We appreciate the comments about the strengths and weaknesses of the paper. To deal with the weaknesses, we have condensed some of the Results to make them less cumbersome and the Discussion has been completely revised, keeping a sharp focus on the actions of JH in Thermobia embryos and how these actions relate to the status quo functions of JH in insects with metamorphosis. As part of the revision of the Discussion, we have replaced Figures 10 and 11.

Reviewer #1 (Recommendations For The Authors):

In keeping with my public review, this paper is very strong and I have very few suggestions for improvement. They are:

(1) Thermobia are extant insects and are not ancestral insects. It is likely that they retain features found in an insect ancestor. However, these insects have been evolving for a very long time, and for any one feature, many changes may have occurred, both gain and loss of gene function and morphology. Further, even for morphological features present in an extant species that are the same as an ancestor, genetic pathways regulating this feature may have changed over time (see for examples papers from the Haag and Pick labs). Although I realize this is a small, possibly almost semantic point, I feel it is important to be precise here. For example, in the title, "before" is speculative as there could have been a different role in the ancestor with the role in embryogenesis arising in lineages leading to Thermobia; similarly in the abstract, "this ancestral role of JH' is an overstatement since we cannot actually measure the ancestral role.

Since the title has already been cited in a Perspectives review, we decided to keep the title as is.

(2) I don't understand the results in Met and myo in Fig. 3B. Perhaps include them in the explanation of Fig.3 and not after the description of Fig. 4 and explain them in more detail (or perhaps not include them at all?). I don't really understand the statistical analysis of these panels either.

We have revised the figure legends to explain the statistics.

(3) Another point regarding language - talking about the embryo being "able" to go through a developmental stage implies decision-making. I would suggest dropping that wording (e.g, in the description of Fig. 5C). Similarly, in explaining Fig. 6B, it would be more correct to say "JH treatment no longer inhibited" than as written "could no longer inhibit" (implying 'no matter how hard it tried, it still couldn't do it')

We have removed the “can’t” wording. Figure 6 has been revised

Reviewer #2 (Public Review):

The authors have studied in detail the embryogenesis of the ametabolan insect Thermobia domestica. They have also measured the levels of the two most important hormones in insect development: juvenile hormone (JH) and ecdysteroids. The work then focuses on JH, whose occurrence concentrates in the final part (between 70 and 100%) of embryo development. Then, the authors used a precocene compound (7-ethoxyprecocene, or 7EP) to destroy the JH producing tissues in the embryo of the firebrat T. domestica, which allowed to unveil that this hormone is critically involved in the last steps of embryogenesis. The 7EP-treated embryos failed to resorb the extraembryonic fluid and did not hatch. More detailed observations showed that processes like the maturational growth of the eye, the lengthening of the foregut and posterior displacement of the midgut, and the detachment of the E2 cuticle, were impaired after the 7EP treatment. Importantly, a treatment with a JH mimic subsequent to the 7EP treatment restored the correct maturation of both the eye and the gut. It is worth noting that the timing of JH mimic application was essential for correcting the defects triggered by the treatment with 7EP.

This is a relevant result in itself since the role of JH in insect embryogenesis is a controversial topic. It seems to have an important role in hemimetabolan embryogenesis, but not so much in holometabolans. Intriguingly, it appears important for hatching, an observation made in hemimetabolan and in holometabolan embryos. Knowing that this role was already present in ametabolans is relevant from an evolutionary point of view, and knowing exactly why embryos do not hatch in the absence of JH, is relevant from the point of view of developmental biology.

The unique and intriguing aspect of juvenile hormone is its status quo action in the control of metamorphosis. Our reason for dealing with an insect group that branched off from the line of insects that eventually evolved metamorphosis, was to gain insight into the ancestral functions of this hormone. Our data from Thermobia as well as that from grasshoppers and crickets indicate that the developmental actions of JH were originally confined to embryogenesis where it promoted the terminal differentiation of the embryo. Its actions in promoting differentiation also included suppressing morphogenesis. This latter function was not pronounced during embryogenesis because JH only appeared after morphogenesis was essentially completed. However, it was a preadaptation that proved useful in more derived insects that delayed aspects of morphogenesis into the postembryonic realm. JH was then used postembryonically to inhibit morphogenesis until late in juvenile growth when JH disappears, and this inhibition is released.

Then, the authors describe a series of experiments applying the JH mimic in early embryogenesis, before the natural peak of JH occurs, and its effects on embryo development. Observations were made under different doses of JHm, and under different temporal windows of treatment. Higher doses triggered more severe effects, as expected, and different windows of application produced different effects. The most used combination was 1 ng JHm applied 1.5 days AEL, checking the effects 3 days later. Of note, 1.5 days AEL is about 15% embryonic development, whereas the natural peak of JH occurs around 85% embryonic development. In general, the ectopic application of JHm triggered a diversity of effects, generally leading to an arrest of development. Intriguingly, however, a number of embryos treated with 1 ng of JHm at 1.5 days AEL showed a precocious formation of myofibrils in the longitudinal muscles. Also, a number of embryos treated in the same way showed enhanced chitin deposition in the E1 procuticle and showed an advancement of at least a day in the deposition of the E2 cuticle.

While the experiments and observations are done with great care and are very exhaustive, I am not sure that the results reveal genuine JH functions. The effects triggered by a significant pulse of ectopic JHm when the embryo is 15% of the development will depend on the context: the transcriptome existing at that time, especially the cocktail of transcription factors. This explains why different application times produce different effects. This also explains why the timing of JHm application was essential for correcting the effects of 7EP treatment. In this reasoning, we must consider that the context at 85% development, when the JH peaks in natural conditions and plays its genuine functions, must be very different from the context at 15% development, when the JHm was applied in most of the experiments. In summary, I believe that the observations after the application of JHm reveal effects of the ectopic JHm, but not necessarily functions of the JH. If so, then the subsequent inferences made from the premise that these ectopic treatments with JHm revealed JH functions are uncertain and should be interpreted with caution.

We disagree with the reviewer. An analogous situation would be in exploring gene function in which both gain-of-function and loss-of-function experiments often provide complementary insights into how a gene functions. We see JH effects only when its receptor, Met, is present and JH can induce its main effector protein, Kr-h1. The latter gives us confidence that we are looking at bona fide JH effects. We have also kept in mind, though, that the nature of the responding tissues is changing through time. Nevertheless, we see a consistent pattern of responses in the embryo and these can be related to its postembryonic effects in metamorphic insects.

Those inferences affect not only the "JH and the progressive nature of embryonic molts" section, but also, the "Modifications in JH function during the evolution of hemimetabolous and holometabolous life histories" section, and the entire "Discussion". In addition to inferences built on uncertain functions, the sections mentioned, especially the Discussion, I think suffer from too many poorly justified speculations. I love speculation in science, it is necessary and fruitful. But it must be practiced within limits of reasonableness, especially when expressed in a formal journal.

We have tried to dial back the speculation.

Finally, In the section "Modifications in JH function during the evolution of hemimetabolous and holometabolous life", it is not clear the bridge that connects the observations on the embryo of Thermobia and the evolution of modified life cycles, hemimetabolan and holometabolan.

Our Figure 12 should put this into perspective.

Reviewer #2 (Recommendations For The Authors):

Main points

(1) Please, reduce the level of overinterpretation of ectopic treatment experiments with JHm, since the resulting observations represent effects, but not necessarily functions of JH.

We have revised this section to indicate that the “effects” of ectopic treatments provide insights into the function of JH. Using a genetic analogy, both “loss-of-function” and “gain-of-function” experiments provide insights into a given gene. (see response to Public Comments)

(2) Especially in the sections "JH and the progressive nature of embryonic molts" and "Modifications in JH function during the evolution of hemimetabolous and holometabolous life histories", and the entire "Discussion", please keep the level of speculation within reasonable limits, avoiding especially the inference of conclusions on the basis of speculation, itself based on previous speculation.

We have toned down some of the speculation and provided reasons why it is worth suggesting.

(3) Please revisit the argued roles of myoglianin in the story, in light of its effects as an inhibitor of JH production, repressing the expression of JHAMT, as has been reliably demonstrated in hemimetabolan species (DOI: 10.1073/pnas.1600612113 and DOI: 10.1096/ fj.201801511R).

Our appreciation to the reviewer. We are more explicit about the relationship between JH and myo.

Minor points

(4) Please keep the consistency of the scientific binomial nomenclature for the species mentioned. For example, read "Manduca sexta" (in italics) at the first mention, and then "M. sexta" (in italics) in successive mentions (instead of reading "Manduca" on page 17, and then "Manduca sexta" on page 18, for example). The same for "Drosophila" ("Drosophila melanogaster" first, and then "D. melanogaster"), "Thermobia" ("Thermobia domestica" first, and then "T. domestica"), etc. In the figure legends, I recommend using the complete name: Thermobia domestica, in the main heading.

Where there is no possibility of confusion, we intend to use Thermobia, rather than T. domestica, etc. We think that it is easier for a non-specialist to read and it is commonly done in endocrine papers.

(5) There is no purpose in evolution and biological processes. Thus, I suggest avoiding expressions that have a teleological aftertaste. For example (capitals are mine), on p. 3 "appears to have been extended into postembryonic life where it acts TO antagonize morphogenic and allow the maintenance of a juvenile state".

We have tried to avoid teleological wording.

(6) The title "The embryonic role of juvenile hormone in the firebrat, Thermobia domestica, reveals its function before its involvement in metamorphosis" contains a redundancy ("role" and "function"), and an apparent obviousness ("before its involvement in metamorphosis"). I suggest a more straightforward title. Something like "Juvenile hormone plays developmental functions in the embryo of the firebrat Thermobia domestica, which predate its status quo action in metamorphosis".

As noted above, we are retaining the title since it has already been cited.

(7) Page 2. "The transition from larva to adult then occurred through a transitional stage, the pupa, thereby providing the three-part life history diagnostic of the "complete metamorphosis" exhibited by holometabolous insects (reviews: Jindra, 2019; Truman & Riddiford, 2002, 2019)". I suggest adding the reference ISBN: 9780128130209 9 7 8 - 0 - 1 2 - 8 1 3 0 2 0 - 9, as the most comprehensive and recent review on complete metamorphosis.

Done

(8) Page 3. "These severe developmental effects suggest that the developmental role of JH in insects was initially CONFINED to the embryonic domain" (capitals are mine). This appears contradictory with the observations of Watson, 1967, on the relationships between the apparition of scales and JH, mentioned shortly before by the authors.

This is explained in the Discussion. Although JH can suppress scale appearance in the J4 stage, we have not been able to show that scales appearance is caused by changes in the juvenile JH titer.

(9) Page 4. "we measured JH III levels during Thermobia embryogenesis at daily intervals starting at 5 d AEL". Why not before, like in the case of ecdysteroids? The authors might perhaps argue that the levels of Kr-h1 expression are consistently low from the very beginning, according to Fernandez-Nicolas et al, 2022 (reference cited later in the manuscript).

(10) Page 4. "Ecdysteroid titers through embryogenesis and the early juvenile instars were measured using the enzyme immunoassay method (Porcheron et al., 1989) that is optimized for detecting 20-hydroxyecdysone (20E)". The antibody generated by Porcheron (and now sold by Cayman) recognizes ecdysone and 20-hydroxyecdysone alike. But that's not relevant here. I would refer to "ecdysteroids" when mentioning measurements. Also in figure 2B (and "juvenile hormone III" without the formula, in Panel A, for harmonization). And I would not expand on specifications, like those at the beginning of page 5, or towards the end of page

We thank the reviewer for this important correction.

(12) ("the fact that we detected only a slight rise in ecdysteroids at this time (Fig 2B) is likely due to the assay that we used being designed to detect 20E rather than ecdysone").

Omitted.

(11) Page 5. "Low levels of Kr-h1 transcripts were present at 12 hr after egg deposition, but then were not detected until about 6 d AEL when JH-III first appeared". There is a very precise Kr-h1 pattern in Fernandez-Nicolas et al. 2023 (reference mentioned later in the manuscript).

(12) Page 5. "notably myoglianin (myo), have become prominent as agents that promote the competence and execution of metamorphosis in holometabolous and hemimetabolous insects (He et al., 2020; Awasaki et al., 2011)". See my note 3 above.

The myoglianin issue has been revised.

(13) Page 5. "a drug that suppresses JH production". Rather, "a drug that destroys the JH producing tissues". Why the way, do the authors know when the CA are formed in T. domestica embryo development?

We prefer to keep our original wording. There have been some cases in which precocene has blocked JH production but did not kill the CA cells. We do not have observations that show that 7EP kills the CA cells in Thermobia embryos.

(14) Page 5. "subsequent treatment with a JHm". I would say here that the JHm is pyriproxyfen, not on page 6 or page 7. Thus, to be consistent, after the first mention of "pyriproxyfen (JHm)" on page 5, I'd consistently use the abbreviation "JHm".

(15) Page 9. "Limb loss in such embryos was often STOCHASTIC, i.e., in a given embryo some limbs were completely lost while others were maintained in a reduced state" (capitals are mine). The meaning of "stochastic" is random, involving a random variable; it is a concept usually associated to probability theory and related fields. I suggest using the less specialized word "variable", since to ascertain that the values are really stochastic would require specific mathematical approaches.

We are still using stochastic because the loss is random.

(16) Page 10. "9E). Indeed, the JH treatment redirects the molt to be more like that to the J2 stage, rather than to the E2 (= J1) stage". Probably too assertive given the evidence available (see my points 1 and 2 above).

We do not see a problem with our conclusion. In response to the JHm treatment, the embryo produced a smooth, rather than a “pebbly” cuticle, failed to make the J1-specific egg tooth, and attempted to make cuticular lenses (a J2 feature). This ability of premature JH exposure to cause embryos to “skip” a stage is also seen in locusts (Truman & Riddiford, 1999) and crickets (Erezyilmaz et al., 2004). The JHm treatment resulted in the production of smooth cuticle, lack of a hatching tooth, and an attempt to make cuticular lenses.

(17) Page 11. "early JHM treatment", read "early JHm treatment".

Corrected

(18) Page 11. "likely. A target of JH, and likely Kr-h1, in Thermobia is myoglianin...". Please see my notes 1, 2, and especially 3, above.

This has been revised

(19) Page 13. "the locust, Locusta americana (Aboulafia-Baginshy et al.,1984)". Please read "the locust, Locusta migratoria (Aboulafia-Baginshy et al.,1984)".

Corrected

(20) Page 13 "Acheta domesticus" three times. The correct name now is "Acheta domestica", after harmonizing the declension of the specific name with the generic one. See additionally my note 4 above.

Acheta domesticus has been used in hundreds (thousands?) of papers since it was originally named by Linnaeus. We will continue to use it.

(21) Page 15, "(also called the vermiform larva (Bernays, 1971) redirects embryonic development to form an embryo with proportions, cuticular pigmentation, cuticular sculpturing and bristles characteristic of a nymph, while pronymph modifications, such as the cuticular surface sculpturing (Bernays, 1971)". The reference "Bernays, 1971" is indeed "Bergot et al., 1971".

There was a mistake in the references. The Bernays reference was omitted from the revised Discussion

(22) Page 16. "Since JH also induces Kr-h1 in embryos of many insects, including Thermobia". I'm not sure that this has been studied in many insects. In any case, any reference would be useful.

(23) Page 17. "Tribolium casteneum". Please read "Tribolium castaneum".

Changed

(24) Page 17. "...results in a permanent larva that continues to molt well after it has surpassed its critical weight (He et al., 2019)". The paper of He et al., 2019 is preceded by two key papers that previously demonstrate (and in hemimetabolan insects) that myoglianin is a determining factor in the preparation for metamorphosis: DOI: 10.1073/pnas.1600612113 and DOI: 10.1096/ fj.201801511R). See my note 3 above.

Corrected in revision

(25) Page 18. "These persisting embryonic primordia join the wing primordia in delaying their morphogenesis into postembryonic life". This reader does not understand this sentence.

Made clearer in the revision.

(26) Page 18. "is first possible in the commercial silkworm (Daimon et al., 2015)". Please mention the scientific Latin name of the species, Bombyx mori.

(27) Page 19. "The functioning of farnesol derivatives in growth versus differentiation control extends deep into the eukaryotes.../... this capacity was eventually exploited by the insects to provide the hormonal system that regulates their metamorphosis". This information appears quite out of place.

We have retained this point.

(28) Page 21. Heading "Hormones". I suggest using the heading "Bioactive compounds", as neither pyriproxyfen nor 7-ethoxyprecocene are hormones.

Done

(29) Page 29, legend of figure 1. "Photomicrographs" is somewhat redundant. The technical word is "micrographs". "Thermobia domestica" appears in the explanation of panel C, but this is not necessary, as the name appears in the main heading of the legend.

Done

(30) Page 30, legend of figure 2. Panel B, see my comment 10 above. Why embryonic age is expressed in % embryo development in panel C (and in days in panels A and B)?

All have been converted to days AEL

(31) Page 35, legend of figure 5. "Photomicrograph" see my note 28 above.

Done

(32) Page 40, figure 10. In panel A, the indication of the properties of JH is misleading. The arrow going to promoting differentiation and maturation is OK, but the repression sign that indicates suppression of morphogenetic growth and cell determination seems to suggest that JH has retroactive effects. In panel B, I suggest to label "Flies" instead of "Higher Diptera", which is an old-fashioned term. In any case, see my general comments 1 and 2, above, about speculation.

Figure has been completely revised

(33) Figure 11. See my general comments 1 and 2, above, about speculation.

Figure has been revised

Reviewer #3 (Public Review):

Summary:

In this manuscript, the authors use inhibitors and mimetics of juvenile hormone (JH) to demonstrate that JH has a key role in late embryonic development in Thermobia, specifically in gut and eye development but also resorption of the extraembryonic fluid and hatching. They then exogenously apply JH early in development (when it is not normally present) to examine the biological effects of JH at these stages. This causes a plethora of defects including developmental arrest, deposition of chitin, limb development, and enhanced muscle differentiation. The authors interpret these early effects on development as JH being important for the shift from morphogenetic growth to differentiation - a role that they speculate may have facilitated the evolution of metamorphosis (hemi- and holo-metaboly). This paper will be of interest to insect evo-devo researchers, particularly those with interests in the evolution of metamorphosis.

Strengths:

The experiments are generally conducted very well with appropriate controls and the authors have included a very detailed analysis of the phenotypes.

The manuscript significantly advances our understanding of Thermobia development and the role of JH in Thermobia development.

The authors interpret this data to present some hypotheses regarding the role of JH in the evolution of metamorphosis, some aspects of which can be addressed by future studies.

Weaknesses:

The results are based on using inhibitors and mimetics of JH and there was no attempt to discern immediate effects of JH from downstream effects. The authors show, for instance, that the transcription of myoglianin is responsive to JH levels, it would have been interesting to see if any of the phenotypic effects are due to myoglianin upregulation/suppression (using RNAi for example). These kinds of experiments will be necessary to fully work out if and how the JH regulatory network has been co-opted into metamorphosis.

We agree completely and should be a feature of future work.

The results generally support the authors' conclusions. However, the discussion contains a lot of speculation and some far-reaching conclusions are made about the role of JH and how it became co-opted into controlling metamorphosis. There are some interesting hypotheses presented and the author's speculations are consistent with the data presented. However, it is difficult to make evolutionary inferences from a single data point as although Thermobia is a basally branching insect, the lineage giving rise to Thermobia diverged from the lineages giving rise to the holo- and hemimetabolous insects approx.. 400 mya and it is possible that the effects of JH seen in Thermobia reflect lineage-specific effects rather than the 'ancestral state'. The authors ignore the possibility that there has been substantial rewiring of the networks that are JH responsive across these 400 my. I would encourage the authors to temper some of the discussion of these hypotheses and include some of the limitations of their inferences regarding the role of JH in the evolution of metamorphosis in their discussion.

We have tried to be less all-encompassing in the Discussion. The strongest comparisons can be made between ametabolous and hemimetabolous insects and we have focused most of the Discussion on the role of JH in that transition. We still include some discussion of holometabolous insects because the ancestral embryonic functions of JH may be somehow related to the unusual reappearance of JH in the prepupal period. We have reduced this discussion to only a few sentences.

Reviewer #3 (Recommendations For The Authors):

(1) The overall manuscript is very long (especially the discussion), and the main messages of the manuscript get lost in some of the details. I would suggest that the authors move some of the results to the supplementary material (e.g. it might be possible to put a lot of the detail of Thermobia embryogenesis into the supplementary text if the authors feel it is appropriate). The discussion contains a lot of speculation and I suggest the authors make this more concise. One example: At the moment there is a large section on the modification in JH function during the evolution of holo and hemi-metabolous life history strategies. There are some interesting ideas in this section and the authors do a good job of integrating their findings with the literature - but I would encourage the authors to limit the bulk of their discussion to the specific things that their results demonstrate. E.g. The first half of p17 contains too much detail, and the focus should be on the relationship with Thermobia (as at the bottom of p17).

Section has been revised and is more focused

(2) I would also suggest a thorough proofread of the manuscript, I have highlighted some of the errors/points of confusion that I found in the list below - but this list is unlikely to be exhaustive . We appreciate catching the errors. Hopefully the final version is better proofed.

(3) It might be me, but I found the wording in the second half of the abstract a bit confusing. Particularly the statement about the redeployment of morphogen systems - could this be stated more clearly?

Abstract has been revised.

(4) Introduction

a. "powered flight" rather than 'power flight'

Done

b. 'brought about a hemimetabolous lifecycle' implies causality which hasn't been shown and directionality to evolution - suggest 'facilitated the evolution of a hemi...". Similar comment for 'subsequent step to complete metamorphosis'.

c. Bottom of p2 - unclear whether you are referring to hemi- holo- or both

d. Suggest removing sentence beginning "besides its effects..." as the relevance of the role of JH in caste isn't clear.

Kept sentence but removed initial clause

e. State that Thermoia is a Zygentoma.

Done

f. Throughout - full species names on first usage only, T. domestica on subsequent usages.

We will continue to use genus names for the reason given above.

Gene names e.g. kr-h1 in italics.

g. 'antagonise morphogens"? rather than 'antagonise morphoentic'.

Done

(5) Results

a. Unclear why drawings are provided rather than embryonic images in Fig. 1A

We think that the points can be made better with diagrams.

b. Top of p4, is 'slot' the correct word?

Corrected

c. Unclear why the measurements of JHIII weren't measured before 5 days AEL, especially given that many of the manipulative experiments are at earlier time points than this. I appreciate that, based on kr-h1, levels that JHIII is also likely to be low.

d. Reference for the late embryonic peak of 20E being responsible for the J2 cuticle?

Clarified that this is an assumption

e. Clarify "some endocrine related transcripts" why were these ones in particular picked? Kr-h1 is a good transcriptional proxy for JH and Met is the JH-receptor, why myoglianin and not some of the other transcriptional proxies of neuroendocrine signalling?

Hopefully, the choice is clearer.

f. Fig 2C rather than % embryo development for the gene expression data please represent this in days (to be consistent with your other figures).

It is now consistent with other parts of figure.

g. In Fig. 3 the authors do t-tests, because there are three groups there needs to be some correction for multiple testing (e.g. Bonferroni) can the authors add this to the relevant methods section?

We think that pair-wise comparisons are appropriate.

h. Fig. 3 legend: you note that you treat stage 2 juveniles with 7EP - I couldn't tell what AEL this corresponded to.

This is after hatching so AEL does not apply.

i. Top of p7 'deformities' rather than 'derangements'?

Done

j. Regarding the dosage effects of embryonic abnormalities - it would be good to include these in the supp material, as it convinces the reader that the effects you have seen aren't just due to toxicity.

It is not clear what the objection is.

k. Bottom of p7 'problematic' not 'problematical'

Done

l. P8 Why are the clusters of Its important? - provide a bit more interpretation for the reader here.

This is clear in the revised version.

m. P9 Why is the modulation of transcription of kr-h1, met, and myo important in this context

Explained

n. P9 'fig. 7F'? there is no Fig. 5F

Thanks for catching the typo.

o. Fig. 7B add to the legend which treatment the dark and light points correspond to.

We think it is obvious from the labeling on Fig 7B.

(6) Discussion:

a. What do we know about how terminal differentiation is controlled in non-insect arthropods? Most of the discussion is focused on insects (which makes sense as JH is an insect-specific molecule), but if the authors are arguing the ancestral role of JH it would be useful to know how their findings relate to non-insect arthropods.

We have not been able to find any information about systemic signals being involved in non-insect arthropods.

b. There is no Fig. 5E (are they referring to 7E?)

Yes, it should have been Fig. 7E.

c. Is myoglianin a direct target of JH in other species?

Other reports are in postembryonic stages and show that myoglianin suppresses JH production. Our paper is the first examination in embryos and we find that the opposite is true – i.e., that JH treatment suppresses myoglianin production. We suspect that these two signaling systems are mutually inhibitory. It would be interesting to see whether treatment of a post-critical weight larva with JH (which would induce a supernumerary larval molt) would also suppress myoglianin production (as we see in Thermobia embryos).

d. P12 What is the evidence that JH interacts with the first 20E peak to alter the embryonic cuticle?

We are not sure what the issue is. The experimental fact is that treatment with JH before the E1 ecdysteroid peak causes the production of an altered E1 cuticle. We are faced with the question of why is this molt sensitive to JH when the latter will not appear until 3 or 4 days later? A possible answer is that the ecdysone response pathway has a component that has inherent JH sensitivity. The mosquito data suggest that Taiman provides another link between JH and ecdysone action

e. Top of p13 - this paragraph can be cut down substantially. Although this is evidence that JH can alter ecdysteriods - it is in a species that is 400 my derived from the target species. Is it likely to be the exact same mechanism? I would encourage the authors to distil and retain the most important points.

This paragraph has been shortened and focused.

f. Bottom of p13 - what does this study add to this knowledge?

The response of Thermobia embryos to JH treatment is qualitatively the same as seen in other short germband embryos. This similarity supports the assumption that the same responses would have been seen in their last common ancestor.

g. P19 the last paragraph in the conclusions is really peripherally relevant to the paper and is a bit of a stretch, I would encourage the authors to leave this section out.

We agree that it is a stretch. JH and its precursor MF are the only sesquiterpene hormones. How did they come about to acquire this function? We think it is worth pointing out the farnesol metabolites have been associated with promoting differentiation in various eukaryotes. An ancient feature of these molecules in promoting (maintaining?) differentiation may have been exploited by the insects to develop a unique class of hormones. It is worth putting the idea out to be considered.

h. P19 "conclusions" rather than 'concluding speculations'.

Changed as suggested.

Methods:

It is standard practice to include at least two genes as reference genes for RT-qPCR analysis (https://doi.org/10.1186/gb-2002-3-7-research0034, https://doi.org/10.1373/clinchem.2008.112797) If there are large-scale differences in the tissues being compared (e.g. as there are here during development) then more than two reference genes may be required and a reference gene study (such as https://doi.org/10.3390%2Fgenes12010021) is appropriate. Have the authors confirmed that rp49 is stably expressed during the stages of Thermobia development that they assay here?

We have explained our choice in the Methods.

eLife assessment

This important study presents findings regarding the role of Juvenile Hormone in development and cell differentiation in the ametabolous insect Thermobia domestica, providing an in-depth analysis of JH's roles in a member of this basally branching group. The evidence supporting the claims of the authors is convincing, drawing on a broad range of approaches and variety of experimental techniques. While the interpretation of this work in the wider context - its relevance for the evolution of metamorphosis - is in some places somewhat speculative, the work will be of interest to evolutionary developmental biologists studying the evolution of metamorphosis, and the evolution of insects in general.

Reviewer #1 (Public Review):

Summary:<br /> This paper provides strong evidence for the roles of JH in an ametabolous insect species. In particular, it demonstrates that:<br /> • JH shifts embryogenesis from a growth mode to a differentiation mode and is responsible for terminal differentiation during embryogenesis. This, and other JH roles, are first suggested as correlations, based on the timing of JH peaks, but then experimentally demonstrated using JH antagonists and rescue thereof with JH mimic. This is a robust approach and the experimental results are very convincing.<br /> • JH redirects ecdysone-induced molting to direct formation of a more mature cuticle<br /> • Kr-h1 is downstream of JH in Thermobia, as it is in other insects, and is a likely mediator of many JH effects<br /> • The results support the proposed model that an ancestral role of JH in promoting and maintaining differentiation was coopted during insect radiations to drive the evolution of metamorphosis. However, alternate evolutionary scenarios should also be considered.

Strengths:

Overall, this is a beautiful, in-depth student. The paper is well-written and clear. The background places the work in a broad context and shows its importance in understanding fundamental questions about insect biology. The researchers are leaders in the field, and a strength of this manuscript is their use of a variety of different approaches (enzymatic assays, gene expression, agonists & antagonists, analysis of morphology using different types of microscopy and detection, and more) to attack their research questions. The experimental data is clearly presented and carefully executed with appropriate controls and attention to detail. The 'multi-pronged' approach provides support for the conclusions from different angles, strengthening conclusions. In sum, the data presented are convincing and the conclusions about experimental outcomes are well-justified based on the results obtained.

Weaknesses:

This paper provides more detail than is likely needed for readers outside the field but also provides sufficient depth for those in the field. This is both a strength and a weakness. I would suggest the authors shorten some aspects of their text to make it more accessible to a broader audience. In particular, the discussion is very long and accompanied by two model figures. The discussion could be tightened up and much of the text used for a separate review article (perhaps along with Figure 11) that would bring more attention to the proposed evolution of JH roles.

Reviewer #2 (Public Review):

The authors have studied in detail the embryogenesis of the ametabolan insect Thermobia domestica. They have also measured the levels of the two most important hormones in insect development: juvenile hormone (JH) and ecdysteroids. The work then focuses on JH, whose occurrence concentrates in the final part (between 70 and 100%) of embryo development. Then, the authors used a precocene compound (7-ethoxyprecocene, or 7EP) to destroy the JH producing tissues in the embryo of the firebrat T. domestica, which allowed to unveil that this hormone is critically involved in the last steps of embryogenesis. The 7EP-treated embryos failed to resorb the extraembryonic fluid and did not hatch. More detailed observations showed that processes like the maturational growth of the eye, the lengthening of the foregut and posterior displacement of the midgut, and the detachment of the E2 cuticle, were impaired after the 7EP treatment. Importantly, a treatment with a JH mimic subsequent to the 7EP treatment restored the correct maturation of both the eye and the gut. It is worth noting that the timing of JH mimic application was essential for correcting the defects triggered by the treatment with 7EP.

This is a relevant result in itself since the role of JH in insect embryogenesis is a controversial topic. It seems to have an important role in hemimetabolan embryogenesis, but not so much in holometabolans. Intriguingly, it appears important for hatching, an observation made in hemimetabolan and in holometabolan embryos. Knowing that this role was already present in ametabolans is relevant from an evolutionary point of view, and knowing exactly why embryos do not hatch in the absence of JH, is relevant from the point of view of developmental biology.

Then, the authors describe a series of experiments applying the JH mimic in early embryogenesis, before the natural peak of JH occurs, and its effects on embryo development. Observations were made under different doses of JHm, and under different temporal windows of treatment. Higher doses triggered more severe effects, as expected, and different windows of application produced different effects. The most used combination was 1 ng JHm applied 1.5 days AEL, checking the effects 3 days later. Of note, 1.5 days AEL is about 15% embryonic development, whereas the natural peak of JH occurs around 85% embryonic development. In general, the ectopic application of JHm triggered a diversity of effects, generally leading to an arrest of development. Intriguingly, however, a number of embryos treated with 1 ng of JHm at 1.5 days AEL showed a precocious formation of myofibrils in the longitudinal muscles. Also, a number of embryos treated in the same way showed enhanced chitin deposition in the E1 procuticle and showed an advancement of at least a day in the deposition of the E2 cuticle.

While the experiments and observations are done with great care and are very exhaustive, I am not sure that the results reveal genuine JH functions. The effects triggered by a significant pulse of ectopic JHm when the embryo is 15% of the development will depend on the context: the transcriptome existing at that time, especially the cocktail of transcription factors. This explains why different application times produce different effects. This also explains why the timing of JHm application was essential for correcting the effects of 7EP treatment. In this reasoning, we must consider that the context at 85% development, when the JH peaks in natural conditions and plays its genuine functions, must be very different from the context at 15% development, when the JHm was applied in most of the experiments. In summary, I believe that the observations after the application of JHm reveal effects of the ectopic JHm, but not necessarily functions of the JH. If so, then the subsequent inferences made from the premise that these ectopic treatments with JHm revealed JH functions are uncertain and should be interpreted with caution.

Those inferences affect not only the "JH and the progressive nature of embryonic molts" section, but also, the "Modifications in JH function during the evolution of hemimetabolous and holometabolous life histories" section, and the entire "Discussion". In addition to inferences built on uncertain functions, the sections mentioned, especially the Discussion, I think suffer from too many poorly justified speculations. I love speculation in science, it is necessary and fruitful. But it must be practiced within limits of reasonableness, especially when expressed in a formal journal.

Finally, In the section "Modifications in JH function during the evolution of hemimetabolous and holometabolous life", it is not clear the bridge that connects the observations on the embryo of Thermobia and the evolution of modified life cycles, hemimetabolan and holometabolan.

Reviewer #3 (Public Review):

Summary:

In this manuscript, the authors use inhibitors and mimetics of juvenile hormone (JH) to demonstrate that JH has a key role in late embryonic development in Thermobia, specifically in gut and eye development but also resorption of the extraembryonic fluid and hatching. They then exogenously apply JH early in development (when it is not normally present) to examine the biological effects of JH at these stages. This causes a plethora of defects including developmental arrest, deposition of chitin, limb development, and enhanced muscle differentiation. The authors interpret these early effects on development as JH being important for the shift from morphogenetic growth to differentiation - a role that they speculate may have facilitated the evolution of metamorphosis (hemi- and holo-metaboly). This paper will be of interest to insect evo-devo researchers, particularly those with interests in the evolution of metamorphosis.

Strengths:

The experiments are generally conducted very well with appropriate controls and the authors have included a very detailed analysis of the phenotypes.<br /> The manuscript significantly advances our understanding of Thermobia development and the role of JH in Thermobia development.<br /> The authors interpret this data to present some hypotheses regarding the role of JH in the evolution of metamorphosis, some aspects of which can be addressed by future studies.

Weaknesses:

The results are based on using inhibitors and mimetics of JH and there was no attempt to discern immediate effects of JH from downstream effects. The authors show, for instance, that the transcription of myoglianin is responsive to JH levels, it would have been interesting to see if any of the phenotypic effects are due to myoglianin upregulation/suppression (using RNAi for example). These kinds of experiments will be necessary to fully work out if and how the JH regulatory network has been co-opted into metamorphosis.

The results generally support the authors' conclusions. However, the discussion contains a lot of speculation and some far-reaching conclusions are made about the role of JH and how it became co-opted into controlling metamorphosis. There are some interesting hypotheses presented and the author's speculations are consistent with the data presented. However, it is difficult to make evolutionary inferences from a single data point as although Thermobia is a basally branching insect, the lineage giving rise to Thermobia diverged from the lineages giving rise to the holo- and hemimetabolous insects approx.. 400 mya and it is possible that the effects of JH seen in Thermobia reflect lineage-specific effects rather than the 'ancestral state'. The authors ignore the possibility that there has been substantial rewiring of the networks that are JH responsive across these 400 my. I would encourage the authors to temper some of the discussion of these hypotheses and include some of the limitations of their inferences regarding the role of JH in the evolution of metamorphosis in their discussion.

Author Response

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

This work describes a new method for sequence-based remote homology detection. Such methods are essential for the annotation of uncharacterized proteins and for studies of protein evolution.

Strengths:

The main strength and novelty of the proposed approach lies in the idea of combining stateof-the-art sequence-based (HHpred and HMMER) and structure-based (Foldseek) homology detection methods with recent developments in the field of protein language models (the ESM2 model was used). The authors show that features extracted from high-dimensional, information-rich ESM2 sequence embeddings can be suitable for efficient use with the aforementioned tools.

The reduced features take the form of amino acid occurrence probability matrices estimated from ESM2 masked-token predictions, or structural descriptors predicted by a modified variant of the ESM2 model. However, we believe that these should not be called "embeddings" or "representations". This is because they don't come directly from any layer of these networks, but rather from their final predictions.

We agree that there is some room for discussion about whether the amino acid probabilities returned by pre-trained ESM-2 and the 3Di sequences returned by ESM-2 3B 3Di can be properly referred to as “embeddings”. The term “embedding” doesn’t have a formal definition, other than some kind of alternative vector representation of the input data which, preferably, makes the input data more suitable for some downstream task. In that simple sense of the word “embedding”, amino acid probabilities and 3Di sequences output by our models are, indeed, types of embeddings. We posed the question on Twitter (https://twitter.com/TrichomeDoctor/status/1715051012162220340) and nobody responded, so we are left to conclude that the community is largely ambivalent about the precise definition of “embedding”.

We’ve added language in our introduction to make it more clear that this is our working definition of an “embedding”, and why that definition can apply to profile HMMs and 3Di sequences.

The benchmarks presented suggest that the approach improves sensitivity even at very low sequence identities <20%. The method is also expected to be faster because it does not require the computation of multiple sequence alignments (MSAs) for profile calculation or structure prediction.

Weaknesses:

The benchmarking of the method is very limited and lacks comparison with other methods. Without additional benchmarks, it is impossible to say whether the proposed approach really allows remote homology detection and how much improvement the discussed method brings over tools that are currently considered state-of-the-art.

We thank the reviewer for the comment. To address the question, we’ve expanded the results by adding a new benchmark and added a new figure, Figure 4. In this new content, we use the SCOPe40 benchmark, originally proposed in the Foldseek paper (van Kempen et al., 2023), to compare our best method, ESM-2 3B 3Di coupled to Foldseek, with several other recent methods. We find our method to be competitive with the other methods.

We are hesitant to claim that any of our proposed methods are state-of-the-art because of the lack of a widely accepted standard benchmark for remote homology detection, and because of the rapid pace of advancement of the field in recent years, with many groups finding innovative uses of pLMs and other neural-network models for protein annotation and homology detection.

Reviewer #2 (Public Review):

Summary:

The authors present a number of exploratory applications of current protein representations for remote homology search. They first fine-tune a language model to predict structural alphabets from sequence and demonstrate using these predicted structural alphabets for fast remote homology search both on their own and by building HMM profiles from them. They also demonstrate the use of residue-level language model amino acid predicted probabilities to build HMM profiles. These three implementations are compared to traditional profile-based remote homology search.

Strengths:

• Predicting structural alphabets from a sequence is novel and valuable, with another approach (ProstT5) also released in the same time frame further demonstrating its application for the remote homology search task.

• Using these new representations in established and battle-tested workflows such as MMSeqs, HMMER, and HHBlits is a great way to allow researchers to have access to the state-of-the-art methods for their task.

• Given the exponential growth of data in a number of protein resources, approaches that allow for the preparation of searchable datasets and enable fast search is of high relevance.

Weaknesses:

• The authors fine-tuned ESM-2 3B to predict 3Di sequences and presented the fine-tuned model ESM-2 3B 3Di with a claimed accuracy of 64% compared to a test set of 3Di sequences derived from AlphaFold2 predicted structures. However, the description of this test set is missing, and I would expect repeating some of the benchmarking efforts described in the Foldseek manuscript as this accuracy value is hard to interpret on its own.

The preparation of training and test sets are described in the methods under the heading “Fine tuning ESM-2 3B to convert amino acid sequences into 3Di sequences”. Furthermore, there is code in our github repository to reproduce the splits, and the entire model training process: https://github.com/seanrjohnson/esmologs#train-esm-2-3b-3di-starting-from-the-esm-2-3bpre-trained-weights

We didn’t include the training/validation/test splits in the Zenodo repository because they are very large: train 33,924,764; validation 1,884,709; test 1,884,710 sequences, times 2 because there are both amino acid and 3Di sequences. It comes out to about 30 Gb total, and is easily rebuilt from the same sources we built it from.

We’ve added the following sentence to the main text to clarify:

“Training and test sets were derived from a random split of the Foldseek AlphaFold2 UniProt50 dataset (Jumper et al., 2021; van Kempen et al., 2023; Varadi et al., 2022), a reducedredundancy subset of the UniProt AlphaFold2 structures (see Methods for details).”

To address the concern about comparing to Foldseek using the same benchmark, we’ve expanded the results section and added a new figure, Figure 4 using the SCOPe40 benchmark originally presented in the Foldseek paper, and subsequently in the ProstT5 paper to compare Foldseek with ESM-2 3B 3Di to Foldseek with ProstT5, AlphaFold2, and experimental structures.

• Given the availability of predicted structure data in AFDB, I would expect to see a comparison between the searches of predicted 3Di sequences and the "true" 3Di sequences derived from these predicted structures. This comparison would substantiate the innovation claimed in the manuscript, demonstrating the potential of conducting new searches solely based on sequence data on a structural database.

See response above. We’ve now benchmarked against both ProstT5 and AF2.

• The profile HMMs built from predicted 3Di appear to perform sub-optimally, and those from the ESM-2 3B predicted probabilities also don't seem to improve traditional HMM results significantly. The HHBlits results depicted in lines 5 and 6 in the figure are not discussed at all, and a comparison with traditional HHBlits is missing. With these results and presentation, the advantages of pLM profile-based searches are not clear, and more justification over traditional methods is needed.

We thank the reviewer for pointing out the lack of clarity in the discussion of lines 5 and 6.

We’ve re-written that section of the discussion, and reformatted Figure 3 to enhance clarity.

We agree, a comparison to traditional HHBlits could be interesting, but we don’t expect to see stronger performance from the pLM-predicted profiles than from traditional HHBlits, just as we don’t see stronger performance from pLM-hmmscan or pLM-Foldseek than from the traditional variants. We think that the advantages of pLM based amino acid hmm searches are primarily speed. There are many variables that can influence speed of generating an MSA and HMM profile, but in general we expect that it will be much slower than generating an HMM profile from a pLM.

We don’t know why making profiles of 3Di sequences doesn’t improve search sensitivity, we just think it’s an interesting result that is worth presenting to the community. Perhaps someone can figure out how to make it work better.

• Figure 3 and its associated text are hard to follow due to the abundance of colors and abbreviations used. One figure attempting to explain multiple distinct points adds to the confusion. Suggestion: Splitting the figure into two panels comparing (A) Foldseek-derived searches (lines 7-10) and (B) language-model derived searches (line 3-6) to traditional methods could enhance clarity. Different scatter markers could also help follow the plots more easily.

We thank the reviewer for this helpful comment. We’ve reformatted Figure 3 as suggested, and we think it is much easier to read now.

• The justification for using Foldseek without amino acids (3Di-only mode) is not clear. Its utility should be described, or it should be omitted for clarity.

To us, the use of 3Di-only mode is of great theoretical interest. From our perspective, this is one of our most significant results. Previous methods, such as pLM-BLAST and related methods, have made use of very large positional embeddings to achieve sensitive remote homology search. We show that with the right embedding, you don’t need very many bits per position to get dramatically improved search sensitivity from Smith-Waterman, compared to amino acid searches. We also doubt that predicted 3Di sequences are the optimal small encoding for remote homology detection. This result and observation opens up an exciting avenue for future research in developing small, learned positional embeddings that are optimal for remote homology detection and amenable to SIMD-optimized pre-filtering and Smith-Waterman alignment steps.

We’ve expanded the discussion, explaining why we are excited about this result.

• Figure 2 is not described, unclear what to read from it.

It's just showing that ESM-2-derived amino acid probabilities closely resemble amino acid frequencies in MSAs. We think it gives readers some visual intuition about why predicted profile HMMs perform as well as they do. We’ve added some additional explanation of it in the text.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

The paper would mainly benefit from a more comprehensive benchmark:

We suggest that the authors extend the benchmark by including the reference methods (HHpred and Foldseek) run with their original representations, i.e., MSAs obtained with 2-3 iterations of hhblits (for HHpred) and experimental or predicted structures (for Foldseek). HHpred profile-profile comparisons and Foldseek structure-structure comparisons would be important reference points for assessing the applicability of the proposed approach in distant homology detection. It is also essential to compare the method with other emerging tools such as EBA (DOI: 10.1101/2022.12.13.520313), pLM-BLAST (DOI: 10.1101/2022.11.24.517862), DEDAL (DOI: 10.1038/s41592-022-01700-2), etc.

We also suggest using an evolutionary-oriented database for the benchmark, such as ECOD or CATH (these databases classify protein domains with known structures, which is important in the context of including Foldseek in the benchmark). We ran a cursory benchmark using the ECOD database and generated HH-suite .hhm files (using the single_seq_to_hmm.py and hhsearch_multiple.py scripts). Precision and recall appear to be significantly lower compared to "vanilla" hhsearch runs with MSA-derived profiles. It would also be interesting to see benchmarks for speed and alignment quality.

The pLM-based methods for homology detection are an emerging field, and it would be important to evaluate them in the context of distinguishing between homology and analogy. In particular, the predicted Foldseek representations may be more likely to capture structural similarity than homology. This could be investigated, for example, using the ECOD classification (do structurally similar proteins from different homology groups produce significant matches?) and/or resources such as MALISAM that catalog examples of analogy.

We’ve added the SCOPe40 benchmark, which we think at least partially addresses these comments, adding a comparison to pLM-BLAST, ProstT5, and AF2 followed by Foldseek. The question of Analogy vs homology is an interesting one. It could be argued that the SCOPe40 benchmark addresses this in the difference between Superfamily (distant homology) and Fold (analogy, or very distant homology).

Our focus is on remote homology detection applications rather than alignment quality, so we don’t benchmark alignment quality, although we agree that those benchmarks would be interesting.

Page 2, lines 60-67. This paragraph would benefit from additional citations and explanations to support the superiority of the proposed approach. The fact that flattened embeddings are not suitable for annotating multidomain proteins seems obvious. Also, the claim that "current search implementations are slow compared to other methods" should be supported (tools such as EBA or pLM-BLAST have been shown to be faster than standard MSA-based methods). Also, as we mentioned in the main review, we believe that the generated pseudo-profiles and fine-tuned ESM2 predictions should not be called "smaller positional embeddings".

Discriminating subdomains was a major limitation of the influential and widely-cited PfamN paper (Bileschi et al., 2022), we’ve added a citation to that paper in that paragraph for readers interested in diving deeper.

To address the question of speed, we’ve included data preparation and search benchmarks as part of our presentation of the SCOPe40 benchmark.

Finally, we were not sure why exactly every 7th residue is masked in a single forward pass. Traditionally, pseudo-log likelihoods are generated by masking every single token and predicting probabilities from logits given the full context - e.g. https://arxiv.org/pdf/1910.14659.pdf. Since this procedure is crucial in the next steps of the pipeline, it would be important to either experiment with this hyperparameter or explain the logic used to choose the mask spacing.

We’ve added discussion of the masking distance to the Methods section.

Reviewer #2 (Recommendations For The Authors):

• While the code and data for the benchmark are available, the generation of searchable databases using the methods described for a popular resource such as Pfam, AFDB, SCOP/CATH which can be used by the community would greatly boost the impact of this work.

3Di sequences predicted by ESM-2 3B 3Di can easily be used as queries against any Foldseek database, such as PDB, AFDB, etc. We’ve added Figure 4E to demonstrate this possibility, and added some related discussion.

• Minor: In line 114, the text should likely read "compare lines 7 and 8" instead of "compare lines 6 and 7."

We’ve clarified the discussion of Figure 3.

eLife assessment

This important study addresses the problem of detecting weak similarity between protein sequences, a procedure commonly used to infer homology or assign putative functions to uncharacterized proteins. The authors present a convincing approach that combines recently developed protein language models with well-established methods. The benchmarks provided show that the proposed tool is fast and accurate for remote homology detection, making this paper of general interest to all researchers working in the fields of protein evolution and genome annotation.

Reviewer #1 (Public Review):

This paper describes a new method for sequence-based remote homology detection. Such methods are essential for the annotation of uncharacterized proteins and for studies of protein evolution.

The main strength and novelty of the proposed approach lies in the idea of combining state-of-the-art sequence-based (HHpred and HMMER) and structure-based (Foldseek) homology detection methods with protein language models (the ESM2 model was used). The authors show that high-dimensional, information-rich representations extracted from the ESM2 model can be efficiently combined with the aforementioned tools.

The benchmarking of the new approach is convincing and shows that it is suitable for homology detection at very low sequence similarity. The method is also fast because it does not require the computation of multiple sequence alignments for profile calculation or structure prediction.

Overall, this is an interesting and useful paper that proposes an alternative direction for the problem of distant homology detection.

Reviewer #3 (Public Review):

Summary:<br /> Kobayashi et al identify MER21C as a common promoter of GPR1-AS/Liz in Euarchontoglires, which establishes a somatic DMR that controls ZFDB2 imprinting. In mice, MER21C appears to have diverged significantly from its primate counterparts and is no longer annotated as such.

Strengths:<br /> The authors used high-quality cross-species RNA-seq data to characterise GPR1-AS-like transcripts, which included generating new data in five different species. The association between MER21C/B elements and the promoter of GPR1-AS in most species is clear and convincing. The expression pattern of MER21C/B elements overall further supports their role in enabling correct temporal expression of GPR1-AS during embryonic development.

Weaknesses:<br /> A deeper comparison of syntenic regions to the GPR1-AS promoter could be performed to provide a clearer picture of how the MER21C/B element evolved. The use of alternative TE annotation software may also be helpful. These analyses would be particularly useful to drive home the conclusion that the mouse (Liz) promoter is derived from the same insertion.

eLife assessment

The findings in the manuscript are important and the strength of evidences from the genomic analyses is convincing. However, the evidence for the existence of functional MER21B/C remnants in mice, as well as for the imprinting status of Zdbf2 in rabbits and non-human primates was viewed as mainly correlative and incomplete. This manuscript will be of interest to developmental biologists and those working on possible novel mechanisms of gene regulation.

Reviewer #1 (Public Review):

Summary:<br /> The study tests the conservation of imprinting of the ZBDF2 locus across mammals. ZDBF2 is known to be imprinted in mice, humans, and rats. The locus has a unique mechanism of imprinting: although imprinting is conferred by a germline DMR methylated in oocytes, the DMR is upstream to ZDBF2 (at GPR1) and monoallelic methylation of the gDMR does not persist beyond early developmental stages. Instead, a lncRNA (GPR1-AS, also known as Liz in mouse) initiating at the gDMR is expressed transiently in embryos and sets up a secondary DMR (by mechanisms not fully elucidated) that then confers monoallelic expression of ZDBF2 in somatic tissues.

In this study, the authors first interrogate existing placental RNA-seq datasets from multiple mammalian species, and detect GPR1-AS1 candidate transcripts in humans, baboons, macaques and mice, but not in about a dozen other mammals. Because of the varying depth, quality, and nature of these RNA-seq libraries, the ability to definitely detect the GPR1-AS1 lncRNA is not guaranteed; therefore, they generate their own deep, directional RNA-seq data from tissues/embryos from five species, finding evidence of GPR1-AS in rabbits and chimpanzees, but not bovine animals, pigs or opossums. From these surveys, the authors conclude that the lncRNA is present only in Euarchontoglires mammals. To test the association between GPR1-AS and ZDBF2 imprinting, they perform RT-PCR and sequencing in tissue from wallabies and cattle, finding biallelic expression of ZDBF2 in these species that also lack a detected GPR1-AS transcript. From inspection of the genomic location of the GPR1-AS first exon, the authors identify an overlap with a solo LTR of the MER21C retrotransposon family in those species in which the lncRNA is observed, except for some rodents, including mice. However, they do detect a degree of homology (46%) to the MER21C consensus at the first exon on Liz in mouse. Finally, the authors explore public RNA-seq datasets to show that GPR1-AS is expression transiently during human preimplantation development, an expression dynamic that would be consistent with the induction of monoallelic methylation of a somatic DMR at ZDBF2 and consequent monoallelic expression.

Strengths:<br /> -The analysis uncovers a novel mechanism by which a retrotransposon-derived LTR may be involved in genomic imprinting.<br /> -The genomic analysis is very well executed.<br /> -New directional and deeply-sequenced RNA-seq datasets from the placenta or the trophectoderm of five mammalian species and marsupial embryos, that will be of value to the community.

Weaknesses:<br /> Although the genomic analysis is very strong, the study remains entirely correlative. All of the data are descriptive, and much of the analysis is performed on RNA-seq and other datasets from the public domain; a small amount of primary data is generated by the authors.<br /> Evidence that the residual LTR in mouse is functionally relevant for Liz lncRNA expression is lacking.

Reviewer #2 (Public Review):

Summary:<br /> This work concerns the evolution of ZDBF2 imprinting in mammalian species via initiation of GPR1 antisense (AS) transcription from a lineage-specific long-terminal repeat (LTR) retrotransposon. It extends previous work describing the mechanism of ZDBF2 imprinting in mice and humans by demonstrating conservation of GPR1-AS transcripts in rabbits and non-human primates. By identifying the origin of GPR1-AS transcription as the LTR MER21C, the authors claim to account for how imprinting evolved in these species but not in those lacking the MER21C insertion. This illustrates the principle of LTR co-option as a means of evolving new gene regulatory mechanisms, specifically to achieve parent-of-origin allele specific expression (i.e., imprinting). Examples of this phenomenon have been described previously, but usually involve initiation of transcription during gametogenesis rather than post-fertilization, as in this work. The findings of this paper are therefore relevant to biologists studying imprinted genes or interested more generally in the evolution of gene regulatory mechanisms.

Strengths:<br /> (1) The authors convincingly demonstrate the existence of GPR1-AS orthologs in specific mammalian lineages using deeply sequenced, stranded, and paired-end RNA-seq libraries collected from diverse mammalian species.

Weaknesses:<br /> (1) The authors do not directly demonstrate imprinting of the ZDBF2 locus in rabbits and non-human primates, which would greatly strengthen their model linking ZDBF2 imprinting to transcription from MER21C.

(2) Experimental evidence linking GPR1-AS transcription to ZDBF2 imprinting in rabbits and non-human primates is currently lacking. Consideration should be given to the challenges associated with studying non-model species and manipulating repeat sequences, which may explain the absence of experimental evidence in this case. Further, this mechanism is established in humans and mice, so the authors' model is arguably sufficiently supported merely by the existence of GPR1-AS orthologs in other mammalian lineages.

eLife assessment

This important study combines experimental and computational data to address crucial aspects of RNA methylation by a vital RNA methyltransferase (MTase). The authors have provided compelling, strong evidence, utilizing well-established techniques, to elucidate aspects of the methyl transfer mechanism of methyltransferase-like protein 3 (METTL3), which is a part of the METTL3-14 complex. This work will be of broad interest to biochemists, biophysicists, and cell biologists alike.

Reviewer #1 (Public Review):

Summary:<br /> This important study nicely integrates a breadth of experimental and computational data to address fundamental aspects of RNA methylation by an important for biology and health RNA methyltransferases (MTases). 



Strengths: The authors offer compelling and strong evidence, based on carefully performed with appropriate and well-established techniques to shed light on aspects of the methyl transfer mechanism of the methyltransferase-like protein 3 (METTL3), which is part of the methyltransferase-like proteins 3 & 14 (METTL3-14) complex. 


There are no weaknesses that we identified in the revised version.

Reviewer #2 (Public Review):

Summary:<br /> Caflisch and coworkers investigate the methyltransferase activity of the complex of methyltransferase-like proteins 3 and 14 (METTL3-14). To obtain an high resolution description of the complete catalytic cycle they have carefully designed a combination of experiments and simulations. Starting from the identification of bisubstrate analogues (BAs) as binder to stabilise a putative transition state of the reaction they have determined multiple crystal structures and validated relevant interactions by mutagenesis and enzymatic assays.

Using the resolved structure and classical MD simulations they obtained a kinetic picture of the binding and release of the substrates. Of note, they accumulate very good statistics on these processes using 16 simulation replicates over a time scale of 500 ns. To compare the time scale of the release of the products with that of the catalytic step they performed state-of-the-art QM/MM free energy calculations (testing multiple levels of theory) and obtain a free energy barrier that indicates how the release of the product is slower than the catalytic step.

Strengths:<br /> All the work proceeds through clear hypothesis testing based on a combination of literature and new results. Eventually, this allows them to present in Figure 10 a detailed step-by-step description of the catalytic cycle. The work is very well crafted and executed.

Reviewer #3 (Public Review):

Summary:<br /> The manuscript by Coberski et al describes a combined experimental and computational study aimed to shed light on the catalytic mechanism in a methyltransferase that transfers a methyl group from S-adenosylmethionine (SAM) to a substrate adenosine to form N6-methyladenosine (m6A).

Strengths:<br /> The authors determine crystal structures in complex with so-called bi-substrate analogs that can bridge across the SAM and adenosine binding sites and mimic a transition state or intermediate of the methyl-transfer reaction. The crystal structures suggest dynamical motions of the substrate(s) that are examined further using classical MD simulations. The authors then use QM/MM calculations to study the methyl-transfer process. Together with biochemical assays of ligand/substrate binding and enzyme turnover, the authors use this information to suggest what the key steps are in the catalytic cycle. The manuscript is in most places easy to read.

Weaknesses:<br /> After revising the manuscript, there are few weaknesses beyond those listed in the paper.

eLife assessment

This study reports important findings on identifying sequence motifs that predict substrate specificity in a class of lipid synthesis enzymes. It sheds light on a mechanism used by bacteria to modify the lipids in their membrane to develop antibiotic resistance. The evidence is convincing, with a careful application of machine learning methods, validated by mass spectrometry-based lipid anlaysis experiments. This interdisciplinary study will be of interest to computational biologists and to the community working on lipids and on enzymes involved in lipid synthesis or modification.

Reviewer #1 (Public Review):

The basic approach is that the authors first train an RBM on all MprF sequences, and then use this analysis to identify a subset of the family that catalyzes the addition of amino acids to PG. Then a second RBM is trained on this subset.

In the initial RBM training a particular hidden unit is identified that has a sparse and bimodal activation in response to the input sequences. The contribution of individual resides is shown in Figure 3c, which highlights one of the strengths of this RBM implementation - it is interpretable in a physically meaningful way. However, there are several decisions here, the justification of which is not entirely clear.

i) Some of the residues in Fig 3c are stated as "relevant" for aminoacylated PG production. But is this the only such hidden unit? Or are there others that are sparse, bimodal, and involve "relevant" AA?<br /> ii) In order to filter the sequences for the second stage, only those that produce an activation over +2.0 in this particular hidden unit were taken. How was this choice made?<br /> iii) How many sequences are in the set before and after this filtering? On the basis of the strength of the results that follow I expect that there are good reasons for these choices, but they should be more carefully discussed.<br /> iv) Do the authors think that this gets all of the aminoacylated PG enzymes? Or are some missed?

The authors show that they can classify members of the family by training a second RBM on the filtered sequences. They do this by identifying two hidden unit activations in particular (Figure 5b) which seem to be useful for determining lipid substrate specificity, and they test several variants that obtain different responses of these two hidden units by experimentally determining what lipids they produce (Table 2). However, some similar criticisms from the last point occur here as well, namely the selection of which weights should be used to classify the enzymes' function. Again the approach is to identify hidden unit activations that are sparse (with respect to the input sequence), have a high overall magnitude, and "involve residues which could be plausibly linked to the lipid binding specificity."

i) Two hidden units are identified as useful for classification, but how many candidates are there that pass the first two criteria? Indeed, how many hidden units are there?<br /> ii) The criterion "involve residues which could be plausibly linked to the lipid binding specificity" is again vague. Do all of the other candidate hidden units *not* involve significant contributions from substrate-binding residues? Maybe one of the other units does a better job of discriminating substrate specificity. (As indicated in Figure 8, there are examples of enzymes that confound the proposed classification.) Why combine the activations of two units for the classification, instead of 1 or 3 or...?

Reviewer #2 (Public Review):

In "Lipid discovery enabled by sequence statistics and machine learning" Christensen et al. address an important question: how can bacteria modify lipid charges to produce cationic lipids, prone to confer resistance to cationic antibiotics? One of the enzymes involved in this process is MprF, which can, through the transfer of amino acids, in particular, lysine, from charged tRNA modify the charge of anionic membrane phospholipid from negative to positive. Recent works have shown that MprF can also modify another substrate, glycolipid glucosyl-diacylglycerol, which is neutral. These findings immediately raise two questions: what are the determinants in the MrpF sequence controlling the lipid substrates it can modify? Are there other substrates for MrpF, so far unknown?

Christensen et al. address both of these questions in an elegant way, combining sequence analysis with machine-learning methods and experimental characterisation of the enzymatic products through mass spectrometry. Using restricted Boltzmann machines (RBM), an unsupervised architecture extracting statistical features from the sequence data, they identify putative amino-acid motifs along the MprF sequences possibly related to the substrate identity, select some bacterial species whose wild-type sequence contains those motifs, and validate the biological role of the motifs by identifying the produced lipids. Remarkably, with this approach, the authors find a novel cationic lipid with two glucosyl groups.

Besides these new results on MrpF and its operation, the present work is appealing, as it shows that the functional characterisation of a very small number of proteins (here, three!) combined with the guided classification of homologous sequence data with appropriate machine-learning methods can lead to the discovery of new functionalities.

Reviewer #3 (Public Review):

Summary:<br /> After the previous identification that the Streptococcus agalactiae MprF enzyme can synthesize also lysyl-glucosyl-diacylglycerol (Lys-Glc-DAG), besides the already known lysyl-phosphatidylglycerol (Lys-PG), the authors aim for the current manuscript was to investigate the molecular determinants of MprF lipid substrate specificity in a variety of bacterial species.

Strengths:<br /> - In general, the manuscript is well constructed and easy to follow, especially taking into account the multidisciplinary aspect of it (computational machine learning combined with lipid biology).<br /> -The added value of the Restricted Boltzmann machines (RBM) approach, in comparison to standard computational pairwise sequence statistics, becomes evident. This is exemplified by a successful, although not perfect, classification and categorization of MprF activity.<br /> - The MS analysis (monoisotopic mass, plus fragmentation pattern), convincingly shows the identification of a novel lipid species Lys-Glc2-DAG.

Weaknesses:<br /> -In many of the analyzed strains, the presence of the lipid species Lys-PG, Lys-Glc-DAG, and Lys-Glc2-DAG is correlated to the presence of the MprF enzyme(s), but one should keep in mind that a multitude of other membrane proteins are present that in theory could be involved in the synthesis as well. Therefore, there is no direct evidence that the MprF enzymes are linked to the synthesis of these lipid species. Although, it is unlikely that other enzymes are involved, this weakens the connection between the observed lipids and the type of MprF.<br /> -Related to this, in a few cases MprF activity is tested, but the manuscript does not contain any information on protein expression levels. Heterologous expression of membrane proteins is in general challenging and due to various reasons, proteins end up not being expressed at all. As an example, the absence of activity for the E. faecalis MprF1 and E. faecium MprF2 could very well be explained by the entire absence of the protein.

Overall, the authors largely achieved their goals, as the applied RBM approach led to specific sequence determinants in MprF enzymes that could categorize the specificity of these enzymes. The experimental data could largely confirm this categorization, although a stronger connection between synthesized lipids and enzyme activity would have further strengthened the observations.

The work now focuses only on MprF enzymes, but could in theory be expanded to other categories of lipid-synthesizing enzymes. In other words, the RBM approach could have an impact on the lipid synthesis field, if it would be a tool that is easily applicable. Moreover, the lipids synthesized by MprF (Lys-PG, but also other cationic lipids) play an important role in bacterial resistance against certain antibiotics.

Author Response

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

Reviewer #1 (Public Review):

The current manuscript focuses on the adenine phosphoribosyltransferase (Aprt) and how the lack of its function affects nervous system function. It puts it into the context of Lesch-Nyhan disease, a rare hereditary disease linked to hypoxanthine-guanine phosphoribosyltransferase (HGPRT). Since HGPRT appears absent in Drosophila, the study focuses initially on Aprt and shows that aprt mutants have a decreased life-span and altered uric acid levels (the latter can be attenuated by allopurinol treatment). Moreover, aprt mutants show defects in locomotor reactivity behaviors. A comparable phenotype can be observed when specifically knocking down aprt in dopaminergic cells. Interestingly, also glia-specific knock-down caused a similar behavioral defect, which could not be restored when re-expressing UAS-aprt, while neuronal re-expression did restore the mutant phenotype. Moreover, mutants, pan-neuronal and pan-neuronal plus glia RNAi for aprt caused sleep-defects. Based on immunostainings Dopamine levels are increased; UPLC shows that adenosine levels are reduced and PCR showed in increase of Ent2 levels are increased (but not AdoR). Moreover, aprt mutants display seizure-like behaviors, which can be partly restored by purine feeding (adenosine and N6methyladenosine). Finally, expression of the human HGPRT also causes locomotor defects.

The authors provide a wide range of genetic experimental data to assess behavior and some molecular assessment on how the defects may emerge. It is clearly written, and the arguments follow the experimental evidence that is provided. The findings provide a new example of how manipulating specific genes in the fruit fly allows the study of fundamental molecular processes that are linked to a human disease.

We thank the reviewer for his clear understanding and positive assessment of our work.

Reviewer #2 (Public Review):

The manuscript by Petitgas et al demonstrates that loss of function for the only enzyme responsible for the purine salvage pathway in fruit-flies reproduces the metabolic and neurologic phenotypes of human patients with Lesch-Nyhan disease (LND). LND is caused by mutations in the enzyme HGPRT, but this enzyme does not exist in fruit-flies, which instead only have Aprt for purine recycling. They demonstrate that mutants lacking the Aprt enzyme accumulate uric acid, which like in humans can be rescued by feeding flies allopurinol, and have decreased longevity, locomotion and sleep impairments and seizures, with striking resemblance to HGPRT loss of function in humans. They demonstrate that both loss of function throughout development or specifically in the adult ubiquitously or in all neurons, or dopaminergic neurons, mushroom body neurons or glia, can reproduce the phenotypes (although knock-down in glia does not affect sleep). They show that the phenotypes can be rescued by over-expressing a wild-type form of the Aprt gene in neurons. They identify a decrease in adenosine levels as the cause underlying these phenotypes, as adenosine is a neurotransmitter functioning via the purinergic adenosine receptor in neurons. In fact, feeding flies throughout development and in the adult with either adenosine or m6A could prevent seizures. They also demonstrate that loss of adenosine caused a secondary up-regulation of ENT nucleoside transporters and of dopamine levels, that could explain the phenotypes of decreased sleep and hyperactivity and night. Finally, they provide the remarkable finding that over-expression of the human mutant HGPRT gene but not its wild-type form in neurons impaired locomotion and induced seizures. This means that the human mutant enzyme does not simply lack enzymatic activity, but it is toxic to neurons in some gain-of-function form. Altogether, these are very important and fundamental findings that convincingly demonstrate the establishment of a Drosophila model for the scientific community to investigate LND, to carry out drug testing screens and find cures.

We thank the reviewer for his clear understanding and positive assessment of our work.

The experiments are conducted with great rigour, using appropriate and exhaustive controls, and on the whole the evidence does convincingly or compellingly support the claims. The exception is an instance when authors mention 'data not shown' and here data should either be provided, or claims removed: "feeding flies with adenosine or m6A did not rescue the SING phenotype of Aprt mutants (data not shown)". It is important to show these data (see below).

As recommended by the reviewer, these results are now shown in the new Figure S15.

Sleep is used to refer to lack of movement of flies to cross a beam for more than 5 minutes. However, lack of movement does not necessarily mean the flies are asleep, as they could be un-motivated to move (which could reflect abnormal dopamine levels) or engaged in incessant grooming instead. These differences are important for future investigation into the neural circuits affect by LND.

We agree that the method we used could overestimate sleep duration because flies that don't move do not necessarily sleep either, as it is the case with brain-dopamine deficient flies (Riemensperger et al., PNAS 2011). To address this issue, we have recorded video data showing that after 5 min of inactivity, wild-type and Aprt5 mutant flies are less sensitive to stimulation, indicating that they were indeed asleep. This is now shown in the new Figure S10 and mentioned on page 17, lines 338-339 in the main text. In addition, in this work we report that Aprt mutant flies have a nocturnal insomnia phenotype. Sleep overestimation is not, therefore, an issue that could challenge these results.

The authors claim that based on BLAST genome searchers, there are no HPRTI (encoding HGPRT) homologues in Drosophila. However, such a claim would require instead structure-based searches that take into account structural conservation despite high sequence divergence, as this may not be detected by regular BLAST.

To reinforce our conclusions about the lack of homologue of the human HPRT1 gene in Drosophila, we have now added a Results section about the evolution of HGPRT proteins on pages 6-7, lines 122150, and two phylogenetic analyses as new Figures S2 and S3 with more details in legends. We have also carried out structural similarity searches against the RCSB PDB repository. The structural analysis did not identify any relevant similarity with HGPRT 3D structures in Insecta (mentioned lines 146-150). We hope these new analyses address the Reviewer's concerns. Furthermore, as shown in Table S2, no enzymatic HGPRT activity could be detected in extracts of wild-type Drosophila. A protein that would be structurally similar to human HGPRT but with a divergent sequence could not be involved in purine recycling without expressing HGPRT-like activity. In contrast, enzymatic Aprt activity could be easily detected in this organism (Figure S4 and Table S1).

This work raises important questions that still need resolving. For example, the link between uric acid accumulation, reduced adenosine levels, increased dopamine and behavioural neurologic consequences remain unresolved. It is important that they show that restoring uric acid levels does not rescue locomotion nor seizure phenotypes, as this means that this is not the cause of the neurologic phenotypes.

We agree with the reviewer about the potential importance of our results and the need to resolve the exact origin of the neurological phenotypes. This would need to be addressed in further studies in our opinion. The fact that allopurinol treatment did not improve the locomotor ability of Aprt5 mutant flies is now shown in Figure 1D, E to emphasize this result. Results showing that allopurinol does not rescue the bang-sensitivity phenotype of Aprt-deficient mutants are shown in Figure S14.

Instead, their data indicate adenosine deficiency is the cause. However, one weakness is that for the manipulations they test some behaviours but not all. The authors could attempt to improve the link between mechanism and behaviour by testing whether over-expression of Aprt in neurons or glia, throughout development or in the adult, and feeding with adenosine and m6A can rescue each of the behavioural phenotypes handled: lifespan, SING, sleep and seizures. The authors could also attempt to knock-down dopamine levels concomitantly with feeding with adenosine or m6A to see if this rescues the phenotypes of SING and sleep.

The reviewer is right. However, carrying out all these experiments properly with enough repeats will require about two more years of work. Because of that, they could not be included in the revision of the present article. Here we show that Aprt overexpression in neurons, but not in glia, rescues the SING phenotype of Aprt5 mutants (Figure 2B and 2E). We have also added in the revised article the new result that Aprt overexpression reduces transcript levels of DTH1, which codes for the neural form of the dopamine-synthesizing enzyme tyrosine hydroxylase (new Figure 5F).

Visualising the neural circuits that express the adenosine receptor could reveal why the deficit in adenosine can affect distinct behaviours differentially, and which neurologic phenotypes are primary and which secondary consequences of the mutations. This would allow them to carry out epistasis analysis by knocking-down AdoR in specific circuits, whilst at the same time feeding Aprt mutants with Adenosine.

Deciphering the specific circuits involved in the various effects of adenosine would indeed be extremely interesting. Unfortunately very few is currently known about the neural circuits that express AdoR in flies. No antibody is available to detect this receptor in situ and mutated AdoR gene coding for a tagged form of the receptor has not been engineered yet to our knowledge.

The revelation that the mutant form of human HGPRT has toxic effects is very intriguing and important and it invites the community to investigate this further into the future.

To conclude, this is a fundamental piece of work that opens the opportunity for the broader scientific community to use Drosophila to investigate LND.

We sincerely thank the reviewer for his thoughtful and positive comments on our work.

Reviewer #3 (Public Review):

The study attempts to develop a Drosophila model for the human disease of LND. The issue here, and the main weakness of this study, is that Drosophila does not express the enzyme, HGPRT, which when mutated causes LND. The authors, instead, mutate the functionally-related Drosophila Aprt enzyme. However, it is unknown whether Aprt is also a structural homologue. Because of this, it will likely not be possible to identify pharmacological compounds that rescue HGPRT activity via a direct interaction (unless modelling predicts high conservation of substrate binding pocket between the two enzymes, etc).

As stated in our Provisional Responses prior to revision of the Reviewed Preprint, the enzymes APRT and HGPRT are actually known to be functionally and structurally related. We apologize for not providing this information in the original submission. This point is now made clearer in the revised article on page 39, lines 785-792. Indeed, both human APRT and HGPRT belong to the type I PRTases family identified by a conserved phosphoribosyl pyrophosphate (PRPP) binding motif, which is used as a substrate to transfer phosphoribosyl to purines. This binding motif is only found in PRTases from the nucleotide synthesis and salvage pathways (see: Sinha and Smith (2001) Curr Opin Struct Biol 11(6):733-9, doi: 10.1016/s0959-440x(01)00274-3). The purine substrates adenine, hypoxanthine and guanine share the same chemical skeleton and APRT can bind hypoxanthine, indicating that APRT and HGPRT also share similarities in their substrate binding sites (Ozeir et al. (2019) J Biol Chem. 294(32):11980-11991, doi: 10.1074/jbc.RA119.009087). Moreover, Drosophila Aprt and Human APRT are closely related as the amino acid sequences of APRT proteins have been highly conserved throughout evolution (see Figure S5B in our paper).

An additional weakness is that the study does not identify a molecule that may act as a lead compound for further development for treating LND. Rather, the various rescues reported are selective for only a subset of the disease-associated phenotypes. Thus, whilst informative, this first section of the study does not meet the study ambitions.

In this study, we identify adenosine and N6-methyladenosine as rescuers of the epileptic behavior in Aprt mutant flies (shown in Figure 7E, F). Interestingly, the same molecules have been found to rescue the viability of fibroblasts and neural stem cells derived from iPSCs of LND patients, in which de novo purine synthesis was prevented (discussed on page 38, lines 747-753). This suggests that the Drosophila model reported here could help to identify new genetic targets and pharmacological compounds capable to rescue HGPRT mutations in humans.

The second approach adopted is to express a 'humanised mutated' form of HGPRT in Drosophila, which holds more promise for the development of a pharmacological screen. In particular, the locomotor defect is recapitulated but the seizure-like activity, whilst reported as being recapitulated, is debatable. A recovery time of 2.3 seconds is very much less than timings for typical seizure mutants. Nevertheless, the SING behaviour could be sufficient to screen against. However, this is not explored.

We agree with the reviewer that it would be very interesting to do a pharmacological screen in this second LND model. However, we did not have the possibility to carry out such a screen yet.

In summary, this is a largely descriptive study reporting the behavioural effects of an Aprt loss-offunction mutation. RNAi KD and rescue expression studies suggest that a mix of neuronal (particularly dopaminergic and possibly adenosinergic signalling pathways) and glia are involved in the behavioural phenotypes affecting locomotion, sleep and seizure. There is insufficient evidence to have confidence that the Arpt fly model will prove valuable for understanding / treating LND.

Here we report many common phenotypes between the Aprt fly model and the symptoms of LND patients (reduced longevity, locomotor problems, sleep defects, overproduction of uric acid that is rescued by allopurinol treatment…). Moreover, APRT and HGPRT enzymes are both functional and structural homologues, as explained in our answers. We also found that the same drugs can rescue the seizure-like phenotype in Aprt-deficient flies and the viability of LND fibroblasts and neural stem cells, derived from iPSCs of LND patients, in which de novo purine synthesis is prevented (Figure 7E, F). In many respects, our results therefore suggest that Aprt mutant flies could be useful to better understand LND, and potentially to screen for new therapeutic compounds.

From the Reviewing Editor:

(1) How are the pathways of purine catabolism different between flies and mammals? How does the absence of HGPRT and presence of only AGPRT affect purine catabolism? When did HGPRT appear in evolution?

Purine catabolism is quite similar in flies and mammals, except for the lack of urate oxidase in primates, as described in Figure S1. We added words in the revised article about purine anabolism/catabolism pathways lines 123-126 (see below our detailed response to Reviewer 1’s Recommandations). HGPRT is present in Bacteria, Archea and Eukaryota, and nearly all animal phyla. However, BLAST search indicates that HGPRT homologues cannot be found in most insect species, such as Drosophila. To reinforce our conclusions about the lack of homologue of the human HPRT1 gene in Drosophila melanogaster, we have now added a Results section about the evolution of HGPRT proteins on pages 6-7, lines 122-150, and two phylogenetic analyses as new Figures S2 and S3 with details in legends.

In addition to BLAST a structural based modelling method should be used to establish the loss of HGPRT in Drosophila.

In agreement with the phylogenetic analyses, we have confirmed that no HGPRT enzymatic activity can be detected in wild-type Drosophila extract (Table S2). To complete these observations, as recommended by reviewer #2, we have carried out 3D structure-based searches in the RCSB Protein Data Bank. This enabled us to compare human HGPRT with all currently available protein structures. W found no Drosophila protein with a divergent sequence showing relevant structural similarity to human HGPRT. In contrast, this search identified proteins similar to human HGPRT in many other species of Eukaryota, Archea and Bacteria. This is now mentioned on page 7, lines 146-150 in the revised article.

(2) Of the three biochemical changes reported the change in dopamine levels should be validated by other methods given the unreliable nature of IHC.

As recommended by Reviewer #1, we have added the results of new experiments carried out by RTqPCR and Western blotting, which confirm the effect of Aprt mutation on brain dopamine levels. In addition, we added the consistent result that Aprt overexpression reduces transcript levels of DTH1. The results are shown in the new panels E to H of Figure 5 and mentioned in the text on page 20, lines 385-389.

(3) As suggested by reviewer 2 it would be helpful to clearly identify which of the three biochemical changes (DA, uric acid, adenosine) are responsible for the numerous behaviours tested. This is important because it is relevant for developing any therapeutic strategy arising from this study.

We agree that it would be very interesting to decipher the relationship between the different behaviors observed in mutant flies and the biochemical changes (dopamine, uric acid or adenosine). However, this would require a large amount of new experiments and it would probably double the size of our paper, which already includes many original data. In our opinion, such a detailed study should logically be the purpose of another article.

(4) There is concern regarding the robustness of the seizure data. Reviewer 3 has suggestions on how to address this.

See our answers to Reviewer 3’s recommendations below.

(5) Editorial corrections and changes suggested by reviewers 2 and 3 need to be addressed.

As indicated in our answers, we have taken into account and when possible addressed the corrections and changes suggested by the reviewers.

(6) It is recommended that the authors tone down the relevance of this model for LND, particularly in the abstract. The focus should be on stating what is actually delivered.

As recommended by the reviewing editor, and to take in account the reserved comments of reviewer #3, we have toned down our affirmation that our new fly models are relevant for LND in the last sentences of the Abstract and Discussion, and also added a question mark in the subtitle of the Discussion on line 777. As mentioned in our provisional responses to the Public Reviews, we would like to emphasize, however, that reviewers #1 and #2 expressed more confidence than reviewer #3 in the potential usefulness of our work. Reviewer #1 indeed stated that: “The findings provide a new example of how manipulating specific genes in the fruit fly allows the study of fundamental molecular processes that are linked to a human disease”, and reviewer #2 further wrote: "Altogether, these are very important and fundamental findings that convincingly demonstrate the establishment of a Drosophila model for the scientific community to investigate LND, to carry out drug testing screens and find cures”, and added: “To conclude, this is a fundamental piece of work that opens the opportunity for the broader scien2fic community to use Drosophila to inves2gate LND”.

Reviewer #1 (Recommendations For The Authors):

• An important prerequisite for the current study is that there appears to be no HGPRT "activity" in Drosophila. It is initially stated that there was previously no "HGPRT activity observed" in two papers form the 70ies. It would be important to corroborate this notion and provide some background on the <br /> /catabolism pathways. How shared or divergent are these pathways between Drosophila and mammals?

In agreement with the pioneering studies of Becker (1974a, b), we have confirmed in this work that no HGPRT enzymatic activity can be detected in wild-type Drosophila extracts, as mentioned in Results on page 6, lines 127-130 and reported in Table S2. Purine catabolism is quite similar in flies and mammals, except for the lack of urate oxidase in primates, as shown in Figure S1. All the enzymes involved in purine anabolism/catabolim or recycling in humans have been conserved in Drosophila and humans, with the notorious exception of HPRT1.

If there is no HGPRT gene, but only the APRT ortholog, what would this mean for the metabolites? Our enzymatic assays on Drosophila extracts indicated that hypoxanthine and guanine cannot be recycled into IMP and GMP, respectively, contrary to adenine which can be converted into AMP in flies. In the absence of HGPRT activity, GMP and IMP could be produced by de novo purine synthesis, or, alternatively, synthesized from AMP, which can be converted into IMP by the enzyme AMPD, and then IMP can be converted into GMP by the enzymes IMPDH and GMPS. These metabolic pathways are depicted in Figure S1A.

Is the lack of HGPRT specific for Drosophila, insects (generally in invertebrates)? I feel clarifying this would provide more insight into the motivation of the experimental approach.

As suggested by the Reviewer and the Reviewing Editor, we have addressed the evolution of HGPRT proteins more precisely in the revision. We have added a section on this subject in Results on pages 67, lines 122-150, and two phylogenetic analyses as Figures S2 and S3 with details in legends. A phylogenetic analysis was carried out a few years ago by Giorgio Matassi, who is now co-author of this paper. The most striking result was the great impact of horizontal gene transfer in the evolution of HGPRT in Insects (Figures S2 and S3). Our analysis of the phyletic distribution of HGPRT proteins revealed their striking rareness in Insecta, and in particular, their absence in Drosophilidae. The PSIBlast search detected however a significant hit in Drosophila immigrans (accession KAH8256851.1). Yet, this sequence is 100% identical to the HGPRT of the Gammaroteobacterium Serratia marcescens. Indeed, a phylogenetic analysis showed that D. immigrans HGPRT clusters with the Serratia genus (see Figure S3). This can be interpreted either a contamination of the sequenced sample, or as a very recent horizontal gene transfer event. The second scenario is more likely for the corresponding nucleotide sequences differ by 5 synonymous substitutions (out of 534 positions). A powerful approach to try to understand the "origin" of the D. immigrans protein would be to analyze whether horizontal gene transfer has affected its chromosomal neighbours. This approach, proposed previously by G. Matassi (BMC Evol Biol, 2017, 17:2, doi: 10.1186/s12862-016-0850-6), is highly demanding in terms of computing time and would require an ad hoc study. We hope that these new analyses address the Reviewer's concerns.

• On the mechanistic side on how the behavioral defects may arise, the authors show that dopaminergic neurons (and glia cells) are involved. One interesting finding is that dopamine immunostainings suggest increased dopamine levels. However, immunostainings are notorious for artifacts and do not provide a strong quantitative assessment. I feel it would be helpful to have an alternative technique to corroborate this finding.

We agree with the reviewer and we added the results of further confirmatory experiments in the four new panels E-H of Figure 5, showing that: 1) the transcript levels of DTH1 (encoding the neuronal isoform of the dopamine-synthesizing enzyme tyrosine hydroxylase in Drosophila) are increased in Aprt5 mutants compared to wild-type flies (new Figure 5E), 2) consistent with this, DTH1 transcript levels were found in contrast to be decreased when Aprt was overexpressed ubiquitously in flies (new Figure 5F), 3) Western blot experiments showed that DTH1 protein levels are also increased in Aprt5 mutant flies compared to controls (new Figure 5G-H).

Reviewer #2 (Recommendations For The Authors):

As mentioned in the public review, the behavioural phenotypes of decreased lifespan, SING, sleep and seizures could be tested for all manipulations: feeding with allopurinol, adenosine and m6A, and combining this with knock-down dopamine levels in PAMs or MBs. This could help dissect the relationship between mutations in Aprt and behaviour.

We thank the reviewer for these suggestions, and, indeed, we would have liked to do all these experiments. However, as mentioned in our responses to the Public Reviews, carrying out these experiments properly with sufficient repeats would require about two more years of work. We have already accumulated a large amount of data, so we have decided to publish our results at this stage in order to make our new fly models available to the scientific community. We are giving careful and due consideration to these experimental proposals and we hope to continue our investigation on this topic in the future.

It would also be helpful to find out which neurons and glia express AdoR. Perhaps there are already tools available the authors could test or at least check with the scRNAseq Fly Atlas (public Scope database).

Following the reviewer’s recommendation, we have checked the scRNAseq Fly Atlas for AdoR expression in the brain, compared to that of ple (encoding tyrosine hydroxylase) and Eaat1 (encoding the astrocytic glutamate transporter). As shown in the image below, the results are not very informative. AdoR appears to be expressed in rather widespread subsets of neurons and glial cells, that partly overlap with ple and Eaat1 expression. Further work would be required to identify more precisely the neurons and glial cells expressing AdoR in the brain.

Author response image 1.

Page 7, line 161: use of the word 'normalize'. "We tried to normalise uric acid content in flies..." would best to use 'rescue' instead, as normalisation in science has a different meaning.

We modified this word as suggested.

Page 9 line 203: 'genomic deficiencies that cover': the genetic term is 'uncover', as a deficiency for a locus reveals a phenotypes, thus it is said 'a gene uncovered by xx deficiency".

Thank you for this helpful remark. We corrected this in line 221.

Page 10, lines 206-208: 'allopurinol treatment did not improve the locomotor activity...". These are important observations that should be best presented within the main manuscript Figure 1.

As recommended, we have transferred the graphs of Figure S5 to new panels D and E of Figure 1.

Figure 4: please indicate genotypes in the figure, where no information is given that these are UASAprt-RNAi experiments.

We added the complete genotype in Figure 4G, and also in Figure S12C and D. Thank you for noting that.

Page 25 line 491: "None of these drugs was able to rescue the SING defects (data not shown)". Either provide the data or remove this claim.

We have added these data in the new Figure S15.

Statistical analyses: details are provided in the methods, but the name of test and multiple comparisons corrections should be also provided in the legends.

Thank you very much for the careful proofreading. This was an oversight and we have added the information in all legends of the revised article.

Reviewer #3 (Recommendations For The Authors):

This is a difficult manuscript to appreciate. The abstract and introduction suggest that the study is to identify novel treatments for a human disease (LND) by development of a Drosophila model. Much of the results, however, are focussed to describing the consequences to purine metabolism of the Aprt mutation. To my mind, a rewrite to focus on the latter would be beneficial. The potential applicability to LND would be best restricted to the discussion.

We apologize for not making our goals clearer. Our purpose was to find out if purine recycling deficiency could lead to metabolic and neurobehavioral disturbances in Drosophila, as it is the case in human LND patients when HGPRT is mutated. Interestingly, we observed that mutation of the only purine recycling enzyme in flies, Aprt, did induce defects in part comparable to that of LND in humans, including overproduction of uric acid that is rescued by allopurinol treatment, reduced longevity, and various neurobehavioral phenotypes including bang-sensitive seizure, sleep defects and locomotor impairments. We also identified adenosine and N6-methyladenosine as rescuers of the epileptic behavior in these mutants. These drugs were also identified as therapeutic candidates in screens based on iPSCs from LND patients. This suggests that Aprt deficiency in Drosophila could be used as a model to better understand this disease and find new therapeutic targets.

Regardless of the above comment, the concluding sentence of the abstract is inappropriate. This study does not show that Drosophila can be used to identify a cure for LND.

We agree with the Reviewer that the last sentence of the abstract was too affimative. As also recommended by the reviewing editor, we have modified this sentence in the abstract and other sentences in the text in order to tone down the affirmation that our new fly models are relevant for LND. See our answers to the Reviewing Editor above for details.

Indeed, I would challenge the premise that screening against a functional, but unknown if structural, homologue (Aprt) will ever provide an exploitable opportunity. To meet this statement, this study needs to identify a treatment that rescues all of the behavioural phenotypes associated with the Aprt mutation, in addition to rescuing the influences of the mis-expression of mutated HGPRT.

APRT and HGPRT are both functionally and structurally related. Both human APRT and HGPRT belong to the type I PRTases family identified by a conserved phosphoribosyl pyrophosphate (PRPP) binding motif, which is used as a substrate to transfer phosphoribosyl to purines. This binding motif is only found in PRTases from the nucleotide synthesis and salvage pathways (see: Sinha and Smith (2001) Curr Opin Struct Biol 11(6):733-9733-9, doi: 10.1016/s0959-440x(01)00274-3). The purine substrates adenine, hypoxanthine and guanine share the same chemical skeleton and APRT can bind hypoxanthine, indicating that APRT and HGPRT also share similarities in their substrate binding sites (Ozeir et al. (2019) J Biol Chem. 294(32): 11980-11991, doi: 10.1074/jbc.RA119.009087)). This point has been made clearer in the Discussion page 39, in lines 785-792.. Finally, Drosophila Aprt and Human APRT are closely related as the amino acid sequences of APRTs have been highly conserved throughout evolution (shown in Figure S5B).

With respect to expression of the mutated HGPRT: the short seizure recovery time of 2.3 seconds is not very convincing evidence of a seizure phenotype. This is far below the timings reported for typical BS mutations. Because of this, the authors should run a positive control (e.g. one of the wellestablished BS mutations: parabss, eas or jus) to validate their assay. Moreover, was the seizure induced by the Aprt mutation (17.3 secs - again a low value) rescued by prior exposure to an antiepileptic? Could this behaviour be, instead, related to the SING locomotor phenotype?

The assay we used to test for bang-sensitivity has been validated in previous articles from different laboratories. We agree that the recovery times we observed were shorter than those of the BS mutations mentioned by the reviewer. However, we could cite another Drosophila BS mutant, porin, that shows similarly short recovery times (2.5 and 6 sec, according to the porin alleles tested, Graham et al. J Biol Chem. 2010, doi: 10.1074/jbc.M109.080317). This is now mentioned on page 36 lines 717-720). In addition, the BS phenotype we observed with Aprt mutants was robust and highly significant compared to control flies (Figure 7). We did not try to rescue this phenotype by exposing the flies to an antiepileptic, but we do not think that it can be related to the SING phenotype. Indeed, providing adenosine or N6-methyladenosine to Aprt5 mutant flies was able to rescue the BS phenotype (Figure 7E, F), but did not rescue the locomotor defects (new Figure S15). Moreover, SING performances of Aprt5 mutant flies at 8 or 30 d a. E. are decreased nearly in almost identical way (Figure 1C), while we observed an effect on BS behavior at 30 d a. E., which implies that the SING and BS behaviors are most likely unrelated.

Line 731 states that 'Aprt mutants show a typical BS phenotype' - whilst accurate to some extent (e.g. the behaviour depicted in the supp videos), it should be made clear, it should be made clear that the recovery time is uncharacteristically short and thus differs from typical BS mutations.

We have corrected the sentence in the revised article to mention that (page 36, lines 717-718).

Line 732 stating that BS phenotype is often linked to neuronal activity - what other links would there be? Even if via glia or other tissues the final effect is via neurons.

We have modified this sentence (page 36, line 720).

The introduction and, particularly, the discussion are overly long and, in the case of the latter, repetitive of the results text. Pruning to make the paper more concise would be very beneficial. Removal of the extensive speculation about how DA and adenosine may interact would help in this regard (line 688 onwards). Indeed, in many places the discussion morphs into a review.

We agree with the reviewer on this point, and have therefore done our best to shorten the Introduction and Discussion, which are now 24% and 21% shorter, respectively, in the revised article compared to the original submission.

The applicability of using Drosophila Aprt mutations to screen for compounds that may treat LND is predicated on some degree of similarity in either enzyme structure or metabolic pathways. A discussion of how relevant, therefore, studying Aprt is needs to be included. Given the authors insights - where should potential new rugs be targeted to?

As stated above, we now mention in the article that APRT and HGPRT share similarities in their structure. In addition, the metabolic pathways between humans and Drosophila have been largely conserved (shown in Figure S1B).

Reviewer #1 (Public Review):

The current manuscript focusses on the adenine phosphoribosyltransferase (Aprt) and how the lack of its function affects nervous system function. It puts it into the context of Lesch-Nyhan disease, a rare hereditary disease linked to hypoxanthine-guanine phosphoribosyltransferase (HGPRT). Since HGPRT appears absent in Drosophila, the study focusses initially on Aprt and shows that aprt mutants have a decreased life-span and altered uric acid levels (the latter can be attenuated by allopurinol treatment). Moreover, aprt mutants show defects in locomotor reactivity behaviors. A comparable phenotype can be observed when specifically knocking down aprt in dopaminergic cells (in an adult-specific fashion). Interestingly, also glia-specific knock-down caused a similar behavioral defect, which could not be restored when re-expressing UAS-aprt, while neuronal re-expression did restore the mutant phenotype. Moreover, mutants, pan-neuronal and glia-specific RNAi for aprt caused sleep-defects. Based on immunostainings Dopamine levels are increased; UPLC shows that adenosine levels are reduced and PCR showed in increase of Ent2 levels are increased (but not AdoR). Moreover, aprt mutants display seizure-like behaviros, which can be partly restored by purine feeding (adenosine and N6-methyladenosine). Finally, expression of the human HGPRT also causes locomotor defects.

The authors provide a wide range of genetic experimental data to assess behavior and some molecular assessment on how the defects may emerge. It is clearly written, and the arguments follow the experimental evidence that is provided.

The findings provide a new example of how manipulating specific genes in the fruit fly allow the study of fundamental molecular processes that are linked to a human disease.

Reviewer #2 (Public Review):

The manuscript by Petitgas et al demonstrates that loss of function for the only enzyme responsible for the purine salvage pathway in fruit-flies reproduces the metabolic and neurologic phenotypes of human patients with Lesch-Nyhan disease (LND). LND is caused by mutations in the enzyme HGPRT, but this enzyme does not exist in fruit-flies, which instead only have Aprt for purine recycling. They demonstrate that mutants lacking the Aprt enzyme accumulate uric acid, which like in humans can be rescued by feeding flies allopurinol, and have decreased longevity, locomotion and sleep impairments and seizures, with striking resemblance to HGPRT loss of function in humans. They demonstrate that both loss of function throughout development or specifically in the adult ubiquitously or in all neurons, or dopaminergic neurons, mushroom body neurons or glia, can reproduce the phenotypes (although knock-down in glia does not affect sleep). They show that the phenotypes can be rescued by over-expressing a wild-type form of the Aprt gene in neurons. They identify a decrease in adenosine levels as the cause underlying these phenotypes, as adenosine is a neurotransmitter functioning via the purinergic adenosine receptor in neurons. In fact, feeding flies throughout development and in the adult with either adenosine or m6A could prevent seizures. They also demonstrate that loss of adenosine caused a secondary up-regulation of ENT nucleoside transporters and of dopamine levels, that could explain the phenotypes of decreased sleep and hyperactivity and night. Finally, they provide the remarkable finding that over-expression of the human mutant HGPRT gene but not its wild-type form in neurons impaired locomotion and induced seizures. This means that the human mutant enzyme does not simply lack enzymatic activity, but it is toxic to neurons in some gain-of-function form. Altogether, these are very important and fundamental findings that convincingly demonstrate the establishment of a Drosophila model for the scientific community to investigate LND, to carry out drug testing screens and find cures.

The authors have dealt with my concerns satisfactorily and have explained the instances in which resolving experimentally the criticisms raised would require a work effort well beyond the scope of a revision for this manuscript.

Reviewer #3 (Public Review):

The revised study provides better evidence to suggest that loss of Aprt activity in Drosophila provides a model for the loss of HGPRT activity in humans, which is causative for LND. Analysis of Drosophila Aprt mutations and RNAi-mediated knockdown reveals similar phenotypes to LND, particularly neurological defects, reduced nighttime sleep, and potentially seizures. LND is currently resistant to treatments and screening of a limited number of compounds in Drosophila has not identified a compound that can reduce all of the associated phenotypes. It is appropriate, therefore, that claims to have developed a clinically exploitable model for human LND have been toned down. Future drug screening may well prove profitable, but currently the evidence that Drosophila Aprt will be a suitable model for LND remains speculative.

The second approach adopted is to express a 'humanised mutated' form of HGPRT in Drosophila, which holds more promise for the development of a pharmacological screen. In particular, the locomotor defect is recapitulated but the seizure-like activity, whilst reported as being recapitulated, is debatable. A recovery time of 2.3 seconds is very much less than timings for typical seizure mutants. Nevertheless, the SING behaviour could be sufficient to screen against. However, this is not explored. With respect the short seizure duration, the authors cite similar findings for porin loss of function, but the cited study similarly did not employ anti-seizure drug exposure to validate that this phenotype is seizure related.

In summary, this is a largely descriptive study reporting the behavioural effects of an Aprt loss-of-function mutation. RNAi KD and rescue expression studies suggest that a mix of neuronal (particularly dopaminergic and possibly adenosinergic signalling pathways) and glia are involved in the behavioural phenotypes affecting locomotion, sleep and seizure. There remains insufficient evidence to have full confidence that the Arpt fly model will prove valuable for understanding / treating LND.

Author Response

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

We thank the editors and reviewers for their tremendously helpful comments. We outline below changes we have made to the manuscript in response to each point. These include new analyses and a substantial rewrite to address the concerns about lack of clarity.

We believe the revisions strengthen the evidence for our conclusion that grid fields can be either anchored to or independent from a task reference frame, and that anchoring is selectively associated with successful path integration-dependent behaviour. Our additional analyses of non-grid cells indicate that while some are coherent with the grid population, many are not, suggesting cell populations within the MEC may implement grid-dependent and grid-independent computations in parallel.

We hope the reviewers will agree that our novel experimental strategy complements and avoids limitations of perturbation-based approaches, and by providing evidence to dissociate the two major hypotheses for whether and when grid cells contribute to behaviour our results are likely to have a substantial impact on the field.

Public Reviews:

Reviewer #1 (Public Review):

In this study, Clark et. al. uncovered an association between the positional encoding of grid cell activity with good performance in spatial navigation tasks that requires path integration, highlighting the contribution of grid firing to behaviour… The conclusions of this paper are mostly well supported by data, the finding about the association between grid cell encoding and behaviour in spatial memory tasks is important. However, some aspects of the analysis need to be clarified or extended.

Thankyou for the overview and constructive comments.

(1) While the current dataset aims to demonstrate a "correlation" between grid cell encoding and task performance, the other variables that could confound this correlation should be carefully examined.

(1.1) The exact breakdown of the fraction of beaconed/non-beaconed/probe trials is never shown. if the session makeup has a significant effect on the coding scheme or other results, this variable should be accounted for.

The lack of information about the trial organisation was a substantial oversight in our preparation of the first version of the manuscript. Session make up can not account for effects on grid stability and its relationship to behavioural outcome but this was not made at all clear.

In all sessions trial types were varied in a fixed repeating sequence. Therefore, continuous blocks of trials on which grid firing is anchored (or independent from) the track can not be explained by the mouse experiencing a particular trial type. We have revised the manuscript to make this clearer, e.g. p 5, ‘These switches could not be explained by variation between trials in the availability of cues or rewards, as these were interleaved in blocks that repeated throughout a session (see Methods), whereas periods in which grid cell activity was in a given mode extended across the repeating blocks (e.g. Figures 3D,E, 4A, 5E,F).’ and methods p 12, ‘Trials were delivered in repeating blocks throughout a recording session…’

(1.2) The manuscript did not provide information about whether individual mice experienced sessions with different combinations of the three trial types, and whether they show different preferences in position or distance encoding even in comparable sessions. This leads to the question of whether different behaviour and activity encoding were dominated by experimental or natural differences between individual mice. Presenting the data per mouse will be helpful.

As we note above, because trial types were interleaved in a fixed sequence, experience of a particular trial type can not account for switching between task-anchored and taskindependent firing modes. This was insufficiently clear in the first version of the manuscript.

We varied the proportions of trials of a particular type between sessions with the aim of maximising the number of non-beaconed and probe trials. This was necessary because we find that if we introduce too high a proportion of these trials early in training then mice appear to ‘lose interest’ in the task and their performance drops off. We therefore used an approach in which we increased the proportions of non-beaconed and probe trials over training days as mice became familiar with the task. This is now described in the methods (p 12).

Because the decision for when to vary the proportion of trial types was based on the previous day’s performance, the experimental design was not optimised for addressing the reviewer’s question about dissociating experimental from natural differences in mice. To provide some initial insight we have analysed the relationship between task anchored coding and proportion of beaconed trials in a session (Figure 3, Figure Supplement 7). While on average there is a higher proportion of trials in which grid fields are task-anchored in sessions with more beaconed trials, this effect is small and most of the variance is independent from the proportion of beaconed trials.

(1.3) Related to the above point, in Figure 5, the mice appeared to behave worse in probe trials than non-beaconed trials. If the mouse did not know if a trial is a probe or a non-beacon trial, they should behave equivalently until the reward location and thus should stop an equal amount. If this difference is because multiple probe trials are placed consecutively, did the mouse learn that it will not get a reward and then stop trying to get rewards? Did this affect switching between position and distance coding?

Thankyou for flagging this. This reflected an inconsistency arising from the way we detected stops that we have now corrected. Briefly, the temporal resolution of the processed location data against which the stop detection threshold was applied was insufficiently high. As a result, stops in the non-beaconed group were picked up, as they tended to be longer because mice remained still to consume rewards, whereas some stops in the probe group were missed because they were relatively short. We have corrected this by repeating the analyses on raw position data at the highest temporal resolution available. This analysis is now clearly described in the Methods (see p13 “A stop was registered in Blender3D if the speed of the mouse dropped below 4.7 cm/s. Speed was calculated on a rolling basis from the previous 100 ms at a rate of 60 Hz.”).

(1.4) It is not shown how the behaviours (e.g., running speed away from the reward zone, licking for reward) in beaconed/non-beaconed/probe trials were different and whether the difference in behaviours led to the different encoding schemes.

Because trial types were interleaved and repeated with a period less than the length of typical trial sequences during which grid cell activity remained either task-anchored or taskindependent, differences between trial types are unlikely to explain use of the different coding schemes. Hopefully, this is clarified by the comments above.

To further describe the relationship between behavioural outcomes, trial types and grid anchoring, we now also show running speed as a function of location for each combination of trial types and trial outcomes (Figure 6, Figure Supplement 1). This illustrates and replicates our previous findings (Tennant et al. 2018) that running speed profiles are similar for a given trial outcome regardless of trial type (Figure 6, Figure Supplement 1A), and further further shows that the behavioural profile for a given trial outcome and trial-type does not differ when grid cells are in task-anchored and task-independent modes (Figure 6, Figure Supplement 1B). This further argues against the possibility that difference in behaviours leads to the different encoding schemes.

(2) Regarding the behaviour and activity encoding on a trial-by-trial basis, did the behavioural change occur first, or did the encoding switch occur first, or did they happen within the same trial? This analysis will potentially determine whether the encoding is causal for the behaviour, or the other way around.

This is a good question but our experimental design lacks sufficient statistical power to address the timing of mode switches within a trial. This is because mode switching is relatively infrequent (so the n for switching is low) and only a subset of trials are uncued (making the relevant n even lower), while at a trial level the behavioural outcome is variable (increasing the required n for adequate power).

(3) The author determined that the grid cell coding schemes were limited to distance encoding and position encoding. However, there could be other schemes, such as switching between different position encodings (with clear spatial fields but at different locations), as indicated by Low et. al., 2021, and switching between different distant encodings (with different distance periods). If these other schemes indeed existed in the data, they might contribute to the variation of the behaviours.

Switching between position encoding schemes appears to be rare within our dataset and unlikely to contribute to variation in behaviour. In most sessions we did not observe switching between grid phases / position encodings (e.g. Figures 2A-B, 3B-E, 4A, 5C-D, F). In one session we found switching between different phases when grid cells were taskanchored. Because the grid period was unchanged, the spatial periodograms remained similar. We report this example in the revised manuscript (Figure 5E).

(4) The percentage of neurons categorised in each coding scheme was similar between nongrid and grid cells. This implies that non-grid cells might switch coding schemes in sync with grid cells, which would mean the whole MEC network was switching between distance and position coding. This raises the question of whether the grid cell coding scheme was important per se, or just the MEC network coding scheme.

We very much appreciate this suggestion. We note first that while the proportion of taskanchored grid and non-grid cells is similar, task-independent periodic firing of non-grid cells is much rarer than for grid cells (Figure 2E), suggesting a dissociation between the populations. To further address the question we have included additional analyses of nongrid cells (Figure 3, Figure Supplement 5). This shows that while some non-grid cells have anchoring that switches coherently with simultaneously recorded grid cells, others do not. Figures 4 and 5 now show examples of non-grid cell activity recorded simultaneously with grid cells.

Together, our data suggest that the MEC implements multiple coding schemes: one that is associated with the grid network and includes some non-grid cells; and one (or more) that can be independent from the grid network. This dissociation adds to the insights into MEC function that are provided by our study and is now highlighted in the abstract and discussion.

(5) In Figure 2 there are several cell examples that are categorised as distance or position coding but have a high fraction of the other coding scheme on a per-trial basis. Given this variation, the full session data in F should be interpreted carefully, since this included all cells and not just "stable" coding cells. It will be cleaner to show the activity comparison only between the stable cells.

We have now included examples in Figure 2A-C where the grid mode is stable throughout a session. As the view of activity at a session level is important, we have not updated Figure 2F, but have clarified the terminology to now clearly refer to classification at either season or trial levels. In addition, we have repeated the analyses shown in Figure 2F but after grouping cells according to whether their firing has a single mode on >85% of the trials (Figure 3 Figure Supplement 4). This analysis supports similar conclusions to those of Figure 2F.

(6) The manuscript is not well written. Throughout the manuscript, there are many unexplained concepts (especially in the introduction) and methods, mis-referenced figures, and unclear labels.

We very much appreciate the feedback and have substantially rewritten the manuscript. We have paid particular attention to explaining key concepts in the introduction and have carefully checked the figures. We welcome further feedback on whether this is now clearer.

Reviewer #2 (Public Review):

Clark and Nolan's study aims to test whether the stability of grid cell firing fields is associated with better spatial behaviour performance on a virtual task… This study is very timely as there is a pressing need to identify/delimitate the contribution of grid cells to spatial behaviours. More studies in which grid cell activity can be associated with navigational abilities are needed.

Thank you for the supportive comments and highlighting the importance of the question.

The link proposed by Clark and Nolan between "virtual position" coding by grid cells and navigational performance is a significant step toward better understanding how grid cell activity might support behaviour. It should be noted that the study by Clark and Nolan is correlative. Therefore, the effect of selective manipulations of grid cell activity on the virtual task will be needed to evaluate whether the activity of grid cells is causally linked to the behavioural performance on this task. In a previous study by the same research group, it was shown that inactivating the synaptic output of stellate cells of the medial entorhinal cortex affected mice's performance of the same virtual task (Tennant et al., 2018). Although this manipulation likely affects non-grid cells, it is still one of the most selective manipulations of grid cells that are currently available.

Again, thank you for the supportive comments. We recognise the previous version of the manuscript did not sufficiently clarify the motivation for our approach, or the benefits of capitalising on behavioural variable variability as a complementary strategy to perturbation approaches. We now make this clearer in the revised introduction (p 2, paragraphs 2 and 3).

When interpreting the "position" and "distance" firing mode of grid cells, it is important to appreciate that the "position" code likely involves estimating distance. The visual cues on the virtual track appear to provide mainly optic flow to the animal. Thus, the animal has to estimate its position on the virtual track by estimating the distance run from the beginning of the track (or any other point in the virtual world).

We appreciate the ambiguity here was confusing. We have re-named the groups to ‘taskanchored’, corresponding to when grid cells encode position on the track (as well as distance as the reviewer correctly points out), and ‘task-independent’, corresponding to the group we previously referred to as distance encoding.

It is also interesting to consider how grid cells could remain anchored to virtual cues. Recent work shows that grid cell activity spans the surface of a torus (Gardner et al., 2022). A run on the track can be mapped to a trajectory on the torus. Assuming that grid cell activity is updated primarily from self-motion cues on the track and that the grid cell period is unlikely to be an integer of the virtual track length, having stable firing fields on the virtual track likely requires a resetting mechanism taking place on each trial. The resetting means that a specific virtual track position is mapped to a constant position on the torus. Thus, the "virtual position" mode of grid cells may involve 1) a trial-by-trial resetting process anchoring the grid pattern to the virtual cues and 2) a path integration mechanism. Just like the "virtual position" mode of grid cell activity, successful behavioural performance on non-beaconed trials requires the animal to anchor its spatial behaviour to VR cues.

Reviewer #3 (Public Review):

This study addresses the major question of 'whether and when grid cells contribute to behaviour'. There is no doubt that this is a very important question. My major concern is that I'm not convinced that this study gives a significant contribution to this question, although this study is well-performed and potentially interesting. This is mainly due to the fact that the relation between grid cell properties and behaviour is exclusively correlative and entirely based on single cell activity, although the introduction mentions quite often the grid cell network properties and dynamics. In general, this study gives the impression that grid cells exclusively support the cognitive processes involved in this task. This problem is in part related to the text.

Thank you for the comments. We recognise now that the previous text was insufficiently clear. We have modified the introduction to clarify the value of an approach that takes advantage of behavioural variability. Importantly, this approach is complementary to perturbation strategies we and others have used previously. In particular it addresses critical limitations of perturbation strategies which can be confounded by off-target effects and possible adaptation, both of which are extremely difficult to fully rule out. We hope that with this additional clarification it is now clear that as for any important question multiple and complementary testing strategies are required to make progres, and second, that our study makes a new and important contribution by introducing a novel experimental approach and by following this up with careful analyses that clearly distinguish competing hypotheses.

However, it would be interesting to look at the population level (even beyond grid cells) to test whether at the network level, the link between behavioural performance and neural activity is more straightforward compared to the single-cell level. This approach could reconcile the present results with those obtained in their previous study following MEC inactivation.

We’re unclear here about what the reviewer means by ‘more straightforward’ as clear relationships between activity of single grid cells and populations of grid cells are well established (Gardner et al., 2021; Waaga et al., 2021; Yoon et al., 2013).

To give a clearer indication of the corresponding population level representations, as mentioned in response to Reviewer #1, we now include additional data showing many simultaneously recorded neurons, and analyses of non-grid as well as grid cells (Figures 4, 5, Figure 5 Figure Supplement 2).

To reconcile results with our previous study of MEC inactivation we have paid additional attention to the roles of non-grid cells (following suggestions by Reviewer #1). We show that while some non-grid cells show transitions between task-anchored and task-independent firing that are coherent with the grid population, many others have more stable firing that is independent of grid representations. This is consistent with the idea that the MEC supports localised behaviour in the cued and uncued versions of the task (Tennant et al., 2018), and suggests that while grid cells preferentially contribute when cues are absent, non-grid cells could also support the cued version. We make this additional implication clear in the revised abstract and discussion.

The authors used a statistical method based on the computation of the frequency spectrum of the spatial periodicity of the neural firing to classify grid cells as 'position-coding' (with fields anchored to the virtual track) and 'distance-coding' (with fields repeating at regular intervals across trials). This is an interesting approach that has nonetheless the default to be based exclusively on autocorrelograms. It would be interesting to compare with a different method based on the similarities between raw maps.

While our main analyses use a periodogram-based method to identify when grid cells are / are not anchored to the task environment, we validate these analyses by examination of the rate maps in each condition (Figures 2-4). For example, when grid cells are task-anchored, according to the periodogram analysis, the rate maps clearly show spatially aligned peaks, whereas when grid cells are not anchored the peaks in their rate maps are not aligned (Figure 2A vs 2B; Figure 3B-E; Figure 4C). We provide further validation by showing that spatial information (in the track reference frame) is substantially higher when grid cell activity is task-anchored vs task-independent (Figures 2F, 3G, 4F and Figure 3 Figure Supplement 4).

To further address this point we have carried out additional complementary analyses in which we identify task anchored vs task independent modes using a template matching method applied to the raw rate maps (Figure 6, Figure Supplement 2). These analyses support similar conclusions to our periodogram-based analyses.

Beyond this minor point, cell categorization is performed using all trial types.

Each trial type (i.e. beacon or non-beacon) is supposed to force mice to use different strategies and should induce different spatial representations within the entorhinal-hippocampal circuit (and not only in the grid cell system). In that context, since all trials are mixed, it is difficult to extrapolate general information.

We recognise that the description of the task design was insufficiently clear but are unsure why ‘it is difficult to extrapolate general information’. Before addressing this point, we should first be clear that mice are not ‘forced’ to adopt any particular strategy. Rather, on uncued trials a path integration strategy is the most efficient way to solve the task. However, mice could instead use a less efficient strategy, for example by stopping at short intervals they still obtain rewards. Detailed behavioural analyses indicate that such random stopping strategies are used by naive mice, while with training mice learn to use spatial stopping strategies (Tennant et al. 2018).

In terms of ‘extracting general information’ from the task, the following findings lead to general predictions: 1) Grid cells can exist in either task-anchored or task-independent periodic firing modes; 2) These modes can be stable across a session, but often modeswitching occurs within a session; 3) While some non-grid cells show task-independent periodic firing, this is much less common than for grid cells, which suggests a model in which many non-grid MEC neurons operate independently from the grid network; 4) When a marker cue is available mice locate a reward equally well when grid cells are in taskanchored versus task-independent modes, which argues against theories in which grid cells are a key part of a general system for localisation; 5) When markers cues are absent taskanchored grid firing is associated with successful reward localisation, which corroborates a key prediction of theories in which grid cells contribute to path integration.

In revising the manuscript we have attempted to improve the writing to make these advances clearer, and have clarified methodological details that made interpretation more challenging than it should have been. For example, as noted in our response to Reviewer #1, we have included additional details to clarify the organisation of trials and relationships between trials, behavioural outcomes and neural codes observed.

On page 5 the authors state that 'Since only position representations should reliably predict the reward location, ..., we reasoned that the presence of positional coding could be used to assess whether grid firing contributes to the ongoing behaviour'. I do not agree with this statement. First of all, position coding should be more informative only in a cue-guided trial. Second, distance coding could be as informative as position coding since at the network level may provide information relevant to the task (such as distance from the reward).

Again, this point perhaps reflects a lack of clarity on our part in writing the manuscript. When grid cells are anchored to the track reference frame (now called ‘tasked anchored’, previously ‘position encoding’), then the location of the rate peaks in grid firing is reliable from trial to trial. This is the case whether or not the trial is cued. When grid cells are independent of the track reference frame (now called ‘task independent’, previously ‘distance encoding’), then the location of the firing rate peaks vary from trial to trial. In the latter case, position can not be read out directly from trial to trial.

In principle, in the task-independent mode track position could be calculated by storing the grid network configuration at the start of the track, which would differ on each trial, and then implementing a mechanism to readout relative distance as mice move along the track. However, if mice do use this computation we would expect them to do so equally well on cued and uncued trials. By contrast, our results clearly show a dissociation between trial types in the relationship between grid firing and behavioural outcome. We highlight and discuss this possibility in the revised manuscript (p 10, ‘Alternatively, mice could in principle estimate track location with a system that utilises information about distance travelled obtained from task-independent grid representations’).

Third, position-coding is interpreted as more relevant because it predominates in correct trials. However, this does not imply that this coding scheme is indeed used to perform correct trials.

We have revised the manuscript to clarify our goal of distinguishing major hypotheses for the roles of grid cells in behaviour (Introduction, ‘On the one hand, theoretical arguments that grid cell populations can generate high capacity codes imply that they could in principle contribute to all spatial behaviours (Fiete et al., 2008; Mathis et al., 2012; Sreenivasan and Fiete, 2011). On the other hand, if the behavioural importance of grid cells follows from their hypothesised ability to generate position representations by integrating self-motion signals (McNaughton et al., 2006), then their behavioural roles may be restricted to tasks that involve path integration strategies.’

By showing that performance on cued trials is similar regardless of whether grid cells are task-anchored or not, we provide strong evidence against the idea that grid firing is in general necessary for location-based behaviours. By showing that task anchoring is associated with successful localisation when cues are absent we corroborate a key prediction of hypothesised roles for grid cells in path integration-dependent behaviour. Therefore, we substantially reduce the space of behaviours to which grid cells might contribute. Importantly, this space is much larger for the MEC, which is required for cued and uncued versions of the task. We have revised the introduction and discussion to make these points clearer.

While we believe our results add a key piece of evidence to the puzzle of when and where grid cells contribute to behaviour, we agree that further work will be required to develop and test more refined hypotheses. Alternative models also remain plausible, for example perhaps the behaviourally relevant computations are implemented elsewhere in the brain with grid anchoring to the track as an indirect consequence. Nevertheless, explanations of this kind are more difficult to reconcile with evidence that inactivation of stellate cells in the MEC impairs learning of the task, and other manipulations that modify grid firing impair performance on similar tasks. We now discuss these possibilities (discussion p 10, ‘mice could in principle estimate track location with a system that utilises information about distance travelled obtained from task-independent grid representations’).

It could be more informative to push forward the correlative analysis by looking at whether behavioural performance can be predicted by the coding scheme on a trial-by-trial basis.

The previous version of the manuscript showed these analyses (now in Figure 6). Thus, task anchored grid firing predicts more successful performance on uncued trials at the session level (Figure 6A-B) and at the trial level (Figure 6C-D).

Reviewer #1 (Recommendations For The Authors):

(1) The author particularly mentioned that the 1D tracks are different from the "cue-rich environments that are typically used to study grid cells". It is not clear what conclusions would hold for a cue-rich environment or a track, which may require relatively less path integration compared to the cue-sparse environment. This point should be discussed.

This is an important point that we did not pay sufficient attention to in the previous version of the manuscript. Our finding of successful localisation in the cued environment when grid cells are not task anchored implies that grid anchoring is not required to solve cued tasks. The implication here is that cue rich environments may then not be the most suitable for investigation of grid roles in behaviour as non-grid mechanisms may suffice, although this does not rule out the possibility that anchored grid codes may play important roles in learning about cue rich environments. We now address this point in the discussion (p 10, ‘An implication of this result is that cue rich tracks often used to investigate grid activity patterns may not engage behaviours that require anchored grid firing.’).

(2) It would be good to see the statistics for the number of different cells (stable position or distance encoding, and unstable cells) identified per mouse/session and the number of grid cells per session.

These are now added to Supplemental Data 2 and will also be accessible through code and datasets that we will make available alongside the version of record.

(3) Figure 2F: any explanation about why AG cells had high spatial information?

Previously the calculation used bits per spike and as aperiodic cells have low firing rates the spatial information was high. We have replaced this with bits per second, which provides a more intuitive measure and no longer implies high spatial information. We have amended this in the methods (p 15, ‘Spatial information was calculated in bits per second…’).

(4) The following methods sections should provide additional details:

(4.1) Details of the training protocol are largely left to reference papers. The reference papers give a general outline of the training protocol, but the details are not completely comparable given the single experiment performed on these mice. More details should be given on training stages and experience at the time of the experiment.

The task is more clearly described in the introduction (p 3), and additional details of the training protocol are now provided in the methods (p 12-13).

(4.2) The methods reference mean speed across sessions, but it is not clear where this was used.

This was very poor wording. We have now changed this to ‘For each session the mean speed was calculated for each trial outcome’.

(4.3) The calculation of the spatial autocorrelogram on a per-trial basis should be more explicitly stated. Is it the average of each 10 cm increment with the centre trial?

We have added additional information to the methods (p 16-18).

(4.4) 1D field detection is not sufficiently explained in Figure 1/S2. This information should also appear in the methods section.

This is now clarified on page 16 in section ‘Analysis of neural activity and behaviour during the location memory task’.

(5) The data in Figure 4A and B only shows speed vs. location for one example mouse. The combined per mouse or per session data should also be shown.

This is now shown in Figure 5A and Figure 5, Figure Supplemental 2

(6) Figure 5 is somewhat confusing. Why are A/B by session and C/D by trial? The methods imply that A/B are originally averaged by cell, but that duplicate cells in the same session are excluded because behaviour versus session type is identical. This method should be valid if all grid cells within a session are all "stable". This is likely given the synchrony of code-switching between grid cells, but not all co-active grid cells behaved identically.

It is understandable that C/D are performed by trial, but it should be made clear that it is not a comparable analysis to A/B. It is unclear what N refers to in C. The figure says by trial, but the legend says the error bar is by cell. If data is calculated by trial and then averaged by cell, this should be more clearly stated.

In Figure 6A/B (previously Figure 5A/B) we focus our analysis on sessions in which the mode of grid firing, either task-anchored or task-independent, was relatively stable on a trialto-trial basis (see Figure 3F for definitions). This enables us to then compare behaviour averaged across each session, with sessions categorised as task-anchored and task independent. This analysis has the advantage that it focuses on large blocks of time (whole sessions) in which the mode of grid firing is unambiguous, but the disadvantage is that it excludes many sessions in which grid firing switches between task-anchored and taskindependent modes.

Figure 6C/D (previously Figure 5C/D) addresses this limitation by carrying out similar analyses with behaviour sorted into task-anchored versus task-independent groups at the level of trials. A potential limitation for this analysis is that grid firing is somewhat variable on a trial-by-trial basis and so some trials may be mis-classified. We don’t expect this to lead to systematic bias, but it may make the data more noisy. Nevertheless, these analyses are important to include as they allow assessment of whether conclusions from 6A/B hold when all sessions are considered.

We have added additional clarification of the rationale for these analyses to the main text (p7-8, ‘’We addressed this by using additional trial-level comparisons’). We have also added clarification in the methods section for categorisation of task-anchored versus taskindependent trials when multiple grid cells were recorded simultaneously (p 17, ‘When assigning a common classification across a group of cells recorded simultaneously...’) and an explanation for the N in the figure legend. We also clarify that the analyses use a nested random effects design to account for dependencies at the levels of sessions and mice (methods, p 20, ‘Random effects had a nested structure to account for animals and sessions…’) .

(7) Panels E and F of Figure 5 are not explained in the main text.

This is now corrected (see p8, ‘Additional analyses…’).

(8) Figure 5: Since stable grid cells and all grid cells are shown, it will be better to show unstable cells, which can be compared with grid cells.

Given that the rationale for differences between Figure 6A/B and C/D (previously Figure 5AD) were not previously clear, the reason for focussing on stable grid cells here was likely also not clear (see point 6 above). We don’t show unstable grid cells in Figure 6A-B as the behaviour averaged at the level of a session would be a mix of trials when they are taskanchored and when they are task-independent. Therefore, the analysis would not test predictions about the relationship between task-anchored vs task-independent modes and behaviour. We hope this is now clear in the manuscript given the revisions introduced to address point 6 above.

(9) The methods describing the statistics for these experiments are also confusing. The methods section should be written more clearly, and it should be made clear in the text or figure legend whether this data is the "original" data or is processed in relation to the model, such as excluding duplicate grid cells within a session. The figure legend should also state that a GLMM was used to calculate the statistics.

We have revised the methods section with the goal of improving clarity, adding detail and removing ambiguity. This includes updates of the methods for the GLMM analysis, which are referred to within the Figure 6 legend. A clear definition of a stable session is now also added to the Figure 6 legend.

Reviewer #2 (Recommendations For The Authors):

When grid fields are anchored to the virtual world (position mode), there is probably small trialto-trial variability in the firing location of the firing fields. Is this trial-to-trial variability related to the variability in the stop location? This would provide a more direct link between path integration in grid cell networks and behaviour that depends on path integration.

When attempting to address this we find that the firing of individual grid cells is too variable to allow sufficiently precise decoding of their fields at a single trial level. This is expected given the Poisson statistics of spike generation and previous evaluations of grid coding (e.g. (Stemmler et al., 2015)).

The conclusion of the abstract is: "Our results suggest that positional anchoring of grid firing enhances the performance of tasks that require path integration." This statement is slightly confusing. The task requires 1) anchoring the behaviour to the visual cues presented at the start of the trial and 2) path integration from thereon to identify the rewarded location. The performance is higher when grid cells anchor to the visual cues presented at the start of the trial. What the results show is that the anchoring of grid firing fields to visual landmarks enhances the performance of tasks that require path integration from visual landmarks (i.e. grid cells being anchored to the reference frame that is behaviorally relevant).

To try to more clearly explain the logic and conclusion we have rewritten the abstract, including the final sentence.

Similar comment for the title of Figure 5: "Positional grid coding is not required for cued spatial localisation but promotes path integration-dependent localisation." Positional coding means that grid cells are anchored to the behaviorally relevant reference frame.

To address the lack of clarity we have modified the little of Figure 6 (previously Figure 5) to read ‘Anchoring of grid firing to the task reference frame promotes localisation by path integration but is not required for cued localisation’.

In Figure 1, there is a wide range of beaconed (40-80%) and non-beaconed (10-60%) trials given. It is not 100% clear whether these refer to the percentage of trials of a given type within the recording sessions. Was the proportion of non-beaconed trials manipulated? If so, was the likelihood of position and distance coding changing according to the percentage of nonbeaconed trials?

The ranges given refer to proportions across different behavioural sessions. Within any given behavioural session the proportion was constant. We now make this clear in the figure legend and in the results and methods sections.

We did not manipulate proportions of trial types during a session. Manipulations betweens sessions were carried out with the goal of maximising the numbers of uncued trials that the mice would carry out (see response to public comments above). While the effect of trial-type at the session level is not relevant to the hypotheses we aim to test here, we have included an additional analysis of the relationship between task anchoring and the proportions of trial types in a session (Figure 3, Figure Supplement 7)(also discussed above). As disentangling the effects of learning and motivation will be complex and likely require new experimental designs we have not drawn strong conclusions or pursued the analysis further..

I was not convinced that the labels "position" and "distance" were appropriate for the two grid cell firing modes. My understanding is that the "position" code also requires the grid cell network to estimate distance. It seems that the main difference between the "position" and "distance" modes is that when in the "position" mode, the activity on the torus is reset to a constant toroidal location when the animal reaches a clearly identifiable location on the virtual track. In the "distance" mode, this resetting does not take place.

As previously mentioned, we agree these terms weren’t the best and have since relabelled these as “task-anchored” and “task-independent”.

There are a few sections in the manuscript that implicitly suggest that a causal link between grid cell activity and behaviour was demonstrated. For instance: "It has been challenging to directly test whether and when grid cells contribute to behaviour.": The assumption here is that the manuscript overcomes this challenge, but the study is correlative.

We have modified the wording to be clear that we are introducing new tests of predictions made by hypotheses about causal relationships between grid coding and behaviour (introduction, p 1-2). We also clarify that our results argue against the hypothesis that grid cells provide a general coded for behaviour, but corroborate predictions of hypotheses in which they are specifically important for path integration (discussion, p 10).

We have modified the title abstract and main text to try to treat claims about causality with care. We now more thoroughly introduce and contrast the approach we report here with previous experiments that use perturbations (introduction, p2). While it is tempting to make stronger claims for causality with these approaches, there are also logical limitations with perturbation-based approaches, for example the challenges of fully excluding off target effects and adaptation. We now explain how these strategies are complementary. Our view is that both strategies will be required to develop strong arguments for whether and when grid cells contribute to behaviour. From this perspective, it is encouraging that our conclusions are in agreement with what are probably the most specific perturbations of grid cells reported to date (Gil et al. 2017), while perturbations that more generally affect MEC function appear to impair cued and path integration-dependent behaviours (Tennant et al. 2018). We now discuss these points more clearly (introduction, p 2).

I am slightly confused by the references to the panels in Figure 4.

"In some sessions, localization of the reward occurred almost exclusively when grid cells were anchored to position and not when they encoded distance (Figure 4C). Figure 4C only shows position coding.

"In other sessions, animals localised the reward when grid firing was anchored to position or distance, but overall performance was improved on positional trials (Figure 4D-E)." The reference should probably point to Figure 4E-F or just to 4E.

"In a few sessions, we observed spatial stopping behaviour comparable to cued trials, even when grid firing almost exclusively encoded distance rather than position (Figure 4F)." From Figure 4F, it seems that the performance on non-beaconed trials is better during "position" coding.

We have now updated Figure 5 (Figure 4 in the original manuscript) and references to the Figure in the text. Now Figure 5 shows the activity of cells recorded in stable and unstable task-anchored and task-independent sessions (see Figure 5C-F).

Minor issues:

Is this correct: (Figure 4A and Figure 4, Figure Supplement 1).

This has been corrected.

Figure 4B: There could be an additional label for position and distance.

Figure 4B from the original manuscript has now been removed.

Figure 4C-F. The panels on the right side should be explained in the Figure Legend.

Legends for Figure 5C-F (previously Figure 4C-F) have now been updated.

Reviewer #3 (Recommendations For The Authors):

Specific questions :

(1) Position coding reflects a coding scheme in which fields are spaced by a fixed distance; previous studies have shown that a virtual track grid map is a slice of the 2D classic grid. In that case, the fields are still anchored to the track but would produce a completely different map. Did the authors check whether it is the case at least for some cells? If not, what could explain such a major difference?

Το avoid confusion we now use the term ‘task-anchored’ rather than ‘position coding’ (see comments above). We should further clarify that our conclusions rest on whether or not the grid fields are anchored to the track. Task anchored firing does not require that grid fields maintain their spacing from 2D environments, only that fields are at the same track position on each trial. Thus, whether the spacing of the fields corresponds to a slice through a 2D grid makes no difference to the hypotheses we test here.

We agree that the relationship between 1D and 2D field organisation could be an interesting future direction, for example anchoring could involve resetting the grid phase while maintaining a stable period, or it could be achieved through local distortions in the grid period. However, since these outcomes would not help distinguish the hypotheses we test here we have not included analyses to address them.

(2) Previous studies have highlighted the role of grid cells in goal coding. Here there is an explicit reward in a particular area. Are there any grid modifications around this area? This question is not addressed in this study.

Again, we note that the hypotheses we test here relate to the firing mode of grid cells - taskanchored or task-independent - and interpretation of our results is independent from the specific pattern of grid fields on the track. This question nevertheless leads to an interesting prediction that if grid fields cluster in the goal area then this clustering should be apparent in the task-anchored but not the task-independent firing mode.

We test this by considering the average distribution of firing fields across all grid cells in each firing mode (Reviewer Figure 1). We find that when grid firing is task-anchored there is a clear peak around the reward zone, which is consistent with previous work by Butler et al. and Boccara et al. Consistent with our other prediction, this peak is reduced when grid cells are in the task-independent mode.

Author response image 1.

Plot shows the grid field distribution during stable grid cell session (> 85 % task-anchored or task-independent) (A) or during task-anchored and task-independent trials (B). Shaded regions in A and B represent standard error of the mean measured across sessions and epochs respectively.

(3) The behavioural procedure during recording is not fully explained. Do trial types alternate within the same session by blocks? How many trials are within a block? Is there any relation between trial alternation and the switch in the coding scheme observed in a large subset of the grid cells?

We agree this wasn’t sufficiently clear in the previous version of the manuscript. Trial types were interleaved in a fixed order within each session. We have updated the results and methods sections to provide details (see responses above).

(4) From the examples in Figure 2 it seems that firing fields tend to shift toward the start position. Is it the case in all cells? Could this reflect some reorganisation at the network level with cells signalling the starting as time progresses?

This is inconsistent between cells. To make this variability clear we have included additional examples of spiking profiles from different grid cells (Figure 2 - 5). Because quantification of the phenomena would not, so far as we can tell, help distinguish our core hypotheses we have not included further analyses here.

(5) Are grid cells with different coding properties recorded in different parts of the MEC? Are there any differences between these cell categories in the 2D map?

The recordings we made are from the dorsal region of the MEC (stated at the start of the results section). We don’t have data to speak to other parts of the MEC.

Minor:

There are very few grid cell examples that repeat in the different figures. I would suggest showing more examples both in the main text and supplementary material.

We have now provided multiple additional examples in Figures 2, 4 and 5. Grid cell examples repeat in the main figures twice, in both cases only when showing additional examples are shown from the same recording session (Figure 2A example #1 with Figure 5C, Figure 3E with Figure 4A). Further similar repeats are found in the supplemental figures (Figure 3D with Figure 5, Figure Supplement 2A, Figure 3C with Figure 5, Figure Supplement 2F).

Fig1 A-B shows the predictions in a 1D track based on distance or position coding. The A inset represents the modification of field distribution from a 2D arena to a 1D track, as performed in this study. The inset B is misleading since it represents the modifications expected from a circular track to a 1D track as in Jacob et al 2019, that is not what the authors studied. It would be better to present either the predictions based on the present study or the prediction based on previous studies. In that case, they should mention the possibility that the 1D map is a slice of the 2D map.

The goal of Figure 1A-B is to illustrate predictions (right) based on conclusions from previous studies (left). Figure 1A shows predicted 1D track firing given anchoring to the environment typically observed in grid cell studies in 2D arenas. Figure 1B shows predicted 1D track firing given the firing shifting firing patterns observed by Jacob et al. in a circular 2D track. To improve clarity, we have modified the legend to make clear that the schematics to the right are predictions given the previous evidence summarised to the left. As we outline above, the critical prediction relates to whether the representations anchor to the track. Whether the 1D representation is a perfect slice isn’t relevant to the hypotheses tested and so isn’t included in the schematic (see comments above).

Author Response

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

We thank the editors and reviewers for their tremendously helpful comments. We outline below changes we have made to the manuscript in response to each point. These include new analyses and a substantial rewrite to address the concerns about lack of clarity.

We believe the revisions strengthen the evidence for our conclusion that grid fields can be either anchored to or independent from a task reference frame, and that anchoring is selectively associated with successful path integration-dependent behaviour. Our additional analyses of non-grid cells indicate that while some are coherent with the grid population, many are not, suggesting cell populations within the MEC may implement grid-dependent and grid-independent computations in parallel.

We hope the reviewers will agree that our novel experimental strategy complements and avoids limitations of perturbation-based approaches, and by providing evidence to dissociate the two major hypotheses for whether and when grid cells contribute to behaviour our results are likely to have a substantial impact on the field.

Public Reviews:

Reviewer #1 (Public Review):

In this study, Clark et. al. uncovered an association between the positional encoding of grid cell activity with good performance in spatial navigation tasks that requires path integration, highlighting the contribution of grid firing to behaviour… The conclusions of this paper are mostly well supported by data, the finding about the association between grid cell encoding and behaviour in spatial memory tasks is important. However, some aspects of the analysis need to be clarified or extended.

Thankyou for the overview and constructive comments.

(1) While the current dataset aims to demonstrate a "correlation" between grid cell encoding and task performance, the other variables that could confound this correlation should be carefully examined.

(1.1) The exact breakdown of the fraction of beaconed/non-beaconed/probe trials is never shown. if the session makeup has a significant effect on the coding scheme or other results, this variable should be accounted for.

The lack of information about the trial organisation was a substantial oversight in our preparation of the first version of the manuscript. Session make up can not account for effects on grid stability and its relationship to behavioural outcome but this was not made at all clear.

In all sessions trial types were varied in a fixed repeating sequence. Therefore, continuous blocks of trials on which grid firing is anchored (or independent from) the track can not be explained by the mouse experiencing a particular trial type. We have revised the manuscript to make this clearer, e.g. p 5, ‘These switches could not be explained by variation between trials in the availability of cues or rewards, as these were interleaved in blocks that repeated throughout a session (see Methods), whereas periods in which grid cell activity was in a given mode extended across the repeating blocks (e.g. Figures 3D,E, 4A, 5E,F).’ and methods p 12, ‘Trials were delivered in repeating blocks throughout a recording session…’

(1.2) The manuscript did not provide information about whether individual mice experienced sessions with different combinations of the three trial types, and whether they show different preferences in position or distance encoding even in comparable sessions. This leads to the question of whether different behaviour and activity encoding were dominated by experimental or natural differences between individual mice. Presenting the data per mouse will be helpful.

As we note above, because trial types were interleaved in a fixed sequence, experience of a particular trial type can not account for switching between task-anchored and taskindependent firing modes. This was insufficiently clear in the first version of the manuscript.

We varied the proportions of trials of a particular type between sessions with the aim of maximising the number of non-beaconed and probe trials. This was necessary because we find that if we introduce too high a proportion of these trials early in training then mice appear to ‘lose interest’ in the task and their performance drops off. We therefore used an approach in which we increased the proportions of non-beaconed and probe trials over training days as mice became familiar with the task. This is now described in the methods (p 12).

Because the decision for when to vary the proportion of trial types was based on the previous day’s performance, the experimental design was not optimised for addressing the reviewer’s question about dissociating experimental from natural differences in mice. To provide some initial insight we have analysed the relationship between task anchored coding and proportion of beaconed trials in a session (Figure 3, Figure Supplement 7). While on average there is a higher proportion of trials in which grid fields are task-anchored in sessions with more beaconed trials, this effect is small and most of the variance is independent from the proportion of beaconed trials.

(1.3) Related to the above point, in Figure 5, the mice appeared to behave worse in probe trials than non-beaconed trials. If the mouse did not know if a trial is a probe or a non-beacon trial, they should behave equivalently until the reward location and thus should stop an equal amount. If this difference is because multiple probe trials are placed consecutively, did the mouse learn that it will not get a reward and then stop trying to get rewards? Did this affect switching between position and distance coding?

Thankyou for flagging this. This reflected an inconsistency arising from the way we detected stops that we have now corrected. Briefly, the temporal resolution of the processed location data against which the stop detection threshold was applied was insufficiently high. As a result, stops in the non-beaconed group were picked up, as they tended to be longer because mice remained still to consume rewards, whereas some stops in the probe group were missed because they were relatively short. We have corrected this by repeating the analyses on raw position data at the highest temporal resolution available. This analysis is now clearly described in the Methods (see p13 “A stop was registered in Blender3D if the speed of the mouse dropped below 4.7 cm/s. Speed was calculated on a rolling basis from the previous 100 ms at a rate of 60 Hz.”).

(1.4) It is not shown how the behaviours (e.g., running speed away from the reward zone, licking for reward) in beaconed/non-beaconed/probe trials were different and whether the difference in behaviours led to the different encoding schemes.

Because trial types were interleaved and repeated with a period less than the length of typical trial sequences during which grid cell activity remained either task-anchored or taskindependent, differences between trial types are unlikely to explain use of the different coding schemes. Hopefully, this is clarified by the comments above.

To further describe the relationship between behavioural outcomes, trial types and grid anchoring, we now also show running speed as a function of location for each combination of trial types and trial outcomes (Figure 6, Figure Supplement 1). This illustrates and replicates our previous findings (Tennant et al. 2018) that running speed profiles are similar for a given trial outcome regardless of trial type (Figure 6, Figure Supplement 1A), and further further shows that the behavioural profile for a given trial outcome and trial-type does not differ when grid cells are in task-anchored and task-independent modes (Figure 6, Figure Supplement 1B). This further argues against the possibility that difference in behaviours leads to the different encoding schemes.

(2) Regarding the behaviour and activity encoding on a trial-by-trial basis, did the behavioural change occur first, or did the encoding switch occur first, or did they happen within the same trial? This analysis will potentially determine whether the encoding is causal for the behaviour, or the other way around.

This is a good question but our experimental design lacks sufficient statistical power to address the timing of mode switches within a trial. This is because mode switching is relatively infrequent (so the n for switching is low) and only a subset of trials are uncued (making the relevant n even lower), while at a trial level the behavioural outcome is variable (increasing the required n for adequate power).

(3) The author determined that the grid cell coding schemes were limited to distance encoding and position encoding. However, there could be other schemes, such as switching between different position encodings (with clear spatial fields but at different locations), as indicated by Low et. al., 2021, and switching between different distant encodings (with different distance periods). If these other schemes indeed existed in the data, they might contribute to the variation of the behaviours.

Switching between position encoding schemes appears to be rare within our dataset and unlikely to contribute to variation in behaviour. In most sessions we did not observe switching between grid phases / position encodings (e.g. Figures 2A-B, 3B-E, 4A, 5C-D, F). In one session we found switching between different phases when grid cells were taskanchored. Because the grid period was unchanged, the spatial periodograms remained similar. We report this example in the revised manuscript (Figure 5E).

(4) The percentage of neurons categorised in each coding scheme was similar between nongrid and grid cells. This implies that non-grid cells might switch coding schemes in sync with grid cells, which would mean the whole MEC network was switching between distance and position coding. This raises the question of whether the grid cell coding scheme was important per se, or just the MEC network coding scheme.

We very much appreciate this suggestion. We note first that while the proportion of taskanchored grid and non-grid cells is similar, task-independent periodic firing of non-grid cells is much rarer than for grid cells (Figure 2E), suggesting a dissociation between the populations. To further address the question we have included additional analyses of nongrid cells (Figure 3, Figure Supplement 5). This shows that while some non-grid cells have anchoring that switches coherently with simultaneously recorded grid cells, others do not. Figures 4 and 5 now show examples of non-grid cell activity recorded simultaneously with grid cells.

Together, our data suggest that the MEC implements multiple coding schemes: one that is associated with the grid network and includes some non-grid cells; and one (or more) that can be independent from the grid network. This dissociation adds to the insights into MEC function that are provided by our study and is now highlighted in the abstract and discussion.

(5) In Figure 2 there are several cell examples that are categorised as distance or position coding but have a high fraction of the other coding scheme on a per-trial basis. Given this variation, the full session data in F should be interpreted carefully, since this included all cells and not just "stable" coding cells. It will be cleaner to show the activity comparison only between the stable cells.

We have now included examples in Figure 2A-C where the grid mode is stable throughout a session. As the view of activity at a session level is important, we have not updated Figure 2F, but have clarified the terminology to now clearly refer to classification at either season or trial levels. In addition, we have repeated the analyses shown in Figure 2F but after grouping cells according to whether their firing has a single mode on >85% of the trials (Figure 3 Figure Supplement 4). This analysis supports similar conclusions to those of Figure 2F.

(6) The manuscript is not well written. Throughout the manuscript, there are many unexplained concepts (especially in the introduction) and methods, mis-referenced figures, and unclear labels.

We very much appreciate the feedback and have substantially rewritten the manuscript. We have paid particular attention to explaining key concepts in the introduction and have carefully checked the figures. We welcome further feedback on whether this is now clearer.

Reviewer #2 (Public Review):

Clark and Nolan's study aims to test whether the stability of grid cell firing fields is associated with better spatial behaviour performance on a virtual task… This study is very timely as there is a pressing need to identify/delimitate the contribution of grid cells to spatial behaviours. More studies in which grid cell activity can be associated with navigational abilities are needed.

Thank you for the supportive comments and highlighting the importance of the question.

The link proposed by Clark and Nolan between "virtual position" coding by grid cells and navigational performance is a significant step toward better understanding how grid cell activity might support behaviour. It should be noted that the study by Clark and Nolan is correlative. Therefore, the effect of selective manipulations of grid cell activity on the virtual task will be needed to evaluate whether the activity of grid cells is causally linked to the behavioural performance on this task. In a previous study by the same research group, it was shown that inactivating the synaptic output of stellate cells of the medial entorhinal cortex affected mice's performance of the same virtual task (Tennant et al., 2018). Although this manipulation likely affects non-grid cells, it is still one of the most selective manipulations of grid cells that are currently available.

Again, thank you for the supportive comments. We recognise the previous version of the manuscript did not sufficiently clarify the motivation for our approach, or the benefits of capitalising on behavioural variable variability as a complementary strategy to perturbation approaches. We now make this clearer in the revised introduction (p 2, paragraphs 2 and 3).

When interpreting the "position" and "distance" firing mode of grid cells, it is important to appreciate that the "position" code likely involves estimating distance. The visual cues on the virtual track appear to provide mainly optic flow to the animal. Thus, the animal has to estimate its position on the virtual track by estimating the distance run from the beginning of the track (or any other point in the virtual world).

We appreciate the ambiguity here was confusing. We have re-named the groups to ‘taskanchored’, corresponding to when grid cells encode position on the track (as well as distance as the reviewer correctly points out), and ‘task-independent’, corresponding to the group we previously referred to as distance encoding.

It is also interesting to consider how grid cells could remain anchored to virtual cues. Recent work shows that grid cell activity spans the surface of a torus (Gardner et al., 2022). A run on the track can be mapped to a trajectory on the torus. Assuming that grid cell activity is updated primarily from self-motion cues on the track and that the grid cell period is unlikely to be an integer of the virtual track length, having stable firing fields on the virtual track likely requires a resetting mechanism taking place on each trial. The resetting means that a specific virtual track position is mapped to a constant position on the torus. Thus, the "virtual position" mode of grid cells may involve 1) a trial-by-trial resetting process anchoring the grid pattern to the virtual cues and 2) a path integration mechanism. Just like the "virtual position" mode of grid cell activity, successful behavioural performance on non-beaconed trials requires the animal to anchor its spatial behaviour to VR cues.

Reviewer #3 (Public Review):

This study addresses the major question of 'whether and when grid cells contribute to behaviour'. There is no doubt that this is a very important question. My major concern is that I'm not convinced that this study gives a significant contribution to this question, although this study is well-performed and potentially interesting. This is mainly due to the fact that the relation between grid cell properties and behaviour is exclusively correlative and entirely based on single cell activity, although the introduction mentions quite often the grid cell network properties and dynamics. In general, this study gives the impression that grid cells exclusively support the cognitive processes involved in this task. This problem is in part related to the text.

Thank you for the comments. We recognise now that the previous text was insufficiently clear. We have modified the introduction to clarify the value of an approach that takes advantage of behavioural variability. Importantly, this approach is complementary to perturbation strategies we and others have used previously. In particular it addresses critical limitations of perturbation strategies which can be confounded by off-target effects and possible adaptation, both of which are extremely difficult to fully rule out. We hope that with this additional clarification it is now clear that as for any important question multiple and complementary testing strategies are required to make progres, and second, that our study makes a new and important contribution by introducing a novel experimental approach and by following this up with careful analyses that clearly distinguish competing hypotheses.

However, it would be interesting to look at the population level (even beyond grid cells) to test whether at the network level, the link between behavioural performance and neural activity is more straightforward compared to the single-cell level. This approach could reconcile the present results with those obtained in their previous study following MEC inactivation.

We’re unclear here about what the reviewer means by ‘more straightforward’ as clear relationships between activity of single grid cells and populations of grid cells are well established (Gardner et al., 2021; Waaga et al., 2021; Yoon et al., 2013).

To give a clearer indication of the corresponding population level representations, as mentioned in response to Reviewer #1, we now include additional data showing many simultaneously recorded neurons, and analyses of non-grid as well as grid cells (Figures 4, 5, Figure 5 Figure Supplement 2).

To reconcile results with our previous study of MEC inactivation we have paid additional attention to the roles of non-grid cells (following suggestions by Reviewer #1). We show that while some non-grid cells show transitions between task-anchored and task-independent firing that are coherent with the grid population, many others have more stable firing that is independent of grid representations. This is consistent with the idea that the MEC supports localised behaviour in the cued and uncued versions of the task (Tennant et al., 2018), and suggests that while grid cells preferentially contribute when cues are absent, non-grid cells could also support the cued version. We make this additional implication clear in the revised abstract and discussion.

The authors used a statistical method based on the computation of the frequency spectrum of the spatial periodicity of the neural firing to classify grid cells as 'position-coding' (with fields anchored to the virtual track) and 'distance-coding' (with fields repeating at regular intervals across trials). This is an interesting approach that has nonetheless the default to be based exclusively on autocorrelograms. It would be interesting to compare with a different method based on the similarities between raw maps.

While our main analyses use a periodogram-based method to identify when grid cells are / are not anchored to the task environment, we validate these analyses by examination of the rate maps in each condition (Figures 2-4). For example, when grid cells are task-anchored, according to the periodogram analysis, the rate maps clearly show spatially aligned peaks, whereas when grid cells are not anchored the peaks in their rate maps are not aligned (Figure 2A vs 2B; Figure 3B-E; Figure 4C). We provide further validation by showing that spatial information (in the track reference frame) is substantially higher when grid cell activity is task-anchored vs task-independent (Figures 2F, 3G, 4F and Figure 3 Figure Supplement 4).

To further address this point we have carried out additional complementary analyses in which we identify task anchored vs task independent modes using a template matching method applied to the raw rate maps (Figure 6, Figure Supplement 2). These analyses support similar conclusions to our periodogram-based analyses.

Beyond this minor point, cell categorization is performed using all trial types.

Each trial type (i.e. beacon or non-beacon) is supposed to force mice to use different strategies and should induce different spatial representations within the entorhinal-hippocampal circuit (and not only in the grid cell system). In that context, since all trials are mixed, it is difficult to extrapolate general information.

We recognise that the description of the task design was insufficiently clear but are unsure why ‘it is difficult to extrapolate general information’. Before addressing this point, we should first be clear that mice are not ‘forced’ to adopt any particular strategy. Rather, on uncued trials a path integration strategy is the most efficient way to solve the task. However, mice could instead use a less efficient strategy, for example by stopping at short intervals they still obtain rewards. Detailed behavioural analyses indicate that such random stopping strategies are used by naive mice, while with training mice learn to use spatial stopping strategies (Tennant et al. 2018).

In terms of ‘extracting general information’ from the task, the following findings lead to general predictions: 1) Grid cells can exist in either task-anchored or task-independent periodic firing modes; 2) These modes can be stable across a session, but often modeswitching occurs within a session; 3) While some non-grid cells show task-independent periodic firing, this is much less common than for grid cells, which suggests a model in which many non-grid MEC neurons operate independently from the grid network; 4) When a marker cue is available mice locate a reward equally well when grid cells are in taskanchored versus task-independent modes, which argues against theories in which grid cells are a key part of a general system for localisation; 5) When markers cues are absent taskanchored grid firing is associated with successful reward localisation, which corroborates a key prediction of theories in which grid cells contribute to path integration.

In revising the manuscript we have attempted to improve the writing to make these advances clearer, and have clarified methodological details that made interpretation more challenging than it should have been. For example, as noted in our response to Reviewer #1, we have included additional details to clarify the organisation of trials and relationships between trials, behavioural outcomes and neural codes observed.

On page 5 the authors state that 'Since only position representations should reliably predict the reward location, ..., we reasoned that the presence of positional coding could be used to assess whether grid firing contributes to the ongoing behaviour'. I do not agree with this statement. First of all, position coding should be more informative only in a cue-guided trial. Second, distance coding could be as informative as position coding since at the network level may provide information relevant to the task (such as distance from the reward).

Again, this point perhaps reflects a lack of clarity on our part in writing the manuscript. When grid cells are anchored to the track reference frame (now called ‘tasked anchored’, previously ‘position encoding’), then the location of the rate peaks in grid firing is reliable from trial to trial. This is the case whether or not the trial is cued. When grid cells are independent of the track reference frame (now called ‘task independent’, previously ‘distance encoding’), then the location of the firing rate peaks vary from trial to trial. In the latter case, position can not be read out directly from trial to trial.

In principle, in the task-independent mode track position could be calculated by storing the grid network configuration at the start of the track, which would differ on each trial, and then implementing a mechanism to readout relative distance as mice move along the track. However, if mice do use this computation we would expect them to do so equally well on cued and uncued trials. By contrast, our results clearly show a dissociation between trial types in the relationship between grid firing and behavioural outcome. We highlight and discuss this possibility in the revised manuscript (p 10, ‘Alternatively, mice could in principle estimate track location with a system that utilises information about distance travelled obtained from task-independent grid representations’).

Third, position-coding is interpreted as more relevant because it predominates in correct trials. However, this does not imply that this coding scheme is indeed used to perform correct trials.

We have revised the manuscript to clarify our goal of distinguishing major hypotheses for the roles of grid cells in behaviour (Introduction, ‘On the one hand, theoretical arguments that grid cell populations can generate high capacity codes imply that they could in principle contribute to all spatial behaviours (Fiete et al., 2008; Mathis et al., 2012; Sreenivasan and Fiete, 2011). On the other hand, if the behavioural importance of grid cells follows from their hypothesised ability to generate position representations by integrating self-motion signals (McNaughton et al., 2006), then their behavioural roles may be restricted to tasks that involve path integration strategies.’

By showing that performance on cued trials is similar regardless of whether grid cells are task-anchored or not, we provide strong evidence against the idea that grid firing is in general necessary for location-based behaviours. By showing that task anchoring is associated with successful localisation when cues are absent we corroborate a key prediction of hypothesised roles for grid cells in path integration-dependent behaviour. Therefore, we substantially reduce the space of behaviours to which grid cells might contribute. Importantly, this space is much larger for the MEC, which is required for cued and uncued versions of the task. We have revised the introduction and discussion to make these points clearer.

While we believe our results add a key piece of evidence to the puzzle of when and where grid cells contribute to behaviour, we agree that further work will be required to develop and test more refined hypotheses. Alternative models also remain plausible, for example perhaps the behaviourally relevant computations are implemented elsewhere in the brain with grid anchoring to the track as an indirect consequence. Nevertheless, explanations of this kind are more difficult to reconcile with evidence that inactivation of stellate cells in the MEC impairs learning of the task, and other manipulations that modify grid firing impair performance on similar tasks. We now discuss these possibilities (discussion p 10, ‘mice could in principle estimate track location with a system that utilises information about distance travelled obtained from task-independent grid representations’).

It could be more informative to push forward the correlative analysis by looking at whether behavioural performance can be predicted by the coding scheme on a trial-by-trial basis.

The previous version of the manuscript showed these analyses (now in Figure 6). Thus, task anchored grid firing predicts more successful performance on uncued trials at the session level (Figure 6A-B) and at the trial level (Figure 6C-D).

Reviewer #1 (Recommendations For The Authors):

(1) The author particularly mentioned that the 1D tracks are different from the "cue-rich environments that are typically used to study grid cells". It is not clear what conclusions would hold for a cue-rich environment or a track, which may require relatively less path integration compared to the cue-sparse environment. This point should be discussed.

This is an important point that we did not pay sufficient attention to in the previous version of the manuscript. Our finding of successful localisation in the cued environment when grid cells are not task anchored implies that grid anchoring is not required to solve cued tasks. The implication here is that cue rich environments may then not be the most suitable for investigation of grid roles in behaviour as non-grid mechanisms may suffice, although this does not rule out the possibility that anchored grid codes may play important roles in learning about cue rich environments. We now address this point in the discussion (p 10, ‘An implication of this result is that cue rich tracks often used to investigate grid activity patterns may not engage behaviours that require anchored grid firing.’).

(2) It would be good to see the statistics for the number of different cells (stable position or distance encoding, and unstable cells) identified per mouse/session and the number of grid cells per session.

These are now added to Supplemental Data 2 and will also be accessible through code and datasets that we will make available alongside the version of record.

(3) Figure 2F: any explanation about why AG cells had high spatial information?

Previously the calculation used bits per spike and as aperiodic cells have low firing rates the spatial information was high. We have replaced this with bits per second, which provides a more intuitive measure and no longer implies high spatial information. We have amended this in the methods (p 15, ‘Spatial information was calculated in bits per second…’).

(4) The following methods sections should provide additional details:

(4.1) Details of the training protocol are largely left to reference papers. The reference papers give a general outline of the training protocol, but the details are not completely comparable given the single experiment performed on these mice. More details should be given on training stages and experience at the time of the experiment.

The task is more clearly described in the introduction (p 3), and additional details of the training protocol are now provided in the methods (p 12-13).

(4.2) The methods reference mean speed across sessions, but it is not clear where this was used.

This was very poor wording. We have now changed this to ‘For each session the mean speed was calculated for each trial outcome’.

(4.3) The calculation of the spatial autocorrelogram on a per-trial basis should be more explicitly stated. Is it the average of each 10 cm increment with the centre trial?

We have added additional information to the methods (p 16-18).

(4.4) 1D field detection is not sufficiently explained in Figure 1/S2. This information should also appear in the methods section.

This is now clarified on page 16 in section ‘Analysis of neural activity and behaviour during the location memory task’.

(5) The data in Figure 4A and B only shows speed vs. location for one example mouse. The combined per mouse or per session data should also be shown.

This is now shown in Figure 5A and Figure 5, Figure Supplemental 2

(6) Figure 5 is somewhat confusing. Why are A/B by session and C/D by trial? The methods imply that A/B are originally averaged by cell, but that duplicate cells in the same session are excluded because behaviour versus session type is identical. This method should be valid if all grid cells within a session are all "stable". This is likely given the synchrony of code-switching between grid cells, but not all co-active grid cells behaved identically.

It is understandable that C/D are performed by trial, but it should be made clear that it is not a comparable analysis to A/B. It is unclear what N refers to in C. The figure says by trial, but the legend says the error bar is by cell. If data is calculated by trial and then averaged by cell, this should be more clearly stated.

In Figure 6A/B (previously Figure 5A/B) we focus our analysis on sessions in which the mode of grid firing, either task-anchored or task-independent, was relatively stable on a trialto-trial basis (see Figure 3F for definitions). This enables us to then compare behaviour averaged across each session, with sessions categorised as task-anchored and task independent. This analysis has the advantage that it focuses on large blocks of time (whole sessions) in which the mode of grid firing is unambiguous, but the disadvantage is that it excludes many sessions in which grid firing switches between task-anchored and taskindependent modes.

Figure 6C/D (previously Figure 5C/D) addresses this limitation by carrying out similar analyses with behaviour sorted into task-anchored versus task-independent groups at the level of trials. A potential limitation for this analysis is that grid firing is somewhat variable on a trial-by-trial basis and so some trials may be mis-classified. We don’t expect this to lead to systematic bias, but it may make the data more noisy. Nevertheless, these analyses are important to include as they allow assessment of whether conclusions from 6A/B hold when all sessions are considered.

We have added additional clarification of the rationale for these analyses to the main text (p7-8, ‘’We addressed this by using additional trial-level comparisons’). We have also added clarification in the methods section for categorisation of task-anchored versus taskindependent trials when multiple grid cells were recorded simultaneously (p 17, ‘When assigning a common classification across a group of cells recorded simultaneously...’) and an explanation for the N in the figure legend. We also clarify that the analyses use a nested random effects design to account for dependencies at the levels of sessions and mice (methods, p 20, ‘Random effects had a nested structure to account for animals and sessions…’) .

(7) Panels E and F of Figure 5 are not explained in the main text.

This is now corrected (see p8, ‘Additional analyses…’).

(8) Figure 5: Since stable grid cells and all grid cells are shown, it will be better to show unstable cells, which can be compared with grid cells.

Given that the rationale for differences between Figure 6A/B and C/D (previously Figure 5AD) were not previously clear, the reason for focussing on stable grid cells here was likely also not clear (see point 6 above). We don’t show unstable grid cells in Figure 6A-B as the behaviour averaged at the level of a session would be a mix of trials when they are taskanchored and when they are task-independent. Therefore, the analysis would not test predictions about the relationship between task-anchored vs task-independent modes and behaviour. We hope this is now clear in the manuscript given the revisions introduced to address point 6 above.

(9) The methods describing the statistics for these experiments are also confusing. The methods section should be written more clearly, and it should be made clear in the text or figure legend whether this data is the "original" data or is processed in relation to the model, such as excluding duplicate grid cells within a session. The figure legend should also state that a GLMM was used to calculate the statistics.

We have revised the methods section with the goal of improving clarity, adding detail and removing ambiguity. This includes updates of the methods for the GLMM analysis, which are referred to within the Figure 6 legend. A clear definition of a stable session is now also added to the Figure 6 legend.

Reviewer #2 (Recommendations For The Authors):

When grid fields are anchored to the virtual world (position mode), there is probably small trialto-trial variability in the firing location of the firing fields. Is this trial-to-trial variability related to the variability in the stop location? This would provide a more direct link between path integration in grid cell networks and behaviour that depends on path integration.

When attempting to address this we find that the firing of individual grid cells is too variable to allow sufficiently precise decoding of their fields at a single trial level. This is expected given the Poisson statistics of spike generation and previous evaluations of grid coding (e.g. (Stemmler et al., 2015)).

The conclusion of the abstract is: "Our results suggest that positional anchoring of grid firing enhances the performance of tasks that require path integration." This statement is slightly confusing. The task requires 1) anchoring the behaviour to the visual cues presented at the start of the trial and 2) path integration from thereon to identify the rewarded location. The performance is higher when grid cells anchor to the visual cues presented at the start of the trial. What the results show is that the anchoring of grid firing fields to visual landmarks enhances the performance of tasks that require path integration from visual landmarks (i.e. grid cells being anchored to the reference frame that is behaviorally relevant).

To try to more clearly explain the logic and conclusion we have rewritten the abstract, including the final sentence.

Similar comment for the title of Figure 5: "Positional grid coding is not required for cued spatial localisation but promotes path integration-dependent localisation." Positional coding means that grid cells are anchored to the behaviorally relevant reference frame.

To address the lack of clarity we have modified the little of Figure 6 (previously Figure 5) to read ‘Anchoring of grid firing to the task reference frame promotes localisation by path integration but is not required for cued localisation’.

In Figure 1, there is a wide range of beaconed (40-80%) and non-beaconed (10-60%) trials given. It is not 100% clear whether these refer to the percentage of trials of a given type within the recording sessions. Was the proportion of non-beaconed trials manipulated? If so, was the likelihood of position and distance coding changing according to the percentage of nonbeaconed trials?

The ranges given refer to proportions across different behavioural sessions. Within any given behavioural session the proportion was constant. We now make this clear in the figure legend and in the results and methods sections.

We did not manipulate proportions of trial types during a session. Manipulations betweens sessions were carried out with the goal of maximising the numbers of uncued trials that the mice would carry out (see response to public comments above). While the effect of trial-type at the session level is not relevant to the hypotheses we aim to test here, we have included an additional analysis of the relationship between task anchoring and the proportions of trial types in a session (Figure 3, Figure Supplement 7)(also discussed above). As disentangling the effects of learning and motivation will be complex and likely require new experimental designs we have not drawn strong conclusions or pursued the analysis further..

I was not convinced that the labels "position" and "distance" were appropriate for the two grid cell firing modes. My understanding is that the "position" code also requires the grid cell network to estimate distance. It seems that the main difference between the "position" and "distance" modes is that when in the "position" mode, the activity on the torus is reset to a constant toroidal location when the animal reaches a clearly identifiable location on the virtual track. In the "distance" mode, this resetting does not take place.

As previously mentioned, we agree these terms weren’t the best and have since relabelled these as “task-anchored” and “task-independent”.

There are a few sections in the manuscript that implicitly suggest that a causal link between grid cell activity and behaviour was demonstrated. For instance: "It has been challenging to directly test whether and when grid cells contribute to behaviour.": The assumption here is that the manuscript overcomes this challenge, but the study is correlative.

We have modified the wording to be clear that we are introducing new tests of predictions made by hypotheses about causal relationships between grid coding and behaviour (introduction, p 1-2). We also clarify that our results argue against the hypothesis that grid cells provide a general coded for behaviour, but corroborate predictions of hypotheses in which they are specifically important for path integration (discussion, p 10).

We have modified the title abstract and main text to try to treat claims about causality with care. We now more thoroughly introduce and contrast the approach we report here with previous experiments that use perturbations (introduction, p2). While it is tempting to make stronger claims for causality with these approaches, there are also logical limitations with perturbation-based approaches, for example the challenges of fully excluding off target effects and adaptation. We now explain how these strategies are complementary. Our view is that both strategies will be required to develop strong arguments for whether and when grid cells contribute to behaviour. From this perspective, it is encouraging that our conclusions are in agreement with what are probably the most specific perturbations of grid cells reported to date (Gil et al. 2017), while perturbations that more generally affect MEC function appear to impair cued and path integration-dependent behaviours (Tennant et al. 2018). We now discuss these points more clearly (introduction, p 2).

I am slightly confused by the references to the panels in Figure 4.

"In some sessions, localization of the reward occurred almost exclusively when grid cells were anchored to position and not when they encoded distance (Figure 4C). Figure 4C only shows position coding.

"In other sessions, animals localised the reward when grid firing was anchored to position or distance, but overall performance was improved on positional trials (Figure 4D-E)." The reference should probably point to Figure 4E-F or just to 4E.

"In a few sessions, we observed spatial stopping behaviour comparable to cued trials, even when grid firing almost exclusively encoded distance rather than position (Figure 4F)." From Figure 4F, it seems that the performance on non-beaconed trials is better during "position" coding.

We have now updated Figure 5 (Figure 4 in the original manuscript) and references to the Figure in the text. Now Figure 5 shows the activity of cells recorded in stable and unstable task-anchored and task-independent sessions (see Figure 5C-F).

Minor issues:

Is this correct: (Figure 4A and Figure 4, Figure Supplement 1).

This has been corrected.

Figure 4B: There could be an additional label for position and distance.

Figure 4B from the original manuscript has now been removed.

Figure 4C-F. The panels on the right side should be explained in the Figure Legend.

Legends for Figure 5C-F (previously Figure 4C-F) have now been updated.

Reviewer #3 (Recommendations For The Authors):

Specific questions :

(1) Position coding reflects a coding scheme in which fields are spaced by a fixed distance; previous studies have shown that a virtual track grid map is a slice of the 2D classic grid. In that case, the fields are still anchored to the track but would produce a completely different map. Did the authors check whether it is the case at least for some cells? If not, what could explain such a major difference?

Το avoid confusion we now use the term ‘task-anchored’ rather than ‘position coding’ (see comments above). We should further clarify that our conclusions rest on whether or not the grid fields are anchored to the track. Task anchored firing does not require that grid fields maintain their spacing from 2D environments, only that fields are at the same track position on each trial. Thus, whether the spacing of the fields corresponds to a slice through a 2D grid makes no difference to the hypotheses we test here.

We agree that the relationship between 1D and 2D field organisation could be an interesting future direction, for example anchoring could involve resetting the grid phase while maintaining a stable period, or it could be achieved through local distortions in the grid period. However, since these outcomes would not help distinguish the hypotheses we test here we have not included analyses to address them.

(2) Previous studies have highlighted the role of grid cells in goal coding. Here there is an explicit reward in a particular area. Are there any grid modifications around this area? This question is not addressed in this study.

Again, we note that the hypotheses we test here relate to the firing mode of grid cells - taskanchored or task-independent - and interpretation of our results is independent from the specific pattern of grid fields on the track. This question nevertheless leads to an interesting prediction that if grid fields cluster in the goal area then this clustering should be apparent in the task-anchored but not the task-independent firing mode.

We test this by considering the average distribution of firing fields across all grid cells in each firing mode (Reviewer Figure 1). We find that when grid firing is task-anchored there is a clear peak around the reward zone, which is consistent with previous work by Butler et al. and Boccara et al. Consistent with our other prediction, this peak is reduced when grid cells are in the task-independent mode.

Author response image 1.

Plot shows the grid field distribution during stable grid cell session (> 85 % task-anchored or task-independent) (A) or during task-anchored and task-independent trials (B). Shaded regions in A and B represent standard error of the mean measured across sessions and epochs respectively.

(3) The behavioural procedure during recording is not fully explained. Do trial types alternate within the same session by blocks? How many trials are within a block? Is there any relation between trial alternation and the switch in the coding scheme observed in a large subset of the grid cells?

We agree this wasn’t sufficiently clear in the previous version of the manuscript. Trial types were interleaved in a fixed order within each session. We have updated the results and methods sections to provide details (see responses above).

(4) From the examples in Figure 2 it seems that firing fields tend to shift toward the start position. Is it the case in all cells? Could this reflect some reorganisation at the network level with cells signalling the starting as time progresses?

This is inconsistent between cells. To make this variability clear we have included additional examples of spiking profiles from different grid cells (Figure 2 - 5). Because quantification of the phenomena would not, so far as we can tell, help distinguish our core hypotheses we have not included further analyses here.

(5) Are grid cells with different coding properties recorded in different parts of the MEC? Are there any differences between these cell categories in the 2D map?

The recordings we made are from the dorsal region of the MEC (stated at the start of the results section). We don’t have data to speak to other parts of the MEC.

Minor:

There are very few grid cell examples that repeat in the different figures. I would suggest showing more examples both in the main text and supplementary material.

We have now provided multiple additional examples in Figures 2, 4 and 5. Grid cell examples repeat in the main figures twice, in both cases only when showing additional examples are shown from the same recording session (Figure 2A example #1 with Figure 5C, Figure 3E with Figure 4A). Further similar repeats are found in the supplemental figures (Figure 3D with Figure 5, Figure Supplement 2A, Figure 3C with Figure 5, Figure Supplement 2F).

Fig1 A-B shows the predictions in a 1D track based on distance or position coding. The A inset represents the modification of field distribution from a 2D arena to a 1D track, as performed in this study. The inset B is misleading since it represents the modifications expected from a circular track to a 1D track as in Jacob et al 2019, that is not what the authors studied. It would be better to present either the predictions based on the present study or the prediction based on previous studies. In that case, they should mention the possibility that the 1D map is a slice of the 2D map.

The goal of Figure 1A-B is to illustrate predictions (right) based on conclusions from previous studies (left). Figure 1A shows predicted 1D track firing given anchoring to the environment typically observed in grid cell studies in 2D arenas. Figure 1B shows predicted 1D track firing given the firing shifting firing patterns observed by Jacob et al. in a circular 2D track. To improve clarity, we have modified the legend to make clear that the schematics to the right are predictions given the previous evidence summarised to the left. As we outline above, the critical prediction relates to whether the representations anchor to the track. Whether the 1D representation is a perfect slice isn’t relevant to the hypotheses tested and so isn’t included in the schematic (see comments above).

eLife assessment

This valuable study examines the relationship between positional anchoring of grid cell activity and performance in spatial navigation tasks that requires path integration. The authors demonstrate that grid cells can either fire in relation to the position relative to task-relevant virtual stimuli or independently based on the distance covered. Their findings convincingly reveal that mice exhibited better performance in the path integration task when grid cell activity was anchored to their position on the virtual track rather than the distance traversed, highlighting the contribution of grid firing to spatial navigation behavior. The work will be of interest to experimental and computational neuroscientists interested in spatial navigation.

Reviewer #1 (Public Review):

Summary:<br /> In this study, Clark et. al. used electrophysiology approaches to measure MEC neuron activity while mice performed spatial memory tasks in one-dimensional virtual tracks, where the mice must stop in a specific reward zone for a reward. The authors identified that grid cell activity could either be anchored to the track reference frame ('task-anchored') or can maintain a periodic firing pattern independent of the track reference frame ('task-independent'). They found that in the task that requires path integration, good task performance is specifically associated with task-anchored grid cell activity.

Strength:<br /> This study took advantage of the variation in neural activity and navigation task behaviors to answer an important question: how grid cell activity is associated with performance of spatial tasks. The mice performed individual trials where they must stop in a specific reward zone for a reward. Individual behavioral sessions could include three types of trials: (1) a visual cue at the reward location (beaconed trials), (2) no cue at the reward location (non-beaconed trials), and (3) no cue and no reward regardless of stopping (probe trials). The authors found that, interestingly, grid cell activity pattern could be anchored to task reference frame or maintain a periodic pattern independent of the reference frame. The anchoring of activity patterns could switch within a behavioral session. On the other hand, spatial firing of non-grid cells was either coherent with the grid population or was stably anchored to the task reference frame. Combining grid cell activity feature with task behaviors, they uncovered an association between the task-anchoring of grid cell activity with good performance in spatial navigation tasks that requires path integration (non-beaconed and probe trials). This work suggests the contribution of grid firing to path integration-dependent navigation.

Weakness:<br /> It would be interesting to find out that on the trial-by-trial basis, whether the activity anchoring switched first, or the task behaviors altered first, or whether they happened within the same trial. This will potentially determine whether the encoding is causal for the behavior, or the other way around. However, based the authors explanation, their experimental design lacks sufficient statistical power to address the timing of mode switches within a trial, because task mode switching is relatively infrequent (so the n for switching is low) and only a subset of trials are uncued (making the relevant n even lower), while at a trial level the behavioral outcome is variable (increasing the required n for adequate power).

In addition, the authors reported that the activity anchoring of some non-grid cells coherently switched with grid cells, while others do not. They propose that the MEC implement multiple coding schemes. However, it is unclear whether and how the coding scheme is associated with behavior. It would be interesting to further investigate this question.

Reviewer #2 (Public Review):

Clark and Nolan's study aims to test whether the stability of grid cell firing fields is associated with better spatial behavior performance on a virtual task. Mice were trained to stop at a rewarded location along a virtual linear track. The rewarded location could be marked by distinct visual stimuli or be unmarked. When the rewarded location was unmarked, the animal had to estimate its distance run from the beginning of the trial to know where to stop. When the mouse reached the end of the virtual track, it was teleported back to the start of the virtual track.

The authors found that grid cells could fire in at least two modes. In the "task-anchored" mode, grid firing fields had stable positions relative to the virtual track. In the "task-independent" mode, grid fields were decoupled from the virtual cues and appeared to be located as a function of distance run on the track. Importantly, on trials in which the rewarded location was unmarked, the behavioral performance of mice was better when grid cells fired in the "task-anchored" mode. When a unique visual cue marked the reward location, navigation performance was not correlated with the grid cells' firing mode.

This study is very timely as there is a pressing need to identify/delimit the contribution of grid cells to spatial behaviors. More studies are needed in which grid cell activity is linked to navigational abilities. The link proposed by Clark and Nolan between "task-anchored" coding by grid cells and navigational performance is a significant step toward better understanding how grid cell activity might support behavioral behavior. The results also highlight that some forms of navigation (approaching a location marked by a visual cue) might be less dependent on the anchoring of grid cells.

It should be noted that the study by Clark and Nolan is correlative. Therefore, the effect of selective manipulations of grid cell activity on the virtual task will be needed to evaluate whether the activity of grid cells is causally linked to the behavioral performance on this task. A previous study by the same research group showed that inactivating the synaptic output of stellate cells of the medial entorhinal cortex affected mice's performance of the same virtual task (Tennant et al., 2018). Although this manipulation likely affects non-grid cells, it is still one of the most selective manipulations of grid cells that are currently available.

It is interesting to consider how grid cells remain anchored to virtual cues. Recent work shows that grid cell activity spans the surface of a torus (Gardner et al., 2022). A run on the track can be mapped to a trajectory on the torus. Assuming that grid cell activity is updated primarily from self-motion cues on the track and that the grid cell period is unlikely to be an integer of the virtual track length, having stable firing fields on the virtual track likely requires a resetting mechanism taking place on each trial. During this resetting event, the active location on the torus is likely to jump to a new toroidal location, independently of self-motion cues. Future studies in which large numbers of grid cells are recorded could pinpoint at which moment such resetting event occurs on each trial.

Author Response

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

eLife assessment

This study is valuable as it sheds light on the pivotal role played by alterations in glycan metabolism within chondrocytes in the onset of cartilage degeneration and early onset of osteoarthritis (OA) through the process of hypertrophic differentiation of chondrocytes, giving insights into the identification of nascent markers for early-stage OA. Although the methods, data, and analyses broadly support the claims, the data shown by the authors are incomplete because the mechanism by which cartilage degeneration induced by changes in glycometabolism occurs has not been fully elucidated. The authors' deductions stand to gain further credence through undertaking additional experiments aimed at analyzing the mechanisms underlying the changes in glycometabolism in cartilage, such as the meticulous identification of the target glycan molecules bearing core fucose and analysis of endochondral ossification in cartilage-specific Fut8 KO mice.

We wish to express our strong appreciation to the Reviewer for his or her insightful comments on our paper. We feel the comments have helped us significantly improve the paper. In particular, we wish to acknowledge the Reviewer’s highly valuable comments on the effect of Fut8 on endochondral ossification.

Reviewer #1 (Public Review): :<br /> Summary:

This study is valuable in that it may lead to the discovery of future OA markers, etc., in that changes in glycan metabolism in chondrocytes are involved in the initiation of cartilage degeneration and early OA via hypertrophic differentiation of chondrocytes. However, more robust results would be obtained by analyzing the mechanisms and pathways by which changes in glycosylation lead to cartilage degeneration.

Strengths:

This study is important because it indicates that glycan metabolism may be associated with pre-OA and may lead to the elucidation of the cause and diagnosis of pre-OA.

We thank reviewer #1 for their interest in our work and their overall positive report.

Weaknesses:

More robust results would be obtained by analyzing the mechanism by which cartilage degeneration induced by changes in glycometabolism occurs.

To understand the mechanisms of cartilage degeneration induced by changes in glycometabolism, we attempted additional experiments using rescue experiments with external administration of TGF-β. We had shown that the addition of mannosidase to an organ culture system of normal wild-type mouse cartilage increased TGF-β gene expression from 6 hours (Fig. 3E) and that TGF-β expression was even suppressed in chondrocytes from Fut8 cKO mice (Fig. 4D). In addition to these results, an early OA model in which mannosidase is added to the cartilage was used to test the effect of exogenous TGF-β. As a result, under TGF-β treated conditions, no degenerative changes occurred when high-mannose type N-glycans were trimmed, and proteoglycan leakage during the recovery period was significantly reduced. This was considered to be a very useful finding and it was decided to include the experimental results in Figure 4F, rather than making them supplement data.

Reviewer #2 (Public Review):

Summary:

This paper consists of mostly descriptive data, judged from alpha-mannosidase-treated samples, in which they found an increase in core fucose, a product of Fut 8.

Strengths:

This paper is interesting in the clinical field, but unfortunately, the data is mostly descriptive and does not have a significant impact on the scientific community in general.

We thank reviewer #2 for their interest in our work and their overall positive report. In response to your comment about our attempts to show that glycan changes occur at the precursor stage of cartilage substrate degeneration and that this glycosylation is also what triggers substrate degeneration, we would like to add that reversing cartilage substrate degeneration is a very ambitious challenge. We are currently in the preparatory stages of characterizing the appropriate glycan-substrate relationships to 'rescue' cartilage tissue from degeneration, and we hope to use this approach to provide information on the pre-developmental stages of OA.

Weaknesses:

If core fucose is increased, at least the target glycan molecules of core fucose should be evaluated. They also found an increase in NO, suggesting that inflammatory processes also play an important role in OA in addition to glycan changes.

As the increase in NO was observed in the organ culture system and cartilage is a tissue without vascular invasion, we thought that the involvement of immune cells could be excluded. On the other hand, our research group has reported that chondrocytes themselves have inflammatory circuits (Ota et al., Arthritis Rheum. 2019. DOI:10.1002/art.41182), but as we did not find increased expression of NF-κB, an indicator of inflammatory amplifier activation, we concluded that inflammation was not involved in this study.

It has already been reported that core fucose is decreased by administration of alpha-mannosidase inhibitors. Therefore, it is expected that alpha-mannosidase administration increases core fucose.

The report by Toegel et al. that the synthesis of complex-type N-glycans (Man2a1, Mgat2) is predicted in human OA chondrocytes along with the expression of Fut8 also led to the expectation that administration of α-mannosidase would increase core fucose. However, there was no conclusive evidence that administration of α-mannosidase increased core fucose; in 1987, Vignon et al performed an enzyme assay on experimental OA cartilage (rabbit ACLT model) and showed that mannosidase was very high in operated joints and that its activity increased and decreased with the severity of fibrosis in the cartilage. The results suggest that glycoprotein hexose degradation is an early transient event in the enzymatic process of cartilage destruction. These findings led to the conception of a novel 'pre-OA model' in which mannosidase is added to the joint. The present study is valuable in its demonstration that glycometabolism is a driver of degeneration.

(see manuscript REF. 25, 9)

Toegel et al., Arthritis Res. Ther. 2013. DOI:10.1186/ar4330

Vignon et al., Clin Rheumatol. 1987. DOI:10.1007/BF02201026

Reviewer #3 (Public Review):

Summary:

In the manuscript "Articular cartilage corefucosylation regulates tissue resilience in osteoarthritis", the authors investigate the glycan structural changes in the context of pre-OA conditions. By mainly conducting animal experiments and glycomic analysis, this study clarified the molecular mechanism of N-glycan core fucosylation and Fut8 expression in the extracellular matrix resilience and unrecoverable cartilage degeneration. Lastly, a comprehensive glycan analysis of human OA cartilage verified the hypothesis.

Strengths:

Generally, this manuscript is well structured with rigorous logic and clear language. This study is valuable and important in the early diagnosis of OA patients in the clinic, which is a great challenge nowadays.

We thank reviewer #3 for their interest in our work and their mainly positive report. This is precisely the purpose of our study, as we are primarily interested in the detection of conditions prior to the onset of OA.

Weaknesses:

I recommend minor revisions:

(1) I would suggest the authors prepare an illustrative scheme for the whole study, to explain the complex mechanism and also to summarize the results.

We would like to thank the reviewer for this comment and have created a new Figure 7 for the overall study scheme.

We included the following statement in the opening discussion part:

"The objective of this work was to provide novel and translational insights into pathogenesis of OA associated with changes in glycan structure. A graphical abstract summarizing our findings is shown in Fig. 7." (line199-201, p9)

(2) Including but not limited to Figures 2A-C, Figures 3A and C, Figure 4B, and Figures 5A and D. The texts in the above images are too small to read, I would suggest the authors remake these images.

The font size of the figures has been reviewed and revised throughout.

(3) The paper is generally readable, but the language could be polished a bit. Several writing errors should be realized during the careful check.

Thanks to your suggestion, I have noticed several writing errors. In addition, we have had the manuscript rewritten by an experienced scientific editor, who has improved the grammar and stylistic expression of the paper.

(4) As several species and OA models were conducted in this study, it would be better if the authors could note the reason behind their choice for it.

The authors agree with the reviewer's argument that since several species and OA models were performed in this study, it would be better to note the reason for their choice.

We first attempted to inject mannosidase into rabbits, matching the animal species to a previous paper showing that N-glycans are altered prior to degeneration of the cartilage matrix. Next, we checked whether similar changes occur in mouse cartilage after mannosidase treatment, assuming that we would verify this in genetically engineered mice. We then used the integrated glycome in human cartilage to see if the corefucosylation phenomenon detected was conserved across species.

For the modeling of OA in Fut8 cKO mice, the instability-induced OA model and the age-associated OA model were adapted. The former emphasizes mechanical stress factors in OA, the latter aging factors. OA is a multifactorial disease. Therefore, we thought it was appropriate to validate both aspects of OA.

We included the following statements in each Methods part:

"We injected mannosidase into rabbit knee joints in accordance with a previous paper showing that N-type glycans are altered prior to cartilage matrix degeneration." (line289-290, p12)

"Organ culture experiments in mice were established to study the effects of mannosidase on articular cartilage without immunoreaction and in anticipation of later candidate gene research using transgenic mice." (line326-328, p14)

"To determine whether the glycosylation detected is conserved across species, we analyzed the total glycome in human cartilage." (line407-408, p17)

We included the following statements in the Discussion part:

"For the modeling of OA in Fut8 cKO mice, the instability-induced OA model and the age-associated OA model were adapted. The former emphasizes mechanical stress factors in OA, the latter aging factors. OA is a multifactorial disease. Therefore, we thought it was appropriate to validate both aspects of OA." (line254-257, p11)

Reviewer #1 (Recommendations For The Authors):

(1) The cited literature states that core fucosylation by FUT8 has a chondroprotective effect via the TGF-β pathway and that the loss of these chondroprotective effects in Fut8 led to cartilage degeneration, but these need to be proven by experiment.

We agree that corefucosylation and the TGF-β signaling pathway are important lines of investigation. We have now acknowledged this and added in the revised manuscript that additional experiments have shown that TGF-β restores the protective effects of Fut8 cKO cartilage by external administration.

We included the following statements in the Results part:

"To evaluate whether TGF-β1 decreases cartilage degeneration after mannosidase stimulation, TGF-β1 was exogenously added to Col2-Fut8−/− cartilage in the presence of α-mannosidase stimulation for 24 h. The samples treated with TGF-β1 leaked significantly less PG following mannosidase stimulation compared to samples not treated with TGF-β1 (Fig. 4F)." (line143-147, p6-7)

We included the following statements in the Discussion part:

"Here, the exogenous addition of TGF-β1 rescued them from cartilage degeneration." (line274-275, p12)

(2) There are skeletal differences in cartilage-specific Fut8 KO mice compared to WT, and the effect of Fut8 on endochondral ossification should also be analyzed.

We agree that Fut8 is associated with various endochondral ossification processes (for example by the TGF-β signaling pathway). Moreover, we would like to thank the reviewer for the proposed experiment.

The growth curve was normal at birth, with differences beginning around weaning (~3 w for mice). Therefore, we evaluated the epiphyseal line of 4-week-old mice stained with toluidine, type 10 collagen, and proliferating cell nuclear antigen. This is similar to the epiphyseal growth plate phenotype of Smad3ex8/ex8 mice by Yang et al. and is consistent with the finding that Smad3 deficiency does not affect chondrogenesis during developmental stages, but the hypertrophic zone is increased in 3-4 week-old Smad3 KO mice. Chondrocytes in Fut8 cKO mice were suppressed of Tgf-β expression (Fig. 4D), suggesting that inhibition of TGF-β signaling, which is suppressive for late hypertrophic chondrocyte differentiation, led to the increased height of the hypertrophic zone.

The results suggested that the growth plate of Fut8 cKO mice had an enlarged hypertrophic layer and decreased primary trabecular bone. Because these results have important implications for the content of the paper, we have included the staining results in Figure 5 and added a graph quantitatively assessing the extent of the hypertrophic zone as supplementary Figure S6.

We included the following statement in the Results part:

"To assess the role of FUT8 in endochondral ossification, we performed an epiphyseal plate analysis of 4-week-old Col2-Fut8−/− mice. This uncovered a significant enlargement of the zone of hypertrophic chondrocytes in the growth plates of the long bones of Col2-Fut8−/− mice compared to controls (Fig. 5C, S6 Figure)." (line154-158, p7)

We included the following statement in the Discussion part:

"The high-mannose/corefucosylation relationship estimated function to maintain formed cartilage. In endochondral ossification, the Fut8 cKO growth plate had an enlarged hypertrophic zone and reduced primary spongiosa because it is involved in the next process of cartilage replacement into bone rather than the process of cartilage formation." (line214-217, p9)

Literature mentioned above (not included in manuscript):

Yang X, et al. TGF-beta/Smad3 signals repress chondrocyte hypertrophic differentiation and are required for maintaining articular cartilage. J Cell Biol. 2001;153(1):35–46.

(3) The DMM model analysis is performed with n=5 for each group. Please consider if the sample size is sufficient.

In the literature, the sample sizes for DMM models have varied in previous studies (Doyran et al., n=5; Liao et al., n=6-7; Ouhaddi et al., n=8). Therefore, we performed a preliminary test of the DMM in WT and Flox mice with n=3 each and a power analysis with the outcome set to the OARSI score at 8 weeks. This resulted in n=4. The sample size for this study was increased to n=5 to account for attrition. The summed OARSI score of the WT in this study was comparable to that of Ouhaddi et al. and the model was judged to be working accurately. The summed OARSI score of the WT in this study was comparable to that of Ouhaddi et al. and the model was judged to be working accurately. The summed OARSI score of the WT in this study was comparable to that of Ouhaddi et al. and the model was judged to be working accurately.

Literature mentioned above (not included in manuscript):

(1) Doyran B, Tong W, Li Q, Jia H, Zhang X, Chen C, et al. Nanoindentation modulus of murine cartilage: a sensitive indicator of the initiation and progression of post-traumatic osteoarthritis. Osteoarthr Cartil. 2017;25(1):108–17.

(2) Liao L, Zhang S, Gu J, Takarada T, Yoneda Y, Huang J, et al. Deletion of Runx2 in Articular Chondrocytes Decelerates the Progression of DMM-Induced Osteoarthritis in Adult Mice. Sci Rep. 2017 24;7(1):2371.

(3) Ouhaddi Y, Nebbaki SS, Habouri L, Afif H, Lussier B, Kapoor M, et al. Exacerbation of Aging-Associated and Instability-Induced Murine Osteoarthritis With Deletion of D Prostanoid Receptor 1, a Prostaglandin D2 Receptor. Arthritis Rheum. 2017;69(9):1784–95.

Reviewer #2 (Recommendations For The Authors):

This paper is suitable for publication in clinical Journals related to osteoarthritis and cartilage.

Identification of core fucosylated glycans from chondrocytes is essential for this type of paper.

We mentioned that we had identified similar corefucosylated glycans in isolated mouse chondrocytes from the cartilage (line117-118, p5), but we have now also added the following to the subtitle of the Results section to avoid any potential confusion: "Corefucosylated N-glycan was formed in resilient cartilage and its isolated chondrocyte" (line109, p5)

Thank you again for your comments on our paper. We trust that the revised manuscript is suitable for publication.

eLife assessment

This valuable study sheds light on the pivotal role of alterations in chondrocyte glycan metabolism in two contexts: The onset of cartilage degeneration and early onset of osteoarthritis (OA). The action is through hypertrophic differentiation of chondrocytes, a finding that provides insights into the identification of nascent markers for early-stage OA. The evidence supporting the claims is solid, with the authors clearly demonstrating the role of articular cartilage corefucosylation in the development of OA. The authors' inferences would be further enhanced through future experiments aimed at analyzing the mechanisms underlying the changes in glycometabolism in cartilage.

Reviewer #1 (Public Review):

Summary:<br /> This study is valuable in that it may lead to the discovery of future OA markers, etc., in that changes in glycan metabolism in chondrocytes are involved in the initiation of cartilage degeneration and early OA via hypertrophic differentiation of chondrocytes. However, more robust results would be obtained by analyzing the mechanisms and pathways by which changes in glycosylation lead to cartilage degeneration.

Strengths:<br /> This study is important because it indicates that glycan metabolism may be associated with pre-OA and may lead to the elucidation of the cause and diagnosis of pre-OA.

Weaknesses:<br /> More robust results would be obtained by analyzing the mechanism by which cartilage degeneration induced by changes in glycometabolism occurs.

Reviewer #2 (Public Review):

Summary:<br /> This paper consists of mostly descriptive data, judged from alpha-mannosidase-treated samples, in which they found an increase in core fucose, a product of Fut 8.

Strengths:<br /> This paper is interesting in the clinical field, but unfortunately the data is mostly descriptive and does not have a significant impact on the scientific community in general.

Weaknesses:<br /> If core fucose is increased, at least the target glycan molecules of core fucose should be evaluated. They also found an increase in NO, suggesting that inflammatory processes also play an important role in OA in addition to glycan changes.<br /> It has already been reported that core fucose is decreased by administration of alpha-mannosidase inhibitors. Therefore, it is expected that alphaa-mannosidase administration increases core fucose.

Author Response

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

eLife assessment

This manuscript describes fundamental single-molecule correlative force and fluorescence microscopy experiments to visualize the 1D diffusion dynamics and long-range nucleosome sliding activity of the yeast chromatin remodelers, RSC and ISW2. Compelling evidence shows that both remodelers exhibit 1D diffusion on bare DNA but utilize different mechanisms, with RSC primarily hopping and ISW2 mainly sliding on DNA. These results will be of interest to researchers working on chromatin remodeling.

Reviewer #1 (Public Review):

Single-molecule visualization of chromatin remodelers on long chromatin templates-a long sought-after goal-is still in its infancy. This work describes the behaviors of two remodelers RSC and ISW2, from SWI/SNF and ISWI families respectively, with well-conducted experiments and rigorous quantitative analysis, thus representing a significant advance in the field of chromatin biology and biophysics. Overall, the conclusions are supported by the data and the manuscript is clearly written. However, there are a few occasions where the strength of the conclusion suffers from low statistics. Some of the statements are too strong given the evidence presented.

We thank the reviewer for the thorough and considerate review of our manuscript. We have increased the statistics when possible and have toned down the conclusions wherever further experimentation to improve statistics could not be done expeditiously.

Specific Comments:

(1) It is confusing what is the difference between the "non-diffusive" behavior of the remodeler upon nucleosome encounter and the nucleosome-translocating behavior in the presence of ATP. For example, in Figure 3F, readers can see a bit of nucleosome translocation in the first segment. Is the lower half-life of "non-diffusive" ISW2 with ATP on a nucleosome array because it is spending more time translocating nucleosomes? The solid and dashed green lines in Figure 3F and 3G are not explained. It is also not explained why Figure 3H and 3I are fit by double exponentials.

We thank the reviewer for calling upon us to clarify these points. In both the case of translocation and stable non-translocating colocalization, the chromatin remodeler is marked as “non-diffusive” because the molecule is not moving quickly enough to be detected by our rolling-window (20 frames considered) diffusion coefficient analysis. We have updated the text to point out the translocation that is occurring in the panels indicated and noted that this type of motion is not detected by our automated analysis. Thus, translocation events were manually segmented for analysis from kymographs; a note of this was added to the results section (Results section # 1; Paragraph # 2).

To address the question of whether the half-life of “non-diffusive” ISW2 with ATP on the nucleosome array is because of increased time spent in translocation, we have computed the percentage of “non-diffusive” time spent translocating in the presence of ATP for both remodelers; for ISW2, 14% of “non-diffusive” times are translocation whereas for RSC, 28% of “non-diffusive” times are translocation. Given that these percentages are not negligible, the reviewer helped identify an important parameter that better describes the effects of ATP hydrolysis on nucleosome binding for ISW2. In addition, we computed and compared the half-life of translocation times for both remodelers to the “non-diffusive” times and found that RSC translocates with a half-life of 20 s (similar to the half-life of “non-diffusion”) whereas ISW2 translocates with a half-life of 17 s (longer than the half-life of “non-diffusion”). We believe that this new information improves understanding of the role of ATP hydrolysis in turning over ISW2-nucleosome binding interactions, which result in the shorter “non-diffusive” lifetime as well as the shorter and more rarely observed ISW2 translocation events. We have updated the text to include these observations and our interpretation (Results section # 3; Paragraph # 3). As was already included in the text (Results section # 3; Final Paragraph), we speculate that this behavior may be due to a hydrolysis-dependent turnover of the ISW2-nucleosome bound state and refer the reader to Tim Richmond’s 2004 EMBO paper titled “Reaction cycle of the yeast Isw2 chromatin remodeling complex” in which bulk experiments show that ATP hydrolysis affects ISW2-nucleosome bound lifetimes.

We thank the reviewer for also pointing out where details were missing from the figure legend and results section regarding Figure 3. We have added a description of the dashed and solid lines to the figure legend (Figure 3; Legend). We have also described why Figures 3H and I are fit to double exponentials to the results section (Results section # 3; Paragraph # 2).

(2) What is the fraction of 1D vs. 3D nucleosome encountered by the remodelers? This is an important parameter to compare between RSC and ISW2.

We thank the reviewer for raising this point. We agree that this is an important parameter to compare between RSC and ISW2; knowledge of this parameter would enable quantitative predictions to be made from our data regarding target localization efficiency increases owed to 1D scanning for each remodeler. We regretfully could not quantify this due to technical limitations of our measurements. A note about this limitation along with an explanation for why we were unable to quantify this parameter have been added to the main text (Results section # 3; end of Paragraph # 1).

(3) A major conclusion stated repeatedly in the manuscript is that nucleosome translocation by a remodeler is terminated by a downstream nucleosome. But this is based on a total of 4 events. The problem of dye photobleaching was mentioned, which is a bit surprising considering that the green excitation was already pulsed. The authors should try to get more events by lowering the laser power or toning down the conclusion that translocation termination is prominently due to blockage by a downstream nucleosome. Quantifying the translocation distances before termination, in addition to the durations (Figure 4G and 4H), would also be helpful.

We thank the reviewer for these observations and feedback. We agree that only 4 observations of direct visualization of remodeler translocation termination by a downstream nucleosome is a small n-value, and have chosen to omit presentation of these rare events in the manuscript.

(4) The claim on nucleosome translocation directionality is also based on a small number of events, particularly for RSC. 6/9 is hardly over 50% if one considers the Poisson counting error (RSC was also found to switch directions.) If the authors would like to make a firm statement to support the "push-pull" model, they should obtain more events.

We thank the reviewer for this critique and agree with the reviewer’s concern. In addition to adding data from two additional experimental replicates of RSC nucleosome translocation (which had the smaller n-value), we have also re-evaluated all events containing translocation for additional evidence in support or against the “push-pull” model. Previously we were only considering events where 1D diffusion on DNA leads immediately to translocation. Now we add the following categories to the count: (1) events where translocation terminates with the remodeler dissociating from the nucleosome and performing a 1D diffusive search, (2) events where 1D diffusion on DNA leads to association with a nucleosome and after a paused colocalization we observe translocation, and (3) the inverse scenario of (2) (see schematics in Figure 5 – figure supplement 1). These new results, detailed below, are now included in place of the older results in (Results Section # 5; Paragraph # 2). Furthermore, we toned down our argument and clarified that a larger n-value would be needed to be definitive, especially since we observe RSC switching directions, as the reviewer points out.

By aggregating in new RSC data and using only events where 1D diffusion leads immediately to translocation, we observe 10/12 events in support of the “push” model. If we include these other categories in addition to aggregating the previous data with the new data, a total of 20/25 events are in support of the “push” model. For RSC, the breakdown in the other categories was as follows: (1) 7/10 events, (2) 1/1 events with a paused time of 5 seconds, and (3) 2/2 events with a paused time of 36 and 50 seconds.

For ISW2, we had previously reported 12/13 events where 1D search lead immediately to translocation. After combing through the data a second time, we decided to omit two events which were less clear; Now we report 10/11 events in support of the “pull” model from this initial category. If we include these other categories in addition to the original, a total of 19/21 events are in support of the “pull” model. For ISW2, the breakdown in the other categories was as follows: (1) 4/4 events, (2) 4/4 events with pause times of 44, 27, 29, and 8 seconds, (3) 1/2 events with paused times of 5 and 19 seconds.

(5) At 5 pN of tether tension, the outer wrap of nucleosomes is destabilized, which could impact nucleosome translocation dynamics. Additionally, a low buffer flow was kept on during data acquisition, which could bias remodeler diffusion behavior. The authors should rule out or at a minimum discuss these possibilities.

We thank the reviewer for raising the important point regarding outer wrap destabilization of the nucleosome occurring at 5pN of tension. We have added an additional section to the discussion that reviews the literature on tension effects on nucleosome stability as well as what is currently known of the effects of tension on remodeler translocation on DNA (Discussion Paragraph # 3). While we cannot exclude the possibility that the 5pN of tension used in this study is a causative factor of the observed fast speed or high processivity nucleosome translocation that we report, we believe that with the modifications made to the text to emphasize to the reader of these possibilities, the reader can draw informed conclusions on the significance of our findings. The topic of force effects on remodeling outcomes is an interesting subject for the future.

We apologize that the experimental details on buffer flow used during imaging was unclear in our initial submission; we do not have buffer flowing during imaging, rather the buffer containing protein is flowed over the DNA at low pressure just prior to imaging. The flow is completely stopped before the DNA or nucleosome array is stretched to 5pN of tension for imaging (See Methods section: Single Molecule Tracking and Analysis).

Reviewer 1 (Recommendations For The Authors):

(1) The figure panels could be better arranged to focus on the main messages of the paper.

(i) Figure 3C-E should go to a supplemental figure.

We thank the reviewer for this helpful suggestion. As recommended, we moved Figure 3C to the supplemental figure as this panel did not pertain to the main message of the paper.

(ii) Figure 4 could be split into two figures, one characterizing processive nucleosome translocation (4C, D, G, H, I, J, K, and relevant panels in S4), and the other showing the differential directionality of each remodeler (4E, F, L, and relevant panels in S4).

We thank the reviewer for their suggestions that help better organize our presentation of the data. As the reviewer suggests, we split figure 4 into two figures: figure 4 which now focuses on translocation characterization and figure 5 which now focuses on the differential directionality of each remodeler.

(iii) The nucleotide condition should be clearly indicated in the figures or legends. For example, it is unclear if the data in Figure 2 were generated with or without ATP.

We thank the reviewer for taking note of this. We have added clear indications of the nucleotide condition to figures where this is relevant, including in Figure 2 as indicated.

(iv) There are many cartoon panels, and some are redundant (e.g., Figure 1A and 1B, Figure 3A and 3B).

We thank the reviewer for bringing up this point. We agree that some cartoons are redundant. We have eliminated Figure panel 1B and Figure panel 3A of the original figures from the new figures.

(2) The last paragraph of the Results section should be moved to Discussion. This paper did not directly address the effects of RSC/ISW2 on NDR length.

We thank the reviewer for this suggestion. We agree and have moved the last paragraph of the Results section to the Discussion..

(3) There are some typos in the text. For example, "Of the two main types of 1D diffusion, hopping and sliding" is not a complete sentence.

We thank the reviewer for catching this typo and bringing our attention to others. Upon a more careful proofreading of the text and figures we have caught and amended this and other typos.

(4) What are the green lines in Figure S1F?

We thank the reviewer for asking this question. The green lines were meant emphasize how the percentage of traces in the majority high diffusion category increases for RSC but not for ISW2 in response to increases in the KCl concentration. Since this was confusing, we removed these green lines.

Reviewer # 2 (Public Review):

Summary:

The authors use a dual optical trap instrument combined with 2-color fluorescence imaging to analyze the diffusion of RSC and ISW2 on DNA, both in the presence and absence of nucleosomes, as well as long-range nucleosome sliding by these remodelers. This allowed them to demonstrate that both enzymes can participate in 1D diffusion along DNA for rather long ranges, with ISW2 predominantly tracking the DNA strand, while RSC diffusion involves hopping. In an elegant two-color assay, the authors were able to analyze interactions of diffusing remodeler molecules, both of the same or different types, observing their collisions, co-diffusion, and bypassing. The authors demonstrate that nucleosomes act as barriers for remodeler diffusion, either repelling or sequestering them upon collision. In the presence of ATP, they observed surprisingly processive unidirectional nucleosome sliding with a strong bias in the direction opposite to where the remodeler approached the nucleosome from for ISW2. These results have fundamentally important implications for the mechanism of nucleosome positioning at promoters in vivo, will be of great interest to the scientific community, and will undoubtedly spark exciting future research.

Strengths:

The mechanism of target search for chromatin-interacting protein machines is a 'hot' topic, and this manuscript provides extremely important and timely new information about how RSC and ISW2 find the nucleosomes they slide. Intriguingly, although both remodelers analyzed in this study can diffuse along DNA, the diffusion mechanisms are substantially different, with extremely interesting mechanistic implications.

The strong directional preference in nucleosome sliding by ISW2 dictated by the direction it approaches the nucleosomes from during 1D sliding on DNA is a very intriguing result with interesting implications for the regulation of nucleosome organization around promoters. It will be of great interest to the scientific community and will undoubtedly inspire future research.

Relatively little is known about nucleosome sliding at longer ranges (>100bp), and this manuscript provides a unique view into such sliding and also establishes a versatile methodology for future studies.

Weaknesses:

All measurements were conducted at 5pN tension, which induces unwrapping of the outer DNA gyre from nucleosomes. This could potentially represent a limitation for experiments involving nucleosomes, since partial nucleosome unwrapping could affect the behavior of remodelers, especially their sliding of nucleosomes.

We thank the reviewer for succinctly summarizing the strengths and weaknesses of our study. We have changed the Discussion to better review the literature on the effects of 5pN of tension on nucleosome wrapping and have more clearly presented the limitations of our studying owing to our conducting measurements at 5pN of tension. In doing so, we have tried to emphasize the strengths of our study identified by the reviewer and better inform the reader of the weaknesses.

Reviewer #2 (Recommendations For The Authors):

Although not required, nucleosome sliding data under lower tensions (e.g., <=2pN) could be a valuable addition to the manuscript. Indeed, to my knowledge, there is no data on force-dependent rates of nucleosome sliding, so a conclusive demonstration of changes in remodeling rate with tension would be an exciting new result and might be discussed in the context of a potential tension in chromatin. If such experiments cannot readily be added, the authors could alternatively discuss this potential limitation in more detail.

We thank the reviewer for this suggestion. We agree that adding data at lower tensions (<= 2pN) would have been valuable. Due to time constraints, this will be the subject for the future. We agree that knowledge of the effects of tension would be especially interesting in light of the possibility that tension on chromatin in cells may be affecting remodeler function. We have added a discussion of this potential significance of future work to the discussion (Discussion Section; Paragraph # 3). We have also elaborated on the potential limitation of only conducting measurements at 5pN to the discussion (Discussion Section; Paragraph # 3), as the reviewer recommends.

The quantitative implications of the proposed mechanism for targeting ISW2 and RSC towards +1 and -1 nucleosomes are highly interesting. To further strengthen the mechanistic implications, the authors could consider quantitatively analyzing how the observed 1D diffusion would affect the probabilities of binding to +1 and -1 versus to other nucleosomes.

We thank the reviewer for their thoughtful suggestion. While we would have liked to present a final quantitative model that integrates the experimental parameters on 1D diffusion that we present in this study with the parameters extracted from live cell single particle tracking studies, there are key parameters for model building that are missing from our study, due to technical limitations. Namely, we were not able to quantify the fraction of 1D vs 3D nucleosome encounters by remodelers, because the majority of the protein that we image has been bound before the start of imaging; very few proteins bind the nucleosome arrays after the start of imaging as the protein concentration in the imaging chamber is very low. This makes observing binding directly to a nucleosome a very rare event, especially due to the sparse density of nucleosomes (~10) on the array (~50,000 kb).

The low-diffusion state is intriguing - could the authors speculate about the nature of this state?

We thank the reviewer for the question. We had added some speculation about the nature of the low-diffusion state to the results section (Results Section # 1; Paragraph 2). One thought that we have is that this may be due to more stable interactions made between remodelers and free DNA when they become trapped in a conformation state that binds more tightly to DNA. Conformational changes may result in different scanning speeds for chromatin remodelers; e.g. SWR1 was shown to scan DNA quicker when bound to ATP (Carcamo, C. et al. eLife 2022). Another possibility is that certain sequences due to their intrinsic curvature, for instance, or their AT-content may trap the remodeler which may make more contacts with the DNA at these sites.

Minor points:

Information on the labeling efficiencies for the remodelers would be helpful.

We thank the reviewer for pointing this out. We assessed labeling saturation by running gels of remodeler labeling with increasing molar ratios of dye to protein and did not observe increased labeling efficiency above the molar ratio used for proteins imaged in our study (see added Figure 1 – figure supplement 1, panel A). From this, we assessed that we have high protein labeling efficiency. We could not assess the labeling efficiency using the standard absorbance method as the extinction coefficient for JFX650 was measured with 1% v/v TFA (PMCID: PMC8154212) which is not compatible for use in assessing our protein labeling efficiency in an aqueous buffer.

How were the experimental conditions adjusted for two-color diffusion experiments in order to optimize the probability of observing two remodeler molecules with different labels at the same time.

We thank the reviewer for this clarifying question. To image both remodelers on the same DNA, we combined the remodelers using the same concentrations that produced single molecule densities when the remodelers were imaged separately. We have clarified this point in the Methods section: “Bimolecular Remodeler-Remodeler Imaging and Interaction Analysis”.

The authors should check the figures for consistency of labeling and provide definitions for abbreviations used in them (e.g. CDF and PDF).

We thank the reviewer for catching inconsistencies in labeling in our figures. We have updated the figures such that there is consistent labeling throughout. We have also provided definitions for abbreviations such as Cumulative Distribution Function (CDF) and Probability Distribution Function (PDF) in the figure legends where applicable.

In the section "Remodeler-remodeler collisions during 1D search" (4th line from the end) reference to Fig3D seems to be out of place.

We thank the reviewer for catching this typo. We have reworded this section such that each figure panel can be discussed sequentially, eliminating this out of place reference to Fig 3D.

eLife assessment

This manuscript describes fundamental single-molecule correlative force and fluorescence microscopy experiments to visualize the 1D diffusion dynamics and long-range nucleosome sliding activity of the yeast chromatin remodelers, RSC and ISW2. Compelling evidence shows that both remodelers exhibit 1D diffusion on bare DNA but utilize different mechanisms, with RSC primarily hopping and ISW2 mainly sliding on DNA. These results will be of interest to researchers working on chromatin remodeling.

Review #1 (Public Review)

Single-molecule visualization of chromatin remodelers on long chromatin templates-a long sought-after goal-is still in its infancy. This work describes the behaviors of two remodelers RSC and ISW2, from SWI/SNF and ISWI families respectively, with well-conducted experiments and rigorous quantitative analysis, thus representing a significant advance in the field of chromatin biology and biophysics.

Review #2 (Public Review)

The authors use a dual optical trap instrument combined with 2-color fluorescence imaging to analyze the diffusion of RSC and ISW2 on DNA, both in the presence and absence of nucleosomes, as well as long-range nucleosome sliding by these remodelers. This allowed them to demonstrate that both enzymes can participate in 1D diffusion along DNA for rather long ranges, with ISW2 predominantly tracking the DNA strand, while RSC diffusion involves hopping. In an elegant two-color assay, the authors were able to analyze interactions of diffusing remodeler molecules, both of the same or different types, observing their collisions, co-diffusion and bypassing. The authors demonstrate that nucleosomes act as barriers for remodeler diffusion, either repelling or sequestering them upon collision. In the presence of ATP, they observed surprisingly processive unidirectional nucleosome sliding with a strong bias in the direction opposite to where the remodeler approached the nucleosome from for ISW2. These results have fundamentally important implications for the mechanism of nucleosome positioning at promoters in vivo, will be of great interest for the scientific community, and will undoubtedly spark exciting future research

eLife assessment

This work describes a novel and powerful affinity interactomics approach that allows investigators to identify networks of protein-protein interactions in cells. The important findings presented here describe the application of this technique to the SH3 domain of the membrane remodeling Bridging Integrator 1 (BIN1), the truncation of which leads to centronuclear myopathy. The authors present solid evidence that BIN1 SH3 engages with an unexpectedly high number of cellular proteins, many of which are linked to skeletal muscle disease, and evidence is presented to suggest that BIN1 may play a role in mitosis creating the potential for new avenues in drug development efforts. Some of the findings, however, are rather preliminary, and questions about differences in affinities between whole intact protein and fragment binding partners are not adequately discussed.

Reviewer #1 (Public Review):

The authors report here interesting data on the interactions mediated by the SH3 domain of BIN1 that expand our knowledge on the role of the SH3 domain of BIN1 in terms of mediating specific interactions with a potentially high number of proteins and how variants in this region alter or prevent these protein-protein interactions. These data provide useful information that will certainly help to further dissect the networks of proteins that are altered in some human myopathies as well as the mechanisms that govern the correct physiological activity of muscle cells.

The work is mostly based on improved biochemical techniques to measure protein-protein interaction and provide solid evidence that the SH3 domain of BIN1 can establish an unexpectedly high number of interactions with at least a hundred cellular proteins, among which the authors underline the presence of other proteins known to be causative of skeletal muscle diseases and not known to interact with BIN1. This represents an unexpected and interesting finding relevant to better define the network of interactions established among different proteins that, if altered, can lead to muscle disease. An interesting contribution is also the detailed identification of the specific sites, namely the Proline-Rich Motifs (PRMs) that in the interacting proteins mediate binding to the BIN1 SH3 domain. Less convincing, or too preliminary in my opinion, are the data supporting BIN1 co-localization with PRC1. Indeed, the affinity of PRC1 is significantly lower than that of DNM2, an established BIN1 interacting protein. Thus, this does not provide compelling evidence to support PRC1 as a significant interactor of BIN1. Similarly, the localization data appears somewhat preliminary to substantiate a role of BIN1 in mitotic processes. These findings may necessitate additional experimental work to be more convincing.

Reviewer #2 (Public Review):

Summary:<br /> In this paper, Zambo and coworkers use a powerful technique, called native holdup, to measure the affinity of the SH3 domain of BIN1 for cellular partners. Using this assay, they combine data using cellular proteins and proline-containing fragments in these proteins to identify 97 distinct direct binding partners of BIN1. They also compare the binding interactome of the BIN1 SH3 domain to the interactome of several other SH3 domains, showing varying levels of promiscuity among SH3 domains. The authors then use pathway analysis of BIN1 binding partners to show that BIN1 may be involved in mitosis. Finally, the authors examine the impact of clinically relevant mutations of the BIN1 SH3 domain on the cellular interactome. The authors were able to compare the interactome of several different SH3 domains and provide novel insight into the cellular function of BIN1. Generally, the data supports the conclusions, although the reliance on one technique and the low number of replicates in each experiment is a weakness of the study.

Strengths:<br /> The major strength of this paper is the use of holdup and native holdup assays to measure the affinity of SH3 domains to cellular partners. The use of both assays using cell-derived proteins and peptides derived from identified binding partners allows the authors to better identify direct binding partners. This assay has some complexity but does hold the possibility of being used to measure the affinity of the cellular interactome of other proteins and protein domains. Beyond the utility of the technique, this study also provides significant insight into the cellular function of BIN1. The authors have strong evidence that BIN1 might have an undiscovered function in cellular mitosis, which potentially highlights BIN1 as a drug target. Finally, the study provides outstanding data on the cellular binding properties and partners of seven distinct SH3 domains, showing surprising differences in the promiscuity of these proteins.

Weaknesses:<br /> There are three major weaknesses of the study. First, the authors rely completely on a single technique to measure the affinity of the cellular interactome. The native holdup is a relatively new technique that is powerful yet relatively unproven. However, it appears to have the capacity to measure the relative affinity of proteins. Second, the authors appear to use a relatively small number of replicates for the holdup assays. There is no information in the legends about the number of replicates but the materials and methods suggest the native holdup data is from a single experimental replicate with multiple technical replicates. Finally, the authors' data using cellular proteins and fragments show that the affinity of the whole proteins is 5-20 fold lower than individual proline-containing fragments. The authors state that this difference suggests that there is cooperativity between different proline-rich sites of the binding partners of BIN1, yet BIN1 only has one SH3 domain. It is unclear what the molecular mechanism of the cooperative interaction would be exactly since there would be only one SH3 domain to bind the partner. An alternative interpretation would be that the BIN 1 SH3 domain requires sequences outside of the short proline-rich regions for high-affinity interactions with cellular partners, a hypothesis that is supported by other studies.