Reviewer #2 (Public review):

Summary:

This manuscript examines decision-making in a context where the information for the decision is not continuous, but separated by a short temporal gap. The authors use a standard motion direction discrimination task over two discrete dot motion pulses (but unlike previous experiments, fill the gaps in evidence with 0-coherence random dot motion of differently coloured dots). Previous studies using this task (Kiani et al., 2013; Tohidi-Moghaddam et al., 2019; Azizi et al., 2021; 2023) or other discrete sample stimuli (Cheadle et al., 2014; Wyart et al., 2015; Golmohamadian et al., 2025) have shown decision-makers to integrate evidence from multiple samples (although with some flexible weighting on each sample). In this experiment, decision-makers tended not to use the second motion pulse for their decision. This allows the separation of neural signatures of momentary decision-evidence samples from the accumulated decision-evidence. In this context, classic electroencephalography signatures of accumulated decision-evidence (central-parietal positivity) are shown to reflect the momentary decision-evidence samples.

Strengths:

The authors present an excellent analysis of the data in support of their findings. In terms of proportion correct, participants show poorer performance than predicted if assuming both evidence samples were integrated perfectly. A regression analysis suggested a weaker weight on the second pulse, and in line with this, the authors show an effect of the order of pulse strength that is reversed compared to previous studies: A stronger second pulse resulted in worse performance than a stronger first pulse (this is in line with the visual condition reported in Golmohamadian et al., 2025). The authors also show smaller changes in electrophysiological signatures of decision-making (central parietal positivity and lateralised motor beta power) in response to the second pulse. The authors describe these findings with a computational model which allows for early decision-commitment, meaning the second pulse is ignored on the majority of trials. The model-predicted electrophysiological components describe the data well. In particular, this analysis of model-predicted electrophysiology is impressive in providing simple and clear predictions for understanding the data.

Weaknesses:

Some readers may be left questioning why behaviour in this experiment is so different from previous experiments, which use almost exactly the same design (Kiani et al., 2013; Tohidi-Moghaddam et al., 2019; Azizi et al., 2021; 2023). The authors suggest this may be due to the staircase procedure used to calibrate the coherence of (single-pulse) dot motion stimuli for individuals at the start of the experiment. But it remains unclear why overall performance in this experiment is so bad. Participants achieved ~85% correct following 400 ms of 33 - 45% coherent motion. In previous work, performance was ~90% correct following 240ms of 12.8% coherent motion. It seems odd that adding the 0% coherent motion in the temporal gaps would impair performance so greatly, given it was clearly colour-coded. There is a lack of detail about the stimulus presentation parameters to understand whether visual processing explains the declined performance, or if there is a more cognitive/motivational explanation.

eLife Assessment

This study uses a valuable combination of functional magnetic resonance imaging and electroencephalography (EEG) to study brain activity related to prediction errors in relation to both sensorimotor and more complex cognitive functions. It provides incomplete evidence to suggest that prediction error minimisation drives brain activity across both types of processing and that elevated inter-regional functional coupling along a superior-inferior axis is associated with high prediction error, whereas coupling along a posterior-anterior axis is associated with low prediction error. The manuscript will be of interest to neuroscientists working on predictive coding and decision-making, but would benefit from more precise localisation of EEG sources and more rigorous statistical controls.

Reviewer #1 (Public review):

Summary:

This study investigates whether prediction error extends beyond lower-order sensory-motor processes to include higher-order cognitive functions. Evidence is drawn from both task-based and resting-state fMRI, with the addition of resting-state EEG-fMRI to examine power spectral correlates. The results partially support the existence of dissociable connectivity patterns: stronger ventral-dorsal connectivity is associated with high prediction error, while posterior-anterior connectivity is linked to low prediction error. Furthermore, spontaneous switching between these connectivity patterns was observed at rest and correlated with subtle intersubject behavioral variability.

Strengths:

Studying prediction error from the lens of network connectivity provides new insights into predictive coding frameworks. The combination of various independent datasets to tackle the question adds strength, including two well-powered fMRI task datasets, resting-state fMRI interpreted in relation to behavioral measures, as well as EEG-fMRI.

Weaknesses:

Major:

(1) Lack of multiple comparisons correction for edge-wise contrast:

The analysis of connectivity differences across three levels of prediction error was conducted separately for approximately 22,000 edges (derived from 210 regions), yet no correction for multiple comparisons appears to have been applied. Then, modularity was applied to the top 5% of these edges. I do not believe that this approach is viable without correction. It does not help that a completely separate approach using SVMs was FDR-corrected for 210 regions.

(2) Lack of spatial information in EEG:

The EEG data were not source-localized, and no connectivity analysis was performed. Instead, power fluctuations were averaged across a predefined set of electrodes based on a single prior study (reference 27), as well as across a broader set of electrodes. While the study correlates these EEG power fluctuations with fMRI network connectivity over time, such temporal correlations do not establish that the EEG oscillations originate from the corresponding network regions. For instance, the observed fronto-central theta power increases could plausibly originate from the dorsal anterior cingulate cortex (dACC), as consistently reported in the literature, rather than from a distributed network. The spatially agnostic nature of the EEG-fMRI correlation approach used here does not support interpretations tied to specific dorsal-ventral or anterior-posterior networks. Nonetheless, such interpretations are made throughout the manuscript, which overextends the conclusions that can be drawn from the data.

Reviewer #2 (Public review):

Summary:

This paper investigates putative networks associated with prediction errors in task-based and resting-state fMRI. It attempts to test the idea that prediction errors minimisation includes abstract cognitive functions, referred to as the global prediction error hypothesis, by establishing a parallel between networks found in task-based fMRI where prediction errors are elicited in a controlled manner and those networks that emerge during "resting state".

Strengths:

Clearly, a lot of work and data went into this paper, including 2 task-based fMRI experiments and the resting state data for the same participants, as well as a third EEG-fMRI dataset. Overall, well written with a couple of exceptions on clarity, as per below, and the methodology appears overall sound, with a couple of exceptions listed below that require further justification. It does a good job of acknowledging its own weakness.

Weaknesses:

(1) The paper does a good job of acknowledging its greatest weakness, the fact that it relies heavily on reverse inference, but cannot quite resolve it. As the authors put it, "finding the same networks during a prediction error task and during rest does not mean that the networks' engagement during rest reflects prediction error processing". Again, the authors acknowledge the speculative nature of their claims in the discussion, but given that this is the key claim and essence of the paper, it is hard to see how the evidence is compelling to support that claim.

(2) Given how uncontrolled cognition is during "resting-state" experiments, the parallel made with prediction errors elicited during a task designed for that effect is a little difficult to make. How often are people really surprised when their brains are "at rest", likely replaying a previously experienced event or planning future actions under their control? It seems to be more likely a very low prediction error scenario, if at all surprising.

(3) The quantitative comparison between networks under task and rest was done on a small subset of the ROIs rather than on the full network - why? Noting how small the correlation between task and rest is (r=0.021) and that's only for part of the networks, the evidence is a little tenuous. Running the analysis for the full networks could strengthen the argument.

(4) Looking at the results in Figure 2C, the four-quadrant description of the networks labelled for low and high PE appears a little simplistic. The authors state that this four-quadrant description omits some ROIs as motivated by prior knowledge. This would benefit from a more comprehensive justification. Which ROIs are excluded, and what is the evidence for exclusion?

(5) The EEG-fMRI analysis claiming 3-6Hz fluctuations for PE is hard to reconcile with the fact that fMRI captures activity that is a lot slower, while some PEs are as fast as 150 ms. The discussion acknowledges this but doesn't seem to resolve it - would benefit from a more comprehensive argument.

Reviewer #3 (Public review):

Bogdan et al. present an intriguing and timely investigation into the intrinsic dynamics of prediction error (PE)-related brain states. The manuscript is grounded in an intuitive and compelling theoretical idea: that the brain alternates between high and low PE states even at rest, potentially reflecting an intrinsic drive toward predictive minimization. The authors employ a creative analytic framework combining different prediction tasks and imaging modalities. They shared open code, which will be valuable for future work.

However, the current manuscript would benefit from further clarification and empirical grounding, especially with regard to its theoretical framing (that PE-like state fluctuations are intrinsic and help us minimize PE), interpretation of results, and broader functional significance. Below, I outline a few major comments and suggestions that I think would strengthen the contribution.

(1) Consistency in Theoretical Framing

The title, abstract, and introduction suggest inconsistent theoretical goals of the study.

The title suggests that the goal is to test whether there are intrinsic fluctuations in high and low PE states at rest. The abstract and introduction suggest that the goal is to test whether the brain intrinsically minimizes PE and whether this minimization recruits global brain networks. My comments here are that a) these are fundamentally different claims, and b) both are challenging to falsify. For one, task-like recurrence of PE states during resting might reflect the wiring and geometry of the functional organization of the brain emerging from neurobiological constraints or developmental processes (e.g., experience), but showing that mirroring exists because of the need to minimize PE requires establishing a robust relationship with behavior or showing a causal effect (e.g., that interrupting intrinsic PE state fluctuations affects prediction).

The global PE hypothesis-"PE minimization is a principle that broadly coordinates brain functions of all sorts, including abstract cognitive functions"-is more suitable for discussion rather than the main claim in the abstract, introduction, and all throughout the paper.

Given the above, I recommend that the authors clarify and align their core theoretical goals across the title, abstract, introduction, and results. If the focus is on identifying fluctuations that resemble task-defined PE states at rest, the language should reflect that more narrowly, and save broader claims about global PE minimization for the discussion. This hypothesis also needs to be contextualized within prior work. I'd like to see if there is similar evidence in the literature using animal models.

(2) Interpretation of PE-Related Fluctuations at Rest and Its Functional Relevance

It would strengthen the paper to clarify what is meant by "intrinsic" state fluctuations. Intrinsic might mean task-independent, trait-like, or spontaneously generated. Which do the authors mean here? Is the key prediction that these fluctuations will persist in the absence of a prediction task?

Regardless of the intrinsic argument, I find it challenging to interpret the results as evidence of PE fluctuations at rest. What the authors show directly is that the degree to which a subset of regions within a PE network discriminates high vs. low PE during task correlates with the magnitude of separation between high and low PE states during rest. While this is an interesting relationship, it does not establish that the resting-state brain spontaneously alternates between high and low PE states, nor that it does so in a functionally meaningful way that is related to behavior. How can we rule out brain dynamics of other processes, such as arousal, that also rise and fall with PE? I understand the authors' intention to address the reverse inference concern by testing whether "a participant's unique connectivity response to PE in the reward-processing task should match their specific patterns of resting-state fluctuation". However, I'm not fully convinced that this analysis establishes the functional role of the identified modules to PE because of the following:

Theoretically, relating the activities of the identified modules directly to behavior would demonstrate a stronger functional role.

a) Across participants: Do individuals who exhibit stronger or more distinct PE-related fluctuations at rest also perform better on tasks that require prediction or inference? This could be assessed using the HCP prediction task, though if individual variability is limited (e.g., due to ceiling effects), I would suggest exploring a dataset with a prediction task that has greater behavioral variance.

Or even more broadly, does this variability in resting state PE state fluctuations predict general cognitive abilities like WM and attention (which the HCP dataset also provides)? I appreciate the inclusion of the win-loss control, and I can see the intention to address specificity. This would test whether PE state fluctuations reflect something about general cognition, but also above and beyond these attentional or WM processes that we know are fluctuating.

b) Within participants: Do momentary increases in PE-network expression during tasks relate to better or faster prediction? In other words, is there evidence that stronger expression of PE-related states is associated with better behavioral outcomes?

(3) Apriori Hypothesis for EEG Frequency Analysis

It's unclear how to interpret the finding that fMRI fluctuations in the defined modules correlate with frontal Delta/Theta power, specifically in the 3-6 Hz range. However, in the EEG literature, this frequency band is most commonly associated with low arousal, drowsiness, and mind wandering in resting, awake adults, not uniquely with prediction error processing. An a priori hypothesis is lacking here: what specific frequency band would we expect to track spontaneous PE signals at rest, and why? Without this, it is difficult to separate a PE-based interpretation from more general arousal or vigilance fluctuations.

(4) Significance Assessment

The significance of the correlation above and all other correlation analyses should be assessed through a permutation test rather than a single parametric t-test against zero. There are a few reasons: a) EEG and fMRI time series are autocorrelated, violating the independence assumption of parametric tests;<br /> b) Standard t-tests can underestimate the true null distribution's variance, because EEG-fMRI correlations often involve shared slow drifts or noise sources, which can yield spurious correlations and inflating false positives unless tested against an appropriate null.

Building a null distribution that preserves the slow drifts, for example, would help us understand how likely it is for the two time series to be correlated when the slow drifts are still present, and how much better the current correlation is, compared to this more conservative null. You can perform this by phase randomizing one of the two time courses N times (e.g., N=1000), which maintains the autocorrelation structure while breaking any true co-occurrence in patterns between the two time series, and compute a non-parametric p-value. I suggest using this approach in all correlation analyses between two time series.

(5) Analysis choices

If I'm understanding correctly, the algorithm used to identify modules does so by assigning nodes to communities, but it does not itself restrict what edges can be formed from these modules. This makes me wonder whether the decision to focus only on connections between adjacent modules, rather than considering the full connectivity, was an analytic choice by the authors. If so, could you clarify the rationale? In particular, what justifies assuming that the gradient of PE states should be captured by edges formed only between nearby modules (as shown in Figure 2E and Figure 4), rather than by the full connectivity matrix? If this restriction is instead a by-product of the algorithm, please explain why this outcome is appropriate for detecting a global signature of PE states in both task and rest.

When assessing the correspondence across task-fMRI and rs-fMRI in section 2.2.2, why was the pattern during task calculated from selecting a pair of bilateral ROIs (resulting in a group of eight ROIs), and the resting state pattern calculated from posterior-anterior/ventral-dorsal fluctuation modules? Doesn't it make more sense to align the two measures? For example, calculating task effects on these same modules during task and rest?

eLife Assessment

This important study concerns the propagation of waves in bacterial biofilms, bridging active matter physics and bacterial biophysics. While the experimental observations are solid, the theoretical interpretation and model validation are currently incomplete and require further refinement. This work will be of interest to microbiologists, biophysicists, and researchers studying collective behavior in biological systems.

Reviewer #1 (Public review):

Summary:

Overall, this is an interesting paper. The authors have found multiple experimental knobs to perturb a mechanical wave behavior driven by pilli feedback. The authors framed this as nonreciprocal interactions - while I can see how nonreciprocity could play a role - what about mechanical feedback? Phenomenological models are fine, but a lack of mechanistic understanding is a weakness. I think it will be more interesting to frame the model based on potential mechanochemical feedback to understand microscopic mechanisms. Regardless, more can be done to better constrain the model through finding knobs to explain experimental observations (in Figures 3, 4, 5, and 7).

Strengths:

The report of mechanical waves in bacterial collectives. The mechanism has potential application in a multicellular context, such as morphogenesis.

Weaknesses:

My most serious concern is about left-right symmetry breaking. I fail to see how the data in Figure 6 shows LR symmetry breaking. All they show is in-out directionality, which is a boundary condition. LR SM means breaking of mirror symmetry - the pattern cannot be superimposed on its mirror image using only rigid body transformations (translation and rotation) - as far as I am aware, this condition is not satisfied in this pattern-forming system.

Reviewer #2 (Public review):

Summary:

This manuscript by Altin et al. examines the dynamics of bacterial assemblies, building on previously published work documenting mechanical spiral waves. The authors show that the emergent dynamics can be influenced by various factors, including the strain of bacteria and water content in the sample. While the topic of this paper would be of broad interest, and the preliminary results are certainly interesting, various aspects of this paper are underdeveloped and require further exploration.

Strengths:

One of the nice features of this system is the ability to transition between the different states based on the addition or withdrawal of water. The authors use a similar experimental model system and mathematical model to previously published work (Reference 49), but extend by showing that the behaviour can be modified through simple interventions. Specifically, the authors show that adding water droplets or drying the sample through heating can result in changes in the observed wave structure. This represents a possible way of controlling active matter.

The mathematical model proposed in this paper involves a phase-oscillator model of Kuramoto-style coupling (similar to previously reported models). A non-reciprocal phase lag is introduced in order to facilitate the patterns seen in experiments. The qualitative agreement in the behaviour is quite striking, showing both spiral waves and travelling waves.

Weaknesses:

The principal observation of the paper - that spiral waves emerge in these systems and can be controlled in various ways - is not linked to microscale dynamics at the cell level. It is recognised that hydrodynamics can introduce non-reciprocity, an essential ingredient of this model. However, in this work the authors have not identified a physical mechanism for the lag, e.g., either through steric interactions or hydrodynamic disturbances. This is also relevant in the phase oscillator modelling section. In low Reynolds number flows, dynamics are instantaneously determined. In this light, what does the phase lag term represent? What is the origin of the coupling term, b? Can this be varied systematically or derived from experimental measurements or parameters?

Classification of wave properties is an important aspect of this paper, but is not accomplished in a quantitative sense. What is the method for distinguishing between travelling and spiral waves? There is a range of quantitative tools that could be used to investigate these dynamics (and also compare quantitatively with the models). For example, examining the correlation functions and order parameters could assist with the extraction of wave features (see extensive literature on oscillator models).

The methodology of changing the dynamics through moisture content appears to be slightly underdeveloped, e.g., adding water involves a droplet, and removing water is accomplished by heating (which presumably could cause other effects). Could the dynamics not be controlled more directly by varying the humidity? At the same time, the authors also mention that temperature itself plays a role in shaping the behaviour. What is the mechanism for this? Is it just through evaporation? Since the frequency increases with temperature, could it just be that activity increases with temperature?

Reviewer #3 (Public review):

Summary:

This manuscript presents a novel investigation into unidirectionally propagating waves observed on the surface of Pseudomonas nitroreducens bacterial biofilms. The authors explore how these waves, initially spiral in form, transition into combinations of spiral, target, and planar patterns. The study identifies the periodic extension-retraction cycles of type IV pili as the driving mechanism for wave propagation, which preferentially moves from the colony's edge to its center. Furthermore, the manuscript proposes two theoretical models-a phase-oscillator model and a continuum active solid model-to reproduce these phenomena, and demonstrates how external manipulations (e.g., water droplets, temperature, PEG) can control wave patterns and direction, often correlating with oscillation frequency gradients. The work aims to bridge the fields of active-matter physics and bacterial biophysics by providing both experimental observations and theoretical frameworks for understanding these complex biological wave phenomena.

Strengths:

The experimental discovery of unidirectionally propagating waves on bacterial biofilms is highly intriguing and represents a significant contribution to both microbiology and active-matter physics. The detailed observations of wave pattern transitions (spiral to target to planar) and their response to various environmental perturbations (water, temperature, PEG) provide valuable empirical data. The identification of type IV pili as the driving force offers a concrete biological mechanism. The observed correlation between frequency gradients and wave direction is a compelling finding with potential for broader implications in understanding biological pattern formation. This work has the potential to stimulate further research in the collective behavior of living systems and the physical principles underlying biological organization.

Weaknesses:

The manuscript attempts to link unidirectional wave propagation to non-reciprocal couplings but ultimately shows that the wave direction is determined by the gradient of the oscillation frequency. The couplings in the two theoretical models are both isotropic and thus cannot dictate the wave direction. A clear distinction should be made between non-reciprocity as a source of wave generation and non-uniformity as a controlling factor of wave direction.

The relationship between the phase oscillator model and the active solid model is unclear. Given that U and P are both dynamical variables evolving in three-dimensional space, defining the phase Φ precisely in the phase space spanned by U and P could be challenging. A graphical illustration of the definition of Φ would be beneficial. To ensure reproducibility of the numerical results, the parameter values used in the numerical simulations and an explicit definition of the elastic force in the active solid model should be provided.

The link between the theoretical models and experimental results is weak. For example, the propagation of the kink from the lower to the higher part of the surface (Figure 1e) could be addressed within the framework of the active solid model. The mechanism of transition from spiral to target waves (Figure 3a), b)) requires clarification, identifying which model parameter is crucial for inducing this transition. The wave propagation toward the lower frequency side is numerically demonstrated using the phase oscillator model, but a physical or intuitive explanation for this phenomenon is missing. Also, the wave transitions induced by the addition of water droplets and temperature rise are not linked to specific parameters in the theoretical models.

eLife Assessment

This paper reports a useful low-cost platform for studying mosquito behaviors such as flight activity, sugar feeding, and host-seeking responses over the course of several weeks, and demonstrates key applications of this platform. While the authors provide a biological proof of principle, the evidence that supports the validation of the tracking algorithm is incomplete; it lacks biological replicates, independent confirmation of the tracking algorithm, and data on mosquito survival.

Reviewer #1 (Public review):

Summary:

This paper describes a behavioral platform "BuzzWatch" and its application in long-term behavioral monitoring. The study tested the system with different mosquito species and Aedes aegypti colonies and monitored behavioral response to blood feeding, change in photoperiod, and host-cue application at different times of the day.

Strengths:

BuzzWatch is a novel, custom-built behavioral system that can be used to monitor time-of-day-specific and long-term mosquito behaviors. The authors provide detailed documentation of the construction of the assay and custom flight tracking algorithm on a dedicated website, making them accessible to other researchers in the field. The authors performed a wide range of experiments using the BuzzWatch system and discovered differences in midday activity level among Aedes aegypti colonies, and reversible change in the daily activity profile post-blood-feeding.

Weaknesses:

The authors report the population metric "fraction flying" as their main readout of the daily activity profile. It is worth explaining why conventional metrics like travel distance/activity level are not reported. Alternatively, these metrics could be shown, considering the development and implementation of a flight trajectory tracking pipeline in this paper.

The authors defined the sugar-feeding index using occupancy on the sugar feeder. However, the correlation between landing on the sugar feeder and active sugar feeding is not mentioned or tested in this paper. Is sugar feeding always observed when mosquitoes land on the sugar feeder? Do they leave the sugar feeding surface once sugar feeding is complete? One can imagine that texture preference and prolonged occupancy may lead to inaccurate reporting of sugar feeding. While occupancy on the sugar feeder is an informative behavioral readout, its link with sugar feeding activity (consumption) needs to be evaluated. Otherwise, the authors should discuss the caveats that this method presents explicitly to avoid overinterpretation of their results.

Throughout the manuscript, the authors mentioned existing mosquito activity monitoring systems and their drawbacks. However, many of these statements are misleading and sometimes incorrect. The authors claim that beam-break monitors are "limited to counting active versus inactive states". Though these systems provide indirect readouts that may underreport activity, the number of beam-breaks in a time interval is correlated with activity level, as is commonly used and reported in Drosophila and mosquitoes and a number of reports in mosquitoes an updated LAM system with larger behavioral arenas and multiple infrared beams. The authors also mentioned the newer, camera-based alternatives to beam-break monitors, but again referred to these systems as "only detecting activity when a moving insect blocks a light beam"; however, these systems actually use video tracking (e.g., Araujo et al. 2020).

The fold change in behavior presented in Figure 4D is rather confusing. Under the two different photoperiods, it is not clear how an hourly comparison is justified (i.e., comparing the light-on activity in the 20L4D condition with scotophase activity in the 12L12D condition). The same point applies to Figure 4H.

The behavioral changes after changing photoperiod (Figure 4) require a control group (12L12D throughout) to account for age-related effects. This is controlled for the experiment in Figure 3 but not for Figure 4.

Reviewer #2 (Public review):

Summary:

This study establishes a platform for studying mosquito flight activity over the course of several weeks and demonstrates key applications of such a paradigm: the comparison of daily activity profiles across different Aedes aegypti populations and the quantification of responses to physiological and environmental perturbations.

Strengths:

(1) Overall, the authors succeed in setting up a low-cost, scalable tracking system that stably records mosquito flight activity for several weeks and uses it to demonstrate compelling use cases.

(2) The text is organized well, is easy to read, and is understandable for a broad audience.

(3) Instructions for constructing housing and for performing tracking with a dedicated GUI are available on an accompanying website, with open-source (and well-organized) code.

(4) A complementary pair of methods (one testing for activity signals at specific times of the day, and the other capturing broader daily patterns) is used effectively.

Weaknesses:

(1) In the interval-based GLMM results, since each time interval is tested independently, p-values should be corrected for multiple hypotheses (for instance, through controlling the false discovery rate).

(2) The accompanying GUI application needs some modifications to fully work out of the box on a sample video.

Reviewer #3 (Public review):

Summary:

The authors in this paper introduce BuzzWatch, an open-source, low-cost (200-300 Euros) platform for long-term monitoring of mosquito flight and behavior. They use a Raspberry Pi with a Noirv2 Camera set up under laboratory conditions to observe 3 different species of mosquitoes. The system captures a variety of multimodal data, like flight activity, sugar feeding, and host-seeking responses, with the help of external modules like CO2 and fructose-soaked cottons. They also release a GUI in addition to automated tracking and behaviour analysis, which doesn't run on Pi but rather on a personal laptop.

Four main use cases are demonstrated:

(1) Characterizing diel rhythms in various Aedes aegypti populations.

(2) Differentiating behaviors of native African vs. invasive human-adapted subspecies.

(3) Assessing physiological (blood-feeding) and environmental (light regime) perturbations.

(4) Testing time-of-day variation in responses to host-associated cues like CO₂ and heat.

Description (Strengths):

(1) The authors introduce a low-cost, scalable system that uses flight tracking in 2D as an alternative to 3D multi-camera systems.

(2) Due to the low pixel quality required by the system, they can record for weeks at a time, capturing long temporal and behavioral activities.

(3) They also integrate external modules such as lights, CO2, and heat as a way to measure responses to a variety of stimuli.

(4) They also introduce a wiki as a guide for building replication and a help in using the GUI module.

(5) They implement both GLMM hourly and PCA of behavior data.

Limitations - Major Comments:

(1) Most experiments are only done with single replicates per colony. If the setup is claimed to be cheap and replicable, there should be clearer replicates across experiments.

(2) No external validation for the flight tracking algorithm using manual annotation or comparison with field data. The authors focus early on biological proof of principle, but the validity of the tracking algorithm is not presented. How accurate is the algorithm at classifying behaviours (e.g., vs human ground truth)? How reliable is tracking?

(3) Why develop a custom GUI instead of using established packages such as rethomics (https://rethomics.github.io/) that are already available for behavioral analysis?

(4) Why use RGB light strips when perceptual white light for humans is not relevant for mosquitoes? The choice of lighting should be based on the mosquito's visual perception. - https://pmc.ncbi.nlm.nih.gov/articles/PMC12077400/ .

(5) Why use GLMMs instead of GAMs (with explicit periodic components)? With GLMMs, you do not account for temporal structure, which is highly relevant and autocorrelated in behavioral time series data.

(6) What is the proportion of mosquitoes that stay alive throughout the experiments? How do you address dead animals in tracking? No data are available on whether all mosquitoes made it through the monitoring period. No survival data is mentioned in the paper, and in the wiki, it is not clear how it is used or how it affects the analyses - https://theomaire.github.io/buzzwatch/analyze.html#diff-cond .

(7 )The sugar feeding behavior is not manually validated.

(8) Figure 4d is difficult to understand - how did you align time? Why is ZT4 aligning with ZT0? Should you "warp" the time series to compare them (e.g., from dawn to dusk)?

(9) No video recordings are made available for demonstration or validation purposes.

Appraisal

(1) The core conclusions---that BuzzWatch can capture multiscale mosquito behavioral rhythms and quantify the effect of genetic, environmental, and physiological variation - show promise but require stronger validation.

(2) Statistical approaches (GLMM, PCA) are chosen but may not be optimal for temporal data with autocorrelation.

(3) The host-seeking module shows a differential response, which is a potentially valuable feature.

eLife Assessment

This study uses the Drosophila mushroom body as a model to understand the molecular machinery that controls the temporal specification of neuronal cell types. With convincing experimental evidence, the authors made fundamental findings that the Pipsqueak domain-containing transcription factor Eip93F is central to the specification of a later-born neuronal subtype and in inhibiting gene expression for earlier subtypes.

Reviewer #1 (Public review):

Summary:

The temporal regulation of neuronal specification and its molecular mechanisms are important problems in developmental neurobiology. This study focuses on Kenyon cells (KCs), which form the mushroom body in Drosophila melanogaster, in order to address this issue. Building on previous findings, the authors examine the role of the transcription factor Eip93F in the development of late-born KCs. The authors revealed that Eip93F controls the activity of flies at night through the expression of the calcium channel Ca-α1T. Thus, the study clarifies the molecular machinery that controls temporal neuronal specification and animal behavior.

Strengths:

The convincing results are based on state-of-the-art molecular genetics, imaging, and behavioral analysis.

Weaknesses:

Temporal mechanisms of neuronal specification are found in many nervous systems. However, the relationship between the temporal mechanisms identified in this study and those in other systems remains unclear.

Reviewer #2 (Public review):

Summary:

Understanding the mechanisms of neural specification is a central question in neurobiology. In Drosophila, the mushroom body (MB), which is the associative learning region in the brain, consists of three major cell types: γ, α'/β', and α/β kenyon cells. These classes can be further subdivided into seven subtypes, together comprising ~2000 KCs per hemi-brain. Remarkably, all of these neurons are derived from just four neuroblasts in each hemisphere. Therefore, a lot of endeavors are put into understanding how the neuron is specified in the fly MB.

Over the past decade, studies have revealed that MB neuroblasts employ a temporal patterning mechanism, producing distinct neuronal types at different developmental stages. Temporal identity is conveyed through transcription factor expression in KCs. High levels of Chinmo, a BTB-zinc finger transcription factor, promote γ-cell fate (Zhu et al., Cell, 2006). Reduced Chinmo levels trigger expression of mamo, a zinc finger transcription factor that specifies α'/β' identity (Liu et al., eLife, 2019). However, the specification of α/β neurons remains poorly understood. Some evidence suggests that microRNAs regulate the transition from α'/β' to α/β fate (Wu et al., Dev Cell, 2012; Kucherenko et al., EMBO J, 2012). One hypothesis even proposes that α/β represents a "default" state of MB neurons, which could explain the difficulty in identifying dedicated regulators.

The study by Chung et al. challenges this hypothesis. By leveraging previously published RNA-seq datasets (Shih et al., G3, 2019), they systematically screened BAC transgenic lines to selectively label MB subtypes. Using these tools, they analyzed the consequences of manipulating E93 expression and found that E93 is required for α/β specification. Furthermore, loss of E93 impairs MB-dependent behaviors, highlighting its functional importance.

Strengths:

The authors conducted a thorough analysis of E93 manipulation phenotypes using LexA tools generated from the Janelia Farm and Bloomington collections. They demonstrated that E93 knockdown reduces expression of Ca-α1T, a calcium channel gene identified as an α/β marker. Supporting this conclusion, one LexA line driven by a DNA fragment near EcR (R44E04) showed consistent results. Conversely, overexpression of E93 in γ and α'/β' Kenyon cells led to downregulation of their respective subtype markers.

Another notable strength is the authors' effort to dissect the genetic epistasis between E93 and previously known regulators. Through MARCM and reporter analyses, they showed that Chinmo and Mamo suppress E93, while E93 itself suppresses Mamo. This work establishes a compelling molecular model for the regulatory network underlying MB cell-type specification.

Weaknesses:

The interpretation of E93's role in neuronal specification requires caution. Typically, two criteria are used to establish whether a gene directs neuronal identity:<br /> (1) gene manipulation shifts the neuronal transcriptome from one subtype to another, and<br /> (2) gene manipulation alters axonal projection patterns.

The results presented here only partially satisfy the first criterion. Although markers are affected, it remains possible that the reporter lines and subtype markers used are direct transcriptional targets of E93 in α/β neurons, rather than reflecting broader fate changes. Future studies using single-cell transcriptomics would provide a more comprehensive assessment of neuronal identity following E93 perturbation.

With respect to the second criterion, the evidence is also incomplete. While reporter patterns were altered, the overall morphology of the α/β lobes appeared largely intact after E93 knockdown. Overexpression of E93 in γ neurons produced a small subset of cells with α/β-like projections, but this effect warrants deeper characterization before firm conclusions can be drawn. While the results might be an intrinsic nature of KC types in flies, the interpretation of the reader of the data should be more careful, and the authors should also mention this in their main text.

Author response:

Public Reviews:

Reviewer #1 (Public review):

Summary:

The temporal regulation of neuronal specification and its molecular mechanisms are important problems in developmental neurobiology. This study focuses on Kenyon cells (KCs), which form the mushroom body in Drosophila melanogaster, in order to address this issue. Building on previous findings, the authors examine the role of the transcription factor Eip93F in the development of late-born KCs. The authors revealed that Eip93F controls the activity of flies at night through the expression of the calcium channel Ca-α1T. Thus, the study clarifies the molecular machinery that controls temporal neuronal specification and animal behavior.

Strengths:

The convincing results are based on state-of-the-art molecular genetics, imaging, and behavioral analysis.

Weaknesses:

Temporal mechanisms of neuronal specification are found in many nervous systems. However, the relationship between the temporal mechanisms identified in this study and those in other systems remains unclear.

We will expand the Discussion section to highlight the temporal mechanisms between different nervous systems.

Reviewer #2 (Public review):

Summary:

Understanding the mechanisms of neural specification is a central question in neurobiology. In Drosophila, the mushroom body (MB), which is the associative learning region in the brain, consists of three major cell types: γ, α'/β', and α/β kenyon cells. These classes can be further subdivided into seven subtypes, together comprising

~2000 KCs per hemi-brain. Remarkably, all of these neurons are derived from just four neuroblasts in each hemisphere. Therefore, a lot of endeavors are put into understanding how the neuron is specified in the fly MB.

Over the past decade, studies have revealed that MB neuroblasts employ a temporal patterning mechanism, producing distinct neuronal types at different developmental stages. Temporal identity is conveyed through transcription factor expression in KCs. High levels of Chinmo, a BTB-zinc finger transcription factor, promote γ-cell fate (Zhu et al., Cell, 2006). Reduced Chinmo levels trigger expression of mamo, a zinc finger transcription factor that specifies α'/β' identity (Liu et al., eLife, 2019). However, the specification of α/β neurons remains poorly understood. Some evidence suggests that microRNAs regulate the transition from α'/β' to α/β fate (Wu et al., Dev Cell, 2012; Kucherenko et al., EMBO J, 2012). One hypothesis even proposes that α/β represents a "default" state of MB neurons, which could explain the difficulty in identifying dedicated regulators.

The study by Chung et al. challenges this hypothesis. By leveraging previously published RNA-seq datasets (Shih et al., G3, 2019), they systematically screened BAC transgenic lines to selectively label MB subtypes. Using these tools, they analyzed the consequences of manipulating E93 expression and found that E93 is required for α/β specification. Furthermore, loss of E93 impairs MB-dependent behaviors, highlighting its functional importance.

Strengths:

The authors conducted a thorough analysis of E93 manipulation phenotypes using LexA tools generated from the Janelia Farm and Bloomington collections. They demonstrated that E93 knockdown reduces expression of Ca-α1T, a calcium channel gene identified as an α/β marker. Supporting this conclusion, one LexA line driven by a DNA fragment near EcR (R44E04) showed consistent results. Conversely, overexpression of E93 in γ and α'/β' Kenyon cells led to downregulation of their respective subtype markers.

Another notable strength is the authors' effort to dissect the genetic epistasis between E93 and previously known regulators. Through MARCM and reporter analyses, they showed that Chinmo and Mamo suppress E93, while E93 itself suppresses Mamo. This work establishes a compelling molecular model for the regulatory network underlying MB cell-type specification.

Weaknesses:

The interpretation of E93's role in neuronal specification requires caution. Typically, two criteria are used to establish whether a gene directs neuronal identity:

(1) gene manipulation shifts the neuronal transcriptome from one subtype to another, and

(2) gene manipulation alters axonal projection patterns.

The results presented here only partially satisfy the first criterion. Although markers are affected, it remains possible that the reporter lines and subtype markers used are direct transcriptional targets of E93 in α/β neurons, rather than reflecting broader fate changes. Future studies using single-cell transcriptomics would provide a more comprehensive assessment of neuronal identity following E93 perturbation.

We do plan to conduct multi-omics experiments to provide a more comprehensive assessment of neuronal identity upon loss-of-function of E93. However, omics results will be summarized in a new manuscript, but not for the revised manuscript.

With respect to the second criterion, the evidence is also incomplete. While reporter patterns were altered, the overall morphology of the α/β lobes appeared largely intact after E93 knockdown. Overexpression of E93 in γ neurons produced a small subset of cells with α/β-like projections, but this effect warrants deeper characterization before firm conclusions can be drawn. While the results might be an intrinsic nature of KC types in flies, the interpretation of the reader of the data should be more careful, and the authors should also mention this in their main text.

We will describe and interpret this part of results in the main text in a more careful manner.

eLife Assessment

This valuable study presents findings on the developmental roles of Nup107, a key nucleoporin, in regulating the larval-to-pupal transition in Drosophila melanogaster through its involvement in ecdysone signaling. The evidence supporting the authors' claims is solid, with robust experimental approaches including RNAi knockdown and rescue experiments. The authors propose that Nup107 influences EcR localization indirectly by reducing the expression of Halloween genes, a consequence of impaired Torso signaling. However, it remains uncertain whether Torso is the sole receptor tyrosine kinase involved, and this disruption ultimately leads to decreased ecdysone production. In addition, finding a mechanism would strengthen the findings as the currently proposed mechanism is not completely supported by the data.

Reviewer #1 (Public review):

This study provides a thorough analysis of Nup107's role in Drosophila metamorphosis, demonstrating that its depletion leads to developmental arrest at the third larval instar stage due to disruptions in ecdysone biosynthesis and EcR signaling. Importantly, the authors establish a novel connection between Nup107 and Torso receptor expression, linking it to the hormonal cascade regulating pupariation.

The authors have addressed most of the concerns raised in my initial review, particularly those outlined in the public comments. However, I note that they have not directly responded to several specific points raised in the "Author Recommendations" section. That said, a key mechanistic question remains unresolved and deserves further experimental or at least conceptual clarification.

It has been previously shown that Nup107 regulates the nuclear translocation of dpERK (Kim et al., 2010). This observation may provide a mechanistic explanation for the developmental arrest observed upon Nup107 depletion in the prothoracic gland (PG). Given that PG growth and ecdysone biosynthesis are driven by several receptor tyrosine kinases, it is plausible that loss of Nup107 impairs dpERK nuclear translocation, thereby functionally shutting down RTK-dependent transcriptional responses, including those activating Halloween gene expression. This model is supported by the finding that activated Ras (rasV12) can rescue the arrest, likely by generating sufficiently high levels of dpERK such that some fraction enters the nucleus despite impaired translocation. This hypothesis may explain the discrepancy between the complete developmental arrest observed upon Nup107 depletion and the developmental delay seen in Torso mutants.

Similarly, the rescue by Torso, but not EGFR, may reflect differences in receptor activation thresholds. It has been proposed that Torso overexpression might leads to ligand-independent dimerization and constitutive activity, whereas EGFR overexpression may remain ligand-dependent and thus insufficient under compromised dpERK transport conditions. A critical experiment to validate this model would be to examine dpERK localization in PG cells upon Nup107 depletion. This would help establish whether defective nuclear import of dpERK underlies the observed developmental arrest. Even if technically challenging, the authors should at least discuss this hypothesis explicitly in the revised manuscript.

In addition, it has been shown that TGFβ/Activin signaling regulates Torso expression in the prothoracic gland (PG). Therefore, it is plausible that this pathway may also be affected by impaired nuclear translocation of downstream effectors due to Nup107 depletion. This raises the possibility that Nup107 plays a broad regulatory role, impacting multiple signaling cascades-such as RTK and TGFβ/Activin pathways-by controlling the nuclear import of their key effectors.

Reviewer #2 (Public review):

Summary:

The manuscript by Kawadkar et al investigates the role of Nup107 in developmental progression via regulation of ecdysone signaling. The authors identify an interesting phenotype of Nup107 whole body RNAi depletion in Drosophila development - developmental arrest at the late larval stage. Nup107-depleted larvae exhibit mis-localization of the Ecdysone receptor (EcR) from the nucleus to the cytoplasm and reduced expression of EcR taret genes in salivary glands, indicative of compromised ecdysone signaling. This mis-localization of EcR in salivary glands was phenocopied when Nup107 was depleted only in the prothoracic gland (PG), suggesting that it is not nuclear transport of EcR but presence of ecdysone (normally secreted from PG) that is affected. Consistently, whole body levels of ecdysone were shown to be reduced in Nup107 KD, particularly at the late third instar stage when a spike in ecdysone normally occurs. Importantly, the authors could rescue the developmental arrest and EcR mis-localization phenotypes of Nup107 KD by adding exogenous ecdysone, supporting the notion that Nup107 depletion disrupts biosynthesis of ecdysone, which arrests normal development. Additionally, they found that rescue of Nup107 KD phenotype can also be achieved by over-expression of the receptor tyrosine kinase torso, which is thought to be the upstream regulator of ecdysone synthesis in the PG. Transcript levels of torso are also shown to be downregulated in the Nup107KD, as are transcript levels of multiple ecdysone biosynthesis genes. Together, these experiments reveal a new role of Nup107 or nuclear pore levels in hormone-driven developmental progression, likely via regulation of levels of torso and torso-stimulated ecdysone biosynthesis.

Strengths:

The developmental phenotypes of an NPC component presented in the manuscript are striking and novel, and the data appears to be of high quality. The rescue experiments are particularly significant, providing strong evidence that Nup107 functions upstream of torso and ecdysone levels in regulation of developmental timing and progression.

Weaknesses:

The underlying mechanism is however not clear, and any insight into how Nup107 may regulate these pathways would greatly strengthen the manuscript. Some suggestions to address this are detailed below.

Major questions:

(1) Determining how specific this phenotype is to Nup107 vs. to reduced NPC levels overall would give some mechanistic insight. Does knocking down other components of the Nup107 subcomplex (the Y-complex) lead to similar phenotypes? Given the published gene regulatory function of Nup107, do other gene regulatory Nups such as Nup98 or Nup153 produce these phenotypes?

(2) In a related issue, does this level of Nup107 KD produce lower NPC levels? It is expected to, but actual quantification of nuclear pores in Nup107-depleted tissues should be added. These and above experiments would help address a key mechanistic question - is this phenotype the result of lower numbers of nuclear pores or specifically of Nup107?

(3) Additional experiments on how Nup107 regulates torso would provide further insight. Does Nup107 regulate transcription of torso or perhaps its mRNA export? Looking at nascent levels of the torso transcript and the localization of its mRNA can help answer this question. Or alternatively, does Nup107 physically bind torso?

(4) The depletion level of Nup107 RNAi specifically in the salivary gland vs. the prothoracic gland should be compared by RT-qPCR or western blotting.

(5) The UAS-torso rescue experiment should also include the control of an additional UAS construct - so Nup107; UAS-control vs Nup107; UAS-torso should be compared in the context of rescue to make sure the Gal4 driver is functioning at similar levels in the rescue experiment.

Minor:

(6) Figures and figure legends can stand to be more explicit and detailed, respectively.

Comments on revisions:

The revised manuscript addresses several outstanding issues, most importantly the question of whether the developmental delay phenotype of Nup107 is exhibited by other Nups.

I recommend that the authors include the data they provide in the rebuttal letter on Nup153 KD not showing the delay phenotype (Figure R1) into the actual manuscript. It's an important mechanistic question raised by multiple reviewers, and would strengthen the authors' conclusions. Ideally, knock downs of other Nups of the Nup107 complex should be investigated, especially given that all those RNAi lines are publicly available.

Figure 6B should also specify whether the torso transcript being measured is mRNA or nascent, as it would help understand whether it's transcription or mRNA stability that is affected by Nup107 KD.

Reviewer #3 (Public review):

These findings suggest that Nup107 is involved in regulating ecdysone signaling during developmental transitions, with depletion of Nup107 disrupting hormone-regulated processes. Moreover, the rescue experiments hint that Nup107 might directly influence EcR signaling and ecdysone biosynthesis, though the precise molecular mechanism remains unclear.

Overall, the manuscript presents compelling data supporting Nup107's role in regulating developmental transitions.

Comments on revisions:

RNAi specificity: The authors now provide a more thorough discussion of off-target effects and justify their reliance on the Nup107KK RNAi line. The explanation regarding the predicted off-target for the GD line and their choice to use the KK line with a known insertion site is appropriate and adequately addresses the original concern.

NPC component specificity: The authors clarify that among the Nup107 complex members tested, only Nup107 knockdown induced developmental arrest. Their inclusion of Nup153 as a control helps to support the specificity of the phenotype, although expanding this analysis beyond a single additional Nup would further strengthen the claim.

Mechanistic clarity: The authors now distinguish between Nup107's upstream role in regulating torso and ecdysone biosynthetic genes versus direct control of EcR translocation. The clarification that EcR nuclear localization is 20E-dependent but Nup107-independent improves interpretive clarity.

The molecular mechanism linking Nup107 to torso regulation remains somewhat speculative. A deeper exploration of whether Nup107 influences transcriptional regulation through chromatin association (as the authors suggest) would strengthen the mechanistic narrative.

Conclusion:

Overall, the authors have addressed the major concerns raised in the initial review, and the revised manuscript presents a more coherent and compelling case for Nup107 as a regulator of developmental timing via the ecdysone signaling axis. While some mechanistic questions remain, the core findings are supported by the data, and the work provides novel insights into NPC function in development.

eLife Assessment

This study employed state-of-the-art quantitative imaging and genomics approaches to address a fundamental question regarding the establishment of Polycomb domains during Drosophila embryogenesis. The critical developmental stage was pinpointed to the maternal-to-zygotic transition, rather than earlier stages, providing clarification for the field. The roles of two factors, Zelda and GAGA-factor, were investigated, which reveal that Zelda, but not GAGA-factor, contributes to this process. These compelling findings have implications for chromatin and developmental biology.

Reviewer #1 (Public review):

This well-conceived manuscript investigates the mechanisms that shape the chromatin landscape following fertilization, using the Drosophila embryo as a model system. Importantly, the authors revisit conflicting data using new approaches and analysis to show that the silent H3K27me3 mark deposited by PRC2 is established de novo in the embryo in coordination with the slowing of the nuclear division cycle and activation of zygotic transcription. Unexpectedly, they demonstrate that the transcription factor GAF is not required for the deposition of this mark, but that the well-studied pioneer factor Zelda, which is required for widespread gene expression, is required for H3K27me3 deposition at a subset of regions. The experiments are rigorously performed, and interpretations are clear. Strengths of this manuscript include the rigor of the experimental design, careful analysis, and well-supported conclusions. Some additional citations, analysis, and broadening of the Discussion section to include additional models and data would further strengthen this manuscript.

Reviewer #2 (Public review):

Epigenetic silencing of target genes by the Polycomb pathway is central to maintenance of cell fates during development and depends on repressive chromatin states involving Polycomb complexes and histone modifications. However, the mechanisms by which these chromatin states are built at the earliest stages of development are unclear. Here, Gonzaga-Saavedra and colleagues use the premier experimental system for studying Polycomb gene regulation, Drosophila development, to investigate when Polycomb domains emerge and how they are assembled. Using a combination of CRISPR gene editing, imaging, and genomic profiling, they determine that while H3K27me3 is initially present in the first nuclear cycles, it quickly dissipates and does not re-emerge until mid-nuclear cycle 14, during the major wave of zygotic genome activation (ZGA). This finding helps resolve current discrepancies in the field, informs potential mechanisms of transgenerational inheritance, and indicates that repressive Polycomb domains are built de novo on target genes in embryogenesis. The authors then set out to examine how Polycomb domains are built. Through live imaging and immunofluorescence, they determine that the histone H3K27 methyltransferase, E(z), is present in nuclei at high levels throughout cleavage and blastoderm stages. By contrast, they determine that several Polycomb proteins that bind PREs (cis elements that demarcate Polycomb targets in the genome) are absent from early cleavage nuclei and progressively increase following nuclear cycle 10. These findings suggest that the absence of H3K27me3 in early embryos may be due to failure to assemble functional Polycomb complexes at target genes. Lastly, the authors test the requirement of two transcription factors with important roles in ZGA, GAF, and ZLD. Despite binding to many PREs and regulating chromatin accessibility in early embryos, they find that GAF is largely dispensable for the emergence of H3K27me3 domains. On the other hand, they find that the pioneer factor ZLD is required for proper H3K27me3 emergence; in its absence, some Polycomb domains accumulate greater levels of H3K27me3, whereas other Polycomb domains accumulate less H3K27me3.

Strengths:

The strengths of this study are manifold. It studies an important topic with broad interest to the chromatin and epigenetics fields. It is well-written with detailed method descriptions. In addition, the experimental design and rigor of execution are exceptional despite working with very small amounts of biological material. Example strengths include that the Polycomb proteins studied were tagged with the same epitope, permitting direct quantitative comparisons in imaging and in genomics experiments. Microscopy studies are quantified and performed both via live imaging and via immunofluorescence. The microscopy studies reinforce and extend conclusions made via ChIP. Sophisticated loss-of-function analyses allow for direct mechanistic tests of Polycomb domain emergence.

Weaknesses:

Overall, the study is quite strong already, but it can be further strengthened in several ways. First, several conclusions should be refined based on the data presented. Second, the extent to which ZLD is important for initiating Polycomb domain formation should be made clearer. Third, additional genomic profiling experiments are needed to provide insight into models explaining why H3K27me3 is absent prior to NC14.

Reviewer #3 (Public review):

Gonzaga-Saavedra et al report an analysis on genomic binding of Polycomb group proteins, and of H2Aub1 and H3K27me3 domain formation in the early Drosophila embryo. Using carefully staged embryos during the nuclear cycles (NC) leading up to the cellular blastoderm stage, the authors provide compelling evidence that H3K27me3 domains at PcG target genes are only established during NC14 and do not exist in NC13. In contrast, H2Aub1 domains already start to appear during NC13. The authors show that E(z), the catalytic subunit of the H3K27 histone methyltransferase PRC2, is readily detected in interphase nuclei during the rapid nuclear divisions in pre-blastoderm embryos. In contrast, the DNA-binding proteins Pho, Cg, and GAF that are known (Pho) or have been postulated (Cg, GAF) to anchor PRC2 and PRC1 to Polycomb Response Elements (PREs) in Polycomb target genes only start to show nuclear localization from NC10 onwards with gradually increasing nuclear concentrations, reaching a maximum during NC14. These data strongly corroborate the simple, straightforward view that targeting of PRC2 and PRC1 to PREs by sequence-specific DNA-binding proteins is a prerequisite for the formation of H3K27me3 and H2Aub1 domains at Polycomb target genes.

The authors then explore the potential role of GAF/Trl in this process. They find that in embryos depleted of GAF/Trl, H3K27me3 domain formation is largely unperturbed.

The authors also depleted the pioneer factor Zelda (Zld) and found that removal of Zld results in a more complex outcome. Zelda appears to counteract the accumulation of H3K27me3 at the Polycomb targets eve and zen, but also appears to be required for effective H3K27me3 domain formation at Polycomb targets such as amos or atonal.

This is a very thorough study that reports data of superior technical quality that are highly relevant for the field. The study by Gonzaga-Saavedra et al extends and strengthens previous work from the labs of Eisen (Li et al, eLife 2014) and Zeitlinger (Chen et al, eLife 2013) to convincingly demonstrate that Polycomb domain formation in the early embryo occurs during ZGA but that such domains do not exist prior to ZGA. This should now finally put to rest earlier claims by the Iovino lab (Zenk et al, Science 2017) that H3K27me3 domains present in the zygote nucleus would be propagated and partially maintained during the rapid nuclear cleavage cycles and serve as seeds for H3K27me3 domain formation during ZGA.

The experiments analyzing H3K27me3 domain formation in embryos depleted of GAF/Trl or Zelda will be of great interest to the field.

Author response:

Reviewer 1:

We appreciate the reviewer’s positive assessment and in revision will expand the Discussion to clarify some of the mechanistic insights of this work, as well as to include expanded treatment of related studies in other model systems.

Reviewer 2:

We are grateful for the reviewer’s thorough and supportive comments. We will carefully revise assertions and conclusions for objectivity. Additional analysis of the Zelda experiments will be performed and experimental data tables will be updated to report these results. For the point about providing “insight into models explaining why H3K27me3 is absent prior to NC14,” we have recently submitted a related preprint that addresses this issue directly (Degen, Gonzaga-Saavedra, and Blythe, bioRxiv 2025). In summary, we find evidence that a maternal PcG imprint is indeed maintained through cleavage divisions, albeit through lower-order methylation states (maximally H3K27me2). We chose not to include these additional results in this manuscript to maintain the focus of this study on ZGA. Our revision of this manuscript will include a section in the Discussion that synthesizes the conclusions of the two studies.

Reviewer 3:

We thank the reviewer for recognizing the strength of our data and conclusions, and we agree that our results help settle conflicting claims in the field. We will emphasize Zelda’s context-dependent effects more clearly in the revised manuscript.

References:

Degen EA, Gonzaga-Saavedra N, Blythe SA. Lower-order methylation states underlie the maintenance and re-establishment of Polycomb modifications in Drosophila embryogenesis. bioRxiv [Preprint]. 2025 Jul 29:2025.07.25.666882. doi: 10.1101/2025.07.25.666882. PMID: 40766521; PMCID: PMC12324246.

eLife Assessment

This work presents a valuable resource combining scRNA-seq and spatial transcriptomics studies to map mouse pre-clinical models of colorectal cancer, identifying distinct cellular programs and microenvironments that could enhance patient stratification and therapeutic approaches in colorectal cancer. While the evidence provided in the manuscript are not fully validated, these solid data were collected and analyzed using a validated methodology that will be of interest to the community in future studies.

Reviewer #2 (Public review):

In their study, Avraham-Davidi et al. combined scRNA-seq and spatial mapping studies to profile two preclinical mouse models of colorectal cancer: Apcfl/fl VilincreERT2 (AV) and Apcfl/fl LSL-KrasG12D Trp53fl/fl Rosa26LSL-tdTomato/+ VillinCreERT2 (AKPV). In the first part of the manuscript, the authors describe the analysis of the normal colon and dysplastic lesions induced in these models following tamoxifen injection. They highlight broad variations in immune and stromal cell composition within dysplastic lesions, emphasizing the infiltration of monocytes and granulocytes, the accumulation of IL-17+gdT cells and the presence of a distinct group of endothelial cells. A major focus the study is the remodeling of the epithelial compartment, where most significant changes are observed. Using no-negative matrix factorization, the authors identify molecular programs of epithelial cell functions, emphasizing stemness, Wnt signaling, angiogenesis and inflammation as majors features associated with dysplastic cells. They conclude that findings from scRNA-seq analyses in mouse models are transposable to human CRC. In the second part of the manuscript, the authors aim to provide the spatial contexture for their scRNA-seq findings using Slide-seq and TACCO. They demonstrate that dysplastic lesions are disorganized and contain tumor-specific regions, which contextualize the spatial proximity between specific cell states and gene programs. Finally, they claim that these spatial organizations are conserved in human tumors and associate region-based gene signatures with patient outcome in public datasets. Overall, the data were collected and analyzed using solid and validated methodology to offer a useful resource to the community.

Main comments:

(1) Clarity. The manuscript would benefit from a substantial reorganization to improve clarity and accessibility for a broad readership. The text could be shortened and the number of figure panels reduced to emphasize the novel contributions of this work while minimizing extensive discussions on general and expected findings, such as tissue disorganization in dysplastic lesions. Additionally, figure panels are not consistently introduced in the correct order, and some are not discussed at all (e.g., Fig. S1D; Fig. 3C is introduced before Fig. 3A; several panels in Fig. 4 are not discussed). The annotation of scRNA-seq cell states is insufficiently explained, with no corresponding information about associated genes provided in the figures or tables. Multiple annotations are used to describe cell groups (e.g., TKN01 = γδ T and CD8 T, TKN05 = γδT_IL17+), but these are not jointly accessible in the figures, making the manuscript challenging to follow. It is also not clear what is the respective value of the two mouse models and timepoints of tissue collection in the analysis.

(2) Novelty. While the study is of interest, it does not present major findings that significantly advance the field or motivate new directions and hypotheses. Many conclusions related to tissue composition and patient outcomes, such as the epithelial programs of Wnt signaling, angiogenesis, and stem cells, are well-established and not particularly novel. Greater exploration of the scRNA-seq data beyond cell type composition could enhance the novelty of the findings. For instance, several tumor microenvironment clusters uniquely detected in dysplastic lesions (e.g., Mono2, Mono3, Gran01, Gran02) are identified, but no further investigation is conducted to understand their biological programs, such as applying nNMF as was done for epithelial cells. Additional efforts to explore precise tissue localization and cellular interactions within tissue niches would provide deeper insights and go beyond the limited analyses currently displayed in the manuscript.

(3) Validation. Several statements made by the authors are insufficiently supported by the data presented in the manuscript and should be nuanced in the absence of proper validation. For example: 1.) RNA velocity analyses: The conclusions drawn from these analyses are speculative and need further support. 2.) Annotations of epithelial clusters as dysplastic: These annotations could have been validated through morphological analyses and staining on FFPE slides. 3.) Conservation of mouse epithelial programs in human tumors: The data in Figure S5B does not convincingly demonstrate enrichment of stem cell program 16 in human samples. This should be more explicitly stated in the text, given the emphasis placed on this program by the authors. 4.) Figure S6E: Cluster Epi06 is significantly overrepresented in spatial data compared to scRNA-seq, yet the authors claim that cell type composition is largely recapitulated without further discussion, which reduces confidence in other conclusions drawn.<br /> Furthermore, stronger validation of key dysplastic regions (regions 6, 8, and 11) in mouse and human tissues using antibody-based imaging with markers identified in the analyses would have considerably strengthened the study. Such validation would better contextualize the distribution, composition, and relative abundance of these regions within human tumors, increasing the significance of the findings and aiding the generation of new pathophysiological hypotheses.

Comments on revisions:

The authors have improved the clarity of the manuscript and responded adequately to all my initial comments.<br /> I don't have any other comments. Congratulations to the authors on this work.

eLife Assessment

In this valuable study, Taber et al. used a battery of biophysical and structural approaches to characterize the impact of erythrocytosis-related mutations in prolyl hydroxylase domain protein 2 (PHD2). The authors show that PHD2 mutant proteins are destabilized, thus supporting the tenet that dysregulation of PHD2/hypoxia induced factor (HIF) axis underpins erythrocytosis, while providing solid evidence that N-terminal ODD prolyl hydroxylation of HIF is indispensable for these phenotypes. These findings were found to be of interest for researchers focusing on oxygen sensing in homeostasis and pathological states.

Reviewer #1 (Public review):

Summary:

Taber et al report the biochemical characterization of 7 mutations in PHD2 that induce erythrocytosis. Their goal is to provide a mechanism for how these mutations cause the disease. PHD2 hydroxylates HIF1a in the presence of oxygen at two distinct proline residues (P564 and P402) in the "oxygen degradation domain" (ODD). This leads to the ubiquitylation of HIF1a by the VHL E3 ligase and its subsequent degradation. Multiple mutations have been reported in the EGLN1 gene (coding for PHD2), which are associated with pseudohypoxic diseases that include erythrocytosis. Furthermore, 3 mutations in PHD2 also cause pheochromocytoma and paraganglioma (PPGL), a neuroendocrine tumour. These mutations likely cause elevated levels of HIF1a, but their mechanisms are unclear. Here, the authors analyze mutations from 152 case reports and map them on the crystal structure. They then focus on 7 mutations, which they clone in a plasmid and transfect into PHD2-KO to monitor HIF1a transcriptional activity via a luciferase assay. All mutants show impaired activation. Some mutants also impaired stability in pulse chase turnover assays (except A228S, P317R, and F366L). In vitro purified PHD2 mutants display a minor loss in thermal stability and some propensity to aggregate. Using MST technology, they show that P317R is strongly impaired in binding to HIF1a and HIF2a, whereas other mutants are only slightly affected. Using NMR, they show that the PHD2 P317R mutation greatly reduces hydroxylation of P402 (HIF1a NODD), as well as P562 (HIF1a CODD), but to a lesser extent. Finally, BLI shows that the P317R mutation reduces affinity for CODD by 3-fold, but not NODD.

Strengths:

(1) Simple, easy-to-follow manuscript. Generally well-written.

(2) Disease-relevant mutations are studied in PHD2 that provide insights into its mechanism of action.

(3) Good, well-researched background section.

Weaknesses:

(1) Poor use of existing structural data on the complexes of PHD2 with HIF1a peptides and various metals and substrates. A quick survey of the impact of these mutations (as well as analysis by Chowdhury et al, 2016) on the structure and interactions between PHD2 peptides of HIF1a shows that the P317R mutation interferes with peptide binding. By contrast, F366L will affect the hydrophobic core, and A228S is on the surface, and it's not obvious how it would interfere with the stability of the protein.

(2) To determine aggregation and monodispersity of the PHD2 mutants using size-exclusion chromatography (SEC), equal quantities of the protein must be loaded on the column. This is not what was done. As an aside, the colors used for the SEC are very similar and nearly indistinguishable.

(3) The interpretation of some mutants remains incomplete. For A228S, what is the explanation for its reduced activity? It is not substantially less stable than WT and does not seem to affect peptide hydroxylation.

(4) The interpretation of the NMR prolyl hydroxylation is tainted by the high concentrations used here. First of all, there is a likely a typo in the method section; the final concentration of ODD is likely 0.18 mM, and not 0.18 uM (PNAS paper by the same group in 2024 reports using a final concentration of 230 uM). Here, I will assume the concentration is 180 uM. Flashman et al (JBC 2008) showed that the affinity of the NODD site (P402; around 10 uM) for PHD2 is 10-fold weaker than CODD (P564, around 1 uM). This likely explains the much faster kinetics of hydroxylation towards the latter. Now, using the MST data, let's say the P317R mutation reduces the affinity by 40-fold; the affinity becomes 400 uM for NODD (above the protein concentration) and 40 uM for CODD (below the protein concentration). Thus, CODD would still be hydroxylated by the P317R mutant, but not NODD.

(5) The discrepancy between the MST and BLI results does not make sense, especially regarding the P317R mutant. Based on the crystal structures of PHD2 in complex with the ODD peptides, the P317R mutation should have a major impact on the affinity, which is what is reported by MST. This suggests that the MST is more likely to be valid than BLI, and the latter is subject to some kind of artefact. Furthermore, the BLI results are inconsistent with previous results showing that PHD2 has a 10-fold lower affinity for NODD compared to CODD.

(6) Overall, the study provides some insights into mutants inducing erythrocytosis, but the impact is limited. Most insights are provided on the P317R mutant, but this mutant had already been characterized by Chowdhury et al (2016). Some mutants affect the stability of the protein in cells, but then no mechanism is provided for A228S or F366L, which have stabilities similar to WT, yet have impaired HIF1a activation.

Comments on revision:

While the authors have addressed my concerns regarding the SEC experiments and the structural interpretation of most mutants, I remain unconvinced by their interpretation of the P317R mutant and affinity measurements. The BLI and MST data remain inconsistent for P317R binding to CODD, and the authors' response is essentially that the fluorescent labeling of P317R (but not other mutants) uniquely interferes with binding to the NODD/CODD peptides, which does not make a lot of sense. The fluorescent labeling target lysine residues; while there are lysine in PHD2 in proximity to the peptide binding site, labeling these sites would affect binding to all mutants, not only P317R (which does not introduce any new labeling site). Furthermore, the authors did not really address the discrepancy with the observations by Flashman et al (2008) that NODD binds more weakly than CODD, which is inconsistent with their BLI results. Another point that makes me doubt the validity of the BLI results is the poor fit of the sensorgrams and the slow dissociation kinetics, which is inconsistent with the relatively low affinity in the 2-6 uM range.

Reviewer #2 (Public review):

Summary:

Mutations in the prolyl hydroxylase, PHD2, cause erythrocytosis and, in some cases, can result in tumorigenesis. Taber and colleagues test the structural and functional consequences of seven patient-derived missense mutations in PHD2 using cell-based reporter and stability assays, and multiple biophysical assays, and find that most mutations are destabilizing. Interestingly, they discover a PHD2 mutant that can hydroxylate the C-terminal ODD, but not the N-terminal ODD, which suggests the importance of N-terminal ODD for biology. A major strength of the manuscript is the multidisciplinary approach used by the authors to characterize the functional and structural consequences of the mutations. However, the manuscript had several major weaknesses, such as an incomplete description of how the NMR was performed, a justification for using neighboring residues as a surrogate for looking at prolyl hydroxylation directly, or a reference to the clinical case studies describing the phenotypes of patient mutations. Additionally, the experimental descriptions for several experiments are missing descriptions of controls or validation, which limits their strength in supporting the claims of the authors.

Strengths:

(1) This manuscript is well-written and clear.

(2) The authors use multiple assays to look at the effects of several disease-associated mutations, which support the claims.

(3) The identification of P317R as a mutant that loses activity specifically against NODD, which could be a useful tool for further studies in cells.

Weaknesses:

Major:

(1) The source data for the patient mutations (Figure 1) in PHD2 is not referenced, and it's not clear where this data came from or if it's publicly available. There is no section describing this in the methods.

(2) The NMR hydroxylation assay.

A. The description of these experiments is really confusing. The authors have published a recent paper describing a method using 13C-NMR to directly detect proly-hydroxylation over time, and they refer to this manuscript multiple times as the method used for the studies under review. However, it appears the current study is using 15N-HSQC-based experiments to track the CSP of neighboring residues to the target prolines, so not the target prolines themselves. The authors should make this clear in the text, especially on page 9, 5th line, where they describe proline cross-peaks and refer to the 15N-HSQC data in Figure 5B.<br /> B. The authors are using neighboring residues as reporters for proline hydroxylation, without validating this approach. How well do CSPs of A403 and I566 track with proline hydroxylation? Have the authors confirmed this using their 13C-NMR data or mass spec?<br /> C. Peak intensities. In some cases, the peak intensities of the end point residue look weaker than the peak intensities of the starting residue (5B, PHD2 WT I566, 6 ct lines vs. 4 ct lines). Is this because of sample dilution (i.e., should happen globally)? Can the authors comment on this?

(3) Data validating the CRISPR KO HEK293A cells is missing.

(4) The interpretation of the SEC data for the PHD2 mutants is a little problematic. Subtle alterations in the elution profiles may hint at different hydrodynamic radii, but as the samples were not loaded at equal concentrations or volumes, these data seem more anecdotal, rather than definitive. Repeating this multiple times, using matched samples, followed by comparison with standards loaded under identical buffer conditions, would significantly strengthen the conclusions one could make from the data.

Minor:

(1) Justification for picking the seven residues is not clearly articulated. The authors say they picked 7 mutants with "distinct residue changes", but no further rationale is provided.

(2) A major finding of the paper is that a disease-associated mutation, P317R, can differentially affect HIF1 prolyhydroxylation, however, additional follow-up studies have not been performed to test this in cells or to validate the mutant in another method. Is it the position of the proline within the catalytic core, or the identity of the mutation that accounts for the selectivity?

Comments on revision:

The revised manuscript addresses most of my concerns, i.e performing SEC experiments under matched sample concentrations, and incorporating additional data to justify the use of surrogate residues to monitor proline hydroxylation. I appreciate the improvements in the text to clarify the NMR experiments, but I still find their description confusing. Although the authors are using neighboring residues to monitor proline hydroxylation (which they justify convincingly using supplementary data), the language in the text suggests they are (and can?) monitor them directly (i.e. referring to proline cross-peaks in an 15N-HSQC spectrum). The axis labels in Figure 5B also seem to have become mislabeled in this revised version.

Reviewer #3 (Public review):

Summary:

This is an interesting and clinically relevant in vitro study by Taber et al., exploring how mutations in PHD2 contribute to erythrocytosis and/or neuroendocrine tumors. PHD2 regulates HIFα degradation through prolyl-hydroxylation, a key step in the cellular oxygen-sensing pathway.

Using a time-resolved NMR-based assay, the authors systematically analyze seven patient-derived PHD2 mutants and demonstrate that all exhibit structural and/or catalytic defects. Strikingly, the P317R variant retains normal activity toward the C-terminal proline but fails to hydroxylate the N-terminal site. This provides the first direct evidence that N-terminal prolyl-hydroxylation is not dispensable, as previously thought.

The findings offer valuable mechanistic insight into PHD2-driven effects and refine our understanding of HIF regulation in hypoxia-related diseases.

Strengths:

The manuscript has several notable strengths. By applying a novel time-resolved NMR approach, the authors directly assess hydroxylation at both HIF1α ODD sites, offering a clear functional readout. This method allows them to identify the P317R variant as uniquely defective in NODD hydroxylation, despite retaining normal activity toward CODD, thereby challenging the long-held view that the N-terminal proline is biologically dispensable. The work significantly advances our understanding of PHD2 function and its role in oxygen sensing, and might help in the future interpretation and clinical management of associated erythrocytosis.

Weaknesses:

There is a lack of in vivo/ex vivo validation. This is actually required to confirm whether the observed defects in hydroxylation-especially the selective NODD impairment in P317R-are sufficient to drive disease phenotypes such as erythrocytosis.

The reliance on HRE-luciferase reporter assays may not reliably reflect the PHD2 function and highlights a limitation in the assessment of downstream hypoxic signaling.

The study clearly documents the selective defect of the P317R mutant, but the structural basis for this selectivity is not addressed through high-resolution structural analysis (e.g., cryo-EM).

Given the proposed central role of HIF2α in erythrocytosis, direct assessment of HIF2α hydroxylation by the mutants would have strengthened the conclusions.

Comments on revision:

The revised manuscript by Taber et al. addresses the key points raised during the review process in a comprehensive and appropriate manner. While some limitations remain, such as the lack of in vivo validation or direct HIF2α assessment, I agree with the authors that these are beyond the scope of the current in vitro-focused study. The authors' primary goal was to define the structural and functional defects caused by disease-associated PHD2 mutations. In this respect, the evidence they present is largely convincing and methodologically appropriate. Additional clarifications and an expanded discussion of the luciferase assay's limitations and the P317R structural context strengthen the manuscript further.

Author response:

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

Reviewer #1 (Public review): 

Summary: 

Taber et al report the biochemical characterization of 7 mutations in PHD2 that induce erythrocytosis. Their goal is to provide a mechanism for how these mutations cause the disease. PHD2 hydroxylates HIF1a in the presence of oxygen at two distinct proline residues (P564 and P402) in the "oxygen degradation domain" (ODD). This leads to the ubiquitylation of HIF1a by the VHL E3 ligase and its subsequent degradation. Multiple mutations have been reported in the EGLN1 gene (coding for PHD2), which are associated with pseudohypoxic diseases that include erythrocytosis. Furthermore, 3 mutations in PHD2 also cause pheochromocytoma and paraganglioma (PPGL), a neuroendocrine tumour. These mutations likely cause elevated levels of HIF1a, but their mechanisms are unclear. Here, the authors analyze mutations from 152 case reports and map them on the crystal structure. They then focus on 7 mutations, which they clone in a plasmid and transfect into PHD2-KO to monitor HIF1a transcriptional activity via a luciferase assay. All mutants show impaired activation. Some mutants also impaired stability in pulse chase turnover assays (except A228S, P317R, and F366L). In vitro purified PHD2 mutants display a minor loss in thermal stability and some propensity to aggregate. Using MST technology, they show that P317R is strongly impaired in binding to HIF1a and HIF2a, whereas other mutants are only slightly affected. Using NMR, they show that the PHD2 P317R mutation greatly reduces hydroxylation of P402 (HIF1a NODD), as well as P562 (HIF1a CODD), but to a lesser extent. Finally, BLI shows that the P317R mutation reduces affinity for CODD by 3-fold, but not NODD.  

Strengths: 

(1) Simple, easy-to-follow manuscript. Generally well-written. 

(2) Disease-relevant mutations are studied in PHD2 that provide insights into its mechanism of action. 

(3) Good, well-researched background section. 

Weaknesses: 

(1) Poor use of existing structural data on the complexes of PHD2 with HIF1a peptides and various metals and substrates. A quick survey of the impact of these mutations (as well as analysis by Chowdhury et al, 2016) on the structure and interactions between PHD2 peptides of HIF1a shows that the P317R mutation interferes with peptide binding. By contrast, F366L will affect the hydrophobic core, and A228S is on the surface, and it's not obvious how it would interfere with the stability of the protein. 

Thank you for the comment.  We have further analyzed the mutations on the available PHD2 crystal structures in complex with HIFα to discern how these substitution mutations may impact PHD2 structure and function.  This analysis has been added into the discussion.

(2) To determine aggregation and monodispersity of the PHD2 mutants using size-exclusion chromatography (SEC), equal quantities of the protein must be loaded on the column. This is not what was done. As an aside, the colors used for the SEC are very similar and nearly indistinguishable. 

Agreed. We have performed an additional experiment as suggested by the reviewer to further assess aggregation and hydrodynamic size.  The colors used in the graph were changed for clearer differentiation between samples.

(3) The interpretation of some mutants remains incomplete. For A228S, what is the explanation for its reduced activity? It is not substantially less stable than WT and does not seem to affect peptide hydroxylation. 

We agree with the reviewer that the causal mechanism for some of the tested disease-causing mutants remain unclear.  The negative findings also raise the notion, perhaps considered controversial, that there may be other substrates of PHD2 that are impacted by certain mutations, which contribute to disease pathogenesis.  A brief paragraph discussing this has been included in the discussion.

(4) The interpretation of the NMR prolyl hydroxylation is tainted by the high concentrations used here. First of all, there is a likely a typo in the method section; the final concentration of ODD is likely 0.18 mM, and not 0.18 uM (PNAS paper by the same group in 2024 reports using a final concentration of 230 uM). Here, I will assume the concentration is 180 uM. Flashman et al (JBC 2008) showed that the affinity of the NODD site (P402; around 10 uM) for PHD2 is 10-fold weaker than CODD (P564, around 1 uM). This likely explains the much faster kinetics of hydroxylation towards the latter. Now, using the MST data, let's say the P317R mutation reduces the affinity by 40-fold; the affinity becomes 400 uM for NODD (above the protein concentration) and 40 uM for CODD (below the protein concentration). Thus, CODD would still be hydroxylated by the P317R mutant, but not NODD. 

The HIF1α concentration was indeed an oversight, which will be corrected to 0.18 mM.  The study by Flashman et al.[1] showing PHD2 having a lower affinity to the NODD than CODD likely contributes to the differential hydroxylation rates via PHD2 WT.  We showed here via MST that PHD2 P317R had K[d] of 320 ± 20 uM for HIF1αCODD, which should have led to a severe enzymatic defect, even at the high concentrations used for NMR (180 uM).  However, we observed only a subtle reduction in hydroxylation efficiency in comparison to PHD2 WT.  Thus, we performed another binding method using BLI that showed a mild binding defect on CODD by PHD2 P317R, consistent with NMR data.  The perplexing result is the WT-like binding to the NODD by PHD2 P317R, which appears inconsistent with the severe defect in NODD hydroxylation via PHD2 P317R as measured via NMR.  These results suggest that there are supporting residues within the PHD2/NODD interface that help maintain binding to NODD but compromise the efficiency of NODD hydroxylation upon PHD2 P317R mutation. 

(5) The discrepancy between the MST and BLI results does not make sense, especially regarding the P317R mutant. Based on the crystal structures of PHD2 in complex with the ODD peptides, the P317R mutation should have a major impact on the affinity, which is what is reported by MST. This suggests that the MST is more likely to be valid than BLI, and the latter is subject to some kind of artefact. Furthermore, the BLI results are inconsistent with previous results showing that PHD2 has a 10-fold lower affinity for NODD compared to CODD. 

The reviewer’s structural prediction that P317R mutation should cause a major binding defect, while agreeable with our MST data, is incongruent with our NMR and the data from Chowdhury et al.[2] that showed efficient hydroxylation of CODD via PHD2 P317R.  Moreover, we have attempted to model NODD and CODD on apo PHD2 P317R structure and found that the mutation had no major impact on CODD while the mutated residue could clash with NODD, causing a shifting of peptide positioning on the protein.  However, these modeling predictions, like any in silico projections, would need experimental validation.  As mentioned in our preceding response, we also performed BLI, which showed that PHD2 P317R had a minor binding defect for CODD, consistent with the NMR results and findings by Chowdhury et al[2].  NODD binding was also measured with BLI as purified NODD peptides were not amenable for soluble-based MST assay, which showed similar K[d]’s for PHD2 WT and P317R.  Considering the absence of NODD hydroxylation via PHD2 P317R as measured by NMR and modeling on apo PHD2 P317R, we posit that P317R causes deviation of NODD from its original orientation that may not affect binding due to the other interactions from the surrounding elements but unfortunately disallows NODD from turnover.  Further study would be required to validate such notion, which we feel is beyond the scope of this manuscript.  

(6) Overall, the study provides some insights into mutants inducing erythrocytosis, but the impact is limited. Most insights are provided on the P317R mutant, but this mutant had already been characterized by Chowdhury et al (2016). Some mutants affect the stability of the protein in cells, but then no mechanism is provided for A228S or F366L, which have stabilities similar to WT, yet have impaired HIF1a activation. 

We thank the reviewer for raising these and other limitations.  We have expanded on the shortcomings of the present study but would like to underscore that the current work using the recently described NMR assay along with other biophysical analyses suggests a previously under-appreciated role of NODD hydroxylation in the normal oxygen-sensing pathway.  

Reviewer #2 (Public review): 

Summary: 

Mutations in the prolyl hydroxylase, PHD2, cause erythrocytosis and, in some cases, can result in tumorigenesis. Taber and colleagues test the structural and functional consequences of seven patientderived missense mutations in PHD2 using cell-based reporter and stability assays, and multiple biophysical assays, and find that most mutations are destabilizing. Interestingly, they discover a PHD2 mutant that can hydroxylate the C-terminal ODD, but not the N-terminal ODD, which suggests the importance of N-terminal ODD for biology. A major strength of the manuscript is the multidisciplinary approach used by the authors to characterize the functional and structural consequences of the mutations. However, the manuscript had several major weaknesses, such as an incomplete description of how the NMR was performed, a justification for using neighboring residues as a surrogate for looking at prolyl hydroxylation directly, or a reference to the clinical case studies describing the phenotypes of patient mutations. Additionally, the experimental descriptions for several experiments are missing descriptions of controls or validation, which limits their strength in supporting the claims of the authors. 

Strengths: 

(1) This manuscript is well-written and clear. 

(2) The authors use multiple assays to look at the effects of several disease-associated mutations, which support the claims. 

(3) The identification of P317R as a mutant that loses activity specifically against NODD, which could be a useful tool for further studies in cells. 

Weaknesses: 

Major: 

(1) The source data for the patient mutations (Figure 1) in PHD2 is not referenced, and it's not clear where this data came from or if it's publicly available. There is no section describing this in the methods. 

Clinical and patient information on disease-causing PHD2 mutants was compiled from various case reports and summarized in an excel sheet found in the Supplementary Information.  The case reports are cited in this excel file.  A reference to the supplementary data has been added to the Figure 1 legend and in the introduction.

(2) The NMR hydroxylation assay. 

A. The description of these experiments is really confusing. The authors have published a recent paper describing a method using 13C-NMR to directly detect proly-hydroxylation over time, and they refer to this manuscript multiple times as the method used for the studies under review. However, it appears the current study is using 15N-HSQC-based experiments to track the CSP of neighboring residues to the target prolines, so not the target prolines themselves. The authors should make this clear in the text, especially on page 9, 5th line, where they describe proline cross-peaks and refer to the 15N-HSQC data in Figure 5B. 

As the reviewer mentioned, the assay that we developed directly measures the target proline residues.  This assay is ideal when mutations near the prolines are studied, such as A403, Y565 (He et al[3]).  In this previous work, we observed that the shifting of the target proline cross-peaks due to change in electronegativity on the pyrrolidine ring of proline in turn impacted the neighboring residues[3], which meant that the neighboring residues can be used as reporter residues for certain purposes.  In this study, we focused on investigating the mutations on PHD2 while leaving the sequence of the HIF-1α unchanged by using solely 15N-HSQC-based experiments without the need for double-labeled samples.  Nonetheless, we thank the reviewer for pointing out the confusion in the text and we have corrected and clarified our description of this assay.

B. The authors are using neighboring residues as reporters for proline hydroxylation, without validating this approach. How well do CSPs of A403 and I566 track with proline hydroxylation? Have the authors confirmed this using their 13C-NMR data or mass spec? 

For previous studies, we performed intercalated 15N-HSQC and 13C-CON experiments for the kinetic measurements of wild-type HIF-1α and mutants.  We observed that the shifting pattern of A403 and I566 in the 15N-HSQC spectra aligned well with the ones of P402 and P564, respectively, in the 13C-CON spectra.  Representative data has been added to Supplemental Data.

C. Peak intensities. In some cases, the peak intensities of the end point residue look weaker than the peak intensities of the starting residue (5B, PHD2 WT I566, 6 ct lines vs. 4 ct lines). Is this because of sample dilution (i.e., should happen globally)? Can the authors comment on this? 

This is an astute observation by the reviewer.  We checked and confirmed that for all kinetic datasets, the peak intensities of the end point residue are always slightly lower than the ones of the starting.  This includes the cases for PHD2 A228S and P317R in 5B, although not as obvious as the one of PHD2 WT.  We agree with the reviewer that the sample dilution is a factor as a total volume of 16 microliters of reaction components was added to the solution to trigger the reaction after the first spectrum was acquired.  It is also likely that rate of prolyl hydroxylation becomes extremely slow with only a low amount of substrate available in the system.  Therefore, the reaction would not be 100% complete which was detected by the sensitive NMR experimentation.

(3) Data validating the CRISPR KO HEK293A cells is missing. 

We thank the reviewer for noting this oversight.  Western blots validating PHD2 KO in HEK293A cells have been added to the Supplementary Data file.

(4) The interpretation of the SEC data for the PHD2 mutants is a little problematic. Subtle alterations in the elution profiles may hint at different hydrodynamic radii, but as the samples were not loaded at equal concentrations or volumes, these data seem more anecdotal, rather than definitive. Repeating this multiple times, using matched samples, followed by comparison with standards loaded under identical buffer conditions, would significantly strengthen the conclusions one could make from the data. 

Agreed.  We have performed an additional experiment as suggested with equal volume and concentration of each PHD2 construct loaded onto the SEC column for better assessment of aggregation.  Notably, our conclusion remained unchanged.

Minor: 

(1) Justification for picking the seven residues is not clearly articulated. The authors say they picked 7 mutants with "distinct residue changes", but no further rationale is provided. 

Additional justification for the selection of the mutants has been added to the ‘Mutations across the PHD2 enzyme induce erythrocytosis’ section.  Briefly, some mutants were chosen based on their frequency in the clinical data and their presence in potential mutational hot spots.  Various mutations were noted at W334 and R371, while F366L was identified in multiple individuals.  Additionally, 9 cases of PHD2-driven disease were reported to be caused from mutations located between residues 200 to 210 while 13 cases were reported between residues 369-379, so G206C and R371H were chosen to represent potential hot spots.  To examine a potential genotype-phenotype relationship, two of the mutants responsible for neuroendocrine tumor development, A228S and H374R, were also selected.  Finally, mutations located close or on catalytic core residues (P317R, R371H, and H374R) were chosen to test for suspected defects.   

(2) A major finding of the paper is that a disease-associated mutation, P317R, can differentially affect HIF1 prolyhydroxylation, however, additional follow-up studies have not been performed to test this in cells or to validate the mutant in another method. Is it the position of the proline within the catalytic core, or the identity of the mutation that accounts for the selectivity? 

This is the very question that we are currently addressing but as a part of a follow-up study.  Indeed, one thought is that the preferential defect observed could be the result of the loss of proline, an exceptionally rigid amino acid that makes contact with the backbone twice, or the addition of a specific amino acid, namely arginine, a flexible amino acid with an added charge at this site.  Although beyond the scope of this manuscript, we will investigate whether such and other characteristics in this region of PHD2/HIF1α interface contribute to the differential hydroxylation. 

Reviewer #3 (Public review): 

Summary: 

This is an interesting and clinically relevant in vitro study by Taber et al., exploring how mutations in PHD2 contribute to erythrocytosis and/or neuroendocrine tumors. PHD2 regulates HIFα degradation through prolyl-hydroxylation, a key step in the cellular oxygen-sensing pathway. 

Using a time-resolved NMR-based assay, the authors systematically analyze seven patient-derived PHD2 mutants and demonstrate that all exhibit structural and/or catalytic defects. Strikingly, the P317R variant retains normal activity toward the C-terminal proline but fails to hydroxylate the N-terminal site. This provides the first direct evidence that N-terminal prolyl-hydroxylation is not dispensable, as previously thought. 

The findings offer valuable mechanistic insight into PHD2-driven effects and refine our understanding of HIF regulation in hypoxia-related diseases. 

Strengths: 

The manuscript has several notable strengths. By applying a novel time-resolved NMR approach, the authors directly assess hydroxylation at both HIF1α ODD sites, offering a clear functional readout. This method allows them to identify the P317R variant as uniquely defective in NODD hydroxylation, despite retaining normal activity toward CODD, thereby challenging the long-held view that the N-terminal proline is biologically dispensable. The work significantly advances our understanding of PHD2 function and its role in oxygen sensing, and might help in the future interpretation and clinical management of associated erythrocytosis. 

Weaknesses: 

(1) There is a lack of in vivo/ex vivo validation. This is actually required to confirm whether the observed defects in hydroxylation-especially the selective NODD impairment in P317R-are sufficient to drive disease phenotypes such as erythrocytosis.

We thank the reviewer for this comment, and while we agree with this statement, the objective of this study per se was to elucidate the structural and/or functional defect caused by the various diseaseassociated mutations on PHD2.  The subsequent study would be to validate whether the identified defects, in particular the selective NODD impairment, would lead to erythrocytosis in vivo.  However, we feel that such study would be beyond the scope of this manuscript.

(2) The reliance on HRE-luciferase reporter assays may not reliably reflect the PHD2 function and highlights a limitation in the assessment of downstream hypoxic signaling. 

Agreed.  All experimental assays and systems have limitations.  The HRE-luciferase assay used in the present manuscript also has limitations such as the continuous expression of exogenous PHD2 mutants driven via CMV promoter.  Thus, we performed several additional biophysical methodologies to interrogate the disease-causing PHD2 mutants.  The limitations of the luciferase assay have been expanded in the revised manuscript. 

(3) The study clearly documents the selective defect of the P317R mutant, but the structural basis for this selectivity is not addressed through high-resolution structural analysis (e.g., cryo-EM). 

We thank the reviewer for the comment.  While solving the structure of PHD2 P317R in complex with HIFα substrate is beyond the scope for this study, a structure of PHD2 P317R in complex with a clinically used inhibitor has been solved (PDB:5LAT).  In analyzing this structure and that of PHD2 WT in complex with NODD, Chowdhury et al[2] stated that P317 makes hydrophobic contacts with LXXLAP motif on HIFα and R317 is predicted to interact differently with this motif.  While this analysis does not directly elucidate the reason for the preferential NODD defect, it supports the possibility that P317R substitution may be more detrimental for enzymatic activity on NODD than CODD.  We have discussed this notion in the revised manuscript. 

(4) Given the proposed central role of HIF2α in erythrocytosis, direct assessment of HIF2α hydroxylation by the mutants would have strengthened the conclusions. 

We thank the reviewer for this comment, but we feel that such study would be beyond the scope of the present study.  We observed that the PHD2 binding patterns to HIF1α and HIF2α were similar, and we have previously assigned >95% of the amino acids in HIF1α ODD for NMR study[3]. Thus, we first focused on the elucidation of possible defects on disease-associated PHD2 mutants using HIF1α as the substrate with the supposition that an identified deregulation on HIF1α could be extended to HIF2α paralog.  However, we agree with the reviewer that future studies should examine the impact of PHD2 mutants directly on HIF2α.  

References:

(1) Flashman, E. et al. Kinetic rationale for selectivity toward N- and C-terminal oxygen-dependent degradation domain substrates mediated by a loop region of hypoxia-inducible factor prolyl hydroxylases. J Biol Chem 283, 3808-3815 (2008).

(2) Chowdhury, R. et al. Structural basis for oxygen degradation domain selectivity of the HIF prolyl hydroxylases. Nat Commun 7, 12673 (2016).

(3) He, W., Gasmi-Seabrook, G.M.C., Ikura, M., Lee, J.E. & Ohh, M. Time-resolved NMR detection of prolyl-hydroxylation in intrinsically disordered region of HIF-1alpha. Proc Natl Acad Sci U S A 121, e2408104121 (2024).

Reviewer #1 (Recommendations for the authors): 

(1) To increase the impact and significance of this work, I would recommend determining the mechanism by which A228S and F366L impair PHD2. Are these mutations affecting interactions with proteins other than HIF1a? Furthermore, does the F366L mutation affect the hydroxylation rate? This should be measured. The authors should also perform a more in-depth structural analysis of these mutations and perhaps use AlphaFold to identify how these sites may be involved in other interactions. 

We thank the reviewer for the recommendations.  A paragraph discussing the quandary of A228S and F366L has been added to the discussion as well as an in-depth structural analysis of each selected mutant.  While AlphaFold is excellent at predicting protein structures overall, its capability to predict the effect of single point mutation, such as those in this study, is limited.  Therefore, it was not utilized for this paper.

(2) For the aggregation assay, I recommended injecting the same quantity of protein on the SEC. If the aggregation-prone mutants' yields were too low, then reduced amounts of the other mutants should be injected. 

Agreed.  An additional experiment was performed in which similar concentrations of each mutant protein was loaded onto the SEC column and chromatograms was normalized according to the molecular concentration.  Results from this experiment have been added to replace the previously performed aggregation assay.  Notably, the data from the revised experiment did not change the outcome or conclusion of the study.

(3) For the NMR kinetics data, the authors should discuss the impact of affinities and concentrations on the reaction rate and incorporate this analysis framework to interpret their data. 

Done.  As discussed in depth in response to Public Reviewer 1’s fourth comment, we observed only a subtle reduction in hydroxylation efficiency of HIF1aCODD by PHD2 P317R in comparison to PHD2 WT.  Upon performing BLI, we found PHD2 P317R displays only a mild binding defect on the CODD and NODD.  The WT-like binding to the NODD by PHD2 P317R appears to be inconsistent with the severe defect in NODD hydroxylation via PHD2 P317R as measured via NMR.   These results suggest that there are supporting residues within the PHD2/NODD interface that help maintain binding to NODD but compromise the efficiency of NODD hydroxylation upon PHD2 P317R mutation.

Reviewer #2 (Recommendations for the authors): 

It is unclear where the source data came from describing the patient mutations, or if it is publicly available. Several minor issues were noted with several of the figures or methods: 

(1) Figure 2C. It is not clear what data are being compared for significance. The lines don't seem to clearly distinguish this. 

Done.  The significance lines have been adjusted in the figure to better convey which data are being compared.

(2) Please incorporate the calculated biophysical constants (KD, TM, etc, average +/- std dev) from the tables into the figures or figure legends that show the data from which they are calculated.  

Done.  References to the corresponding tables have been added to the appropriate figure legends.

(3) Figure 3C, the data for F366L do not appear normalized in the same way as the other constructs. 

CD melt values for F366L were normalized in the same way as other constructs but due to noisier data acquired between 25-37°C, the top value of the sigmoidal curve is slightly higher than the other constructs (F366L: 1.066, WT: 1.007, A228S: 1.000, P317R: 1.015, R371H: 1.005). 

(4) For Figure 1B, it would be helpful to highlight the mutants characterized in the current study with a different color/symbol to help show the number of cases. 

Done.  Dots representing the selected mutants have been highlighted in red in Figure 1B.

(5) A description of the isotopic labeling of PHD2 is missing from the methods.

Due to the nature of the NMR assay, no isotopic labeling was required for PHD2.

Reviewer #3 (Recommendations for the authors): 

(1) To further strengthen the manuscript, the authors could consider exploring the relevance of their in vitro findings in a more physiological context. 

We thank the reviewer for the suggestion, and we will certainly consider furthering our investigation in a more physiological context for future studies.

(2) If technically feasible, integrating direct analyses of HIF2α regulation by the PHD2 mutants would better reflect the clinical phenotype, given the known importance of HIF2α in erythrocytosis. 

We agree that HIF2α is important in the context of erythrocytosis, but through MST we observed no difference in binding pattern between HIF1 and HIF2 and the selected PHD2 mutants.  As we had previously assigned >95% of residues for HIF1α ODD for NMR assay, we analyzed HIF1 with the supposition that any defects observed would likely apply to HIF2.  However, we agree that future studies on the impact of PHD2 mutants directly on HIF2 would be beneficial to supplement our understanding of pseudohypoxic disease.

(3) Additionally, although perhaps more suitable for future work or discussion, structural modeling or highresolution structural studies of the P317R variant could offer valuable insight into the observed NODD selectivity defect. 

We thank the reviewer for the suggestion. While solving the structure of PHD2 P317R in complex with NODD is beyond the scope of this manuscript, a crystal structure of PHD2 P317R in complex with an inhibitor has been solved and insights from this structure have been added to the discussion. 

(4) Finally, a brief clarification or discussion of the limitations of the luciferase reporter assay-especially in the context of aggregation-prone mutants-would help readers better interpret the functional data. 

We thank the reviewer for the suggestion.  The limitations of the luciferase reporter assay in regard to its inability to detect defects with aggregation-prone mutants have been elaborated on in the discussion.

eLife Assessment

This study presents a valuable and interesting finding that a combination of arginine methyltransferase inhibitors synergize with PARP inhibitors to eliminate ovarian and triple negative cancer cell lines in vitro and in vivo using preclinical mouse models. The data were collected and analyzed using solid and validated methodology and can be used as a starting point for the development of novel therapeutics. The work will be of broad interest to scientists working in the field of breast cancer and ovarian cancer.

Reviewer #2 (Public review):

Summary:

The authors show that a combination of arginine methyltransferase inhibitors synergize with PARP inhibitors to kill ovarian and triple negative cancer cell lines in vitro and in vivo using preclinical mouse models.

Strengths and weaknesses

The experiments are well-performed, convincing and have the appropriate controls (using inhibitors and genetic deletions) and use statistics.

They identify the DNA damage protein ERCC1 to be reduced in expression with PRMT inhibitors. As ERCC1 is known to be synthetic lethal with PARPi, this provides a mechanism for the synergy. They use cell lines only for their study in 2D as well as xenograph models.

Comments on revisions:

The authors have addressed by final concerns.

Author response:

The following is the authors’ response to the previous reviews

Reviewer #2 (Public Review):

Summary:

The authors show that a combination of arginine methyltransferase inhibitors synergize with PARP inhibitors to kill ovarian and triple negative cancer cell lines in vitro and in vivo using preclinical mouse models.

Strengths and weaknesses

The experiments are well-performed, convincing and have the appropriate controls (using inhibitors and genetic deletions) and use statistics.

They identify the DNA damage protein ERCC1 to be reduced in expression with PRMT inhibitors. As ERCC1 is known to be synthetic lethal with PARPi, this provides a mechanism for the synergy. They use cell lines only for their study in 2D as well as xenograph models.

We sincerely thank Reviewer #2 for the insightful and constructive feedback, as well as for the kind recognition of the scientific quality of our work: “The experiments are well-performed, convincing and have the appropriate controls (using inhibitors and genetic deletions) and use statistics.” We sincerely thank Reviewer #2 for their thoughtful and constructive comments during both rounds of review, which have significantly improved the quality of our manuscript. In response, we have incorporated new results from additional experiments into the figures (Figures 6M and 6N) and made comprehensive revisions throughout the text, figures, and supplementary materials. Following the reviewer’s valuable suggestions, we also revised the Discussion section. In the “Recommendations for the authors” sections, we have provided detailed point-by-point responses to each comment, which were instrumental in guiding our revisions. We believe these updates have substantially strengthened the manuscript and fully addressed all reviewer concerns.

Reviewer #2 (Recommendations for the authors): 

Although the authors have addressed each recommendation from the reviewer, further revision of the manuscript are still necessary, as outlined below.

Add these additional comments in the text to further enhance the comprehension and clarity of the data.

(1) If the authors kept the tumors of various sizes in Figure 7I, it would be important to assess the protein and/or mRNA level of ERCC1 to further support their mechanism.

Question (1): Please add the figures of new experiments (treatment diagram, curves for tumor volume and qRT-PCR data) to Figure 6.

We thank the reviewers for their constructive suggestions. In response to the reviewers’ comments, we have added the treatment diagram and qPCR results to Figure 6. In this experiment, we shortened the treatment duration to seven days to assess early molecular responses to therapy rather than downstream effects. As expected, such short-term treatment did not result in significant differences in tumor growth among groups. The new results are now presented in Figure 6, panels M and N. The corresponding results and figure legends will also be included in the revised version of the manuscript

(2) Figure 2G: please explain why two bands remain for sgPRMT1.

Question (2): In the answer, the authors stated, "Upon knockdown of the major isoforms by CRISPR/Cas9, expression of this minor isoform may have increased as part of a compensatory feedback mechanism, rendering it detectable by immunoblotting." Please put the statement into the discussion section.

We sincerely thank the reviewers for their thoughtful and constructive suggestions. In response to these comments, we have carefully revised the manuscript and incorporated the corresponding information into the Discussion section to provide greater clarity and context for our findings.

(3) (Previously point 5) What is the link with ERCC1 splicing because reduced overall ERCC1 expression is clear?

Question (5): Please add the explanation you provide of links between ERCC1 splicing and PRMTi into the discussion section.

"Furthermore, as shown in Figure 4G, we observed a reduction in the total ERCC1 mRNA reads following PRMTi treatment. This decrease may be attributed, at least in part, to the instability of the alternatively spliced ERCC1 transcripts, which could be more prone to degradation. In combination with the transcriptional downregulation of ERCC1 induced by PRMT inhibition, these alternative splicing events may lead to a further reduction in functional ERCC1 protein levels. This dual impact on ERCC1 expression, through both decreased transcription and the generation of unstable or nonfunctional isoforms, likely contributes to the enhanced cellular sensitivity to PARP inhibitors observed in our study."

We sincerely thank the reviewers for their thoughtful and constructive suggestions. In response to these comments, we have carefully revised the manuscript and incorporated the corresponding information into the Discussion section to provide greater clarity and context for our findings.

(4) (Previously 6) Figure 7J: From the graph, it seems like Olaparib+G715 and G715+G025 have a similar effect on tumor volume (two curves overlap). Please discuss.

Question (6): In the answer, the authors stated, "Our in vitro and in vivo findings, together with previously published data, consistently demonstrate that GSK715 is more potent than both GSK025 and Olaparib. Notably, treatment with GSK715 alone led to significantly greater inhibition of tumor growth compared to either GSK025 or Olaparib administered individually. This higher potency of GSK715 also explains the comparable levels of tumor suppression observed in the combination groups, including GSK715 plus Olaparib and GSK715 plus GSK025. These results suggest that GSK715 is likely the primary driver of efficacy in the two drug combination settings." Please put the statement in the corresponding result section for Figure 6J.

We sincerely thank the reviewers for their thoughtful and constructive suggestions. In response to these comments, we have carefully revised the manuscript and incorporated the corresponding information into the result section for Figure 6J to provide greater clarity and context for our findings.

eLife Assessment

This valuable study provides a comprehensive description of the Nematostella vectensis matrisome - the genes encoding the proteins of the extracellular matrix. The authors combine new mass spectrometry data with bioinformatic analyses of previously published genomic and single-cell RNAseq data. The analysis is thorough, and the discussion and conclusions are convincing. This work will be of interest to biologists working on the evolution of the matrisome, as well as more broadly those working with non-bilaterian animals.

Reviewer #1 (Public review):

Summary:

In this manuscript entitled "Molecular dynamics of the matrisome across sea anemone life history", Bergheim and colleagues report the prediction, using an established sequence analysis pipeline, of the "matrisome" - that is, the compendium of genes encoding constituents of the extracellular matrix - of the starlet sea anemone Nematostella vectensis. Re-analysis of an existing scRNA-Seq dataset allowed the authors to identify the cell types expressing matrisome components and different developmental stages. Last, the authors apply time-resolved proteomics to provide experimental evidence of the presence of the extracellular matrix proteins at three different stages of the life cycle of the sea anemone (larva, primary polyp, adult) and show that different subsets of matrisome components are present in the ECM at different life stages with, for example, basement membrane components accompanying the transition from larva to primary polyp and elastic fiber components and matricellular proteins accompanying the transition from primary polyp to the adult stage.

Strengths:

The ECM is a structure that has evolved to support the emergence of multicellularity and different transitions that have accompanied the complexification of multicellular organisms. Understanding the molecular makeup of structures that are conserved throughout evolution is thus of paramount importance.

The in-silico predicted matrisome of the sea anemone has the potential to become an essential resource for the scientific community to support big data annotation efforts and better understand the evolution of the matrisome and of ECM proteins, an important endeavor to better understand structure/function relationships. Toward this goal, the authors provide a comprehensive list with extensive annotations and cross-referencing of the 551 genes encoding matrisome proteins in the sea anemone genome.

This study is also an excellent example of how integrating datasets generated using different -omic modalities can shed light on various aspects of ECM metabolism, from identifying the cell types of origins of matrisome components using scRNA-Seq to studying ECM dynamics using proteomics.

Weakness:

- Prior proteomic studies on the ECM of vertebrate organisms have shown the importance of allowing certain post-translational modifications during database search to ensure maximizing peptide-to-spectrum matching and accurately evaluating protein quantification. Such PTMs include the hydroxylation of lysines and prolines that are collagen-specific PTMs. Multiple reports have shown that omitting these PTMs while analyzing LC-MS/MS data would lead to underestimating the abundance of collagens and the misidentification of certain collagens. While the authors in their response state that the inclusion of these PTMs only led to a modest increase in protein identification, they do not comment on the impact of including these PTMs on PSMs or protein abundance (precursor ion intensity).

Author response:

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

Reviewer #1 (Public review): 

Summary: 

In this manuscript entitled "Molecular dynamics of the matrisome across sea anemone life history", Bergheim and colleagues report the prediction, using an established sequence analysis pipeline, of the "matrisome" - that is, the compendium of genes encoding constituents of the extracellular matrix - of the starlet sea anemone Nematostella vectensis. Re-analysis of an existing scRNA-Seq dataset allowed the authors to identify the cell types expressing matrisome components and different developmental stages. Last, the authors apply time-resolved proteomics to provide experimental evidence of the presence of the extracellular matrix proteins at three different stages of the life cycle of the sea anemone (larva, primary polyp, adult) and show that different subsets of matrisome components are present in the ECM at different life stages with, for example, basement membrane components accompanying the transition from larva to primary polyp and elastic fiber components and matricellular proteins accompanying the transition from primary polyp to the adult stage. 

Strengths: 

The ECM is a structure that has evolved to support the emergence of multicellularity and different transitions that have accompanied the complexification of multicellular organisms. Understanding the molecular makeup of structures that are conserved throughout evolution is thus of paramount importance. 

The in-silico predicted matrisome of the sea anemone has the potential to become an essential resource for the scientific community to support big data annotation efforts and understand better the evolution of the matrisome and of ECM proteins, an important endeavor to better understand structure/function relationships. This study is also an excellent example of how integrating datasets generated using different -omic modalities can shed light on various aspects of ECM metabolism, from identifying the cell types of origins of matrisome components using scRNA-Seq to studying ECM dynamics using proteomics. 

We greatly appreciate the positive feedback regarding the design of our study and the evolutionary significance of our findings.

Weaknesses: 

My concerns pertain to the three following areas of the manuscript: 

(1) In-silico definition of the anemone matrisome using sequence analysis: 

a) While a similar computational pipeline has been applied to predict the matrisome of several model organisms, the authors fail to provide a comprehensive definition of the anemone matrisome: In the text, the authors state the anemone matrisome is composed of "551 proteins, constituting approximately 3% of its proteome (see page 6, line 14), but Figure 1 lists 829 entries as part of the "curated" matrisome, Supplementary Table S1 lists the same 829 entries and the authors state that "Here, we identified 829 ECM proteins that comprise the matrisome of the sea anemone Nematostella vectensis" (see page 17, line 10). Is the sea anemone matrisome composed of 551 or 829 genes? If we refer to the text, the additional 278 entries should not be considered as part of the matrisome, but what is confusing is that some are listed as glycoproteins and the "new_manual_annotation" proposed by the authors and that refer to the protein domains found in these additional proteins suggest that in fact, some could or should be classified as matrisome proteins. For example, shouldn't the two lectins encoded by NV2.3951 and NV2.3157 be classified as matrisome-affiliated proteins? Based on what has been done for other model organisms, receptors have typically been excluded from the "matrisome" but included as part of the "adhesome" for consistency with previously published matrisome; the reviewer is left wondering whether the components classified as "Other" / "Receptor" should not be excluded from the matrisome and moved to a separate "adhesome" list. 

In addition to receptors, the authors identify nearly 70 glycoproteins classified as "Other". Here, does other mean "non-matrisome" or "another matrisome division" that is not core or associated? If the latter, could the authors try to propose a unifying term for these proteins? Unfortunately, since the authors do not provide the reasons for excluding these entries from the bona fide matrisome (list of excluding domains present, localization data), the reader is left wondering how to treat these entries. 

Overall, the study would gain in strength if the authors could be more definitive and, if needed, even propose novel additional matrisome annotations to include the components for now listed as "Other" (as was done, for example, for the Drosophila or C. elegans matrisomes). 

The reviewer is correct to point out the confusing terminology used throughout our manuscript, where both the total of 829 proteins constituting the curated list of ECM domain proteins and the actual matrisome (excluding "others") were referred to as "matrisomes". In general, we followed the example set by Naba & Hynes in their 2012 paper (Mol Cell Proteomics. 2012 Apr;11(4):M111.014647. doi: 10.1074/mcp.M111.014647), where they define the "matrisome" as encompassing all components of the extracellular matrix ("core matrisome") and those associated with it ("matrisome-associated" proteins). This corresponds to our group of 551 proteins, comprising both core matrisome and matrisomeassociated proteins. The Naba & Hynes paper also contains the inclusive and exclusive domain lists for the matrisome that we applied for our dataset. In the revised manuscript, we have now labelled the group of 829 proteins as "curated ECM domain proteins/genes", which includes all proteins positively selected for containing a bona fide ECM domain. After excluding non-matrisomal proteins such as receptors, we arrive at the 551 proteins that constitute the "Nematostella matrisome". We have maintained this terminology throughout the revised manuscript and have revised Figures 1B and 4B accordingly.

Regarding the category of "other" proteins, which by definition are not part of the matrisome although containing ECM domains, we have taken the reviewer's advice and classified these in more detail. We categorized all receptors as "adhesome" (202 proteins).  The remaining group of “other” secreted ECM domain proteins were then further subcategorized. Those exhibiting significant matches in the ToxProt database were subclassified as "putative venoms" (15 proteins). This group also includes the two lectins (NV2.3951 and NV2.3157), which had been originally shifted to the “other” category due to their classification as venoms. We categorized as “adhesive proteins” (28 proteins) factors such as coadhesins that due to their domain architecture resemble bioadhesive proteins described in proteomic studies of other invertebrate species, such as corals or sponges (see also https://doi.org/10.1016/j.jprot.2022.104506). Further sub-categories are stress/injury response proteins (9 proteins) and ion channels (6 proteins). The remaining 17 proteins were categorized as “uncharacterized ECM domain proteins”. These include highly diverse proteins possessing either single ECM domains or novel domain combinations. We decided to retain those in our dataset as candidates for future functional characterization.

b) It is surprising that the authors are not providing the full currently accepted protein names to the entries listed in Supplementary Table S1 and have used instead "new_manual_annotation" that resembles formal protein names. This liberty is misleading. In fact, the "new_manual_annotation" seems biased toward describing the reason the proteins were positively screened for through sequence analysis, but many are misleading because there is, in fact, more known about them, including evidence that they are not ECM proteins. The authors should at least provide the current protein names in addition to their "new_manual_annotations". 

c) To truly serve as a resource, the Table should provide links to each gene entry in the Stowers Institute for Medical Research genome database used and some sort of versioning (this could be added to columns A, B, or D). Such enhancements would facilitate the assessment of the rigor of the list beyond the manual QC of just a few entries. 

d) Since UniProt is the reference protein knowledge database, providing the UniProt IDs associated with the predicted matrisome entries would also be helpful, giving easy access to information on protein domains, protein structures, orthology information, etc. 

e) In conclusion, at present, the study only provides a preliminary draft that should be more rigorously curated and enriched with more comprehensive and authoritative annotations if the authors aspire the list to become the reference anemone matrisome and serve the community. 

Table S1 has been updated to include links to the respective Stowers Institute IDs (first two columns), as well as SwissProt IDs and current descriptions from both the Stowers Institute (SI) and Swissprot.

In our manual annotations, we prioritized these over automated ones due to the considerable effort invested in examining each sequence individually. The cnidaria-specific minicollagens and NOWA proteins might serve as an example. According to the SI descriptions, the minicollagens are annotated as “keratin-associated protein, predicted or hypothetical protein, collagen-like protein and pericardin”. We classified these as minicollagens on the basis of overall domain architecture and of signature domains and sequence motifs, such as minicollagen cysteine-rich domains (CRDs) and polyproline stretches (doi: 10.1016/j.tig.2008.07.001). NOWA is a CTLD/CRD-containing protein that is part of nematocyst tubules (doi:10.1016/j.isci.2023.106291). The first two NOWA isoforms, according to Si descriptions, were annotated as aggrecan and brevican core proteins, which is very misleading. We therefore feel that our manual annotations better serve the cnidarian research community in classifying these proteins.

Automated annotations of ECM proteins often rely on similarities between individual domains, neglecting overall domain composition. For example, Swissprot descriptions annotate 31 TSP1 domain-containing proteins in our list as "Hemicentin-1", but closer inspection reveals that only one sequence (NV2.24790) qualifies as Hemicentin-1 due to its characteristic vWFA, Ig-like, TSP1, G2 nidogen, and EGF-like domain architecture. Regarding novel protein annotations, NV2.650 might serve as an example. While SI descriptions annotate this protein as "epidermal growth factor" based on the presence of several EGF-like domains, our analysis reveals two integrin alpha N-terminal domains that classify this sequence as integrin-related. We have therefore assigned a description (Secreted integrin-N-related protein) that references this defining domain and avoids misclassification within the EGF family.

In cases where the automated annotation (including those in Genbank) matched our own findings, we adopted the existing description, as seen with netrin-1 (NV2.7734). We acknowledge that our manual annotations are not flawless and will be refined by future research. Nonetheless, we offer them as an approximation to a more accurate definition of the identified protein list.

(2) Proteomic analysis of the composition of the mesoglea during the sea anemone life cycle: 

a) The product of 287 of the 829 genes proposed to encode matrisome components was detected by proteomics. What about the other ~550 matrisome genes? When and where are they expressed? The wording employed by the authors (see line 11, page 13) implies that only these 287 components are "validated" matrisome components. Is that to say that the other ~550 predicted genes do not encode components of the ECM? This should be discussed. 

Obviously, our wording was not sufficiently accurate here. In the revised Fig. 1B we indicated that 210 of the 551 matrisome (core and associated) proteins were confirmed by mass spectrometry. In total, 287 proteins were identified by mass spectrometry, meaning that 77 of those are non-matrisomal proteins belonging to the “adhesome” (47) and “other” (30) groups. The fact that the remaining 542 proteins of the matrisome predicted by our in silico analysis could not be identified has two major reasons: (1) Our study was focussed on the molecular dynamics of the mesoglea. Therefore, only mesogleas were isolated for the mass spectrometry analysis and nematocysts were mostly excluded by extensive washing steps. As nematocysts contribute significantly to the predicted matrisome, this group of proteins is underrepresented in the mass spectrometry analysis. (2) A significant fraction of the predicted ECM proteins constitutes soluble factors and transmembrane receptors. These might not be necessarily part of the mesoglea isolates. In addition, the isolation and solubilization method we applied might have technical limitations. Although we used harsh conditions for solubilizing the mesoglea samples (90°C and high DTT concentrations), we cannot exclude that we missed proteins which resisted solubilization and thus trypsinization. We confirmed that all genes predicted by the in silico analysis have transcriptomic profiles as demonstrated in supplementary table S4. We have clarified these points in the revised results part (p.6) and also revised the statement in line 16, page 13.

b) Can the authors comment on how they have treated zero TMT values or proteins for which a TMT ratio could not be calculated because unique to one life stage, for example? 

We did not include these proteins in the analysis of the respective statistical comparison. This involved only very few proteins (about 10).  

c) Could the authors provide a plot showing the distribution of protein abundances for each matrisome category in the main figure 4? In mammals, the bulk of the ECM is composed of collagens, followed by fibrillar ECM glycoproteins, the other matrisome components being more minor. Is a similar distribution observed in the sea anemone mesoglea? 

We have included such a plot showing protein abundances across life stages and protein categories (Fig. 4A). Collagens and basement membrane proteoglycans (perlecan) are the most abundant protein categories in the core matrisome while secreted factors dominate in the matrisome-associated group.

d) Prior proteomic studies on the ECM of vertebrate organisms have shown the importance of allowing certain post-translational modifications during database search to ensure maximizing peptide-to-spectrum matching. Such PTMs include the hydroxylation of lysines and prolines that are collagen-specific PTMs. Multiple reports have shown that omitting these PTMs while analyzing LC-MS/MS data would lead to underestimating the abundance of collagens and the misidentification of certain collagens. The authors may want to reanalyze their dataset and include these PTMs as part of their search criteria to ensure capturing all collagen-derived peptides. 

Thank you for this suggestion. We have re-analyzed our dataset including lysine and proline hydroxylation as PTM. While we obtained in total 70 more proteins using this approach, this additional group did not contain any large collagen or minicollagen we had not detected before. We only obtained two additional collagen-like proteins with very short triple helical domains (V2t013973001.1, NV2t024002001.1), one being a fragment. We don’t feel this justifies implementing a re-analysis of the proteome in our study.

e) The authors should ensure that reviewers are provided with access to the private PRIDE repository so the data deposited can also be evaluated. They should also ensure that sufficient meta-data is provided using the SRDF format to allow the re-use of their LCMS/MS datasets. 

We apologize for not providing the reviewer access in our initial submission and have asked the editorial office to forward the PRIDE repository link to all reviewers immediately after receiving the reviews. We did upload a metadata.csv file with the proteomics dataset. This file contains an annotation of all TMT labels to the samples and conditions and replicates used in the manuscript. It contains similar information as an SRDF format file. In addition, the search output files on protein and psm level have been provided. So, from our point of view, we provided all necessary information to reproduce the analysis.

(3) Supplementary tables: 

The supplementary tables are very difficult to navigate. They would become more accessible to readers and non-specialists if they were accompanied by brief legends or "README" tabs and if the headers were more detailed (see, for example, Table S2, what does "ctrl.ratio_Larvae_rep2" exactly refer to? Or Table S6 whose column headers using extensive abbreviations are quite obscure). Similarly, what do columns K to BX in Supplementary Table S1 correspond to? Without more substantial explanations, readers have no way of assessing these data points. 

We have revised the tables and removed any redundant data columns. We also included detailed explanations of the used abbreviations, both in the headers and in a separate README file. Some of the information was apparently lost during the conversion to pdf files. We will therefore upload the original .xls files when submitting the revised manuscript.

Reviewer #2 (Public review): 

This work set out to identify all extracellular matrix proteins and associated factors present within the starlet sea anemone Nematostella vectensis at different life stages. Combining existing genomic and transcriptomic datasets, alongside new mass spectometry data, the authors provide a comprehensive description of the Nematostella matrisome. In addition, immunohistochemistry and electron microscopy were used to image whole mount and decellularized mesoglea from all life stages. This served to validate the de-cellularization methods used for proteomic analyses, but also resulted in a very nice description of mesoglea structure at different life stages. A previously published developmental cell type atlas was used to identify the cell type specificity of the matrisome, indicating that the core matrisome is predominantly expressed in the gastrodermis, as well as cnidocytes. The analyses performed were rigorous and the results were clear, supporting the conclusions made by the authors. 

Thank you. We greatly appreciate the positive assessment of our study.

Reviewer #3 (Public review): 

Summary: 

This manuscript by Bergheim et al investigates the molecular and developmental dynamics of the matrisome, a set of gene products that comprise the extracellular matrix, in the sea anemone Nematostella vectensis using transcriptomic and proteomic approaches. Previous work has examined the matrisome of the hydra, a medusozoan, but this is the first study to characterize the matrisome in an anthozoan. The major finding of this work is a description of the components of the matrisome in Nematostella, which turns out to be more complex than that previously observed in hydra. The authors also describe the remodeling of the extracellular matrix that occurs in the transition from larva to primary polyp, and from primary polyp to adult. The authors interpret these data to support previously proposed (Steinmetz et al. 2017) homology between the cnidarian endoderm with the bilaterian mesoderm. 

Strengths: 

The data described in this work are robust, combining both transcriptome and proteomic interrogation of key stages in the life history of Nematostella, and are of value to the community. 

Thank you for your positive assessment of our dataset. 

Weaknesses: 

The authors offer numerous evolutionary interpretations of their results that I believe are unfounded. The main problem with extending these results, together with previous results from hydra, into an evolutionary synthesis that aims to reconstruct the matrisome of the ancestral cnidarian is that we are considering data from only two species. I agree with the authors' depiction of hydra as "derived" relative to other medusozoans and see it as potentially misleading to consider the hydra matrisome as an exemplar for the medusozoan matrisome. Given the organismal and morphological diversity of the phylum, a more thorough comparative study that compares matrisome components across a selection of anthozoan and medusozoan species using formal comparative methods to examine hypotheses is required. 

Specifically, I question the author's interpretation of the evolutionary events depicted in this statement: 

"The observation that in Hydra both germ layers contribute to the synthesis of core matrisome proteins (Epp et al. 1986; Zhang et al. 2007) might be related to a secondary loss of the anthozoan-specific mesenteries, which represent extensions of the mesoglea into the body cavity sandwiched by two endodermal layers." 

Anthozoans and medusozoans are evolutionary sisters. Therefore, the secondary loss of "anthozoan-like mesenteries" in hydrozoans is at least as likely as the gain of this character state in anthozoans. By extension, there is no reason to prefer the hypothesis that the state observed in Nematostella, where gastroderm is responsible for the synthesis of the core matrisome components, is the ancestral state of the phylum. Moreover, the fossil evidence provided in support of this hypothesis (Ou et al. 2022) is not relevant here because the material described in that work is of a crown group anthozoan, which diversified well after the origin of Anthozoa. The phylogenetic structure of Cnidaria has been extensively studied using phylogenomic approaches and is generally well supported (Kayal et al. 2018; DeBiasse et al. 2024). Based on these analyses, anthozoans are not on a "basal" branch, as the authors suggest. The structure of cnidarian phylogeny bifurcates with Anthozoa forming one clade and Medusozoa forming the other. From the data reported by Bergheim and coworkers, it is not possible to infer the evolutionary events that gave rise to the different matrisome states observed in Nematostella (an anthozoan) and hydra (a medusozoan). Furthermore, I take the observation in Fig 5 that anthozoan matrisomes generally exhibit a higher complexity than other cnidarian species to be more supportive of a lineage-specific expansion of matrisome components in the Anthozoa, rather than those components being representative of an ancestral state for Cnidaria. Whatever the implication, I take strong issue with the statement that "the acquisition of complex life cycles in medusozoa, that are distinguished by the pelagic medusa stage, led to a secondary reduction in the matrisome repertoire." There is no causal link in any of the data or analyses reported by Bergheim and co-workers to support this statement and, as stated above, while we are dealing with limited data, insufficient to address this question, it seems more likely to me that the matrisome expanded in anthozoans, contrasting with the authors' conclusions. While the discussion raises many interesting evolutionary hypotheses related to the origin of the cnidarian matrisome, which is of vital interest if we are to understand the origin of the bilaterian matrisome, a more thorough comparative analysis, inclusive of a much greater cnidarian species diversity, is required if we are to evaluate these hypotheses. 

DeBiasse MB, Buckenmeyer A, Macrander J, Babonis LS, Bentlage B, Cartwright P, Prada C, Reitzel AM, Stampar SN, Collins A, et al. 2024. A Cnidarian Phylogenomic Tree Fitted With Hundreds of 18S Leaves. Bulletin of the Society of Systematic Biologists [Internet] 3. Available from: https://ssbbulletin.org/index.php/bssb/article/view/9267

Epp L, Smid I, Tardent P. 1986. Synthesis of the mesoglea by ectoderm and endoderm in reassembled hydra. J Morphol [Internet] 189:271-279. Available from: https://pubmed.ncbi.nlm.nih.gov/29954165/ 

Kayal E, Bentlage B, Sabrina Pankey M, Ohdera AH, Medina M, Plachetzki DC, Collins AG, Ryan JF. 2018. Phylogenomics provides a robust topology of the major cnidarian lineages and insights on the origins of key organismal traits. BMC Evol Biol [Internet] 18:1-18. Available from: https://bmcecolevol.biomedcentral.com/articles/10.1186/s12862-018-1142-0

Ou Q, Shu D, Zhang Z, Han J, Van Iten H, Cheng M, Sun J, Yao X, Wang R, Mayer G. 2022. Dawn of complex animal food webs: A new predatory anthozoan (Cnidaria) from Cambrian. The Innovation 3:100195 

Steinmetz PRH, Aman A, Kraus JEM, Technau U. 2017. Gut-like ectodermal tissue in a sea anemone challenges germ layer homology. Nature Ecology & Evolution 2017 1:10 [Internet] 1:1535-1542. Available from: https://www.nature.com/articles/s41559-017-0285-5

Zhang X, Boot-Handford RP, Huxley-Jones J, Forse LN, Mould AP, Robertson DL, Li L, Athiyal M, Sarras MP. 2007. The collagens of hydra provide insight into the evolution of metazoan extracellular matrices. J Biol Chem [Internet] 282:6792-6802. Available from: https://pubmed.ncbi.nlm.nih.gov/17204477/ 

We agree with the reviewer that only the analysis of several additional anthozoan and medusozoan representatives will yield a valid basis for a reconstruction of the ancestral cnidarian matrisome and allow statements about ancestral or novel features within the phylum. We have therefore revised our statements in the discussion part of the manuscript by implementing the cited literature and also findings from medusozoan genome analysis (e.g. Gold et al., 2018) demonstrating that changes in gene content are as common in the anthozoans as in medusozoans, which questioned the previously stated “basal” state of Nematostella or of anthozoans in general.

Reviewer #1 (Recommendations for the authors): 

(1) In Figure 2A, an "o" is missing in the labeling of the "developing cnidcytes" population. 

Thank you, we have corrected the typo.

(2) It would be helpful to have the different life stages indicated as headers of the heat maps presented in Figure 4. 

We have included symbolic representations for the different life stages on top of the heat maps in addition to the respective labels at the bottom.

Reviewer #2 (Recommendations for the authors): 

Important changes: 

(1) Figure 2B The x-axis tissue names should be changed to something more easily readable/understandable - some are clear, but others are not. Perhaps abbreviations could be expanded in the legend. 

We have expanded the legend in Fig. 2B to render it more easily readable. We have also rotated the maps in A to have them aligned with the ones in Fig.3B.

(2) Figure 3B This figure would be improved by the inclusion of cluster names, to understand better the mapping. 

We have added relevant cluster names to Fig. 3B and as stated above aligned the orientation of the maps in Fig. 2B and Fig. 3B.

(3) Figure 3C As with 2B, I find the y-axis cnidocyte cell state names to be unclear at times. Perhaps abbreviations could be expanded in the legend. 

All abbreviations were expanded in Fig.3C axis labels.

(4) Many of the supplementary tables are not well exported or easily readable as is (gene names are truncated, headers truncated, etc), which means that they may not be easily usable by researchers in the field interested in following up on this work in other contexts. Indeed, to be more usable, please consider sharing these supplementary data as .csv files, for example, instead of as .pdfs. 

We are sorry for this inconvenience, which was obviously caused by the conversion to pdf files. We will upload the original csv files when submitting the revised manuscript.

Smaller nitpicky comments: 

(5) Page 2 line 4 & page 3 line 7: Please consider a term other than "pre-bilaterian". The drawing/ordering of a phylogeny of extant species is not meaningful in terms of more or less ancestral. e.g. if the tips are flipped in the drawing of the tree, can we say that bilaterians are pre-cnidarians? What does that mean? 

We have used that term on the basis that cnidarians existed before the appearance of bilaterians according to the fossil record and molecular phylogenies (McFadden et al., 2021; Adoutte et al., 2000;Cavalier-Smith et al., 1996; Collins, 1998; Kim et al., 1999; Medina et al., 2001; Wainright et al., 1993). To acknowledge remaining uncertainties in the timing of origin of animals, we will use the term “early-diverging metazoans” instead, which is widely accepted in the cnidarian community. 

(6) Page 3 line 9 I was confused by the use of "gastrula-shaped body" to describe cnidarians, which are on the whole very morphologically diverse and don't all resemble gastrulae (that can also be quite diverse). 

This term is sometimes used to refer to the diploblastic cnidarian body plan (outer ectoderm, inner endoderm) with a mouth that corresponds to the blastopore. To avoid misunderstandings, we changed it in the revised manuscript to “Cnidarians, the sister group to bilaterians, are characterized by a simple body plan with a central body cavity and a mouth opening surrounded by tentacles.”

Reviewer #3 (Recommendations for the authors): 

(1) In general, I felt there was a lot of discussion about protein structure and diversity that is difficult to follow without a figure. I think some of the information in Supplementary Figures S5, S9, and S11 should be in the main figures. 

Following the reviewer’s suggestion, we have integrated Fig. S5 (collagens) into the main Fig. 2 and Fig. S9 (polydoms) into Fig. 4. As metalloproteases are not extensively discussed in the manuscript (and also due to the large size of the figure) we have kept Fig. S11 as a supplementary figure.

(2) Page 3, Line 7: The use of the term "pre-bilaterian" is inappropriate. Cnidarians and bilaterians are evolutionary sisters. Therefore, each lineage derives from the same split and is the same age. The cnidarian lineage is not older than the bilaterian lineage. 

Following a similar request by reviewer 2 we have replaced this term by “early diverging metazoans”.

(3) Page 5, Line 10. How were in silico matrisomes from early-branching metazoan species predicted? 

We applied the same bioinformatic pipeline as for the Nematostella matrisome. We clarified this in the respective methods part.

(4) Page 16, Line 8: This should be Thus. 

Obviously, the wording of this sentence was ambiguous. We changed it to ”In contrast, the adult mesoglea is significantly enriched in elastic fiber components, such as fibrillins and fibulin. This compositional shift likely adds to the visco-elastic properties (Gosline 1971a, b) of the growing body column (Fig. 4B,D, supplementary table S7).”

eLife Assessment

This fundamental work demonstrates that compartmentalized cellular metabolism is a dominant input into cell size control in a variety of mammalian cell types and in Drosophila. The authors show that increased pyruvate import into the mitochondria in liver-like cells and in primary hepatocytes drives gluconeogenesis but reduces cellular amino acid production, suppressing protein synthesis. The evidence supporting the conclusions is compelling, with a variety of genetic and pharmacologic assays rigorously testing each step of the proposed mechanism. This work will be of interest to cell biologists, physiologists, and researchers interested in cell metabolism, and is significant because stem cells and many cancers exhibit metabolic rewiring of pyruvate metabolism.

Reviewer #1 (Public review):

Summary:

The study examines how pyruvate, a key product of glycolysis that influences TCA metabolism and gluconeogenesis, impacts cellular metabolism and cell size. It primarily utilizes the Drosophila liver-like fat body, which is composed of large post-mitotic cells that are metabolically very active. The study focuses on the key observations that over-expression of the pyruvate importer MPC complex (which imports pyruvate from the cytoplasm into mitochondria) can reduce cell size in a cell-autonomous manner. They find this is by metabolic rewiring that shunts pyruvate away from TCA metabolism and into gluconeogenesis. Surprisingly, mTORC and Myc pathways are also hyper-active in this background, despite the decreased cell size, suggesting a non-canonical cell size regulation signaling pathway. They also show a similar cell size reduction in HepG2 organoids. Metabolic analysis reveals that enhanced gluconeogenesis suppresses protein synthesis. Their working model is that elevated pyruvate mitochondrial import drives oxaloacetate production and fuels gluconeogenesis during late larval development, thus reducing amino acid production and thus reducing protein synthesis.

Strengths:

The study is significant because stem cells and many cancers exhibit metabolic rewiring of pyruvate metabolism. It provides new insights into how the fate of pyruvate can be tuned to influence Drosophila biomass accrual, and how pyruvate pools can influence the balance between carbohydrate and protein biosynthesis. Strengths include its rigorous dissection of metabolic rewiring and use of Drosophila and mammalian cell systems to dissect carbohydrate:protein crosstalk.

Comments on revised version:

The study remains an important dissection of how metabolic compartmentalization can influence cell size. It nicely uses Drosophila and a variety of metabolic approaches. The various pathway analyses and rigorous quantitation are strengths.

Reviewer #2 (Public review):

In this manuscript, the authors leverage multiple cellular models including the drosophila fat body and cultured hepatocytes to investigate the metabolic programs governing cell size. By profiling gene programs in the larval fat body during the third instar stage - in which cells cease proliferation and initiate a period of cell growth - the authors uncover a coordinated downregulation of genes involved in mitochondrial pyruvate import and metabolism. Enforced expression of the mitochondrial pyruvate carrier restrains cell size, despite active signaling of mTORC1 and other pathways viewed as traditional determinants of cell size. Mechanistically, the authors find that mitochondrial pyruvate import restrains cell size by fueling gluconeogenesis through the combined action of pyruvate carboxylase and phosphoenolpyruvate carboxykinase. Pyruvate conversion to oxaloacetate and use as a gluconeogenic substrate restrains cell growth by siphoning oxaloacetate away from aspartate and other amino acid biosynthesis, revealing a tradeoff between gluconeogenesis and provision of amino acids required to sustain protein biosynthesis. Overall, this manuscript is extremely rigorous, with each point interrogated through a variety of genetic and pharmacologic assays. The major conceptual advance is uncovering the regulation of cell size as a consequence of compartmentalized metabolism, which is dominant even over traditional signaling inputs. The work has implications for understanding cell size control in cell types that engage in gluconeogenesis but more broadly raise the possibility that metabolic tradeoffs determine cell size control in a variety of contexts.

Comments on revised version:

I have had a chance to review the manuscript and response to reviewer comments. I was extremely positive about this manuscript at first submission, and thought that the manuscript rigorously reported a surprising observation with broad implications across fields. The notion that intracellular metabolic networks can be dominant determinants of cell size, even over traditional signaling inputs, is surprising and important. The authors also provide convincing mechanistic insights into how the observed metabolic changes could affect cell size regulation. I think my previous comments and summary remain applicable for the revised manuscript.

Reviewer #3 (Public review):

Summary:

In this article, Toshniwal et al. investigate the role of pyruvate metabolism in controlling cell growth. They find that elevated expression of the mitochondrial pyruvate carrier (MPC) leads to decreased cell size in the Drosophila fat body, a transformed human hepatocyte cell line (HepG2), and primary rat hepatocytes. Using genetic approaches and metabolic assays, the authors find that elevated pyruvate import into cells with forced expression of MPC increases the cellular NADH/NAD+ ratio, which drives the production of oxaloacetate via pyruvate carboxylase. Genetic, pharmacological, and metabolic approaches suggest that oxaloacetate is used to support gluconeogenesis rather than amino acid synthesis in cells over-expressing MPC. The reduction in cellular amino acids impairs protein synthesis, leading to impaired cell growth.

Strengths:

This study shows that the metabolic program of a cell, and especially its NADH/NAD+ ratio, can play a dominant role in regulating cell growth.

The combination of complementary approaches, ranging from Drosophila genetics to metabolic flux measurements in mammalian cells, strengthens the findings of the paper and shows a conservation of MPC effects across evolution.

eLife Assessment

This study presents a screen for small-molecule activators of the kinase GCN2 that phosphorylates the eukaryotic translation initiation factor 2 alpha (eIF2α) in response to diverse stress stimuli. Among the compounds identified, one stands out as a potent activator that functions independently of GCN1, which is important for probing mechanisms of Integrated Stress Response regulation and may have translational relevance in the context of pathogenic GCN2 mutations. While some reviewers found the biochemical analyses convincing, others viewed the cellular evidence as limited, particularly with respect to time points, endogenous readouts, and broader cell-type validation, which prevents a clear assessment of the compound's potential potency in a physiological context.

Reviewer #1 (Public review):

Summary:

This manuscript describes a chemical screen for activators of the eIF2 kinase GCN2 (EIF2AK4) in the integrated stress response (ISR). Recently, reported inhibitors of GCN2 and other protein kinases have been shown at certain concentrations to paradoxically activate GCN2. The study uses CHO cells and ISR reporter screens to identify a number of GCN2 activator compounds, including a potent "compound 20." These activators have implications for the development of new therapies for ISR-related diseases. For example, although not directly pursued in this study, these GCN2 activators could be helpful for the treatment of PVOD, which is reported for patients with certain GCN2 loss-of-function mutations. The identified activators are also suggested to engage with the GCN2 directly and can function while devoid of GCN1, a co-activator of GCN2.

Strengths:

The manuscript appears to be a largely rigorous study that flows in a logical manner. The topic is interesting and significant.

Weaknesses:

Portions of the manuscript are not fully clear. There are some experimental presentation and design concerns that should be addressed to support the stated conclusions.

Reviewer #2 (Public review):

Summary:

In this manuscript, Zhu, Emanuelli, and colleagues describe a novel pharmacological activator of the Integrated Stress Response kinase GCN2. The work is conclusive and biochemically solid. This work significantly adds to the pharmacological arsenal targeting the ISR and, in particular, GCN2.

Strengths:

Strong biochemistry, novel molecular activator of GCN2 (GCN1 independent).

Weaknesses:

The rationale for the screen is not exploited in the results (e.g., pathogenic GCN2 mutants), and lots of cell-based read-outs are not endogenous.

Major points

(1) Regarding the justification of the work. Since the authors justify the screen for GCN2 activators with loss-of-function mutants associated with diseases, it would be of interest to evaluate whether the best compounds identified in the study are indeed able to prompt activation of those mutants (or at least of the most prevalent). This approach could actually go in parallel with the docking experiments carried out in the last figure of the manuscript, where mutants could be modelized as well.

(2) The compounds are only tested using « artificial » proximal signaling outputs. It would be interesting to evaluate whether the best identified compounds are capable of prompting endogenous eIF2alpha phosphorylation in cellular models.

(3) Other GCN2 activators (other than GCN2iB, e.g., HC-7366) were recently identified. In this context, it would be of interest to carry out a small benchmarking study to evaluate how the compounds identified in the current study perform against the previously identified molecules.

Reviewer #3 (Public review):

Summary:

In this manuscript, the authors describe the results of a high-throughput screen for small-molecule activators of GCN2. Ultimately, they find 3 promising compounds. One of these three, compound 20 (C20), is of the most interest both for its potency and specificity. The major new finding is that this molecule appears to activate GCN2 independent of GCN1, which suggests that it works by a potentially novel mechanism. Biochemical analysis suggests that each binds in the ATP-binding pocket of GCN2, and that at least in vitro, C20 is a potent agonist. Structural modeling provides insight into how the three compounds might dock in the pocket and generates testable hypotheses as to why C20 perhaps acts through a different mechanism than other molecules.

Strengths:

Of the 3 compounds identified by the authors, C20 is the most interesting, not just for its intriguing mechanistic distinction as being GCN1-independent (shown genetically in two distinct cell lines, CHO and 293T in Figure 4, and in contrast to other GCN2 activators) but also for its potency. In in-cellulo assays, compound 21 appears as more of an ISR enhancer than an activator per se, and although compound 18 and compound 21 lead to upregulation of the ISR targets (Figure 2), that degree of upregulation is probably not significantly different from that induced by those compounds in Gcn2-/- cells. For C20, the effect appears stronger (although it is unclear whether the authors performed statistical analysis comparing the two genotypes in Figure 2D). In Figure 3, only C20 activates the ISR robustly in both CHO and 293T. Ultimately, C20 might be a tool for providing mechanistic insight into the details of GCN2 activation and regulation, and could be exploited therapeutically.

Weaknesses:

There are some limitations to the existing work. As the authors acknowledge, they do not use any of the compounds in animals; their in vivo efficacy, toxicity, and pharmacokinetics are unknown. But even in the context of the in cellulo experiments, it is puzzling that none of the three compounds, including C20, has any effects in HeLa cells when Neratinib does. It's beyond the scope of this paper to address definitively why that is, but it would at least be reassuring to know that C20 activates the ISR in a wider range of cells, including ideally some primary, non-immortalized cells. In addition, the ISR is a complex, feedback-regulated response whose output varies depending on the time point examined. The in cellulo analysis in this paper is limited to reporter assays at 18 hours and qRT-PCR assays at 4 and 8 hours. A more extensive examination of the behavior of the relevant ISR mRNAs and proteins (eIF2, ATF4, CHOP, cell viability, etc.) for C20 across a more extensive time course would give the reader a clearer sense of how this molecule affects ISR output. I also find it a bit strange that the authors describe C20 as "demonstrat(ing) weak inhibition of ... PKR"-the measured IC50 is ~4 μM, which is right around its EC50 for GCN2 activation. This raises the confounding possibility that C20 would simultaneously activate GCN2 while inhibiting PKR. While perhaps inhibition of PKR is not relevant under the conditions when GCN2 would be activated either experimentally or therapeutically, examining in cells the effects of C20 on GCN2 and PKR across a dose range would shed light on whether this cross-reactivity is likely to be of concern.

Author response:

We thank the editors and reviewers for their encouraging comments and constructive feedback. We will revise the text to enhance clarity as suggested. New experiments are planned to address questions raised regarding the time course of responses to the hit compounds. We also intend to examine additional endogenous readouts of the integrated stress response, including effects on translation. The effects of lead compound 20 will be examined in a wider range of cells, including primary cells.

eLife Assessment

This fundamental work substantially advances our understanding of tissue deformation and growth patterns during the earliest stages of mammalian heart development. One of the strengths of the work is the compelling quantitative approach to analyzing time-lapse imaging data using an original computational pipeline, which goes beyond the current state of the art and provides new insights into heart tube formation. Overall, this rigorous study will be of broad interest to computational and developmental biologists studying tissue dynamics.

Reviewer #1 (Public review):

Summary:

The study by Raiola et al. conducted a quantitative analysis of tissue deformation during the formation of the primitive heart tube from the cardiac crescent in mouse embryos. Using the tools developed to analyze growth, anisotropy, strain, and cell fate from time-lapse imaging data of mouse embryos, the authors elucidated the compartmentalization of tissue deformation during heart tube formation and ventricular expansion. This paper describes how each region of the cardiac tissue changes to form the heart tube and ventricular chamber, contributing to our understanding of the earliest stages of cardiac development.

Strengths:

In order to understand tissue deformation in cardiac formation, it is commendable that the authors effectively utilized time-lapse imaging data, a data pipeline, and in silico fate mapping.

The study clarifies the compartmentalization of tissue deformation by integrating growth, anisotropy, and strain patterns in each region of the heart.

Weaknesses:

The significance of the compartmentalization of tissue deformation for the heart tube formation remains unclear.

Reviewer #2 (Public review):

The authors address an important challenge in developmental biology: the quantitative description of tissue deformation during organogenesis. They have developed a new pipeline to quantify early heart tube morphogenesis in the mouse, with cellular resolution. They adopt an elegant approach by integrating multiple 3D time-lapse datasets into a dynamic atlas of cardiac morphogenesis in order to compute spatio-temporal deformation patterns. The main findings highlight a strong compartmentalization of cell behaviors, with tissue growth and anisotropy exhibiting complementary and spatially segregated patterns. Using these data, the authors developed an in-silico fate mapping tool to interrogate cell displacement within the myocardium. This virtual model provides new mechanistic insights into how the bilateral cardiac primordia converge and transform into a three-dimensional heart tube. The authors identify "belt-like" constraints at the arterial and venous poles that prevent tissue expansion and thus shape the ventricular barrel morphology.

The computational framework is highly innovative and impressive, providing an unprecedented 3D model of tissue deformation during heart morphogenesis. It also opens avenues for testing hypotheses regarding tissue growth and the forces that cause cell motion. However, the proposed model of ventricular chamber formation with the two constraining belts remains hypothetical, lacking biological validation and requiring strengthening or modulation.

Overall, this carefully performed study provides a new model for exploring tissue deformation during organogenesis and will be of broad interest to computational and developmental biologists.

Reviewer #3 (Public review):

Summary:

The manuscript by Raiola and colleagues entitled "Quantitative computerized analysis demonstrates strongly compartmentalized tissue deformation patterns underlying mammalian heart tube formation" takes a highly quantitative approach to interrogating the earliest stages of cardiogenesis (12 hours, from early cardiac crescent to early heart tube) in a new and innovative way. The paper presents a new computational framework to help identify both regional and temporal patterns of tissue deformation at cellular resolution. The method is applied to live embryo imaging data (newly generated and from the group's previous pioneering work). In the initial setup, the new model was applied directly to raw time-lapse data, and the results were compared to actual cell tracks identified manually, showing close correlations of the model with the manual tracking. Next, they integrated spatial and temporal information from different embryos to generate a new model for tissue movement, driven by parameters such as tissue growth and anisotropy. Key findings from their model suggest that there are distinct compartments of tissue deformation patterns as the bilateral cardiac crescent develops into the linear heart tube, and that the ventricular chamber forms by a defined expansion pattern, as a 'hemi-barrel shape', with the aterial and venous poles (IFT and OFT) acting as the harnessing belts constraining the expansion of the chamber further. Lastly, the model is tested for its ability to predict future residence of cardiac crescent cells in the heart tube, which it seems to be able to do successfully based on fate tracking validation experiments.

Strengths:

The manuscript provides an exceptionally careful analysis of a critical stage during heart development - that of the earliest stages of morphogenesis, when the heart forms its first tube and chamber structures. While numerous studies have interrogated this stage of heart development, few studies have performed time-lapse imaging, and, to my knowledge, no other report has performed such in in-depth quantitative analysis and modeling of this complex process. The computational model applied to normal heart development of the myocardium (labelled by Nkx2-5) has revealed multiple new and interesting concepts, such as the distinct compartments of tissue deformation patterns and the growth trajectories of the emerging ventricle. The fact that the model operates at cellular resolution and over a nearly continuous time period of approximately 12 hours allows for unprecedented depth of the analysis in a largely unbiased manner. Going forward, one can imagine such models revealing additional information on these processes, performing analyses of subpopulations that form the heart, and maybe most importantly, applying the model to various perturbation models (genetic or otherwise). The manuscript is very well written, and the data display is accessible and transparent.

Weaknesses:

No major weaknesses are noted with the study. It would have been very exciting to see the model applied to any kind of perturbation, for example, a left-right defect model, or a model with compromised cardiac progenitor populations. However, the amount of live imaging required for such analyses renders this out of scope for the current study.

Author response:

We are going to modify the text following Reviewer’s comments and perform embryo direct labelling experiments to experimentally address the contraction of the two “belts” proposed in our model. We feel that this aspect is feasible in a reasonable time and important for the model proposed. We appreciate the relevance of using this framework to identify molecular drivers of the regionalized tissue behaviours uncovered and how these might be altered in mutant models, but feel that these aspects demand efforts beyond the the reasonable revision periods.

eLife Assessment

This work presents valuable new data on the role of D-Serine and how it competes with its stereoisomer L-Serine to influence metabolism. The work presents a variety of solid experimental data combined with simulated results to investigate the mechanisms focused on one-carbon metabolism, which is relevant for several research fields. However, some claims are only partially supported by data, and critical areas comparing L- vs D-Serine and further mechanistic studies are incomplete. Furthermore, while the work has potential for various fields, the work has only been studied in a limited cell type and context.

Reviewer #1 (Public review):

Summary:

The authors demonstrate the stereoselective role of D-serine in 1C metabolism, showing that D-serine competes with L-serine and inhibits mitochondrial L-serine transport. They observe expression of 1C metabolites in their metabolomics approach in primary cortical neurons treated with L-serine, D-serine, and a mixture of both. Their conclusions are based on the reduction in levels of glycine, polyamines, and their intermediates and formate. Single-cell RNA sequencing of N2a cells showed that cells treated with D-serine enhanced expression of genes associated with mitochondrial functions, such as respiratory chain complex assembly, and mitochondrial functions, with downregulation of genes related to amino acid transport, cellular growth, and neuron projection extension. Their work demonstrates that D-serine inhibits tumor cell proliferation and induces apoptosis in neural progenitor cells, highlighting the importance of D-serine in neurodevelopment.

Strengths:

D-amino acids are a marvel of nature. It is fascinating that nature decided to make two versions of the same molecule, in this case, an amino acid. While the L-stereoisomer plays well-known roles in biology, the D-stereoisomer seems to function in obscurity. Research into these novel signaling molecules is gathering momentum, with newer stereoisomers being discovered. D-serine has been the most well-studied among the different stereoisomers, and we still continue to learn about this novel neurotransmitter. The roles of these molecules in the context of metabolism is not well studied. The authors aim to elucidate the metabolic role of D-serine in the context of neuronal maturation with implications for 1C metabolism and in cell proliferation. The metabolic role of these molecules is just beginning to be uncovered, especially in the context of mammalian biology. This is the strength of the manuscript. The authors have done important work in prior publications elucidating the role of D-amino acids. The advancement of the field of D-amino acids in mammalian biology is significant, as not much is known. The presentation of RNA seq data is a valuable resource to the community, however, with caveats as mentioned below.

Weaknesses:

The following are some of the issues that come out in a critical reading of the manuscript. Addressing these would only strengthen and clarify the work.

(1) Kinetic assessment of D-serine versus L-serine: While the authors mention that D-serine is not a good substrate for SHMT2 compared to L-serine, the kinetic data are presented for only D-serine. In a substrate comparison with an enzyme, data must be presented for L-serine as well to make the conclusion about substrate specificity and affinity. Since the authors talk about one versus another substrate, there needs to be a kinetic comparison of both with Km (affinity). (Ref Figure 2 panel).

(2) Molecular Dynamics simulations, while a good first step in modeling interactions at the active site, rely on force fields. These force fields are approximations and do not represent all interactions occurring in the natural world. Setting up the initial conditions in the simulations can impact the final results in non-equilibrium scenarios. The basic question here is this: Is the simulated trajectory long enough so that the system reaches thermodynamic equilibrium and the measured properties converge? Prior studies have shown mixed results with the conclusion that properties of biological systems tend to converge in multi-second trajectories (not nanosecond scales as reported by the authors) and transition rates to low probability conformations require more time. (Ref Figure 2C).

(3) The authors use N2a cell line to demonstrate D-serine burden on primary cortical neurons. N2a is an immortalized cell line, and its properties are very different from primary neurons. The authors need to mention a rationale for the use of an immortalized cell line versus primary neurons. The transcriptomic profile of an immortalized cell line is different compared to a primary cell. Hence, the response to D-serine may vary between the two different cell types.

(4) In Figure 4D, the authors mention that D-serine activates the cleavage of caspase 3. Figure 4D shows only cleaved caspase 3 as a single band. They need to show the full blot that contains the cleaved fragments along with the major caspase 3 band.

(5) In Figure panel 4, the authors use neural progenitor cells (NPCs). They need to demonstrate that the population they are working with is NPCs and not primary neurons. There must be a figure panel staining for NPC markers like SOX2 and PAX6. Also, Figure S5 needs to be properly labeled. It is confusing from the legend what panels B-E refer to? Also, scale bars are not indicated.

(6) In Supplementary Figure panel 7F, the authors mention phosphatidyl L-serine and phosphatidyl D-serine. A chromatogram of the two species would clarify their presence as they used 2D-HPLC. On an MS platform, these 2 species are not distinguishable. Including a chromatogram of the 2 species would be helpful to the readers.

(7) The authors mention about enantiomeric shift of serine metabolism during neural development, which appears to be a discussion of prior published data from Hubbard et al, 2013, Burk et al, 2020, and Bella et a,l 2021 in Supplementary Figure panels 8 A-E. This should not be presented as a figure panel, as it gives the false impression that the authors have performed the experiment, which is clearly not the case. However, its discussion can well serve as part of the manuscript in the discussion section.

(8) The entire presentation of the section on enantiomeric shift of serine metabolism during neural development (lines 274-312) is a discussion and should be part of the discussion section and not in the results section. This is misleading.

(9) The discussion section is not well written. There is no mention of recent work related to D-serine that has a direct bearing on its metabolic properties. In the discussion section, paragraph 1, the authors mention that their work demonstrates the selective synthesis of D-serine in mature neurons as opposed to neural progenitor cells. This concept has been referred to in prior publications:

(a) Spatiotemporal relationships among D-serine, serine racemase, and D-amino acid oxidase during mouse postnatal development. PMID:14531937.

(b) D-cysteine is an endogenous regulator of neural progenitor cell dynamics in the mammalian brain. PMID:34556581.

(10) In the abstract, in lines 101 and 102, the authors mention "how D-serine contributes to cellular metabolism beyond neurotransmission remains largely unknown". In 2023, a paper in Stem Cell Reports by Roychaudhuri et al (PMID:37352848) showed that D and L-serine availability impacts lipid metabolism in the subventricular zone in mice, affecting proliferative properties of stem-cell derived neurons using a comprehensive lipidomics approach. There is no mention of this work even in the discussion section, as it bears directly on L and D-serine availability in neurons, which the authors are investigating. In the discussion section in lines 410-411, the authors mention the role of D-serine in neurogenesis, but surprisingly don't refer to the above reference. The role of D-serine in neurogenesis has been demonstrated in the Sultan et al (lines 855-857) and Roychaudhuri et al references.

(11) Both D-serine and the structurally similar stereoisomer D-cysteine (sulfur versus oxygen atom) have a bearing on 1C metabolism and the folate cycle. With reference to the folate cycle, Roychaudhuri et al in 2024 (PMID:39368613) have shown in rescue experiments in mice that supplementing a higher methionine diet provides folate cycle precursors to rescue the high insulin phenotype in SR-deficient mice. Since 1C metabolism is being discussed in this manuscript, the authors seem to overlook prior work in the field and not include it in their discussion, even when it is the same enzyme (SR) that synthesizes both serine and cysteine. Since the field of D-amino acid research is in its infancy, the authors must make it a point to include prior work related to D-serine at least, and not claim that it is not known. The known D-stereoisomers are not many, hence any progress in the area must include at least a discussion of the other structurally related stereoisomers.

(12) Racemases (serine and aspartate) in general are promiscuous enzymes and known to synthesize other stereoisomers in addition to D-serine, D-cysteine, and D-aspartate. A few controls, like D-aspartate, D-cysteine, or even D-alanine must be included in their study to demonstrate the specific actions of D-serine, especially in the N2a cell treatment experiments. Cysteine and Serine are almost identical in structure (sulfur versus oxygen atom), and both are synthesized by serine racemase (published). Cysteine has also been very recently shown to inhibit tumor growth and neural progenitor cell proliferation. (PMIDs: 40797101 and 34556581). How the authors' work relates to the existing findings must be discussed, and this would put things in perspective for the reader.

Reviewer #2 (Public review):

Summary:

This study by Suzuki et al. reports an interesting stereo-selective role of D-serine in regulating one-carbon metabolism during neurodevelopment to adapt the functional transition, probably through the competition with mitochondrial transport of L-serine. The authors provide a multi-layered set of evidence, including metabolomics, enzyme assays, mitochondrial transport competition, and functional assays in immature/neural progenitor cells, to build up a conceptual integration of D-serine as both a neurotransmitter and a metabolic regulator in the central neural system, which raises a broad potential interest to the neuroscience and metabolism communities.

Strengths:

This work provides a conceptual advance that D-serine not only serves as a traditional neurotransmitter in the central neural system but also critically contributes to metabolic regulation of neural cells. The authors performed solid metabolomic assays to validate the suppressive effect of D-serine on the one-carbon metabolic pathway, providing some evidence that D-serine competitively inhibits mitochondrial serine transport, but not directly impairs SHMT2 enzymatic activity. All these data indicate a critical role of D-serine synthesis during neural maturation and suggest a potential translational strategy for targeting serine metabolism in neural tumors.

Weaknesses:

(1) The detailed mechanism by which D-serine competes with L-serine for its mitochondrial transport is not investigated. For example, although the authors made some discussion, they did not provide direct genetic or biochemical evidence linking these effects to the specific transporters, such as SFXN1.

(2) Unlike tumor cells, where SHMT2 usually plays a predominant role in catalyzing serine/THF-derived one-carbon metabolism, normal cells may employ both SHMT1 and SHMT2 to do the work. Even under certain conditions that SHMT2-mediated one-carbon metabolism is suppressed, the activity of SHMT1 could be elevated for compensation. Thus, it is important to investigate whether D-serine affects SHMT1 activity or changes the balance between SHMT1- and SHMT2-mediated one-carbon metabolism. To this aim, the authors are strongly encouraged to perform a metabolic flux assay (MFA) by using 13C-labeled L-serine in the model cells in the presence and absence of D-serine.

(3) A defect in serine-derived one-carbon metabolism may cause multiple cellular stress responses. It is valuable to detect whether cellular NADPH/NADH, GSH, or ROS is altered before and after D-serine treatment.

(4) The physiological relevance between D-serine and neural cell maturation/death should be further tested and discussed, since the dosage of D-serine used in the in vitro assay is much higher than that in physiological conditions.

Reviewer #3 (Public review):

Summary:

This manuscript presents a comprehensive and well-executed investigation into the metabolic role of D-serine in the central nervous system. The authors provide solid evidence that D-serine competitively inhibits mitochondrial L-serine transport, thereby impairing one-carbon metabolism. This stereoselective mechanism reduces glycine and formate production, suppresses cellular proliferation, and induces apoptosis in immature neural cells and glioblastoma stem cells. Developmental analyses further reveal a physiological enantiomeric shift in serine metabolism during neurogenesis, aligning with the transition from proliferation to maturation. Overall, the study bridges developmental neurobiology, cancer metabolism, and amino acid transport, uncovering a previously unrecognized metabolic function of D-serine beyond its role in neurotransmission.

Strengths:

(1) The discovery that D-serine inhibits one-carbon metabolism by competing for mitochondrial L-serine transport-rather than through enzymatic inhibition or receptor-mediated signaling-represents a significant and previously underappreciated mechanism. This finding has broad implications for understanding metabolic regulation during neurodevelopment and offers potential relevance for targeting metabolic vulnerabilities in cancer.

(2) The authors integrate metabolomics, mitochondrial transport assays, molecular dynamics simulations, genetic and pharmacologic perturbations, transcriptomics, and both in vitro and ex vivo models. The breadth of experimental approaches, combined with the coherence of the findings across systems, provides strong support for the central conclusions and enhances the overall impact of the study.

(3) The temporal shift in D-/L-serine levels during neurodevelopment is elegantly linked to the transition from proliferative to mature neuronal states. The selective vulnerability of neural progenitors and tumor cells-contrasted with the resistance of mature neurons-highlights a biologically meaningful and potentially targetable metabolic distinction.

Weaknesses:

(1) While the authors attribute D-serine's metabolic effects to competition with mitochondrial L-serine transport, the specific identity of the transporter(s) mediating this process remains undefined. This represents a meaningful mechanistic gap, as the central conclusion depends on D-serine limiting mitochondrial L-serine availability to inhibit one-carbon metabolism.

(2) The effective concentrations of D-serine used in vitro (IC₅₀ ≈ 1-2 mM) exceed typical brain levels (~0.3 mM). While the authors acknowledge this, a more focused discussion on whether higher local D-serine concentrations could arise in specific microenvironments - such as synaptic compartments, tumor niches, or pathological states-would help contextualize the in vitro findings and strengthen their physiological relevance. For example, disruptions in D-serine clearance or altered expression of serine racemase and transporters in disease contexts could lead to localized accumulation. Moreover, differences between extracellular and intracellular D-serine pools - and the mechanisms governing their regulation - may further influence its metabolic impact in vivo.

(3) While the manuscript focuses on neural stem/progenitor cells and neural tumors, it remains unclear whether the anti-proliferative effects of D-serine are specific to neural lineages or extend to other highly proliferative non-neural cell types. A brief discussion addressing this point would help clarify the scope of D-serine's metabolic impact and whether its mechanism of action reflects a unique vulnerability in neural cells or a more general feature of proliferative metabolism. This distinction is particularly relevant for assessing the broader therapeutic potential of targeting mitochondrial L-serine transport.

eLife Assessment

Plasmodesmata are channels that allow cell-cell communication in plants; based on the functional similarities between facilitated transport within plasmodesmata and into the nucleus, the authors speculate that nuclear pore complex proteins might be involved in plasmodesmata function. If supported, this would transform our understanding of cell-to-cell communication in plants. The authors localize nuclear pore complex proteins to plasmodesmata using proteomics and heterologous overexpression; however, the data are incomplete since key controls for localization, functionality, and expression level of fluorescent protein fusions are absent.

Reviewer #1 (Public review):

Summary:

Plasmodesmata are channels that allow cell-cell communication in plants; based on the functional similarities between facilitated transport within plasmodesmata and into the nucleus, the authors speculate that nuclear pore complex proteins might be involved in plasmodesmata function. In this manuscript, they localize nuclear pore complex proteins to plasmodesmata using proteomics and heterologous overexpression. They also document a possible plasmodesmata transport defect in a mutant affecting one nuclear pore complex protein.

Strengths:

The main strength of this manuscript is the interesting and novel hypothesis. This work could open exciting new directions in our understanding of plasmodesmata function and cell-cell communication in plants. They also localized many NUPs (12/35 Arabidopsis NUPs).

Weaknesses:

The main weakness of this manuscript is that the data are incomplete. While the authors appropriately and frequently acknowledge caveats to their data, two controls are essential to interpret the results that fluorescently-tagged NUPs localize to the plasmodesmata: (1) assessment of the expression level of these fluorescently-tagged NUPs to determine whether the plasmodesmata localization might be an overexpression artefact; (2) assessment of the function of the fluorescently-tagged NUPs, either by molecular complementation of a knockout mutant phenotype or by biochemical methods to test whether the fluorescently-tagged NUP incorporates into nuclear pore complexes. Conducting these experiments for even one fluorescently-tagged NUP would substantially strengthen this manuscript.

Reviewer #2 (Public review):

Summary:

The authors aim to address whether nuclear pore complex components localize and function at PD in plant cells to mediate cell-to-cell communication.

Strengths:

(1) Novelty and Significance:<br /> The core hypothesis, drawing parallels between PD and NPC transport, is highly original and addresses a critical gap in understanding plant intercellular communication. The idea that phase-separated domains formed by FG-NUPs could act as diffusion barriers at PD offers a plausible and sophisticated explanation for their complex transport properties, including size exclusion and facilitated translocation. This could fundamentally change how we view PD function.

(2) Comprehensive Evidence:<br /> The study employs a rigorous and diverse set of experimental approaches, including a comprehensive bioinformatic analysis of both moss and Arabidopsis NUPs in available PD proteomic datasets, extensive imaging analysis of Nup localization in vivo, and functional transport assays using a loss-of-function nup mutant (cpr5). The transport assay is particularly important to provide functional evidence linking CPR5 to PD-mediated transport. The finding that callose levels were not significantly different in cpr5 mutants under these conditions is helpful and supports a distinct, callose-independent mechanism of transport regulation.

(3) Objectivity:<br /> The authors are forthright in discussing the limitations and potential artifacts of their own data, clearly distinguishing between observations and definitive conclusions.

Weaknesses:

While the claims are generally justified as hypotheses or consistent observations, the authors themselves extensively detail the caveats, which are worth reiterating for clarity:

(1) Potential Overexpression Artifacts in Localization:<br /> Although efforts were made to control expression levels, the authors acknowledge that transient overexpression could still lead to NUP accumulation at PD, either as a physiologically relevant accumulation under excess conditions or due to mis-targeting, or even as storage depots. The resolution of confocal microscopy also does not allow for a definitive conclusion on the nature of the location.

(2) Proteomics Purity:<br /> The authors note that the presence of NUPs in PD fractions/proteomics cannot definitively rule out contamination, as PD cannot currently be purified to absolute homogeneity and is often contaminated with other organelles, including the nucleus.

(3) CPR5 Mutant Interpretation:<br /> While cpr5 mutants exhibited reduced macromolecular transport, the authors state that they cannot exclude that the reduced transport is due to secondary effects in the cpr5 mutants, which show rather severe phenotypic defects. This is an important distinction, as CPR5 has known roles in defense responses and hormone signaling that could indirectly influence PD integrity, independent of callose deposition. The lack of effect on small molecule transport is a good control, but the broader pleiotropic effects of cpr5 mutants remain a consideration.

(4) Conceptual Distinction between NPC and PD:<br /> The authors correctly point out that while similarities exist, the physical assembly of NUPs at PD must differ from that at the NPC due to the presence of the desmotubule and smaller cytoplasmic sleeve width at PD. Moreover, nucleocytoplasmic transport depends on karyopherin proteins that interact with the NPC central channel to complete the transport. Yet the role of karyopherins in this case is not clear. Therefore, the proposed "PD pore complex" may bear some NPC features, but not be identical.

Reviewer #3 (Public review):

Summary:

This manuscript presents a step towards testing the hypothesis that plasmodesmata have homology to nuclear pores. The similarities between the two structures have long been noted as both structures allow the transport of proteins and nucleic acids, and both structures are composed of curved membranes. The manuscript has identified nuclear pore proteins (NUPs) in plasmodesmal protein fractions and uses live imaging in a non-endogenous system and functional assays of a mutant to propose that this might be a bona fide association.

The conclusions the authors seek to draw are that: NUPs are present in plasmodesmal protein fractions; NUPs localise at plasmodesmata; NUPs might form a pore-gating complex at plasmodesmata, regulating non-specific (2xGFP) and specific (SHR) transport through plasmodesmata

The authors then use these conclusions to propose the possibility that phase separation mediates transport through plasmodesmata. If there is phase separation at plasmodesmata or a nuclear pore-like complex, it would revolutionise the community. However, this data is insufficient to act as a cornerstone for such a discovery.

Strengths:

The strength of the manuscript lies in the boldness and novelty of the idea.

Weaknesses:

The weaknesses lie in the lack of informative controls. The authors' own assessments of their data suggest they agree with this - in their abstract alone, they point out that the transport defects they observe might be off-target effects, and suggest there is a requirement in the future to determine whether the NUPs are bona fide PD components.

Across the proteomic and live imaging experiments, the conclusions could be stronger if they compared the NUP localisation and accumulation with ER proteins - the question of whether NUPs behave like other ER proteins is not addressed. As NUPs reside in the nuclear envelope, continuous with the ER, and the ER traverses plasmodesmata, a comparison between the NUPs and ER proteins would be extremely informative.

Regarding the proteomic identification of NUPs in plasmodesmal fractions, the authors place significant weight on their own metric for PD enrichment, the PD score. As I understand it, this a metric derived from addition of two factors: a two component enrichment score that is the difference between intensity of peptides of a given protein in the PD fraction and cell wall fraction, added to the difference between intensity of peptides of a given protein in the PD fraction and total cell fraction, and a feature score that is a factor that describes representation of protein domains contained in said given protein in the plasmodesmal fraction relative to the representation of that domain in proteins in the whole proteome. The features chosen for analysis are not indicated, and the feature factor, as I understand it, is a score common to all proteins with a given feature. While each of the factors carries a measure of meaning and information, I do not understand how adding them is mathematically or biologically meaningful.

eLife Assessment

This important study demonstrates the potential of synthetic gene circuits to detect and target aberrant RAS activity in cancer cell lines. The circuit design is novel and the evidence supporting the claims is convincing. As a proof-of-concept, this will be of broad interest to researchers in synthetic biology and therapeutics development, while future work will be required to help translate this technology toward clinical applications in cancer therapeutics and address potential limitations of the strategy.

Reviewer #1 (Public review):

Summary:

The manuscript presents a comprehensive study on the developing synthetic gene circuits targeting mutant RAS expressing cells. The aim of this study is to use these RAS targeting circuits as cancer cell classifiers and enable the selective expression of an output protein in correlation with RAS activity. The system is based on the bacterial two-component system NarX/NarL. A RAS-binding domain is fused to a NarX mutant either defective in the ATP binding (N509A) or the phosphorylation site (H399Q). Nanocluster formation of RAS-GTP reconstitutes an active histidine kinase sensor dimer that phosphorylates the response regulator NarL thus leading to the expression of an output protein. The integration of RAS-dependent MAPK responsive elements to express the RAS sensor components generates RAS circuits with an extended dynamic range between mutant and wild-type RAS. The selectivity of the RAS circuits is confirmed in a set of cancer cell lines expressing endogenous levels of mutant or wild-type RAS or oncogenes affecting RAS signaling upstream or downstream. Expression of the suicide gene HSV thymidine kinase as an outcome protein kills RAS-driven cancer cells demonstrating the functionality of the system.

Strengths:

This proof-of-concept study convincingly demonstrates the potential of synthetic gene circuits to target oncogenic RAS in tumor cell lines, act as RAS mutant cell classifier, and induce the killing of RAS-driven cells.

Weaknesses:

A therapeutic strategy based on of this four-plasmid system may be difficult to implement in RAS-driven solid cancers. However, potential solutions are discussed.

Reviewer #2 (Public review):

The manuscript describes an interesting approach towards designing genetic circuits to sense different RAS mutants in the context of cancer therapeutics. The authors created sensors for mutant RAS and incorporated feed-forward control that leverages endogenous RAS/MAPK signaling pathways in order to dramatically increase the circuits' dynamic range. The modularity of the system is explored through the individual screening of several RAS binding domains, transmembrane domains, and MAPK response elements, and the author further extensively screened different combinations of circuit components. This is an impressive synthetic biology demonstration that took it all the way to cancer cell lines. However, given the sole demonstrated output in the form of fluorescent proteins, the authors' claims related to therapeutic implications require additional empirical evidence or, otherwise, expository revision.

Major comments:

"These therapies are limited to cancers with KRASG12C mutations" is technically accurate. However, in this fast-moving field, there are examples such as MRTX1133 which holds the promise to target the very G12D mutation that is the focus of this paper. There are broader efforts too. It would help the readers better appreciate the background if the authors could update the intro to reflect the most recent landscape of RAS-targeting drugs.

Only KRASG12D was used as a model in the design and optimization work of the genetic circuits. Other mutations should be quite experimentally feasible and comparisons of the circuits' performances across different KRAS mutations would allow for stronger claims on the circuits' generalizability. Particularly, the cancer cell line used for circuit validation harbored a KRASG13D mutation. While the data presented do indeed support the circuit's "generalizability," the model systems would not have been consistent in the current set of data presented.

In Figure 2a, the text claims that "inactivation of endogenous RAS with NF1 resulted in a lower YFP/RBDCRD-NarX expression," but Figure 2a does not show a statistically significant reduction in expression of SYFP (measured by "membrane-to-total signal ratio [RU]).

The therapeutic index of the authors' systems would be better characterized by a functional payload, other than florescent proteins, that for example induce cell death, immune responses, etc.

Regarding data presented in "Mechanism of action" (Figure 2), the observations are interesting and consistent across different fluorescent reporters. However, with regard to interpretations of the underlying molecular mechanisms, it is not clear whether the different output levels in 2b, 2c, and 2d are due to the pathway as described by the authors or simply from varied expression levels of RBDCRD-NarX itself (2a) that is nonlinearly amplified by the rest of the circuit. From a practical standpoint, this caveat is not critical with respect to the signal-to-noise ratios in later parts of the paper. From a mechanistic interpretation standpoint, claims made forth in this section are not clearly substantiated. Some additional controls would be nice. For example, if the authors express NarXs that constitutively dimerize on the membrane, what would the RasG12D-responsiveness look like? Does RasG12D alter the input-output curve of NarL-RE? How would Figure 4f compare to a NaxR constitutively dimerized control that only relies on transcriptional amplification of the Ras-dependent promoters? It's also possible that these Ras could affect protein production at the post-transcriptional or even post-translational levels, which were not adequately considered.

The text claims that "in contrast to what we saw in HEK293 overexpressing RAS (Figure 5d), the "AND-gate" RAS-targeting circuits do not generate higher output than the EF1a-driven, binding-triggered RAS sensor in HCT-116. Instead, the improved dynamic range results from decreased leakiness in HCT- 116k.o." Comparing the experiment from Figure 5d, which looks at activation in KRASG12D and KRASWT, to the experiments in Figure 6b-d, which looks at activation in HCT-116WT and HCT-116KO is misleading. In Fig 5d., cells are transfected with KRASG12D and KRASWT to emulate high levels of mutant RAS and high levels of wild-type RAS. In Figures 6b-d, HCT-116WT has endogenous levels of mutant RAS, while the KCT-116KO is a knock-out cell line, and does not have mutant or WT RAS. Therefore, the improved dynamic range or "decreased leakiness in HCT-116KO" in comparison to Figure 5d. is more comparable to the NF1 condition from Figure 2, which deactivates endogenous RAS. While this may not be feasible, the most accurate comparison would have been an HCT-116KO line with KRASWT stably integrated.

We couldn't locate the citation or discussion of Figure 4d in the text. Conversely, based on the text description, Figure 6g would contain exciting results. But we couldn't find Figure 6g anywhere ... unless it was a typo and the authors meant Figure 6f, in which case the cool results in Figure S8 could use more elaboration in the main text.

Comments on revisions:

Now that the authors have extensively addressed my comments through text and additional experiments, I am supportive of its conclusions. I thank them for the rigorous updates and congratulate them on an important piece demonstrating the potential of synthetic biology circuits.

Reviewer #3 (Public review):

Summary:

Mutations that result in consistent RAS activation constitute a major driver of cancer. Therefore, RAS is a favorable target for cancer therapy. However, since normal RAS activity is essential for the function of normal cells, a mechanism that differentiates aberrant RAS activity from normal one is required to avoid severe adverse effects. To this end, the authors designed and optimized a synthetic gene circuit that is induced by active RAS-GTP. The circuit components, such as RAS-GTP sensors, dimerization domains, and linkers. To enhance the circuit selectivity and dynamic range, the authors designed a synthetic promoter comprised of MAPK-responsive elements to regulate the expression of the RAS sensors, thus generating a feed-forward loop regulating the circuit components. Circuit outputs with respect to circuit design modification were characterized in standard model cell lines using basal RAS activity, active RAS mutants, and RAS inactivation.

This approach is interesting. The design is novel and could be implemented for other RAS-mediated applications. The data support the claims, and while this circuit may require further optimization for clinical application, it is an interesting proof of concept for targeting of aberrant RAS activity. I therefore recommend accepting this paper.

Strengths:

Novel circuit design, through optimization and characterization of the circuit components, solid data.

Weaknesses:

This manuscript could significantly benefit from testing the circuit performance in more realistic cell lines, such as patient-derived cells driven by RAS mutations, as well as in corresponding non-cancer cell lines with normal RAS activity. Furthermore, testing with therapeutic output proteins in vitro, and especially in vivo, would significantly strengthen the findings and claims.

Summary:

Given the revision made, I would recommend a minor revision that discusses the specificity limitations of this experimental setup.

Author response:

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

Reviewer #1 (Public review): 

Summary: 

The manuscript by Senn and colleagues presents a comprehensive study on the developing synthetic gene circuits targeting mutant RAS-expressing cells. This study aims to exploit these RAS-targeting circuits as cancer cell classifiers, enabling the selective expression of an output protein in correlation with RAS activity. The system is based on the bacterial two-component system NarX/NarL. A RAS-binding domain, the RBDCRD domain of the RAS effector protein CRAF, is fused to the histidine kinase domain, which carries an inactivating amino acid exchange either in its ATP-binding site (N509A) or in its phosphorylation site (H399Q). Dimerization or nanocluster formation of RAS-GTP reconstitutes an active histidine kinase sensor dimer that phosphorylates the response regulator NarL. The phosphorylated DNA-binding protein NarL, fused to the transcription activator domain VP48, binds its responsive element and induces the expression of the output protein. In comparison to mutated RAS, the effect of the RAS activator SOS-1 and the RAS inhibitor NF1 on the sensing ability as well as the tunability of the RAS sensor were examined. A RAS targeting circuit with an AND gate was designed by expressing the RAS sensor proteins under the control of defined MAPK response elements, resulting in a large increase in the dynamic range between mutant and wild-type RAS. Finally, the RAS targeting circuits were evaluated in detail in a set of twelve cancer cell lines expressing endogenous levels of mutant or wild-type RAS or oncogenes affecting RAS signaling upstream or downstream. 

Strengths: 

This proof-of-concept study convincingly demonstrates the potential of synthetic gene circuits to target oncogenic RAS in tumor cell lines and to function, at least in part, as an RAS mutant cell classifier. 

Weaknesses: 

The use of an appropriate "therapeutic gene" might revert the oncogenic properties of RAS mutant cell lines. However, a therapeutic strategy based on this four-plasmid-based system might be difficult to implement in RAS-driven solid cancers. 

Thank you for the insightful comments. We agree that the delivery of a four-plasmid system represents a major challenge for translating RAS-targeting circuits into therapeutic applications. Reducing the number of plasmids –ideally consolidating all components onto a single vector– will be critical for clinical implementation.

Viral delivery is generally the most efficient strategy for DNA-based therapies, but viral vectors have limited packaging capacities, which differ by virus type[1]. The RAS_sensor_F.L.T. circuit under the EF1α promoter requires ~7.7 kb for the sensing components alone, excluding the output gene. This exceeds the packaging limit of adeno-associated virus (AAV) and is at the upper boundary for lentiviral vectors but could potentially be accommodated by larger vectors such as γ-retroviruses, poxviruses, or herpesviruses¹. Co-transduction with dual AAVs [2] or ongoing engineering to expand packaging capacity [3] may also offer future solutions. An additional route to reduce construct size could be alternative splicing, especially given redundancy between the two NarX fusion proteins[4]. 

An advantage of our current architecture is that synthetic response elements replace constitutive promoters, reducing construct size. For example, the MAPK-driven PY2_NarX&NarL circuits range between 4.9 and 5.2 kb depending on the transactivation domain, bringing them within AAV packaging limits for the sensor module[5], though co-delivery of the output gene would still be necessary. For lentiviruses, this is within the packaging capacity of 8 kb¹ and would allow for inclusion of ~3 kb output genes.

Still, assembling multiple modules onto a single vector introduces new challenges, including possible crosstalk or interference between neighboring promoters [6]. For example, placing the output gene too close to MAPK response elements may trigger unwanted MAPKdependent expression, potentially bypassing the intended AND-gate logic. Moreover, expressing three genes under separate response elements may shift expression ratios and reduce circuit functionality. Nonetheless, the absence of constitutive promoters and the RAS-dependence of MAPK response elements could provide partial robustness, since even unintended activation would still reflect RAS signaling to some extent. Further, our data (Fig. 1d) show that some deviation in component levels can be tolerated, provided all parts are sufficiently expressed. Nonetheless, assembling the circuit on a single vector will require careful design and rigorous validation to ensure optimal performance. 

While addressing this is beyond the scope of the current study, we agree that future efforts should focus on vector consolidation and delivery strategies. We now include a paragraph discussing these challenges in the revised manuscript.

Reviewer #2 (Public review): 

The manuscript describes an interesting approach towards designing genetic circuits to sense different RAS mutants in the context of cancer therapeutics. The authors created sensors for mutant RAS and incorporated feed-forward control that leverages endogenous RAS/MAPK signaling pathways in order to dramatically increase the circuits' dynamic range. The modularity of the system is explored through the individual screening of several RAS binding domains, transmembrane domains, and MAPK response elements, and the author further extensively screened different combinations of circuit components. This is an impressive synthetic biology demonstration that took it all the way to cancer cell lines. However, given the sole demonstrated output in the form of fluorescent proteins, the authors' claims related to therapeutic implications require additional empirical evidence or, otherwise, expository revision. 

Thank you very much for the thoughtful evaluation, precise critique, and constructive suggestions.

As correctly noted, our study initially focused on developing and optimizing input sensors and processing units for synthetic gene circuits targeting mutated RAS. To address the concern regarding therapeutic relevance, we have now incorporated functional validation using a clinically relevant output protein: herpes simplex virus thymidine kinase (HSV-TK), which converts ganciclovir into a cytotoxic compound. We replaced the mCerulean reporter with HSV-TK and tested the resulting RAS-targeting circuits in both RAS-mutant and wild-type cancer cell lines. The results, now presented in a new chapter (Figure 8 and Supplementary Fig. 14), demonstrate robust killing of RAS-mutant cells and support the potential therapeutic utility of these circuits.

Major comments: 

"These therapies are limited to cancers with KRASG12C mutations" is technically accurate. However, in this fast-moving field, there are examples such as MRTX1133 which holds the promise to target the very G12D mutation that is the focus of this paper. There are broader efforts too. It would help the readers better appreciate the background if the authors could update the intro to reflect the most recent landscape of RAS-targeting drugs. 

Thank you for this helpful suggestion. We have updated the introduction to reflect the rapidly evolving landscape of RAS-targeting therapies, including the development of inhibitors for nonG12C mutations such as KRASG12D (e.g., MRTX1133). Given the pace and breadth of these advances, we also refer readers to a recent comprehensive review that provides an in-depth overview of current RAS-targeting strategies.

Only KRASG12D was used as a model in the design and optimization work of the genetic circuits. Other mutations should be quite experimentally feasible and comparisons of the circuits' performances across different KRAS mutations would allow for stronger claims on the circuits' generalizability. Particularly, the cancer cell line used for circuit validation harbored a KRASG13D mutation. While the data presented do indeed support the circuit's "generalizability," the model systems would not have been consistent in the current set of data presented. 

To further support the generalizability of our RAS sensor, we titrated plasmid doses for a panel of oncogenic RAS variants, including multiple KRAS mutants as well as HRAS<sup>G12D</sup and NRAS<sup>G12D</sup. Across all tested variants, we observed concentration-dependent activation of the RAS sensor. At 1.67 ng/well, the sensor output for all oncogenic RAS variants was at least as high as that for KRAS^G12D, suggesting that the behavior observed in our initial design and optimization is representative of a broader set of RAS mutations.

We also noted that high overexpression of wildtype HRAS and NRAS can lead to substantial activation of the sensor, exceeding that observed with wildtype KRAS. This underscores the importance of considering all RAS isoforms when assessing circuit specificity and avoiding potential off-target activation in healthy cells.

In Figure 2a, the text claims that "inactivation of endogenous RAS with NF1 resulted in a lower YFP/RBDCRD-NarX expression," but Figure 2a does not show a statistically significant reduction in expression of SYFP (measured by "membrane-to-total signal ratio [RU]). 

Thank you for pointing this out. We repeated the experiment to reassess the effect of NF1 on RBDCRD-NarX-SYFP2 expression and were able to confirm statistical significance. Accordingly, we have replaced Figure 2a with updated data. To facilitate better visual comparison across conditions, we also standardized the y-axis range across all relevant flow cytometry plots.

The therapeutic index of the authors' systems would be better characterized by a functional payload, other than florescent proteins, that for example induce cell death, immune responses, etc. 

Thank you for this insightful comment. We agree that fluorescent reporters are limited to approximating expression levels, and that a functional output protein is more appropriate for assessing therapeutic potential. To address this, we replaced mCerulean with the therapeutic suicide-gene, HSV-TK, and tested the circuits in RAS-mutant and wild-type cancer cell lines. These experiments demonstrate that our circuits can express functional proteins and induce cell death in two RAS-mutant cell lines while showing low toxicity in a RAS wild type cell line (new chapter including Fig. 8 and Supplementary Fig.14). 

Comparing confluence of cells transfected with the RAS-targeting circuits to cells transfected with non-toxic GFP-output negative control or the constitutively expressed EF1αHSV-TK positive control allowed us to estimate the killing-strength of the circuits in each cell line. In RAS-mutant HCT-116 the confluence curves were similar to the positive control, indicating effective killing (Fig. 8b). At lower DNA dose in HCT-116, or in SW620 with lower transfection efficiency, the killing of transfected RAS-driven cancer cells was less pronounced, falling approximately midway between the controls (Fig. 8g&j). In the RAS wild type cell line, Igrov-1, the RAS circuits showed continued growth similar to the non-toxic negative control (Fig. 8d), suggesting low toxicity. 

While this may indicate low circuit activation in Igrov-1, an alternative explanation for the low toxicity could also be insufficient transfection efficiency. Testing in SW620 –which had similar transfection efficiency as Igrov-1 (Supplementary Fig. 14a)– showed that this moderate transfection efficiency was sufficient for RAS-circuit-dependent killing (Fig. 8d & 8g), supporting the notion of low activation in Igrov-1 and selective cytotoxicity in RAS-driven cancer cells.

Nonetheless, it is important to note that comparisons between the cell lines need to be interpreted cautiously because of inter-cell line differences in transfection, growth, and HSV-TK/ganciclovir (GCV)-sensitivity (Supplementary Fig. 14) and further validation will be essential. 

A conclusive assessment will require more efficient delivery strategies, such as viral vectors (as discussed above). Efficient delivery would allow to investigate selectivity in a more realistic setting with patient-derived RAS-mutant cancer and healthy cells as well as testing in an vivo model. While beyond the scope of the current study, we view it as a critical direction for future work and have therefore added a paragraph about this to our discussion.

Regarding data presented in "Mechanism of action" (Figure 2), the observations are interesting and consistent across different fluorescent reporters. However, with regard to interpretations of the underlying molecular mechanisms, it is not clear whether the different output levels in 2b, 2c, and 2d are due to the pathway as described by the authors or simply from varied expression levels of RBDCRD-NarX itself (2a) that is nonlinearly amplified by the rest of the circuit. From a practical standpoint, this caveat is not critical with respect to the signal-to-noise ratios in later parts of the paper. From a mechanistic interpretation standpoint, claims made forth in this section are not clearly substantiated. Some additional controls would be nice. For example, if the authors express NarXs that constitutively dimerize on the membrane, what would the RasG12Dresponsiveness look like? Does RasG12D alter the input-output curve of NarL-RE? How would Figure 4f compare to a NaxR constitutively dimerized control that only relies on transcriptional amplification of the Ras-dependent promoters? 

This is a great point. We agree that the observed differences in output levels (Fig. 2) could arise from non-linear amplification due to increased expression of RBDCRD-NarX, rather than RAS binding or dimerization alone. To further investigate this possibility, we performed titrations of KRAS^G12D in combination with the functional RAS sensor and a series of constitutively active and inactive control constructs (Supplementary Fig. 4).

Inactive controls lacking NarX dimerization showed only a modest increase in output expression, similar to direct mCerulean expression under the EF1α promoter. Transfection of the output plasmid alone, with NarL, or with NarL and non-RAS-binding RBD^R89L CRD^C168S -NarX, resulted in minimal RAS-dependent increases (Supplementary Fig. 4a). Importantly, after normalization using the EF1α-driven mCherry transfection control, these effects were fully or even slightly over-compensated (Supplementary Fig. 4b), showing that we don’t include the effect of EF1α-dependent increased leakiness in the data presented throughout the manuscript, but also that –due to the normalization– we potentially underestimate the dynamic range of the RAS-targeting circuits.

In contrast, constitutively dimerizing NarX controls (both membrane-bound and cytosolic dimerized via the FKBP–FRB system) exhibited a more pronounced RAS-dependent increase in output –even after normalization– confirming the presence of non-linear amplification (up to 3–4fold). However, this effect was still lower than that achieved with the functional RAS-binding sensor (8-fold at 1.67 ng/well KRAS^G12D; 14-fold at 5–15 ng/well), indicating that the increase in expression of the sensor parts is not the full explanation of the effect we see. Instead, RAS binding and dimerization further amplify the response and are necessary for full activation (Supplementary Fig. 4b).

We also addressed the reviewer’s suggestion by testing the MAPK response elements used in Fig. 4f with constitutively dimerizing NarX. These controls generally showed lower fold changes between KRAS^G12D; and KRAS^WT than the corresponding RAS-binding circuits  (Supplementary Fig. 7), with one exception: the combination of SRE_NarX and PY2_NarL-VP48. 

Together, these data show that non-linear amplification via increased expression and dimerization contributes to output activation. However, RAS binding and induced dimerization of the NarX sensor are required for full functionality and enhanced signal strength. This underscores that integrating the MAPK response elements with the binding-based RAS sensor into RAS-targeting circuits generally improves the distinction between cells with KRAS^G12D;  and KRAS^WT and that it was the combination that allowed to reach maximal fold changes.

It's also possible that these Ras could affect protein production at the post-transcriptional or even post-translational levels, which were not adequately considered. 

Thank you for this comment. We now mention in the manuscript the potential mechanisms by which (over-)activated RAS or MAPK signaling can increase protein synthesis. We cite relevant reports of the mechanisms we found, including upregulation of translational initiation and machinery[10]  and ribosomal biogenesis[11].

The text claims that "in contrast to what we saw in HEK293 overexpressing RAS (Figure 5d), the "AND-gate" RAS-targeting circuits do not generate higher output than the EF1a-driven, bindingtriggered RAS sensor in HCT-116. Instead, the improved dynamic range results from decreased leakiness in HCT- 116k.o." Comparing the experiment from Figure 5d, which looks at activation in KRASG12D and KRASWT, to the experiments in Figure 6b-d, which looks at activation in HCT-116WT and HCT-116KO is misleading. In Fig 5d., cells are transfected with KRASG12D and KRASWT to emulate high levels of mutant RAS and high levels of wild-type RAS. In Figures 6b-d, HCT-116WT has endogenous levels of mutant RAS, while the KCT-116KO is a knock-out cell line, and does not have mutant or WT RAS. Therefore, the improved dynamic range or "decreased leakiness in HCT-116KO" in comparison to Figure 5d. is more comparable to the NF1 condition from Figure 2, which deactivates endogenous RAS. While this may not be feasible, the most accurate comparison would have been an HCT-116KO line with KRASWT stably integrated. 

Thank you for this input. We understand that comparing the results from HEK293 cells transfected with KRAS^G12D;  or KRAS^WT (Fig. 5d) to those from HCT-116^WT    and HCT-116^k.o. cells (Fig. 6b–d) may be misleading if interpreted as a direct comparison of RAS signaling levels. Our intent was not to compare HEK293 with KRAS^WT directly to HCT-116^k.o.., but rather to contrast the behavior of the EF1α-driven RAS sensor and the MAPK-responsive RAS-targeting circuits within each cell line context.

Specifically, we observed that in HEK293 cells expressing KRAS^G12D, the MAPK-based RAS-targeting circuits produced higher output than the EF1α-expressed RAS sensor. In contrast, in HCT-116^WT cells, the EF1α-expressed RAS sensor resulted in higher output levels than the RAS-targeting circuits. Despite this, the MAPK-driven circuits showed an improved dynamic range compared to the EF1α-expressed RAS sensor in HCT-116, due to the reduced background expression in the HCT-116^k.o.. cells. We have revised the manuscript text to clarify this distinction.

We agree that an HCT-116^k.o cell line with stable integration of KRAS^WT would provide a more direct comparison. Nonetheless, HCT-116^k.o.. cells still express endogenous NRAS and HRAS, both of which are capable of activating the RAS sensor (as shown in Fig. 1g). Therefore, we believe that HCT-116^k.o. cells are more comparable to HEK293 with KRAS^WT than to the NF1 condition in Fig. 2, in which all endogenous RAS isoforms are inactivated.

We couldn't locate the citation or discussion of Figure 4d in the text. Conversely, based on the text description, Figure 6g would contain exciting results. But we couldn't find Figure 6g anywhere ... unless it was a typo and the authors meant Figure 6f, in which case the cool results in Figure S8 could use more elaboration in the main text. 

Thank you for this helpful observation. The figure references were indeed incorrect due to a typo. The results discussed in the text refer to Figure 6f (not 6g), which is now Figure 7a in the revised version. To further highlight these findings, we have added a new Figure 7b that better illustrates how different MAPK response elements enabled us to identify, for each RAS-mutant cell line, a RAS-targeting circuit that showed stronger activation than in all RAS wild-type lines. We have also expanded the corresponding section in the main text to elaborate on these results and their significance.

Reviewer #3 (Public review): 

Summary: 

Mutations that result in consistent RAS activation constitute a major driver of cancer. Therefore, RAS is a favorable target for cancer therapy. However, since normal RAS activity is essential for the function of normal cells, a mechanism that differentiates aberrant RAS activity from normal one is required to avoid severe adverse effects. To this end, the authors designed and optimized a synthetic gene circuit that is induced by active RAS-GTP. The circuit components, such as RAS-GTP sensors, dimerization domains, and linkers. To enhance the circuit selectivity and dynamic range, the authors designed a synthetic promoter comprised of MAPK-responsive elements to regulate the expression of the RAS sensors, thus generating a feed-forward loop regulating the circuit components. Circuit outputs with respect to circuit design modification were characterized in standard model cell lines using basal RAS activity, active RAS mutants, and RAS inactivation. 

This approach is interesting. The design is novel and could be implemented for other RASmediated applications. The data support the claims, and while this circuit may require further optimization for clinical application, it is an interesting proof of concept for targeting aberrant RAS activity. 

Strengths: 

Novel circuit design, through optimization and characterization of the circuit components, solid data. 

Weaknesses: 

This manuscript could significantly benefit from testing the circuit performance in more realistic cell lines, such as patient-derived cells driven by RAS mutations, as well as in corresponding non-cancer cell lines with normal RAS activity. Furthermore, testing with therapeutic output proteins in vitro, and especially in vivo, would significantly strengthen the findings and claims. 

Thank you very much for the thoughtful and supportive comments. We fully agree with the reviewer’s suggestions for improving the translational potential of the RAS-targeting circuits.

As a first step toward therapeutic relevance, we replaced the fluorescent reporter with HSV-TK, a clinically validated suicide gene, and demonstrated killing in RAS-mutant cancer cell lines. This is described above and in the new section of the manuscript (Figure 8).

We also agree that testing in patient-derived cancer cells and especially healthy cells with wild-type RAS activity will be essential. However, testing in primary or patient-derived cells presents delivery challenges: transient transfection of our current four-plasmid system is unlikely to achieve sufficient expression. As discussed in our response to Reviewer #1, development of a more efficient delivery strategy –such as viral vector-based delivery– is a necessary next step.

Once a delivery system is established, identifying relevant off-target tissues throughout the body with high physiological RAS signaling will be key to assessing selectivity. While comparative data on RAS activation across healthy tissues are scarce[12,13], recent atlases of transcription factor activity[14,15] provide insights to identify off-target cells with high activation of RAS-dependent transcription factors and may even approximate RAS activity across healthy tissue. Alternatively, our single-input sensors for RAS and MAPK pathway activity could be used in vivo to identify off-target cells based on endogenous activity.

Once relevant target and off-target cells have been identified, patient-derived cancer and healthy cells can help select and adapt cancer-specific RAS-targeting circuits and nominate therapeutic candidates for further safety and efficacy assessment[6,8].

Reviewer #1 (Recommendations for the authors): 

For the most part, the data in this study are very convincing and very well presented. The cartoons make it easier to understand the complex experimental setups. 

(1) Did the authors use wild-type Sos-1 or a constitutively active membrane-bound catalytic domain in their studies? How is SOS-1 activated when in case Sos-1 wild-type was used? 

Thank you for this feedback. We used the constitutively active catalytic domain of Sos-1 (AA5641049; PDB ID 2II0). 

(2) Figure 1f: In case of KRAS-G12D, it looks like the output expression does not really correlate with the RAS-GTP level. Can the authors give an explanation? 

Thank you for this interesting question. We believe the observed discrepancy arises primarily from differences in the sensitivity and readout dynamics of the two assays. The RAS-GTP pulldown ELISA appears insufficiently sensitive to detect small changes in RAS-GTP levels at lower KRAS^G12D plasmid doses (0.19, 0.56, or 1.67 ng). Only at 5 ng and 15 ng do we observe clear increases in RAS-GTP signal (25% and 700%, respectively). In contrast, the RAS sensor shows strong activation already in the 0.56–5 ng range but begins to saturate at higher doses (see Figure 1f and Figure 1e).

Beyond the differing technical sensitivities of the ELISA (plate reader) and flow cytometry, an important conceptual distinction may further explain this behavior: the RAS sensor likely integrates RAS signaling over time. Once NarX binds RAS-GTP and dimerizes, it activates NarL, triggering mCerulean expression. If the rate of mCerulean production exceeds its degradation, signal accumulates throughout the assay duration. Thus, the flow cytometry readout reflects time-integrated signaling, allowing small differences in RAS-GTP to be amplified into measurable differences in output—especially at low input levels. This may explain why flow cytometry detects circuit activation earlier and more steeply than the pulldown assay, which provides a snapshot of RAS-GTP abundance at a single time point and saturates less readily at high input levels.

Together, these factors likely explain the observed differences in signal dynamics: the RAS sensor exhibits steep activation followed by saturation at high plasmid doses (flow cytometry), while the ELISA shows limited sensitivity at low doses but a broader linear range at higher doses.

(3) Figure 2b: It appears that even in the case of KRAS-G12D and Sos-1, only a few cells are positive. Does this result depend on low cell density, low transfection efficiency, or a wide range of the expression level? As a control, nuclear staining could be shown. 

Thank you for this question. In the experiment shown in Figure 2b, our goal was to assess the membrane localization of the RBD^CRD-NarX-SYFP2 construct, which serves as a proxy for RAS-bound sensor. To enable accurate computational segmentation and separation of membrane signal from adjacent cells, we intentionally reseeded cells at low density in glassbottom plates for confocal imaging.

The observed variability in signal likely reflects a combination of transient transfection and heterogeneous expression levels. While the overall transfection efficiency was approximately 70%, expression varied between individual cells. To account for this, we analyzed the membrane-to-total signal ratio per cell, which internally normalizes the membrane signal to the total cellular expression of SYFP2 and controls for differences in transfection efficiency.

In response to the reviewer’s suggestion, we have updated the figure to include nuclear staining to aid interpretation. We would like to emphasize, however, that the images are intended to illustrate subcellular localization per cell, not expression frequency or intensity across the population.

Minor points 

(1) Figure 1b: "The third plasmid expresses NarL, .." should be changed to "The third plasmid expresses NarL-VP48, .." 

Done

(2) Figure 1c, right part: The orange arrow should be labeled NarX-H399Q (not N509A). 

Done

(3) Supplementary Table 6 and 7: [cells/wells] - should probably be [cells 10*3/well]. 

Thank you for these points, we updated the manuscript accordingly

Reviewer #2 (Recommendations for the authors): 

Minor comments: 

(1) N509A seems mislabeled in Figure 1b. 

(2) It would help the readers if the authors could elaborate a bit on what is known about the RBD and CRD mutations used here. 

Thank you for the input, we added a paragraph in the paper to expand on the effect of these commonly used mutations.

(3) The KRASWT&Sos1 condition is not explained within the text for Figure 1f, which is the first figure with the KRASWT&Sos1 condition, but rather later on for Figure 2a. Adding a description of this condition to the discussion of Figure 1f would add clarity to this figure. 

Thank you, we corrected this.

(4) Citing AlphaFold2 structural predictions as having "revealed that longer linkers between the sensor's RBDCRD and NarX-derived domains could bring the NarX domains into closer proximity" is probably an overstatement. AlphaFold2 generally has low confidence in the placement of long flexible linkers, and the longer linkers in the illustration could facilitate NarX and NarL being even farther apart than they are in the original design. 

Thank you for this input. We agree that AlphaFold2 predictions generally have low confidence in the placement of long, flexible linkers, and we did not intend to imply that the structural models were predictive of actual linker conformations. Rather, the models were used heuristically to generate the hypothesis that longer linkers might facilitate better positioning of the NarX domains for dimerization.

As described in the Methods, we manually rotated the flexible linker regions to explore plausible conformations. These exploratory models showed that with a short (1x GGGGS) linker, it was more challenging to bring the NarX domains into close proximity, whereas longer linkers allowed greater positional flexibility. This modeling exercise provided a structural rationale for experimentally testing longer linkers. We have revised the manuscript text to clarify that the structural predictions were used to motivate linker design –not to validate or predict structural outcomes.

(5) Figure 3b shows that the fold change (KRASG12D/KRASWT) is higher at shorter linker lengths and lower at longer linker lengths, and that the output expression of mCerulean is lower at shorter linker lengths and higher at longer linker lengths. Having a bar plot with the output expression mCerulean levels comparing KRASG12D and KRASWT next to each other would be a significantly more informative representation of this data. In particular, the readers might be interested in understanding the effect of linker length on off-target activation from the sensor, which is not clear from this figure. 

Thank you for the suggestion. We adapted Figure 3b to better present this. 

(6) While it is implied that the sentence "Among the tested binding domains, the Ras association domain (RA) of the natural RAS effector Rassf5, the RAS association domain 2 (RA2) of the phospholipase C epsilon (PLCe)33, and the synthetic RAS binder K5534 showed a slightly higher or similar dynamic range." is comparing these RAS binding domains to RBDCRD, for clarity it should be noted what the point of reference is for this "slightly higher or similar dynamic range." 

(7) Claims are made throughout the text that require supporting data, and thus require a reference to a figure, but there are a few instances where the reference is several sentences after the discussion of data and findings begins. For example, the discussion of Figure 3c begins with the claim "Among the tested binding domains, the Ras association domain (RA) of the natural RAS effector Rassf5, the RAS association domain 2 (RA2) of the phospholipase C epsilon (PLCe)33, and the synthetic RAS binder K5534 showed a slightly higher or similar dynamic range," but there is no reference to the data or figure being discussed until the end of the discussion of Figure 3c. This formatting is also present in Figure 3d and Figure 6f. 

Thank you for mentioning these imprecisions and inconsistencies, we addressed them in the manuscript. 

(8) In Figures 5d and 5e, the formatting of underscores and dashes is occasionally inconsistent within the text. (ex. "PY2_NarX_FLT or PY2_NarL-FLT" on page 13.). 

Thank you for this precise observation. The formatting differences were intentional and reflect distinct design principles. Specifically:

An underscore (e.g., PY2_NarX_FLT) denotes that two separate proteins are expressed –here, PY2-driven RBDCRD-NarX and EF1α-driven NarL-F.L.T.

A dash (e.g., PY2_NarL-F.L.T.) indicates a fusion protein –i.e., PY2-driven NarL-F.L.T. combined with EF1α-driven RBDCRD-NarX.

This notation is used to distinguish expression sources and fusion constructs while avoiding redundancy with the base circuit (EF1α_NarX + EF1α_NarL-VP48). We hope the included schematic diagrams in each relevant figure helps the reader interpret these combinations.

(9) The text claims that "loss-of-function mutations in RBDCRD decreased activation. However, the dynamic range was only 3-fold" and attributes this claim to Figure 6a. For a claim about specific fold-change activation, one would expect a corresponding figure with quantitative measurements of this fluorescence to be referenced. 

Thank you for this remark. We made a supplementary figure (Supplementary Fig. 11) to show the quantitative measurement of the 3-fold dynamic range between HCT-116^WT and HCT-116^k.o. when using the EF1a-expressed RAS sensor with NarL-VP48.

(10) The claim of this Figure 2d is that the effect of RAS-GTP levels on mCerulean output is amplified in comparison to Figures 2a, 2b, and 3c, representing expression, RAS binding, and dimerization respectively. While visually this might be true from the figure, the readers might be confused by the lack of significance between the control and the NF1 condition, alongside the variation between the triplicates. Could this experiment be repeated to gain clearer data and to support their claim more effectively? 

Thank you for this important observation. To address the concern regarding variability and statistical significance in Figure 2d, we repeated the experiment using 24-well plates to increase the number of cells analyzed per condition. This improved the consistency of the data and allowed us to reduce variability across replicates. As a result, we now observe a statistically significant difference between the control and the NF1 condition. The updated results are shown in the revised Figure 2.

(11) The readers might be less familiar with the concept of "composability" than "modularity" and it would be good to explain it if the authors did intend to use the former. 

Thank you for this comment. We changed it to modularity to avoid confusion. 

References

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(2) Mcclements, M. E. & Maclaren, R. E. Adeno-Associated Virus (AAV) Dual Vector Strategies for Gene Therapy Encoding Large Transgenes. YALE JOURNAL OF BIOLOGY AND MEDICINE vol. 90 (2017).

(3) Wagner, H. J., Weber, W. & Fussenegger, M. Synthetic Biology: Emerging Concepts to Design and Advance Adeno-Associated Viral Vectors for Gene Therapy. Advanced Science vol. 8 Preprint at https://doi.org/10.1002/advs.202004018 (2021).

(4) Doshi, J., Willis, K., Madurga, A., Stelzer, C. & Benenson, Y. Multiple Alternative Promoters and Alternative Splicing Enable Universal Transcription-Based Logic Computation in Mammalian Cells. Cell Rep 33, 108437 (2020).

(5) Wu, Z., Yang, H. & Colosi, P. Effect of genome size on AAV vector packaging. Molecular Therapy 18, 80–86 (2010).

(6) Dastor, M. et al. A Workflow for in Vivo Evaluation of Candidate Inputs and Outputs for Cell Classifier Gene Circuits. ACS Synth Biol 7, 474–489 (2018).

(7) Preuß, E. et al. TK.007: A novel, codon-optimized HSVtk(A168H) mutant for suicide gene therapy. Hum Gene Ther 21, 929–941 (2010).

(8) Angelici, B., Shen, L., Schreiber, J., Abraham, A. & Benenson, Y. An AAV gene therapy computes over multiple cellular inputs to enable precise targeting of multifocal hepatocellular carcinoma in mice. Sci Transl Med 13, (2021).

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eLife Assessment

This study constitutes a fundamental advance for the uveal melanoma research field that might be exploited to target this deadly cancer and, more generally, for targeting transcriptional dependency in cancers. This work substantially advances our understanding of pharmacological inhibition of SWI/SNF as a therapeutic approach for cancer. The study is well written and provides compelling evidence, including comprehensive datasets, compound screens, gene expression analysis, epigenetics, as well as animal studies.

Reviewer #1 (Public review):

Summary:

The presented study by Centore and colleagues investigates the inhibition of BAF chromatin remodeling complexes. The study is well written and includes comprehensive datasets, including compound screens, gene expression analysis, epigenetics, as well as animal studies. This is an important piece of work for the uveal melanoma research field, and sheds light on a new inhibitor class, as well as a mechanism that might be exploited to target this deadly cancer for which no good treatment options exist.

Strengths:

This is a comprehensive and well-written study.

Weaknesses:

There are minimal weaknesses.

Reviewer #2 (Public review):

Summary:

The authors generate an optimized small molecule inhibitor of SMARCA2/4 and test it in a panel of cell lines. All uveal melanoma (UM) cell lines in the panel are growth inhibited by the inhibitor making the focus of the paper. This inhibition is correlated with loss of promoter occupancy of key melanocyte transcription factors e.g. SOX10. SOX10 overexpression and a point mutation in SMARCA4 can rescue growth inhibition exerted by the SMARCA2/4 inhibitor. Treatment of a UM xenograft model results in growth inhibition and regression which correlates with reduced expression of SOX10 but not discernible toxicity in the mice. Collectively, the data suggest a novel treatment of uveal melanoma.

Strengths:

There are many strengths of the study, including the strong challenge of the on-target effect, the assays used and the mechanistic data. The results are compelling as are the effects of the inhibitor. The in vivo data is dose-dependent and doses are low enough to be meaningful and associated with evidence of target engagement.

Weaknesses:

The authors have addressed weaknesses in the revised version.

Reviewer #3 (Public review):

Summary:

This manuscript reports the discovery of new compounds that selectively inhibit SMARCA4/SMARCA2 ATPase activity and have pronounced effects on uveal melanoma cell proliferation. They induce apoptosis and suppress tumor growth, with no toxicity in vivo. The report provides biological significance by demonstrating that the drugs alter chromatin accessibility at lineage specific gene enhancer regions and decrease expression of lineage specific genes, including SOX10 and SOX10 target genes.

Strengths:

The study provides compelling evidence for the therapeutic use of these compounds and does a thorough job at elucidating the mechanisms by which the drugs work. The study will likely have a high impact on the chromatin remodeling and cancer fields. The datasets will be highly useful to these communities.

Weaknesses:

The authors have addressed all my concerns.

Author response:

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

Reviewer 1:

While BAP1 mutant UM cell lines were included for some of the experiments, it seems the in-vivo data mentioned in the response to the reviewers comment is missing? The authors stated that "MP46 (Supplementary Fig. 3a) is BAP1-null uveal melanoma cell line with no detectable protein expression (Amirouchene-Angelozzi et al., Mol Oncol 2014), and we have observed strong tumor growth inhibition in this CDX model with our BAF ATPase inhibitor." But the CDX model data shown in Figure 4 is from 92.1 cells. If this data is available, then the manuscript would benefit from its addition.

We thank the reviewer for bringing this to our attention. As the reviewer mentioned, we show 92-1 CDX model in our manuscript. Additionally, strong tumor growth inhibition in MP-46  CDX model treated with our BAF ATPase inhibitor can be found in Vaswani et al., 2025 (PMID:39801091, https://pubmed.ncbi.nlm.nih.gov/39801091/).

Reviewer 3:<br /> Supplementary Figure 2C<br /> Is the T910M mutation in the parental MP41 cells heterozygous? If so, the authors should indicate this in the figure legend. If this is a homozygous mutation, the authors should explain how the inhibitors suppress SMARCA4 activity in cells that have a LOF mutation.

We thank the reviewer for bringing this to our attention. We updated the figure legend accordingly to reflect the genotype of the mutations highlighted in the table.

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

Reviewer #1 (Public Review):

Summary:

The presented study by Centore and colleagues investigates the inhibition of BAF chromatin remodeling complexes. The study is well-written, and includes comprehensive datasets, including compound screens, gene expression analysis, epigenetics, as well as animal studies. This is an important piece of work for the uveal melanoma research field, and sheds light on a new inhibitor class, as well as a mechanism that might be exploited to target this deadly cancer for which no good treatment options exist.

Strengths:

This is a comprehensive and well-written study.

Weaknesses:

There are minimal weaknesses.

We thank the reviewer for the positive comments.

Reviewer #2 (Public Review):

Summary:

The authors generate an optimized small molecule inhibitor of SMARCA2/4 and test it in a panel of cell lines. All uveal melanoma (UM) cell lines in the panel are growth-inhibited by the inhibitor making the focus of the paper. This inhibition is correlated with the loss of promoter occupancy of key melanocyte transcription factors e.g. SOX10. SOX10 overexpression and a point mutation in SMARCA4 can rescue growth inhibition exerted by the SMARCA2/4 inhibitor. Treatment of a UM xenograft model results in growth inhibition and regression which correlates with reduced expression of SOX10 but not discernible toxicity in the mice. Collectively the data suggest a novel treatment of uveal melanoma.

Strengths:

There are many strengths of the study including the strong challenge of the on-target effect, the assays used, and the mechanistic data. The results are compelling as are the effects of the inhibitor. The in vivo data is dose-dependent and doses are low enough to be meaningful and associated with evidence of target engagement.

Weaknesses:

The authors introduce the field stating that SMARCA4 inhibitors are more effective in SMARCA2 deficient cancers and the converse. Since the desirable outcome of cancer therapy would be synthetic lethality it is not clear why a dual inhibitor is desirable. Wouldn't this be associated with more side effects? It is not known how the inhibitor developed here impacts normal cells, in particular T cells which are essential for any durable response to cancer therapies in patients. Another weakness is that the UM cell lines used do not molecularly resemble metastatic UM. These UM most frequently have mutations in the BAP1 tumor suppressor gene. It is not clear if the described SMARCA2/4 inhibitor is efficacious in BAP1 mutant UM cell lines in vitro or BAP1 mutant patient-derived xenografts in vivo.

We thank the reviewer for their insightful and constructive comments. As we demonstrate in Fig. 1d, uveal melanoma cells are selectively and deeply sensitive to BAF ATPase inhibition, and provides a therapeutic window. This is confirmed in Fig. 4a-c, as we demonstrated robust tumor growth inhibition, achieved at a dose well-tolerated in xenograft study. FHD-286, a dual BRM/BRG1 inhibitor similar to FHT-1015 with optimized physical properties, has been evaluated in a Phase I trial in patients with metastatic uveal melanoma (NCT04879017) and manuscript describing results of this clinical trial is currently in preparation.

As the reviewer mentioned, BAP1 loss is a signature of metastatic uveal melanoma. MP38 is a BAP1 mutant uveal melanoma cell line, and we demonstrated growth inhibition and robust caspase 3/7 activity in response to FHT-1015 (Supplementary Fig. 3a and 3f). MP46 (Supplementary Fig. 3a) is BAP1-null uveal melanoma cell line with no detectable protein expression (Amirouchene-Angelozzi et al., Mol Oncol 2014), and we have observed strong tumor growth inhibition in this CDX model with our BAF ATPase inhibitor.

Reviewer #3 (Public Review):

Summary:

This manuscript reports the discovery of new compounds that selectively inhibit SMARCA4/SMARCA2 ATPase activity that work through a different mode as previously developed SMARCA4/SMARCA2 inhibitors. They also demonstrate the anti-tumor effects of the compounds on uveal melanoma cell proliferation and tumor growth. The findings indicate that the drugs exert their effects by altering chromatin accessibility at binding sites for lineage-specific transcription factors within gene enhancer regions. In uveal melanoma, altered expression of the transcription factor, SOX10, and SOX10 target gene underlies the anti-proliferative effects of the compounds. This study is significant because the discovery of new SMARCA4/SMARCA2 inhibitory compounds that can abrogate uveal melanoma tumorigenicity has therapeutic value. In addition, the findings provide evidence for the therapeutic use of these compounds in other transcription factor-dependent cancers.

Strengths:

The strengths of this manuscript include biochemical evidence that the new compounds are selective for SMARCA4/SMARCA2 over other ATPases and that the mode of action is distinct from a previously developed compound, BRM014, which binds the RecA lobe of SMARCA2. There is also strong evidence that FHT1015 suppresses uveal melanoma proliferation by inducing apoptosis. The in vivo suppression of tumor growth without toxicity validates the potential therapeutic utility of one of the new drugs. The conclusion that FHT1015 primarily inhibits SMARCA4 activity and thereby suppresses chromatin accessibility at lineage-specific enhancers is substantiated by ATAC-seq and ChIP-seq studies.

Weaknesses:

The weaknesses include a lack of more precise information on which SMARCA4/SMARCA2 residues the drugs bind. Although the I1173M/I1143M mutations are evidence that the critical residues for binding reside outside the RecA lobe, this site is conserved in CHD4, which is not affected by the compounds. Hence, this site may be necessary but not sufficient for drug binding or specifying selectivity. A more precise evaluation of the region specifying the effect of the new compounds would strengthen the evidence that they work through a novel mode and that they are selective. Another concern is that the mechanisms by which FHT1015 promotes apoptosis rather than simply cell cycle arrest are not clear. Does SOX10 or another lineage-specific transcription factor underlie the apoptotic effects of the compounds?

We thank the reviewer for the valuable comments.

We believe that our dual ATPase inhibitor is selective and additional insights into binding specificity and selectivity for earlier stage compounds of this series were recently published in Vaswani et al., 2025 (PMID:39801091, https://pubmed.ncbi.nlm.nih.gov/39801091/).

The reviewer also poses a great question regarding the mechanism of apoptosis. The mechanism of apoptosis is extremely complex, but we observed a decrease in pro-survival BCL-2 protein expression in response to FHT-1015, in the experiment corresponding to Supplementary Fig. 5e. In the experiment described in Fig. 3k, we also monitored caspase 3/7 activity over time, and SOX10 overexpression rescued 92-1 cells from FHT-1015 induced apoptosis. This suggests the role of SOX10 as an important mediator of response to BAF ATPase inhibition, including apoptosis induced by FHT-1015.

Additional Reviews:

The referees would like to draw the authors' attention to the following issues that would best benefit from additional revision. 

The clinical relevance of the study would be strengthened by the use of uveal melanoma cell lines with BAP1 mutations that better represent metastatic uveal melanoma. The use of patient-derived xenografts would also be pertinent and would be a useful addition. Similarly, attention to the effects of the inhibitor on non-cancerous proliferative cells such as blood/T/immune cells would also strengthen the manuscript. As the study reports the administration of one of the inhibitors in mice for the xenograft experiments, it would be important to assess any potential effects on blood cell counts and better discuss the eventual toxicity or lack of toxicity and how it was assessed. 

The authors should better explain how SOX10 over expression can rescue viability in the presence of the inhibitor. Similarly given the critical roles of BRG1, SOX10, and MITF in cutaneous melanoma some specific discussion on the sensitivity of cutaneous melanoma cells to the inhibitor should be considered, and potential differences with uveal melanoma highlighted. 

Aside from these issues, the authors are urged to consider the other points mentioned below. 

Reviewer #1 (Recommendations For The Authors): 

Figure 1d, as well as the text in the manuscript referring to this figure, would benefit from indicating specific cell lines used for UM. The same for the sentence in line 153. 

We thank the reviewer for bringing this to our attention. We have added the cell line names and updated the manuscript accordingly.

For any of the studies conducted, is there any link with the genetics of UM? E.g. BAP1 wildtype/BAP1 mutant? 

As addressed above in the public review section, MP38 is a BAP1 mutant uveal melanoma cell line, and we demonstrated growth inhibition and robust caspase 3/7 activity in response to FHT-1015 (Supplementary Fig. 3a and 3f). MP46 (Supplementary Fig. 3a) is BAP1-null uveal melanoma cell line with no detectable protein expression (Amirouchene-Angelozzi et al., Mol Oncol 2014), and we have observed strong tumor growth inhibition in this CDX model with our BAF ATPase inhibitor.

Row 191 - How were peaks classified as enhancer-occupied? 

We used annotatePeaks function of HOMER package to annotate genomic locations, as well as H3K27ac ChIP-seq to annotate peaks as enhancer-occupied. We thank the reviewer to pointing it out and have updated the manuscript accordingly to include this information.

Row 259, the two cell lines should be named, also in Figure 3i. 

We have added the cell line names and updated the manuscript accordingly.

Reviewer #2 (Recommendations For The Authors): 

As a proof of concept, this study is truly excellent and the authors should be commended. However, it is desirable that new knowledge in cancer is translated to the clinic. To this end there are a few things needed to strengthen the study. 

I am rephrasing my statements from the public review to say that I would recommend testing the inhibitor in T cells (side effects) and BAP1 mutant cell lines (for clinical relevance). 

As addressed in the public review section, MP38 is a BAP1 mutant uveal melanoma cell line, and we demonstrated growth inhibition and robust caspase 3/7 activity in response to FHT-1015 (Supplementary Fig. 3a and 3f). MP46 (Supplementary Fig. 3a) is BAP1-null uveal melanoma cell line with no detectable protein expression (Amirouchene-Angelozzi et al., Mol Oncol 2014), and we have observed strong tumor growth inhibition in this CDX model with our BAF ATPase inhibitor.

Regarding concerns for any potential side effect on T cells, we observed an increase in both CD4 and CD8 T-cell populations in the peripheral blood and the spleen, when naïve, non-tumor bearing CD-1 mice were dosed with SMARCA2/4 dual ATPase inhibitor FHD-286 once daily for 14 days. FHD-286 is a compound similar to FHT-1015 described in Vaswani et al., 2025 (PMID:39801091, https://pubmed.ncbi.nlm.nih.gov/39801091/). In addition, FHD-286 has been tested in tumor bearing syngeneic models. When B16F10 tumor bearing C57BL/6 were dosed with FHD-286 for 10 days, we observed an increase in CD69+ activated CD8 T-cell infiltration in the tumor microenvironment (doi:10.1136/jitc-2022-SITC2022.0888).

Reviewer #3 (Recommendations For The Authors): 

(1) Determine drug binding by crystal structure or generate additional SMARCA4 or SMARCA2 mutations in the region near I1173/I1143 that are not conserved in CHD4 and test them in an ATPase assay for effects on drug inhibition. For example, Q1166 in SMARCA4 and Q1136 in SMARCA4 could be changed to Alanine as in CHD4. Would this abrogate drug inhibition? 

We believe that our dual ATPase inhibitor is selective and additional insights into binding specificity and selectivity for earlier stage compounds of this series were recently published in Vaswani et al., 2025 (PMID:39801091, https://pubmed.ncbi.nlm.nih.gov/39801091/).

(2) The finding that SOX10 can rescue the antiproliferative effects of FHT1015 suggests that SMARCA4 is primarily needed for SOX10 expression. However, the co-occupancy of SMARCA4 and SOX10 at enhancers suggests that they cooperate to promote chromatin accessibility. It is unclear how over-expression of SOX10 can promote chromatin accessibility in drug-inhibited cells since SOX10 does not have chromatin remodeling activity. ATAC-seq in cells over-expressing SOX10 and treated with the drug could identify SOX10-dependent targets that do not require SMARCA4 activity and clarify the mechanism. It would also be informative to determine if SOX10 over-expression abrogates the effects of FHT1015 on both cell cycle and apoptosis, helping to resolve whether it is a partial or complete rescue of proliferation. 

We agree that running ATAC-seq in cells overexpressing SOX10 would clarify this mechanism. However, shifts in corporate strategy deprioritized any further experiments for this project. One potential mechanism that SOX10 overexpression can partially rescue BAF inhibition phenotype is through overexpressed SOX10 localizing to open chromatin regions (mostly promoters) across the genome. We know from our ATAC-seq data (Fig. 2) that BAF inhibition leads to loss of chromatin accessibility at SOX10 enhancer sites, while promoter regions are only partially affected. Therefore, we think that overexpression of SOX10 would allow upregulation of its target genes via binding to the promoter regions. In this model, the enhancer-driven SOX10 target genes are likely to remain silenced.  

(3) Although the in vivo studies indicate that the drugs are well-tolerated, additional in vitro studies to determine the effects of the drug on the proliferation/survival of non-cancerous cells would further validate their therapeutic utility.

Author Response: The reviewer raises a critical question. FHD-286, a dual BRM/BRG1 inhibitor similar to FHT-1015 with optimized physical properties, has been evaluated in a Phase I trial in patients with metastatic uveal melanoma (NCT04879017), and it was well tolerated at continuous daily dose of up to 7.5 mg QD and at intermittent dose of up to 17.5 mg QD.  Manuscript describing results of this clinical trial is currently in preparation.

eLife Assessment

The is a valuable evaluation of a previously published simulation model on the role of heterozygote advantage in shaping MHC diversity, showing that the conclusions from this model hold only within a narrow parameter range that might be unrealistic. The author presents an alternative model, in which MHC homozygotes with duplicated MHC genes outperform heterozygotes with single genes, thereby challenging the explanation that heterozygote advantage will lead to high allelic variation at a given MHC gene. The topic is highly relevant for eco-immunology and evolutionary genetics, but several major aspects of the author's claim need to be clarified to make the model interpretable. While the work has the potential to improve our understanding of the question of how the extraordinary diversity at the MHC locus evolves, without this addition, the conclusions remain incomplete.

Reviewer #1 (Public review):

The manuscript "Heterozygote advantage cannot explain MHC diversity, but MHC diversity can explain heterozygote advantage" explores two topics. First, it is claimed that the recently published conclusion by Mattias Siljestam and Claus Rueffler (in the following referred to as [SR] for brevity) that heterozygote advantage explains MHC diversity does not withstand even a very slight change in ecological parameters. Second, a modified model that allows an expansion of the MHC gene family shows that homozygotes outperform heterozygotes. This is an important topic and could be of potential interest to readers if the conclusions are valid and non-trivial.

Let me first comment on the second part of the manuscript that describes the fitness advantage of the 'gene family expansion'. I think this, by itself, is a totally predictable result. It appears obvious that with no or a little fitness penalty, it becomes beneficial to have MHC-coding genes specific to each pathogen. A more thorough study that takes into account a realistic (most probably non-linear in gene number) fitness penalty, various numbers of pathogens that could grossly exceed the self-consistent fitness limit on the number of MHC genes, etc, could be more informative. Yet, as I understood the narrative of the manuscript, the expansion of the gene family serves as a mere counter-example to the disputed finding of [SR], rather than a systematic study of the eco-evolutionary consequences of this process.

Now to the first part of the manuscript, which claims that the point made in [RS] is not robust and breaks down under a small change in the parameters. An addition or removal of one of the pathogens is reported to affect "the maximum condition", a key ecological characteristic of the model, by an enormous factor 10^43, naturally breaking down all the estimates and conclusions made in [RS]. This observation is not substantiated by any formulas, recipes for how to compute this number numerically, or other details, and is presented just as a self-standing number in the text. The only piece of information given in the manuscript is that, unlike in [SR], the adjustable parameter c_{max} is kept constant when the number of pathogens is changed.

In my opinion, the information provided in the manuscript does not allow one to conclude anything about the relevance and the validity of its main claim. At the same time, the simulations done in [SR] are described with a fair amount of detail. Which allows me to assume that the conclusions made in [SR] are fairly robust and, in particular, have been demonstrated not to be too sensitive to changes in the main "suspect', c_{max}. Let me briefly justify my point.

First, it follows from Eqs (4,5) in the main text and (A12-A13) in the Appendix that c_{max} and K do not independently affect the dynamics of the model, but it's rather their ratio K/c_max that matters. It can be seen by dividing the numerator and denominator of (5) by c_max. Figure 3 shows the persistent branching for 4 values of K that cover 4 decades. As it appears from the schemes in the top row of Figure 3, those simulations are done for the same positions and widths/virulences of pathogens. So the position of x* should be the same in all 4 cases, presumably being at the center of pathogens, (x*,x*) = (0,0). According to the definition of x* given in the Appendix after Eqs (A12-A13), this means that c_max remains the same in all 4 cases. So one can interpret the 4 scenarios shown in Figure 3 as corresponding not to various K, but to various c_max that varied inversely to K. That is, the results would have been identical to those shown in Figure 3 if K were kept constant and c_max were multiplied by 0.1, 1, 10, and 100, or scaled as 1/K. This begs the conclusion that the branching remains robust to changes in c_max that span 4 decades as well.

Naturally, most, if not all, the dynamics will break down if one of the ecological characteristics changes by a factor of 10^43, as it is reported in the submitted manuscript. As I wrote above, there is no explanation behind this number, so I can only guess that such a number is created by the removal or addition of a pathogen that is very far away from the other pathogens. Very far in this context means being separated in the x-space by a much greater distance than 1/\nu, the width of the pathogens' gaussians. Once again, I am not totally sure if this was the case, but if it were, some basic notions of how models are set up were broken. It appears very strange that nothing is said in the manuscript about the spatial distribution of the pathogens, which is crucial to their effects on the condition c. In [SP], it is clearly shown where the pathogens are.

Another argument that makes me suspicious in the utility of the conclusions made in the manuscript and plays for the validity of [SP] is the adaptive dynamics derivation of the branching conditions. It is confirmed by numerics with sufficient accuracy, and as it stands in its simple form of the inequality between two widths, the branching condition appears to be pretty robust with respect to reasonable changes in parameters.

Overall, I strongly suspect that an unfortunately poor setup of the model reported in the manuscript has led to the conclusions that dispute the much better-substantiated claims made in [SD].

Reviewer #2 (Public review):

Summary:

This study addresses the population genetic underpinnings of the extraordinary diversity of genes in the MHC, which is widespread among jawed vertebrates. This topic has been widely discussed and studied, and several hypotheses have been suggested to explain this diversity. One of them is based on the idea that heterozygote genotypes have an advantage over homozygotes. While this hypothesis lost early on support, a reason study claimed that there is good support for this idea. The current study highlights an important aspect that allows us to see results presented in the earlier published paper in a different light, changing strongly the conclusions of the earlier study, i.e., there is no support for a heterozygote advantage. This is a very important contribution to the field. Furthermore, this new study presents an alternative hypothesis to explain the maintenance of MHC diversity, which is based on the idea that gene duplications can create diversity without heterozygosity being important. This is an interesting idea, but not entirely new.

Strengths:

(1) A careful re-evaluation of a published model, questioning a major assumption made by a previous study.

(2) A convincing reanalysis of a model that, in the light of the re-analysis-loses all support.

(3) A convincing suggestion for an alternative hypothesis.

Weaknesses:

(1) The statement that the model outcome of Siljestam and Rueffler is very sensitive to parameter values is, in this form, not correct. The sensitivity is only visible once a strong assumption by Siljestam and Rueffler is removed. This assumption is questionable, and it is well explained in the manuscript by J. Cherry why it should not be used. This may be seen as a subtle difference, but I think it is important to pin done the exact nature of the problem (see, for example, the abstract, where this is presented in a misleading way).

(2) The title of the study is very catchy, but it needs to be explained better in the text.

Reviewer #3 (Public review):

This manuscript describes a careful and thorough evaluation of an evolutionary simulation model published previously. The model and this report address the question, whether heterozygote advantage (HA) by itself as a selection mechanism can explain a substantial level of allelic diversity as it is often seen in MHC immune genes. Despite decades of research on the topic of pathogen-mediated selection for MHC diversity, it remains an open question by which specific selection mechanisms this exceptional allelic diversity is maintained.

The previously published paper posits, in contrast to various previous studies, that HA is, in fact, able to maintain a level of allelic diversity as seen in many populations, just by itself, given certain conditions. The current manuscript now challenges this conclusion by highlighting that the previous model results only hold under very narrow parameter ranges.

Besides criticizing some of the conceptual points of the previous paper, the author carefully rebuilt the previously published model and replicated their results, before then evaluating the robustness of the model results to reasonable variation in different parameters. From this evaluation, it becomes clear that the previously reported results hinge strongly on a certain scaling or weighing factor that is adjusted for every parameter setting and essentially counteracts the changes induced by changing the parameters. The critical impact of this one parameter is not clearly stated in the previous paper, but raises serious doubts about the generalizability of the model to explain MHC allelic variation across diverse vertebrate species.

Given the fact that the MHC genes are among the most widely studied genes in vertebrates, and that understanding their evolution will shed light on their association with various complex diseases, the insights from this report and the general discussion of how MHC diversity evolved are of interest to at least some of the community. The manuscript is very well written and makes it easy to follow the theoretical and methodological details of the model and the arguments. I have only a few minor comments that I am detailing below. Furthermore, I would be very interested to read a response by the previous authors, especially on the relevance of this scaling/weighing factor that they introduced into their model, as it is possible that I might have missed something about its meaning.

Author response:

Reviewer #1 (Public review):

It appears obvious that with no or a little fitness penalty, it becomes beneficial to have MHC-coding genes specific to each pathogen. A more thorough study that takes into account a realistic (most probably non-linear in gene number) fitness penalty, various numbers of pathogens that could grossly exceed the self-consistent fitness limit on the number of MHC genes, etc, could be more informative.

The reviewer seems to be referring to the cost of excessively high presentation breadth.  Such a cost is irrelevant to the inferior fitness of a polymorphic population with heterozygote advantage compared to a monomorphic population with merely doubled gene copy number.  It is relevant to the possibility of a fitness valley separating these two states, but this issue is addressed explicitly in the manuscript.

An addition or removal of one of the pathogens is reported to affect "the maximum condition", a key ecological characteristic of the model, by an enormous factor 10^43, naturally breaking down all the estimates and conclusions made in [RS]. This observation is not substantiated by any formulas, recipes for how to compute this number numerically, or other details, and is presented just as a self-standing number in the text.

It is encouraging that the reviewer agrees that this observation, if correct, would cast doubt on the conclusions of Siljestam and Rueffler.  I would add that it is not the enormity of this factor per se that invalidates those conclusions, but the fact that the automatic compensatory adjustment of c_max conceals the true effects of removing a pathogen, which are quite large.

I am not sure why the reviewer doubts that this observation is correct.  The factor of 2.7∙10⁴³ was determined in a straightforward manner in the course of simulating the symmetric Gaussian model of Siljestam and Rueffler with the specified parameter values.  A simple way to determine this number is to have the simulation code print the value to which c_max  is set, or would be set, by the procedure of Siljestam and Rueffler for different parameter values.  In another section of this response I will describe how to do this with the simulation code written and used by Siljestam and Rueffler; doing so confirms the value that I obtained with my own code.  Furthermore, I will now give a theoretical derivation of this factor.

As specified by Siljestam and Rueffler, the positions of the m pathogens in (m-1)-dimensional antigenic space correspond to the vertices of a regular simplex centered at the origin, with distance between vertices equal to 1.  The squared distance from the origin to each of the m vertices of such a simplex is (m-1)/2m (https://polytope.miraheze.org/wiki/Simplex).  Thus, the sum of the m squared distances is (m-1)/2.  For the (0, 0) homozygote, condition is multiplied by a factor of exp(-(vr)²/2) for each pathogen, where r is the distance from the origin.  It follows that, with v=20, all the pathogens together decrease condition by a factor of exp(20²∙(m-1)/4) = exp(100∙(m-1)).  Thus, increasing or decreasing m by 1 changes this value by a factor of exp(100) = 2.7∙10⁴³.

This begs the conclusion that the branching remains robust to changes in c_max that span 4 decades as well.

That shows only that the results are not extremely sensitive to c_max or K.  They are, nonetheless, exquisitely sensitive to m and v.  This difference in sensitivities is the reason that a relatively small change to m leads to such a large compensatory change in c_max a change large enough to have a major effect on the results.

As I wrote above, there is no explanation behind this number, so I can only guess that such a number is created by the removal or addition of a pathogen that is very far away from the other pathogens. Very far in this context means being separated in the x-space by a much greater distance than 1/\nu, the width of the pathogens' gaussians. Once again, I am not totally sure if this was the case, but if it were, some basic notions of how models are set up were broken. It appears very strange that nothing is said in the manuscript about the spatial distribution of the pathogens, which is crucial to their effects on the condition c.

I did not explicitly describe the distribution of pathogens in antigenic space because it is exactly the same as in Siljestam and Rueffler, Fig. 4: the vertices of a regular simplex, centered at the origin, with unity edge length.

The number in question (2.7∙10⁴³) pertains to the Gaussian model with v=20.  As specified by Siljestam and Rueffler, each pathogen lies at a distance of 1 from every other pathogen, so the distance of any pathogen from the others is indeed much greater than 1/v.  This condition holds, however, for most of the parameter space explored by Siljestam and Rueffler (their Fig. 4), and for all of the parameter space that seemingly supports their conclusions.  Thus, if this condition indicates that “basic notions of how models are set up were broken”, they must have been broken by Siljestam and Rueffler.

Overall, I strongly suspect that an unfortunately poor setup of the model reported in the manuscript has led to the conclusions that dispute the much better-substantiated claims made in [SD].

The reviewer seems to be suggesting that my simulations are somehow flawed and my conclusions unreliable.  I will therefore describe how my conclusions about sensitivity to parameter values can be verified using the simulation code provided by Siljestam and Rueffler themselves, with only small, easily understood modifications.  I will consider adding this description as a supplement when I revise the manuscript.

The starting point is the Matlab file MHC_sim_Dryad.m, available at https://doi.org/10.5061/dryad.69p8cz98j.  First, we can add a line that prints the value of the variable logcmax, which represents the natural logarithm of cmax determined and used by the code.  Below line 116 (‘prework’), add the line ‘logcmax’ (with no semicolon).

Now, at the Matlab prompt, execute MHC_sim_Dryad(false, 8, 20, 1) to run the simulation for the Gaussian model with m=8, v=20, and K=1.  The output will indicate that logcmax=700, in accord with the theoretical factor exp(100*(m-1)) derived above.  The allelic diversity, n_e, will rise to a steady state-level of about 140, as in the red curve of my Fig. 2.

Now lower m to 7, i.e,  run MHC_sim_Dryad(false, 7, 20, 1).  The output will indicate that logcmax=600.  This confirms that lowering m by 1 causes the code to lower the value of c_max by a factor exp(100)=2.7∙10⁴³, which must also be the factor by which the condition of the most fit homozygote would increase without this adjustment.

With the change of m to 7 and the compensatory change in c_max, steady-state allelic diversity remains high.  But what if m changes but c_max remains the same, as it would in reality?

To find out, we can fix the value of c_max to the value used with m=8 by adding the following line below the line previously added: ‘logcmax = 700’.  With this additional modification in place, executing MHC_sim_Dryad(false, 7, 20, 1) confirms that without a compensatory change to c_max, lowering m from 8 to 7 mostly eliminates allelic diversity, in accord with the corresponding curve in my Fig. 2.  Similarly, raising m from 8 to 9, or changing v from 20 to 19.5 or 20.5 (executing MHC_sim_Dryad(false, 8, 19.5, 1) or MHC_sim_Dryad(false, 8, 20.5, 1)), largely eliminates diversity, confirming the other results in my Fig. 2.  Results for the bitstring model can also be confirmed, though this requires additional changes to the code.

Thus, the extreme sensitivity of the results of Siljestam and Rueffler to parameter values can be verified with the code that they used for their simulations, indicating that my conclusions are not consequences of my having done a “poor setup of the model”.

Response to Reviewer #2 (Public review):

(1) The statement that the model outcome of Siljestam and Rueffler is very sensitive to parameter values is, in this form, not correct. The sensitivity is only visible once a strong assumption by Siljestam and Rueffler is removed. This assumption is questionable, and it is well explained in the manuscript by J. Cherry why it should not be used. This may be seen as a subtle difference, but I think it is important to pin done the exact nature of the problem (see, for example, the abstract, where this is presented in a misleading way).

I appreciate the distinction, and the importance of clearly specifying the nature of the problem.  However, Siljestam and Rueffler do not invoke the implausible assumption that changes to the number of pathogens or their virulence will be accompanied by compensatory changes to c_max.  Rather, they describe the adjustment of c_max (Appendix 7) as a “helpful” standardization that applies “without loss of generality”.  Indeed, my low-diversity results could be obtained, despite such adjustment, by combining the small change to m or v with a very large change to K (e.g., a factor of 2.7∙10⁴³).  In this sense there is no loss of generality, but the automatic adjustment of c_max obscures the extreme sensitivity of the results to m and v.

(2) The title of the study is very catchy, but it needs to be explained better in the text.

I had hoped that the final paragraph of the Discussion would make the basis for the title clear.  I will consider whether this can be clarified in a revision.

eLife Assessment

This valuable study reveals that the GSK-3 inhibitor AZD2858 inhibits the formation of TOPBP1 condensates and hence DNA damage responses in colorectal cancer cells. The evidence supporting the claims of the authors is convincing, although uncovering how this drug blocks bio-condensate formation would have strengthened the study. The work will be of interest to cancer researchers searching for synergistic drug combination strategies.

[Editors' note: this paper was reviewed by Review Commons.]

Reviewer #1 (Public review):

Summary:

Laura Morano and colleagues have performed a screen to identify compounds that interfere with the formation of TopBP1 condensates. TopBP1 plays a crucial role in the DNA damage response, and specifically the activation of ATR. They found that the GSK-3b inhibitor AZD2858 reduced the formation of TopBP1 condensates and activation of ATR and its downstream target CHK1 in colorectal cancer cell lines treated with the clinically relevant irinotecan active metabolite SN-38. This inhibition of TopBP1 condensates by AZD2858 was independent from its effect on GSK-3b enzymatic activity. Mechanistically, they show that AZD2858 thus can interfere with intra-S-phase checkpoint signaling, resulting in enhanced cytostatic and cytotoxic effects of SN-38 (or SN-38+Fluoracil aka FOLFIRI) in vitro in colorectal carcinoma cell lines.

Comments on latest version:

The requested plots are in figure S7 of the latest manuscript version, and look convincing. My last point is now adequately addressed.

Reviewer #2 (Public review):

Summary:

In 2021 (PMID: 33503405) and 2024 (PMID: 38578830) Constantinou and colleagues published two elegant papers in which they demonstrated that the Topbp1 checkpoint adaptor protein could assemble into mesoscale phase-separated condensates that were essential to amplify activation of the PIKK, ATR, and its downstream effector kinase, Chk1, during DNA damage signalling. A key tool that made these studies possible was the use of a chimeric Topbp1 protein bearing a cryptochrome domain, Cry2, which triggered condensation of the chimeric Topbp1 protein, and thus activation of ATR and Chk1, in response to irradiation with blue light without the myriad complications associated with actually exposing cells to DNA damage.

In this current report Morano and co-workers utilise the same optogenetic Topbp1 system to investigate a different question, namely whether Topbp1 phase-condensation can be inhibited pharmacologically to manipulate downstream ATR-Chk1 signalling. This is of interest, as the therapeutic potential of the ATR-Chk1 pathway is an area of active investigation, albeit generally using more conventional kinase inhibitor approaches.

The starting point is a high throughput screen of 4730 existing or candidate small molecule anti-cancer drugs for compounds capable of inhibiting the condensation of the Topbp1-Cry2-mCherry reporter molecule in vivo. A surprisingly large number of putative hits (>300) were recorded, from which 131 of the most potent were selected for secondary screening using activation of Chk1 in response to DNA damage induced by SN-38, a topoisomerase inhibitor, as a surrogate marker for Topbp1 condensation. From this the 10 most potent compounds were tested for interactions with a clinically used combination of SN-38 and 5-FU (FOLFIRI) in terms of cytotoxicity in HCT116 cells. The compound that synergised most potently with FOLFIRI, the GSK3-beta inhibitor drug AZD2858, was selected for all subsequent experiments.

AZD2858 is shown to suppress the formation of Topbp1 (endogenous) condensates in cells exposed to SN-38, and to inhibit activation of Chk1 without interfering with activation of ATM or other endpoints of damage signalling such as formation of gamma-H2AX or activation of Chk2 (generally considered to be downstream of ATM). AZD2858 therefore seems to selectively inhibit the Topbp1-ATR-Chk1 pathway without interfering with parallel branches of the DNA damage signalling system, consistent with Topbp1 condensation being the primary target. Importantly, neither siRNA depletion of GSK3-beta, or other GSK3-beta inhibitors were able to recapitulate this effect, suggesting it was a specific non-canonical effect of AZD2858 and not a consequence of GSK3-beta inhibition per se.

To understand the basis for synergism between AZD2858 and SN-38 in terms of cell killing, the effect of AZD2858 on the replication checkpoint was assessed. This is a response, mediated via ATR-Chk1, that modulates replication origin firing and fork progression in S-phase cell under conditions of DNA damage or when replication is impeded. SN-38 treatment of HCT116 cells markedly suppresses DNA replication, however this was partially reversed by co-treatment with AZD2858, consistent with the failure to activate ATR-Chk1 conferring a defect in replication checkpoint function.

Figures 4 and 5 demonstrate that AZD2858 can markedly enhance the cytotoxic and cytostatic effects of SN-38 and FOLFIRI through a combination of increased apoptosis and growth arrest according to dosage and treatment conditions. Figure 6 extends this analysis to cells cultured as spheroids, sometimes considered to better represent tumor responses compared to single cell cultures.

Significance:

Liquid phase separation of protein complexes is increasingly recognised as a fundamental mechanism in signal transduction and other cellular processes. One recent and important example was that of Topbp1, whose condensation in response to DNA damage is required for efficient activation of the ATR-Chk1 pathway. The current study asks a related but distinct question; can protein condensation be targeted by drugs to manipulate signalling pathways which in the main rely on protein kinase cascades?

Here, the authors identify an inhibitor of GSK3-beta as a novel inhibitor of DNA damage-induced Topbp1 condensation and thus of ATR-Chk1 signalling.

This work will be of interest to researchers in the fields of DNA damage signalling, biophysics of protein condensation, and cancer chemotherapy.

Comments on latest version:

Having read the revised manuscript and rebuttal I am satisfied that the authors have resolved my various original concerns through a combination of clarification/ explanation and textual changes necessary to make the description of certain data precise. My impression is that they have also largely or completely satisfied the concerns of the other reviewers, with the possible exception of reviewer 1's point about the relative toxicity of AZD and FOLFIRI in colorectal cancer cell lines versus the untransformed CCD841 cell line. This is of course an important point with respect to the possible practical application of this combination for cancer therapy, however this seems somewhat subsidiary to the main novelty and significance of the findings, which are that protein liquid phase separation/ condensation can be manipulated pharmacologically to modify signal transduction processes and that existing drugs can be re-purposed to this end.

Reviewer #3 (Public review):

Summary:

The authors have extended their previous research to develop TOPBP1 as a potential drug target for colorectal cancer by inhibiting its condensation. Utilizing an optogenetic approach, they identified the small molecule AZD2858, which inhibits TOPBP1 condensation and works synergistically with first-line chemotherapy to suppress colorectal cancer cell growth. The authors investigated the mechanism and discovered that disrupting TOPBP1 assembly inhibits the ATR/Chk1 signaling pathway, leading to increased DNA damage and apoptosis, even in drug-resistant colorectal cancer cell lines.

Comments on latest version:

This reviewer does not have further comments to the paper.

Author response:

The following is the authors’ response to the original reviews

Reviewer #1:

Comments on revised version: 

I have reviewed the revised manuscript and read the rebuttal. The authors have carefully addressed my concerns. There is however one point that needs further work: 

This follows up on my major point #1 in my initial review. I had I asked the authors to demonstrate that FOLFIRI + AZD are less toxic to untransformed colorectal cells than colorectal cancer cell lines.  It is good to see that the authors took my advice and show effects of the drug treatments on the untransformed colorectal cell line CCD841. It seems to be less sensitive to AZD and FOLFIRI in the figure in the rebuttal. What surprises me is that I cannot find these new figures anywhere in the revised manuscript. Also, the data seem preliminary, because I do not see any standard errors in the graphs, and I cannot find a description of the time of drug incubation. I ask the authors to make sure that the CCD841 data are reproducible, and make sure they incorporate the data in the revised manuscript. 

We thank the reviewer for this insightful comment. In the initial revised version of the manuscript, we did not include results from the untransformed colorectal cell line CCD841, as those experiments had only been performed once and were considered preliminary. However, we fully agree with the reviewer on the importance of including these data.

To address this, we have repeated the experiments in CCD841 cells to ensure reproducibility. We now report the results from three independent experiments testing the combination of AZD2858 and FOLFIRI on healthy epithelial colon cells. These results are shown in Supplementary Figure S7, where blue matrices represent cell viability and black matrices reflect the level of synergy between AZD2858 and FOLFIRI.

Our results confirm that, individually, each drug has little to no effect on healthy cells, and no consistent synergistic interaction was observed, except in Experiment 1, which could not be reproduced. Importantly, the drug concentrations used were identical to those applied in the cancer cell experiments, allowing for direct comparison between normal and malignant cell responses.

Reviewer #2:

Comments on latest version: 

Morano et al. have revised their manuscript in response to the points raised by reviewer #3 as follows.

(1) Fig. 2E: Correcting the previously erroneous labelling of this Fig. makes it match the textual description. 

(2) Figs 3A and B: The revised textual description of the flow cytometry BrdU incorporation is now precise. 

(3) Fig. 3E: Removing the suspect WB images is a pragmatic decision that does not significantly affect the overall conclusions of the paper. 

(4) Fig. 3D: Despite its puzzling appearance this data is now described accurately in the text as "DSBs remained elevated after the combined treatment" rather than "increased after the combined treatment. A more convincing increase in the presumed damaged DNA band is evident in Fig. 4D when AZD2858 is combined with a much lower concentration of SN38 (1.5nM) which could mean that the concentration used in Fig. 3D (300nM) induced maximal damage that could not be further enhanced. 

We thank the reviewer for their thoughtful comments and constructive feedback, which have helped us improve the clarity and rigor of the manuscript.

Reviewer #3:

Comments on latest version: 

The authors have addressed most of the concerns that I raised in the first round of revision and I have no further questions. I appreciate the authors's efforts in carrying out an preliminary in vivo work, although as the authors pointed out the compound seems to be not effective in vivo. Future work is desired to address this to clarify the significance of the work. 

We thank the reviewer for acknowledging our efforts in addressing the previous concerns. We also appreciate the recognition of our preliminary in vivo work. While these results suggest limited in vivo efficacy of the compound at this stage, we agree that additional studies will be necessary to fully evaluate its therapeutic relevance. We consider this an important next step and are committed to pursuing it in future work.

eLife Assessment

This study reports the important finding that the dynamin inhibitor Dyngo-4a broadly affects lipid packing and plasma membrane dynamics, independently of its action on dynamin. While solid computational, biophysical, and cell-based evidence supports this conclusion, there is incomplete support for the authors' main claim on the role of lipid packing in caveolae internalization, as the causal relationship remains unclear and direct analyses are lacking. With stronger evidence, this work would be of significant interest to cell biologists, biophysicists, and chemists interested in membrane remodeling and drug-membrane interactions.

[Editors' note: this paper was reviewed by Review Commons.]

Reviewer #1 (Public review):

Summary:

The authors use Dyngo-4a, a known Dynami inhibitor to test its influence on caveolar assembly and surface mobility. They investigate whether it incorporates into membranes with Quartz-Crystal Microbalance, they investigate how it is organized in membranes using simulations. Finally, they use lipid-packing sensitive dyes to investigate lipid packing in the presence of Dyngo-4a, membrane stiffness using AFM and membrane undulation using fluorescence microscopy. They also use a measure they call "caveola duration time" to claim that something happens to caveolae after Dyngo-4a addition and using this parameter, they do indeed see an increase in it in response to Dyngo-4a, which is reduced back to the baseline after addition of cholesterol.

Overall, the authors claim: 1) Dyngo-4a inserts into the membrane and this 2) results in "a dramatic dynamin-independent inhibition of caveola scission". 3) Dyngo-4a was inserted and positioned at the level of cholesterol in the bilayer and 4) Dyngo-4a-treatment resulted in decreased lipid packing in the outer leaflet of the plasma membrane 5) but Dyngo-4a did not affect caveola morphology, caveolae-associated proteins, or the overall membrane stiffness 6) acute addition of cholesterol counteracts the block in caveola scission caused by Dyngo-4a.

Overall, in this reviewers opinion, claims 1, 3, 4, 5 are well-supported by the presented data from electron and live cell microscopy, QCM-D and AFM.

However, there is no convincing assay for caveolar endocytosis presented besides the "caveola duration" which although unclearly described seems to be the time it takes in imaging until a caveolae is not picked up by the tracking software anymore in TIRF microscopy.

Since the main claim of the paper is a mechanism of caveolar endocytosis being blocked by Dyngo-4a, a true caveolar internalization assay is required to make this claim. This means either the intracellular detection of not surface connected caveolar cargo or the quantification of caveolar movement from TIRF into epifluorescence detection in the fluorescence microscope. Otherwise, the authors could remove the claim and just claim that caveolar mobility is influenced.

Significance:

A number of small molecule inhibitors for the GTPase dynamics exist, that are commonly used tools in the investigation of endocytosis. This goes as far that the use of some of these inhibitors alone is considered in some publications as sufficient to declare a process to be dynamin-dependent. However, this is not correct, as there are considerable off-target effects, including the inhibition of caveolar internalization by a dynamin-independent mechanism. This is important, as for example the influence of dynamin small molecule inhibitors on chemotherapy resistance is currently investigated (see for example Tremblay et al., Nature Communications, 2020).

The investigation of the true effect of small molecules discovered as and used as specific inhibitors and their offside effects is extremely important and this reviewer applauds the effort. It is important that inhibitors are not used alone, but other means of targeting a mechanism are exploited as well in functional studies. The audience here thus is besides membrane biophysicists interested in the immediate effect of the small molecule Dyngo-4a also cell biologists and everyone using dynamic inhibitors to investigate cellular function.

Comments on revised version:

Please include the promised data on caveolar internalization and remove the above mentioned claim on membrane undulations from the text.

Reviewer #2 (Public review):

Summary:

In this manuscript, the authors probe the mechanisms by which Dyngo-4a, a dynamin inhibitor used to block endocytosis, disrupts caveolae dynamics. They provide compelling evidence that Dyngo-4a inhibits caveolae dynamics and endocytosis (as well as several other aspects of plasma membrane dynamics) by a dynamin-independent mechanism. They also provide strong computational and experimental data showing that Dyngo-4a inserts into membranes and decreases lipid packing in the outer leaflet of the plasma membrane. Finally, they demonstrate that the addition of excess cholesterol to cells reverses the effects of Dyngo-4a on caveolae dynamics, presumably by reversing lipid packing defects. Based on these findings they conclude that lipid packing regulates caveolae dynamics and endocytosis in a cholesterol-dependent manner.

This work should be of value to cell biologists interested in plasma membrane remodeling and membrane trafficking, biophysicists that study small molecule/membrane interactions and membrane remodeling processes, and chemists interested in designing drugs to target membrane trafficking machinery and pathways.

Strengths:

This work addresses the important topic of how a widely used endocytic inhibitor actually works. In the process of addressing this question, the authors uncover unexpected connections between how lipids are packed in cell membranes and membrane dynamics. The methods are appropriate and many of the claims made in this work are well supported by data.

Weaknesses:

I appreciate that the manuscript has already gone through one round of revisions and that many of the concerns from the previous reviewers appear to have been addressed. However, as an interested reader, I would like to offer several additional comments for the authors to consider.

(1) It is not clear based on the data presented whether the effects of Dyngo-4a on lipid packing give rise to defects in caveolae dynamics or if these effects are merely correlated. To show this more definitively, one might expect additional experimental approaches to be used to perturb lipid packing. I appreciate this is probably beyond the scope of the current study. However, it seems important for the manuscript to be clear about how far this interpretation can be pushed in the absence of additional independent lines of evidence.

(2) On a related note, it is not obvious how changes in lipid packing in the outer leaflet could impact caveolae dynamics. It would be helpful to include a cartoon illustrating how this might work.

(3) The authors note that Dyngo-4a inhibits several dynamic processes including generalized plasma membrane mobility (Fig 4A&B), transferrin uptake (Fig S4C), and fusion of fusogenic liposomes (Fig S4G). This clearly indicates there is a major disruption of the plasma membrane going on here that is not limited to caveolae. They go on to show that the addition of cholesterol reverses the effects of Dyngo-4a on caveolae dynamics. However, they do not discuss whether adding back cholesterol has similar effects on plasma membrane mobility and transferrin uptake. This information could help to further pinpoint whether the mechanisms of action are shared, and if the role of cholesterol is more general in controlling these events or is instead specific to caveolae.

(4) In Fig 4C, the morphology of the neck region of the Dyngo04a treated caveolae structure appears to be "pinched" compared to the control. I appreciate that more EM studies are underway. It would be useful to specifically compare the morphology of the caveolae as part of those studies.

(5) In Line 91, a statement is made that 8S complex formation requires cholesterol. This is debatable, as they appear to form in E. coli in the absence of cholesterol (reference 14).

Author response:

General Statements

In this paper we demonstrate that the lipid packing of the plasma membrane has a huge impact on the stability of caveolae. By using interdisciplinary techniques, we show that the widely used dynamin inhibitor Dyngo-4a adsorbs and inserts to lipid bilayers leading to a decreased lipid packing and hence reduced caveolae dynamics and internalization even in cells lacking dynamin. We have added experiments that validates that Dyngo-4a treatment does not result in fragmentation or disassembly of the caveolae.  A FRAP assay of cytosolic caveolae has been employed to address questions concerning scission. Moreover, as suggested by the reviewers, we have also included new simulation data that show and expand on the fact that Dyngo-4a positions in the lipid leaflet similar to cholesterol and preferentially associates with cholesterol clusters, affecting the spatial distribution of cholesterol in the membrane. We believe that these added data have greatly improved the paper and strengthened our conclusions that the lipid packing is a critical determinant in the balance between internalization and stable plasma membrane association of membrane vesicles.

As requested, we have expanded the introduction to provide more detailed information about previous findings in the field. Changes and addition to the text has been highlighted in red for easier tracking.

Point-by-point description of the revisions

Reviewer #1 (Evidence, reproducibility and clarity):

The authors use Dyngo-4a, a known Dynami inhibitor to test its influence on caveolar assembly and surface mobility. They investigate, whether it incorporates into membranes with Quartz-Crystal Microbalance, they investigate how it is organized in membranes using simulations. Finally, they use lipid-packing sensitive dyes to investigate lipid packing in the presence of Dyngo-4a, membrane stiffness using AFM and membrane undulation using fluorescence microscopy. They also use a measure they call "caveola duration time" to claim that something happens to caveolae after Dyngo-4a addition and using this parameter, they do indeed see an increase in it in response to Dyngo-4a, which is reduced back to the baseline after addition of cholesterol.

Overall, the authors claim: 1) Dyngo-4a inserts into the membrane and this 2) results in "a dramatic dynamin-independent inhibition of caveola scission". 3) Dyngo-4a was inserted and positioned at the level of cholesterol in the bilayer and 4) Dyngo-4a-treatment resulted in decreased lipid packing in the outer leaflet of the plasma membrane 5) but Dyngo-4a did not affect caveola morphology, caveolae-associated proteins, or the overall membrane stiffness 6) acute addition of cholesterol counteracts the block in caveola scission caused by Dyngo-4a.

Overall, in this reviewers opinion, claims 1, 3, 4, 5 are well-supported by the presented data from electron and live cell microscopy, QCM-D and AFM.

However, there is no convincing assay for caveolar endocytosis presented besides the "caveola duration" which although unclearly described seems to be the time it takes in imaging until a caveolae is not picked up by the tracking software anymore in TIRF microscopy.

Since the main claim of the paper is a mechanism of caveolar endocytosis being blocked by Dyngo-4a, a true caveolar internalization assays is required to make this claim. This means either the intracellular detection of not surface connected caveolar cargo or the quantification of caveolar movement from TIRF into epifluorescence detection in the fluorescence microscope. Otherwise, the authors could remove the claim and just claim that caveolar mobility is influenced.

We thank the reviewer for the nice constructive comments, and we very much appreciate the positive critique. We have now included a FRAP experiment of endocytic Cav1-GFP supporting the effect on internalization. In addition, we are currently preforming CTxB HRP experiments to quantify the number of caveolae at PM using EM but due to reasons out of our control we have not managed to finish these on time, they will be included in the manuscript once they are ready in hopefully not too long.

Reviewer #1 (Significance):

A number of small molecule inhibitors for the GTPase dynamics exist, that are commonly used tools in the investigation of endocytosis. This goes as far that the use of some of these inhibitors alone is considered in some publications as sufficient to declare a process to be dynamin-dependent. However, this is not correct, as there are considerable off-target effects, including the inhibition of caveolar internalization by a dynamin-independent mechanism. This is important, as for example the influence of dynamin small molecule inhibitors on chemotherapy resistance is currently investigated (see for example Tremblay et al., Nature Communications, 2020).

The investigation of the true effect of small molecules discovered as and used as specific inhibitors and their offside effects is extremely important and this reviewer applauds the effort. It is important that inhibitors are not used alone, but other means of targeting a mechanism are exploited as well in functional studies. The audience here thus is besides membrane biophysicists interested in the immediate effect of the small molecule Dyngo-4a also cell biologists and everyone using dynamic inhibitors to investigate cellular function.

Reviewer #2 (Evidence, reproducibility and clarity):

This manuscript uses the small molecule dynamin inhibitors dynasore and dyngo to show that in dynamin triple knockout cells that these inhibitors impact lipid packing and organization in the plasma membrane. Data showing that dyngo affects caveolin dynamics using tirf microscopy is also shown and is interpreted to reflect inhibition of caveolae scission from the membrane.

This data showing that dyngo and dynasore target membrane order is quite compelling and argues that the effects of these inhibitors is not dynamin specific and that inhibition of endocytosis by these small molecule inhibitors is dynamin-independent. The in vitro and in vivo data they provide is convincing.

Similarly, the data showing that dynasore and dyngo affect caveolin dynamics and clathrin endocytosis (transferrin) is quite convincing and argues that altered lipid packing is impacting membrane dynamics at the plasma membrane.

What is less convincing is the conclusion that dyngo is preventing caveolae scission from the membrane. Study of caveolae endocytosis is based on a TIRF assay that has inherent limitations:

- Caveolae are defined as bright cav1-positive spots in diffraction limited TIRF and their disappearance presumed to be endocytic events. Cav1 spots are presumed to be caveolae but the authors do not consider that they may be flat non-caveolar oligomers. The diffraction limited TIRF approach interprets the large structures as caveolae but evidence to that effect is lacking.

This is a valid comment and to address this we have now included data showing colocalization of cavin1 and EHD2 to the Cav1-GFP spots. We can however not determine if they are flat or invaginated. We do have extensive experience imaging caveolae using TIRF microscopy and carefully chose cells that display low expression of fluorescently labelled caveolin to avoid non-caveolar structures.

- The analysis (and the diagram presented in figure 4) considers that caveolae can either diffuse laterally in the membrane or internalize and does not consider that caveolae can flatten and possibly fragment in the membrane. Is it not possible that loss of Cav1 spots is a fragmentation event and not necessarily a scission event?

This is a good question, yet, fragmentation and disassembly would result in shorter track durations and this is not what is observed in data. We have now also included data showing that cavin1 is persistently associated with the Cav1 spots identified as caveolae during Dyngo-4a treatment indicating that these are caveolae. Furthermore, IF stainings showing colocalization of Cav1GFP with cavin1 or EHD2 after Dyngo-4a treatment have also been added. We have now also expanded on the different interpretations of the data in the results section.

- The analysis is based on overexpression of Cav1-GFP that may alter the stoichiometry between Cav1 and cavin1 such that while caveolae may be expressed, larger non-caveolar structures may accumulate.

Yes, this is correct, we have specifically imaged cell expressing low levels of Cav1-GFP to avoid accumulated non-caveolar structures that can be spotted in cells with high expression.

- Cav1 has been shown to be internalized via the CLIC pathway (Chaudary et al, 2014) and if dyngo is impacting clathrin then maybe it is also impacting CLIC endocytosis and thereby Cav1 endocytosis via this pathway?

Dyngo-4a has been shown to not affect CLIC endocytosis (McCluskey et al., 2013) and in our data we do not see internalization following Dyngo-4a treatment.

- The longer Cav1 TIRF track time and shorter displacement with dyngo is consistent with inhibition of caveolae scission. However, as the authors discuss, could not reduced membrane undulations due to dyngo's impact on membrane order be responsible for the longer tracks? Alternatively, perhaps the altered lipid packing is corralling Cav1 movement and reducing non-caveolar Cav1 endocytosis, resulting in shorter tracks of longer duration? The proposed interaction of dyngo with cholesterol could prevent scission but also stabilize large (flat?) Cav1 oligomers in the membrane, perhaps reducing Cav1 oligomer fragmentation.

We completely agree that membrane undulations contribute to instability of the TIRF-field and therefore disruption of cav1-GFP tracks as we discuss in the results section and have been described in previous work (Larsson et al., 2023). Yet, we have also shown that internalization of caveolae results in shorter tracks (Hubert et al., 2020; Larsson et al., 2023; Mohan et al., 2015). Furthermore, the tracked Cav1-GFP spots are persistently positive for cavin1 both with and without Dyngo-4a treatment showing that the majority do not disassemble become internalized by other pathways. Additionally, the added IF stainings after 30 min Dyngo-4a treatment also show that the Cav1-GFP spots remain positive for cavin1 and EHD2 just as ctrl-treated cells.

My point here is not to discredit the data but only to suggest that the TIRF approach used is an indirect measure of caveolae scission from the membrane that requires substantiation using other approaches.

We appreciate these comments and have tried to address these by adding new data and discussions on the interpretation of the tracking data in the results section.

Dyngo is certainly generally affecting lipid packing via cholesterol and thereby affecting Cav1 dynamics in the plasma membrane. The claim of caveolae scission should be qualified and alternative possibilities considered and discussed. If the authors persist in arguing that dyngo is affecting caveolae scission then the effect should be substantiated by accumulation of caveolae by quantitative EM and high spatial and temporal resolution imaging of Cav1 and cavin1 to define the endocytic events. As the latter represents a new, and potentially very challenging, line of experimentation, I would suggest that it is beyond the scope of the current study. As indicated above the additional experiments are not necessary and qualification of the claims would be sufficient.

We have now included a FRAP experiment of endocytic Cav1-GFP supporting the effect on internalization. We are also currently preforming CTxB HRP experiments to quantify the number of caveolae at the PM using EM but due to reasons out of our control we have not managed to finish these on time, they will be included in the manuscript once they are ready in hopefully not too long.

Other points

Figure 1C - Cav1 positive spots cannot be interpreted to be caveolae from diffraction limited confocal images. Same comment applies to Fig 4G - caveola? duration.

We completely agree with this and that the claims should be qualified. We have added IF stainings showing that the Cav1-GFP structures are also positive for cavin1. We have now clarified that we cannot distinguish between flat or different curved states of caveolae using this methodology. We have also changed the labelling of Fig. 4G.

Figure 4C - it is not clear why this EM data is not quantified - for both the number of caveolae and clathrin coated pits - as this would help clarify the interpretation of the effect reported.

We are currently preforming CTxB HRP experiments to quantify the number of caveolae using EM but due to reasons out of our control we have not managed to finish these on time, they will be included in the manuscript once they are ready in hopefully not too long.

Figure 4D - the AFM experiments should perhaps be repeated as the non-significant effect of dyngo on the Young's modulus may be a result of insufficient n values.

We would like to clarify that to ensure the robustness of our AFM measurements, we performed the experiments with sufficient biological and technical replicates. Specifically, each data point shown in Figure 4D represents a Young’s modulus value averaged from approximately sixty force-distance curves per cell. For each condition, we collected force-distance maps on eight to nine individual cells, obtained from two separate petri dishes per day. We repeated this process on two independent days. In total, we analysed thirty-one cells for the DMSO control and thirty-three cells for the Dyngo-4a treatment. We performed the “student’s t-test with Welch’s correction” to access the statistical significance between the two conditions, as described in the main text. We believe that the sample size and statistical approach are sufficient to support the conclusions presented. Furthermore, we also analysed cell stiffness by calculating the slope of the linear portion of the force-distance curves. This analysis also did not reveal any statistically significant differences between the conditions (data not shown), further supporting our conclusion that Dyngo-4a treatment does not significantly alter the Young’s modulus under our experimental setup (or conditions).

Reviewer #2 (Significance):

This data showing that dyngo and dynasore target membrane order is quite compelling and argues that the effects of these inhibitors is not dynamin specific and that inhibition of endocytosis by these small molecule inhibitors is dynamin-independent. The in vitro and in vivo data they provide is convincing.

Similarly, the data showing that dynasore and dyngo affect caveolin dynamics and clathrin endocytosis (transferrin) is quite convincing and argues that altered lipid packing is impacting membrane dynamics at the plasma membrane.

What is less convincing is the conclusion is that dyngo is preventing caveolae scission from the membrane.

Reviewer #3 (Evidence, reproducibility and clarity):

Larsson et al present experimental and computational data on the role of Dyngo4a (a compound that was developed to inhibit dynamin) on the dynamics of caveolae. The manuscript mostly documents effects of Dyngo on caveolae, with one experiment to suggest a mechanism for this result. This one rather unconvincing result forms the focus of the manuscript contributing to a disconnect between the data and the presentation. Additionally, there are concerns with data interpretation. The writing could also benefit from revision to address grammar mistakes, strengthen referencing, and increase precision. Overall, the manuscript requires substantial revisions before being considered for publication. The central claim, in particular, needs stronger evidence to support the proposed mechanism.

We thank the reviewer for the thorough review and for experimental suggestions that we believe has strengthened our data further.

Significant issues (in approximate order of importance):

(1) The data supporting the central mechanistic explanation appears limited. There is no evidence that Dyngo remains in one leaflet

The simulations show that the energy barrier for moving in between bilayers is very high. Furthermore, simulations of C-Laurdan has shown that it does not readily flip in between membrane leaflets (Barucha-Kraszewska et al., 2013) supporting that it reports on the outer lipid leaflet when added to cells. We have however now changed this and state that Dyngo-4a decreased the lipid order in the plasma membrane.

- the GP of the PM is very low compared to previous measurements,

The absolute GP-values will vary between setups depending on what filters are used so they are not comparable between laboratories. What is of importance is that we found a significant change in the relative GP-values in cells treated with Dyngo-4a and control cells. It is this change that we report. We have not performed any GP-measurements on this cell type earlier so it is unclear what previous measurements reviewer #3 are referring to.

- effects on other membranes are not explored,

The order of the intracellular membranes is as expected lower than that of the plasma membrane. Differentiating different intracellular membranes of interest like endocytotic vesicles from other intracellular membranes would be very difficult but, more importantly, our study is focused on what is happening in the plasma membrane where caveolae reside and would be of minor interest for plasma membrane dynamics.

- dynamin-directed effects of Dyngo are not considered,

In the discussion section we discuss the difficulties with disentangling dynamin-direct and indirect effects.

(2) The QCM-D measurements and claims require explanation as several aspects remains unclear. In Fig S2, the 'softness' (what does this mean?) changes by 4-fold with DMSO alone (what does this mean?), then fractionally more with Dyngo. Then fractionally more again when Dyngo is removed (why?). Then it remains somewhat higher when both Dyngo and DMSO are removed, which is somehow interpreted as Dyngo remaining in the bilayer, but not DMSO.

We understand the confusion of the reviewer and hope our explanations provide clarity. QCM-D measurements are based on an oscillating quartz crystal sensor. Specifically, alterations in oscillation frequency (ΔF) and the rate of energy dissipation from the sensor surface (ΔD) are what is measured. Allowing the measurement of: 1) materials adsorbing to the sensor surface, 2) changes in the viscoelastic properties of a solution in contact with the sensor surface, 3) changes in the material adsorbed to the sensor surface upone exposure to different solutions. The ratio of ΔD/-ΔF reports the mechanical softness or rigidity of an adsorbed material, in this case the SLB.

A “buffer shift” is the term used when there is not an adsorption to the sensor surface, but rather an effect from altering the solution above the sensor surface. One reason is because different solutions can have different densities (e.g., a DMSO-buffer mixture vs buffer alone), which impacts the oscillations of the sensor. It was observed that the DMSO-buffer mixture alone gave a large buffer shift in comparison to the adsorption of the Dyngo-4a into the SLB, thereby muddling the data interpretation. Thus, in Fig. S2 the system was first equilibrated with the DMSO-buffer mixture prior to addition of the Dyngo-4a solution to allow for clearer visualization of the two events. In QCMD to assess if something has made a permeant change to the system you change back to the solutions used before the addition, thus first we washed with a DMSO-Buffer mixture followed by buffer alone. Control experiments were carried out in which no Dyngo-4a was added (also shown in Fig. S2). The control shows the same “buffer shift” from the DMSO-buffer mixture occurs in both systems and that upon returning to a buffer only condition there is no permanent change to the system caused from exposure to the DMSO. In contrast, once the system that received Dyngo-4a is changes back to a buffer only system we see that mass has been added to the system (ΔF) with little change to the dissipation (ΔD), thereby resulting in a lower ratio of ΔD/-ΔF, which is to say that the SLB after the adsorption of Dyngo-4a was more rigid that the SLB without Dyngo-4a.

These interpretations are difficult to grasp, as the authors seem to be implying simple amphiphilic partitioning into the membrane, which should all be removable by efficient washing.

Amphiphilic partitioning is not fully reversible by “efficient washing” it depends on partitioning coefficients.

I do not doubt that this compound interacts with membranes, but the quantifications appear ambiguous. A bilayer with 16 mol% (or worse, 30% if all in one leaflet) Dyngo is very unlikely (to remain a bilayer). Even if such a bilayer was conceivable, the authors are claiming an ADDITION of Dyngo that would INCREASE the area of one leaflet by 30%, which needs explanation as it appears unlikely.

We understand that in our attempt provide numbers in the results section for the amount of binding observed in QCM-D, this can easily be interpreted as this is what is observed to insert into the PM. However, as discussed in the discussion, we also see aggregations of Dyngo-4a that associate with the membrane in the simulations which likely could contribute to the binding observed in QCM-D prior to washing. The precise amount of membrane inserted Dyngo-4a is difficult to measure as we discuss in the text. In order to make this clearer, we have now moved all these details to the discussion section where we elaborate on this. Furthermore, since Dyngo-4a, like cholesterol, is intercalating in between the head groups of the lipids the area would not increase in direct proportion to the mol%.

Also, there are no replicates shown, so unclear how reproducible these effects are?

For clarity, only single experiments are shown. However, multiple experiments were performed and the range in measured values for 3 technical repeats can be observed in the standard deviations found in the main text (e.g., 6 ± 2 mol%).

(3) The simulations are insufficiently described and difficult to interpret. How big are these systems? Why do the figures show the aqueous system with lateral boundaries?

There are no explicit boundaries used in the simulations, periodic boundary conditions are applied in all three dimensions. The lateral boundaries observed in the figures correspond to the simulation box edges and are a visual artifact of 2D projections with QuickSurf representation. No artificial wall or constraints were introduced laterally. Additional technical details, including the system size and periodic boundary conditions have now been added to the methods section.

It seems quite important that multiple Dyngo molecules aggregate rather than partition into membranes - is this likely to occur in experiment?

Yes, this is important and with the additional simulation experiments suggested by Reviewer #3 it has been clarified that they contribute a great deal to the change in lipid packing of lipid bilayers containing cholesterol.  However, it is hard to test aggregation is the cellular system, but we believe that this happens and contribute to the effect on membranes. We have now emphasized the effect of the aggregates in the text.

PMF simulations are strongly suggesting that Dyngo does not spontaneously cross membranes, which is inconsistent with its drug-like amphiphilicity (cLogP~2.5 is optimally suited for membrane permeation) and known effects on intracellular proteins. This suggests an artefact in these PMFs.

As stated in the submitted version of the manuscript, logP was used to validate the topology and the observed value was in a very good agreement with cLogP. Moreover, this validation complemented the standard procedure of CHARMM-GUI ligand modelling, that provided a reasonable penalty score (around 20) for the Dyngo-4a topology. POPC and cholesterol molecules are standard in the force field and validated by numerous studies. The parameters used for the membrane simulations and AWH in particular are very common for this type of studies. Thus, we do not see what may cause any artifacts in the free energy profile construction. In fact, amphiphilicity of the molecule may be one of the key reasons that Dyngo-4a molecule remains at the aqueous interface of the membrane and does not cross the membrane spontaneously. Also, we believe that the energy barrier of 40-60 kJ/mol is not prohibitively high and Dyngo-4a molecules may still overcome the barrier eventually, though we expect majority to reside in the upper leaflet.

The authors should experimentally measure the permeation of Dyngo through bilayers (or lack thereof), to more robustly support their finding that Dyngo does not cross membranes spontaneously.

We thank the reviewer for the suggestion, however this if very technically challenging and would require establishment of precise systems which is beyond the scope of this manuscript.

(4) Why not measure effect of Dyngo on lipid packing directly and more broadly in model membranes?

With the added modelling experiments supporting the previous simulations and the calculated GP values from the C-Laurdan experiments on cellular plasma membrane, we do not find it necessary to include more model membranes experiments than the already existing ones on lipid monolayers and supported lipid bilayers.

(5) Statistics should not be done on individual cells (n>26), but rather on independent experiment (N=3?)

We have performed the statistics on live cell particle tracking according to previous literature on similar systems (Boucrot et al., 2011; Larsson et al., 2023; Shvets et al., 2015; Stoeber et al., 2012).

(6) Fig 1G is important but rather unclear. Firstly, these kymographs are an odd way to show that the caveolae are not moving. More importantly, caveolae in normal cells have been shown to be quite stable and immobile (eg doi: 10.1074/jbc.M117.791400), yet here they are claimed to be very mobile.

Although this might be an odd and unconventional way to depict dynamic processes, we believe that this is a very illustrative way to show track stability over time in bulk rather than just a kymograph over a few structures in a cell. Furthermore, we are not claiming that caveolae are very mobile but rather the opposite very stable in agreement with previous work (Boucrot et al., 2011; Larsson et al., 2023; Mohan et al., 2015). We have now edited the text to make this even clearer.

Also, if Dyngo prevents caveolae scission, there should be more of them at the membrane - why no quantification like Fig 1C to show accumulation of caveolae upon Dyngo treatment? Or directly counting caveolae via EM, as in Fig 4C?

We are currently preforming CTxB HRP experiments using EM but due to reasons out of our control we have not managed to finish these on time, they will be included in the manuscript once they are ready in hopefully not too long. However, Dynasore has previously been shown, by EM, to increase the number of caveolae at the PM (Moren et al., 2012; Sinha et al., 2011).

(7) The writing can be made more precise and referencing could be strengthened.

The introduction was written in a short format, and we have now extended this and made it more precise.

Some examples:

(a) 'scissoned' is not a word in English,

Thanks, we have now changed this.

(b) what is meant by "Cav1 assembly is driven by high chol content"? There are many types of caveolin assemblies.

We agree that this can be made more precise and have now clarified this in the introduction.

(c) "This generates a unique membrane domain with distinct lipid packing and a very high curvature." Unclear what 'this' refers to and there is no reference here, so what is the evidence for either of these claims? Caveolin-8S oligomers are not curved. Perhaps 'this' is caveolae, but they are relatively large and also not very highly curved and I am unaware of measurements of lipid packing therein.

Caveolae are around 50 nm which in biology is a very high curvature of a membrane. It has been extensively proven that caveolae have a distinct lipid composition highly enriched in cholesterol and sphingolipids, which thereby also will generate a unique lipid packing as compared to the surrounding membrane. Yet, the reviewer is correct that lipid packing has not been measured in a caveola for obvious technical challenges. Thus, we have now changed the text to “special lipid composition”.

The sentence following that one again makes a specific, but unreferenced, claim.

(d) intro claims that lipid packing is critical for fission, but it is unclear quite what is meant by this claim. The references do not help, as they are often about the basic biophysics of lipids, rather than how packing affects fission.

We have now edited the text.  

(e) intro strongly implies that caveolae remain membrane attached because of stalled scission. How strong is the evidence for this? The fact that EHD2 is at the neck is not definitive,

We used the term stalled scission to describe that all omega shaped membrane invaginations do not scission in the same automatic way as clathrin coated vesicles. We have now changed this in the text. Caveolae are shown to be released (undergo scission) and be detected as internal caveolae if the protein EHD2 is removed. Hence this must be interpreted as if EHD2 stalls scission. The evidence includes data compiled over the last 12 years from others and us which include for example: 1) Caveolae with EHD2 have a longer duration time (Larsson et al., 2023; Mohan et al., 2015; Moren et al., 2012; Stoeber et al., 2012), Knock down of EHD2 results in more internalized caveolae as measured by CTxB HRP using EM (Moren et al., 2012) and shorter duration time at the PM (Hubert et al., 2020; Larsson et al., 2023; Mohan et al., 2015; Stoeber et al., 2012). 2) EHD2 overexpression results in less internalized caveolae as measured by CTxB HRP using EM (Stoeber et al., 2012). Furthermore, 3) overexpression or acute addition of purified EHD2 via microinjection counteracts lipid induced scission of caveolae and hence result in caveolae stabilization at the PM (Hubert et al., 2020). It is very hard to see that the release and internalization of caveolae could result from anything else than that these have undergone scission. EHD2 has been found around the rim of caveolae (Matthaeus et al., 2022) and overexpression of EHD2 oligomerizing mutants have been shown to expand the caveola neck (Hoernke et al., 2017; Larsson et al., 2023).

(f) unclear what is meant by 'lipid packing frustration' and how Dyngo supposedly induces it.

Lipid packing frustration refers to what is usually referred to as lipid packing defect, but since lipid membranes are describe as a fluid system it should not have defects whereby, we believe that lipid packing frustration is more accurate. However, we have now changed the text and use “decreased lipid packing” or “decreased lipid order” more thoroughly to describe the effect on the plasma membrane.

(8) IF of Cav1 is insufficient to claim puncta as caveolae. Co-stained puncta of caveolin with cavin are much stronger evidence. Same issue for Cav1-GFP puncta.

We agree and have now provided IF showing cavin1 and EHD2 colocalization to Cav1GFP in non and Dyngo-4a-treated cells.

(9) Fig 3E claims that "preferred position of Dyngo-4a was closer to the head groups" but the minimum looks to be in similar place as Fig 3B without cholesterol. Response:

We appreciate the reviewer’s observation. The PMF minima in the POPC and POPC:Chol membranes are indeed close in absolute position (~1.1–1.2 nm from the bilayer center). However, as clarified in the revised text, the presence of cholesterol leads to a slight shift of Dyngo-4a closer to the headgroup region and broadens the positional distribution. This is also evident from the added density profiles (Fig. S3A) and is now described more precisely in the manuscript.

Critically, these results do not support the notion that Dyngo affects lipid packing sufficiently, which is not measured in the simulations (though could be).

We thank the reviewer for the excellent suggestion. In response, we have now included a detailed analysis of Dyngo-4a’s effect on lipid packing in the simulations. As described in the revised manuscript, we measured deuterium order parameters, area per lipid (APL), and lipid–Dyngo–cholesterol spatial distributions (Figs. 3-H, S3C-E). The results demonstrate that Dyngo-4a decreases lipid order in POPC:Chol membranes. Both single molecules and clusters reduce the order parameter by up to 0.04 units, particularly in the upper leaflet, where Dyngo-4a reside.The reduction is most pronounced in the midchain region of the sn1 tail and around the double bond of the sn2 tail. These effects were accompanied by increased APL in POPC:Chol membranes and by colocalization of Dyngo-4a near cholesterol-rich regions. Together, these data confirm that Dyngo-4a perturbs membrane organization and lipid packing in a composition-dependent manner. We believe these additions directly address the concern and demonstrate that the simulations indeed support the conclusion that Dyngo-4a modulates lipid packing.

Finally, the simulation data do not show "that Dyngo-4a is competing with cholesterol"; it is unclear what 'competition' means in this context, but regardless, the data only shows that Dyngo sits at a similar location as cholesterol.

We agree with the reviewer that “competition” was an imprecise term. We have rephrased the relevant sections to clarify that Dyngo-4a and cholesterol localize to overlapping regions and exhibit spatial coordination. As now stated in the manuscript, cholesterol appears to partially displace Dyngo-4a from its preferred depth seen in pure POPC, broadens its membrane distribution, and alters lipid packing. According to the order parameters there is an interplay between chol and Dyngo-4a and the heatmaps show that the distribution of chol in the membrane gets less uniform in the presence of Dyngo-4a. These interactions suggest that Dyngo-4a perturbs cholesterol-rich domains.

As new analysis routines were added to the study, we have now also added the details on those to the Methods section of the text.

(10) AFM measures the stiffness of the cell (as correctly explained in Results section) not "overall stiffness of the PM" as stated in the Discussion.

We thank the reviewer for pointing this out, we have now altered this in the discussion section.

(11) Fig2A: what was the starting lipid surface pressure? How does Dyngo insertion depend on initial lipid packing?

The starting pressure lipid pressure was 20 mN m<sup>-1</sup which we now have incorporated in the figure legend. We performed several such experiments with a starting pressure ranging from 20-23 mN m^-1 showing consistent results which we described in the materials and methods section. Given that we also performed QCMD analysis and simulations on bilayers showing that Dyngo-4a adsorbed and inserted respectively, we have not performed a titration of starting pressures resulting in a MIP of Dygo-4a.

(12) Fig 4B is a strange approach to measure membrane motion. Why not RMSD or some other displacement based method? As its shown, it implies that the area of the cell changes.

The method that we used to quantify the area of the cell which is attached (or close to) the glass and thereby is visible in TIRF microscopy. This is area indeed changes over time which has been frequently observed and used to describe and quantify the mobility, lamellipodia and filopodia formation among other things. We agree that RMSD can also be used to analyze the data before and after treatments and we have now included RMSD­­­­ analysis in the manuscript.

Reviewer #3 (Significance):

The title, abstract, and introduction of the manuscript are largely framed around lipid packing, but most of the data investigate other unexpected effects of treating cells with Dyngo4a. The only measurement for lipid packing (or any other membrane properties) is Fig 4E-F. Therefore, this paper is effectively an investigation of an artefact of a common reagent, which itself could be a valuable contribution. However, the mechanism to explain its effect requires stronger evidence, and its broad biological significance needs further exploration.

Overall, the impact of documenting the effects of Dyngo4a on membranes appears modest but may be valuable to the membrane trafficking community.

Barucha-Kraszewska, J., S. Kraszewski, and C. Ramseyer. 2013. Will C-Laurdan dethrone Laurdan in fluorescent solvent relaxation techniques for lipid membrane studies? Langmuir. 29:1174-1182.

Boucrot, E., M.T. Howes, T. Kirchhausen, and R.G. Parton. 2011. Redistribution of caveolae during mitosis. J Cell Sci. 124:1965-1972.

Hoernke, M., J. Mohan, E. Larsson, J. Blomberg, D. Kahra, S. Westenhoff, C. Schwieger, and R. Lundmark. 2017. EHD2 restrains dynamics of caveolae by an ATP-dependent, membrane-bound, open conformation. Proc Natl Acad Sci U S A. 114:E4360-E4369.

Hubert, M., E. Larsson, N.V.G. Vegesna, M. Ahnlund, A.I. Johansson, L.W. Moodie, and R. Lundmark. 2020. Lipid accumulation controls the balance between surface connection and scission of caveolae. Elife. 9.

Larsson, E., B. Moren, K.A. McMahon, R.G. Parton, and R. Lundmark. 2023. Dynamin2 functions as an accessory protein to reduce the rate of caveola internalization. J Cell Biol. 222.

Matthaeus, C., K.A. Sochacki, A.M. Dickey, D. Puchkov, V. Haucke, M. Lehmann, and J.W. Taraska. 2022. The molecular organization of differentially curved caveolae indicates bendable structural units at the plasma membrane. Nat Commun. 13:7234.

McCluskey, A., J.A. Daniel, G. Hadzic, N. Chau, E.L. Clayton, A. Mariana, A. Whiting, N.N. Gorgani, J. Lloyd, A. Quan, L. Moshkanbaryans, S. Krishnan, S. Perera, M. Chircop, L. von Kleist, A.B. McGeachie, M.T. Howes, R.G. Parton, M. Campbell, J.A. Sakoff, X. Wang, J.Y. Sun, M.J. Robertson, F.M. Deane, T.H. Nguyen, F.A. Meunier, M.A. Cousin, and P.J. Robinson. 2013. Building a better dynasore: the dyngo compounds potently inhibit dynamin and endocytosis. Traffic. 14:1272-1289.

Mohan, J., B. Moren, E. Larsson, M.R. Holst, and R. Lundmark. 2015. Cavin3 interacts with cavin1 and caveolin1 to increase surface dynamics of caveolae. J Cell Sci. 128:979-991.

Moren, B., C. Shah, M.T. Howes, N.L. Schieber, H.T. McMahon, R.G. Parton, O. Daumke, and R. Lundmark. 2012. EHD2 regulates caveolar dynamics via ATP-driven targeting and oligomerization. Mol Biol Cell. 23:1316-1329.

Shvets, E., V. Bitsikas, G. Howard, C.G. Hansen, and B.J. Nichols. 2015. Dynamic caveolae exclude bulk membrane proteins and are required for sorting of excess glycosphingolipids. Nat Commun. 6:6867.

Sinha, B., D. Koster, R. Ruez, P. Gonnord, M. Bastiani, D. Abankwa, R.V. Stan, G. Butler-Browne, B. Vedie, L. Johannes, N. Morone, R.G. Parton, G. Raposo, P. Sens, C. Lamaze, and P. Nassoy. 2011. Cells respond to mechanical stress by rapid disassembly of caveolae. Cell. 144:402-413.

Stoeber, M., I.K. Stoeck, C. Hanni, C.K. Bleck, G. Balistreri, and A. Helenius. 2012. Oligomers of the ATPase EHD2 confine caveolae to the plasma membrane through association with actin. EMBO J. 31:2350-2364.

eLife Assessment

This manuscript describes a novel method for determining the mechanical parameters of the kinesin, KIF1A, that uses fluorescence microscopy and does not require an optical tweezer apparatus. The length of a tethered fluorescent DNA nanospring is measured as the kinesin moves processively along the microtubule and then stalls. The work reports important findings, and (barring a few exceptions) the evidence supporting the claims is generally convincing.

Reviewer #1 (Public review):

Summary:

This study uses a novel DNA origami nanospring to measure the stall force and other mechanical parameters of the kinesin-3 family member, KIF1A, using light microscopy. The key is to use SNAP tags to tether a defined nanospring between a motor-dead mutant of KIF5B and the KIF1A to be integrated. The mutant KIF5B binds tightly to a subunit of the microtubule without stepping, thus creating resistance to the processive advancement of the active KIF1A. The nanospring is conjugated with 124 Cy3 dyes, which allows it to be imaged by fluorescence microscopy. Acoustic force spectroscopy was used to measure the relationship between the extension of the NS and force as a calibration. Two different fitting methods are described to measure the length of the extension of the NS from its initial diffraction-limited spot. By measuring the extension of the NS during an experiment, the authors can determine the stall force. The attachment duration of the active motor is measured from the suppression of lateral movement that occurs when the KIF1A is attached and moving. There are numerous advantages of this technology for the study of single molecules of kinesin over previous studies using optical tweezers. First, it can be done using simple fluorescence microscopy and does not require the level of sophistication and expense needed to construct an optical tweezer apparatus. Second, the force that is experienced by the moving KIF1A is parallel to the plane of the microtubule. This regime can be achieved using a dual beam optical tweezer set-up, but in the more commonly used single-beam set-up, much of the force experienced by the kinesin is perpendicular to the microtubule. Recent studies have shown markedly different mechanical behaviors of kinesin when interrogated by the two different optical tweezer configurations. The data in the current manuscript are consistent with those obtained using the dual-beam optical tweezer set-up. In addition, the authors study the mechanical behavior of several mutants of KIF1A that are associated with KIF1A-associated neurological disorder (KAND).

Strengths:

The technique should be cheaper and less technically challenging than optical tweezer microscopy to measure the mechanical parameters of molecular motors. The method is described in sufficient detail to allow its use in other labs. It should have a higher throughput than other methods.

Weaknesses:

The experimenter does not get a "real-time" view of the data as it is collected, which you get from the screen of an optical tweezer set-up. Rather, you have to put the data through the fitting routines to determine the length of the nanospring in order to generate the graphs of extension (force) vs time. No attempts were made to analyze the periods where the motor is actually moving to determine step-size or force-velocity relationships.

Reviewer #2 (Public review):

Summary:

This work is important because it complements other single-molecule mechanics approaches, in particular optical trapping, which inevitably exerts off-axis loads. The nanospring method has its own weaknesses (individual steps cannot be seen), but it brings new clarity to our picture of KIF1A and will influence future thinking on the kinesins-3 and on kinesins in general.

Strengths:

By tethering single copies of the kinesin-3 dimer under test via a DNA nanospring to a strong binding mutant dimer of kinesin-1, the forces developed and experienced by the motor are constrained into a single axis, parallel to the microtubule axis. The method is imaging-based, which should improve accessibility. In principle, at least, several single-motor molecules can be simultaneously tested. The arrangement ensures that only single molecules can contribute. Controls establish that the DNA nanospring is not itself interacting appreciably with the microtubule. Forces are convincingly calibrated, and reading the length of the nanospring by fitting to the oblate fluorescent spot is carefully validated. The excursions of the wild-type KIF1A leucine zipper-stabilised dimer are compared with those of neuropathic KIF1A mutants. These mutants can walk to a stall plateau, but the force is much reduced. The forces from mutant/WT heterodimers are also reduced.

Weaknesses:

The tethered nanospring method has some weaknesses; it only allows the stall force to be measured in the case that a stall plateau is achieved, and the thermal noise means that individual steps are not apparent. The nanospring does not behave like a Hookean spring - instead linearly increasing force is reported by exponentially smaller extensions of the nanospring under tension. The estimated stall force for Kif1A (3.8 pN) is in line with measurements made using 3-bead optical trapping, but those earlier measurements were not of a stall plateau, but rather of limiting termination (detachment) force, without a stall plateau. More confidence in the 3.7 pN stall plateau determined in the current work could be obtained by demonstrating that a stall at a higher force is obtained using the nanospring method on kinesin-1, which stalls at >7 pN in single bead optical trapping.

Author response:

Reviewer #1 (Public review):

We greatly appreciate Reviewer #1’s accurate public review of our study on the kinesin motor using the DNA origami nanospring (NS). With respect to the strengths, we fully agree with Reviewer #1’s comments. Regarding the weakness, we would like to respond as follows.

It is true that, unlike optical tweezers, our method does not provide real-time data display. Optical tweezers enable real-time observation and manipulation of kinesin molecules at arbitrary time points. Achieving real-time observation and manipulation is indeed an important challenge for the future development of the NS technique. On the other hand, Iwaki et al. (our co-corresponding author) has already investigated dynamic properties of motor proteins under load, such as step size and force–velocity relationship of myosin VI using NS. We are now preparing high spatiotemporal resolution microscopy experiments on the KIF1A system to measure its step size and force–velocity relationship, which inherently require such resolution.

Reviewer #2 (Public review):

We would like to thank Reviewer #2 for providing a highly accurate assessment of the strengths of our experiments. Regarding the weaknesses, we would like to respond as follows.

First, Iwaki et al. (our co-corresponding author) have already succeeded in observing the stepping motion of myosin VI using the nanospring (NS) in their previous work. We are also currently preparing high spatiotemporal resolution microscopy experiments to observe the stepping motion of KIF1A in our system. Second, while it is true that the NS does not follow Hooke’s law, it is possible to design and construct NSs with an appropriate dynamic range by tuning the spring constant to match the forces exerted by protein molecules. Finally, we agree that our first observation of the stall plateau in KIF1A using the NS is a meaningful achievement. However, with respect to the suggestion that “increasing validity requires also studying kinesin-1,” we have a somewhat different perspective. The validity of the NS method has already been thoroughly examined in the previous work on myosin VI by Iwaki et al., where results were compared with those obtained using optical tweezers. Moreover, the focus of this manuscript is on KAND caused by KIF1A mutations. From this perspective, although we appreciate the suggestion, we consider it important to keep the present study focused on KIF1A and its implications for KAND.

eLife Assessment

This paper presents valuable findings on how autophagosomes are positioned along microtubules for their efficient fusion with lysosomes, providing significant insights into the mechanism. The evidence supporting the conclusions is solid, with high-quality fluorescence microscopy combined with Drosophila genetics. This work will be of broad interest to cell biologists interested in autophagy and related cell biology fields.

Reviewer #1 (Public review):

Summary:

It is well known that autophagosomes/autolysosomes move along microtubules. However, as these previous studies did not distinguish between autophagosomes and autolysosomes, it remains unknown whether autophagosomes begin to move after fusion with lysosomes or even before fusion. In this manuscript, the authors show using fusion-deficient vps16a RNAi cells that both pre-fusion autophagosomes and lysosomes can move along the microtubules towards the minus end. This was confirmed in snap29 RNAi cells. By screening motor proteins and Rabs, the authors found that autophagosomal traffic is primarily regulated by the dynein-dynactin system and can be counter-regulated by kinesins. They also show that Rab7-Epg5 and Rab39-ema interactions are important for autophagosome trafficking.

Strengths:

This study uses reliable Drosophila genetics and high-quality fluorescence microscopy. The data are properly quantified and statistically analyzed. It is a reasonable hypothesis that gathering pre-fusion autophagosomes and lysosomes in close proximity improves fusion efficiency.

Weaknesses:

(1) This study investigates the behavior of pre-fusion autophagosomes and lysosomes using fusion-incompetent cells (e.g., vps16a RNAi cells). However, the claim that these cells are truly fusion-incompetent relies on citations from previous studies. Since this is a foundational premise of the research, it should be rigorously evaluated before interpreting the data. It's particularly awkward that the crucial data for vps16a RNAi is only presented at the very end of Figure 10-S1; this should be among the first data shown (the same for SNAP29). It would be important to determine the extent to which autophagosomes and lysosomes are fusing (or tethered in close proximity), within each of these cell lines.

(2) In the new Figures 8 and 9, the authors analyze autolysosomes without knocking down Vps16A (i.e., without inhibiting fusion). However, as this reviewer pointed out in the previous round, it is highly likely that both autophagosomes and autolysosomes are present in these cells. This is particularly relevant given that the knockdown of dynein-dynactin, Rab7, and Epg5 only partially inhibits the fusion of autophagosomes and lysosomes (Figure 10H). If the goal is to investigate the effects of fusion, it would be more appropriate to analyze autolysosomes and autophagosomes separately. The authors mention that they can differentiate these two structures based on the size of mCherry-Atg8a structures. If this is the case, they should perform separate analyses for both autophagosomes and autolysosomes.

(3) This is also a continued Issue from the previous review. The authors suggest that autophagosome movement is crucial for fusion, based on the observed decrease in fusion rates in Rab7 and Epg5 knockdown cells (Fig. 10). However, this conclusion is not well supported. It is known that Rab7 and Epg5 are directly involved in the fusion process itself. Therefore, the possibility that the observed decrease is simply due to a direct defect in fusion, rather than an impairment of movement, has not been ruled out.

(4) The term "autolysosome maturation" appears multiple times, yet its meaning remains unclear. Does it refer to autolysosome formation (autophagosome-lysosome fusion), or does it imply a further maturation process occurring after autolysosome formation? This is not a commonly used term in the field, so it requires a clear definition.

(5) In Figure 1-S1D, the authors state that the disappearance of the mCherry-Atg8a signal after atg8a RNAi indicates that the observed structures are not non-autophagic vacuoles. This reasoning is inappropriate. Naturally, knocking down Atg8 will abolish its signal, regardless of the nature of the vacuoles. This does not definitively distinguish autophagic from non-autophagic structures.

Reviewer #2 (Public review):

Summary:

This manuscript by Boda et al. describes the results of a targeted RNAi screen in the background of Vps16A-depleted Drosophila larval fat body cells. In this background, lysosomal fusion is inhibited, allowing the authors to analyze the motility and localization specifically of autophagosomes, prior to their fusion with lysosomes to become autolysosomes. In this Vps16A-deleted background, mCherry-Atg8a labeled autophagosomes accumulate in the perinuclear area, through an unknown mechanism.

The authors found that depletion of multiple subunits of the dynein/dynactin complex caused an alternation of this mCherry-Atg8a localization, moving from the perinuclear region to the cell periphery. Interactions with kinesin overexpression suggest these motor proteins may compete for autophagosome binding and transport. The authors extended these findings by examining potential upstream regulators including Rab proteins and selected effectors, and they also examined effects on lysosomal movement and autolysosome size. Altogether, the results are consistent with a model in which specific Rab/effector complexes direct movement of lysosomes and autophagosomes toward the MTOC, promoting their fusion and subsequent dispersal throughout the cell.

Strengths:

Although previous studies of the movement of autophagic vesicles have identified roles for microtubule-based transport, this study moves the field forward by distinguishing between effects on pre- and post-fusion autophagosomes, and by its characterization of the roles of specific Dynein, Dynactin, and Rab complexes in regulating movement of distinct vesicle types. Overall, the experiments are well controlled, appropriately analyzed, and largely support the authors' conclusions..

Weaknesses:

One limitation of the study is the genetic background that serves as basis for the screen. In addition to preventing autophagosome-lysosome fusion, disruption of Vps16A has been shown to inhibit endosomal maturation and to block trafficking of components to the lysosome from both the endosome and Golgi apparatus. Additional effects previously reported by the authors include increased autophagosome production and reduced mTOR signaling. Thus Vps16A-depleted cells have a number of endosome, lysosome and autophagosome-related defects, with unknown downstream consequences. Additionally, the cause and significance of the perinuclear localization of autophagosomes in this background is unclear. Thus, interpretations of the observed reversal of this phenotype are difficult, and have the caveat that they may apply only to this condition, rather than to normal autophagosomes. Additional experiments to observe autophagosome movement or positioning in a more normal environment would improve the manuscript.

Comments on revision:

The revised manuscript and author responses have satisfactorily met my concerns. I have no further issues and congratulate the authors on this work.

Reviewer #3 (Public review):

Summary:

In multicellular organisms, autophagosomes are formed throughout the cytosol, while late endosomes/lysosomes are relatively enriched in the perinuclear region. It is known that autophagosomes gain access to the lysosome-enriched region by microtubule-based trafficking. The mechanism by which autophagosomes move along microtubules remains incompletely understood. In this manuscript, Péter Lőrincz and colleagues investigated the mechanism driving the movement of nascent autophagosomes along microtubule towards non-centrosomal microtubule organizing center (ncMTOC) using fly fat body as a model system. The authors took an approach by examining autophagosome positioning in cells where autophagosome-lysosome fusion was inhibited by knocking down the HOPS subunit Vps16A. Despite being generated at random positions in the cytosol, autophagosomes accumulate around the nucleus when Vps16A is depleted. They then performed an RNA interference screen to identify the factors involved in autophagosome positioning. They found that the dynein-dynactin complex is required for trafficking of autophagosomes toward ncMTOC. Dynein loss leads to the peripheral relocation of autophagosomes. They further revealed that a pair of small GTPases and their effectors, Rab7-Epg5 and Rab39-ema, are required for bidirectional autophagosome transport. Knockdown of these factors in Vps16a RNAi cells causes scattering of autophagosomes throughout the cytosol.

Strengths:

The data presented in this study help us to understand the mechanism underlying the trafficking and positioning of autophagosomes.

Weaknesses:

(1) The experiments were performed in Vps16A RNAi KD cells. Vps16A knockdown blocks fusion of vesicles derived from the endolysosomal compartments such as fusion between lysosomes. The pleiotropic effect of Vps16A RNAi may complicate the interpretation.

(2) In this study, the transport of autophagosomes is investigated in fly fat cells. In fat cells, a large number of large lipid droplets accumulate and the endomembrane systems are distinct from that in other cell types. The knowledge gain from this study may not apply to other cell types.

Author response:

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

Reviewer #1 (Public review):  

(1) To distinguish autophagosomes from autolysosomes, the authors used vps16 RNAi cells, which are supposed to be fusion deficient. However, the extent to which fusion is actually inhibited by knockdown of Vps16A is not shown. The co-localization rate of Atg8 and Lamp1 should be shown (as in Figure 8). Then, after identifying pre-fusion autophagosomes and lysosomes, the localization of each should be analyzed.

Thank you for this insightful comment. We analyzed the colocalization of 3xmCherry-Atg8a and GFP-Lamp1, which label autophagic structures and lysosomes, respectively, in Vps16A RNAi fat body cells. As expected, Vps16A silencing markedly reduced the overlap between these two signals, indicating a strong block in autophagosome–lysosome fusion. Moreover, both 3xmCherry-Atg8a and GFP-Lamp1 became more perinuclearly localized compared to the control (luciferase RNAi) cells.

It is also possible that autophagosomes and lysosomes are tethered by factors other than HOPS (even if they are not fused). If this is the case, autophagosomal trafficking would be affected by the movement of lysosomes.  

Thank you for raising this possibility. While we cannot fully exclude that autophagosomes might be indirectly transported via tethering to lysosomes, we consider this unlikely. We believe that in Drosophila fat cells, autophagosomes and lysosomes rapidly fuse once in close proximity. Therefore, even if alternative tethering mechanisms exist, they are unlikely to permit prolonged joint trafficking without fusion.

(2) The authors analyze autolysosomes in Figures 6 and 7. This is based on the assumption that autophagosome-lysosome fusion takes place in cells without vps16A RNAi. However, even in the presence of Vps16A, both pre-fusion autophagosomes and autolysosomes should exist. This is also true in Figure 8H, where the fusion of autophagosomes and lysosomes is partially suppressed in knockdown cells of dynein, dynactin, Rab7, and Epg5. If the effect of fusion is to be examined, it is reasonable to distinguish between autophagosomes and autolysosomes and analyze only autolysosomes.  

Thank you for this careful observation. The 3xmCherry-Atg8a reporter is well suited to identify both autophagosomes and autolysosomes, as the mCherry fluorophore is resistant to degradation in the acidic environment of autolysosomes. Notably, mCherry-Atg8a–positive autolysosomes appear larger and brighter than pre-fusion autophagosomes, which are typically smaller and dimmer, especially under fusion-deficient conditions (e.g., Figure 4). Therefore, we use these morphological differences as a proxy to distinguish between the two.

To improve structural assignment, we incorporated endogenous Lamp1 staining (Figure 10) and a Lamp1-GFP reporter (Figure 10—figure supplement 1). Vesicles positive for mCherryAtg8a but negative for Lamp1 are considered pre-fusion autophagosomes. Structures double-positive for mCherry-Atg8a and Lamp1 represent autolysosomes, while Lamp1positive, Atg8a-negative vesicles correspond to non-autophagic lysosomes. To clarify these interpretations, we revised the Results section and explained these reporters in more detail.

(3) In this study, only vps16a RNAi cells were used to inhibit autophagosome-lysosome fusion. However, since HOPS has many roles besides autophagosome-lysosome fusion, it would be better to confirm the conclusion by knockdown of other factors (e.g., Stx17 RNAi).  

Thank you for this valuable suggestion. We initially considered using Syntaxin17 RNAi; however, our recent findings indicate that loss of Syx17 results in a HOPS-dependent tethering lock between autophagosomes and lysosomes (DOI: 10.1126/sciadv.adu9605). In this case, tethered vesicles would likely move together, confounding the interpretation of autophagosome-specific trafficking.

Therefore, we turned to other SNAREs such as Vamp7 and Snap29. One Snap29 RNAi was located on the appropriate chromosome needed for our genetic experiments. We generated a transgenic fly line expressing both Snap29 RNAi and the mCherry-Atg8a reporter under a fat body-specific R4 promoter. When we tested our key trafficking hits in this background, we observed similar autophagosome localization phenotypes as in Vps16A RNAi cells. These results, now included in the revised manuscript (see Figure 6), confirm that the observed transport phenotypes are not specific to Vps16A or HOPS complex loss.

(4) Figure 8: Rab7 and Epg5 are also known to be directly involved in autophagosomelysosome tethering/fusion. Even if the fusion rate is reduced in the absence of Rab7 and Epg5, it may not be the result of defective autophagosome movement, but may simply indicate that these molecules are required for fusion itself. How do the authors distinguish between the two possibilities?

Thank you for this important point. While Rab7 and Epg5 indeed participate in autophagosome–lysosome tethering and fusion, our data suggest they also contribute to autophagosome movement. This is evident from the distinct phenotypes observed upon Rab7 or Epg5 RNAi compared to Vps16A or SNARE RNAi. Depletion of Vps16A, Syx17, Vamp7, or Snap29 (factors involved specifically in fusion) results in perinuclear accumulation of autophagosomes. In contrast, Rab7 or Epg5 RNAi leads to a dispersed autophagosome pattern throughout the cytoplasm.

These differences suggest that Rab7 and Epg5 play additional roles in positioning autophagosomes. Supporting this, our co-immunoprecipitation experiments show that Epg5 interacts with dynein motors. Therefore, we propose that Rab7 and Epg5 influence both autophagosome fusion and their microtubule-based transport.

Reviewer #2 (Public review):  

One limitation of the study is the genetic background that serves as the basis for the screening. In addition to preventing autophagosome-lysosome fusion, disruption of Vps16A has been shown to inhibit endosomal maturation and block the trafficking of components to the lysosome from both the endosome and Golgi apparatus. Additional effects previously reported by the authors include increased autophagosome production and reduced mTOR signaling. Thus Vps16A-depleted cells have a number of endosome, lysosome, and autophagosome-related defects, with unknown downstream consequences. Additionally, the cause and significance of the perinuclear localization of autophagosomes in this background is unclear. Thus, interpretations of the observed reversal of this phenotype are difficult, and have the caveat that they may apply only to this condition, rather than to normal autophagosomes. Additional experiments to observe autophagosome movement or positioning in a more normal environment would improve the manuscript.  

Thank you for highlighting this limitation. We have tried to conduct time-lapse imaging of live fat body cells expressing 3xmCherry-Atg8a and GFP-Lamp1 to visualize the movement and fusion events of pre-fusion autophagosomes (3xmCherry-Atg8a positive and GFP-Lamp1 negative) and lysosomes (GFP-Lamp1 positive). Despite different experimental setups and durations of starvation, no vesicle movement was observed at all, so live imaging of larval Drosophila fat tissue will require time-consuming optimizations of in vitro culture conditions. Consistent with this, we did not find any literature data where organelle motility in fat body cells was successfully observed. Nuclear positioning in fat body cells was investigated in detail in an excellent study, however the authors were able to observe only very little movement of the nuclei by live imaging (Zheng et al. Nat Cell Biol. 2020 Mar;22(3):297-309. doi: 10.1038/s41556-020-0470-7), further highlighting the technical difficulties of live or timelapse imaging in this tissue type.

Specific comments  

(1) Several genes have been described that when depleted lead to perinuclear accumulation of Atg8-labeled vesicles. There seems to be a correlation of this phenotype with genes required for autophagosome-lysosome fusion; however, some genes required for lysosomal fusion such as Rab2 and Arl8 apparently did not affect autophagosome positioning as reported here. Thus, it is unclear whether the perinuclear positioning of autophagosomes is truly a general response to disruption of autophagosome-lysosome fusion, or may reflect additional aspects of Vps16A/HOPS function. A few things here would help. One would be an analysis of Atg8a vesicle localization in response to the depletion of a larger set of fusionrelated genes. Another would be to repeat some of the key findings of this study (effects of specific dynein, dynactin, rabs, effectors) on Atg8a localization when Syx17 is depleted, rather than Vps16A. This should generate a more autophagosome-specific fusion defect.  

Thank you for this insightful suggestion. We recently discovered that Syx17 depletion induces a HOPS-dependent tethering lock between autophagosomes and lysosomes (DOI: 10.1126/sciadv.adu9605), making it unsuitable for modeling autophagosome-specific fusion defects. In contrast, Vamp7 and Snap29 knockdowns do not appear to cause such tethering lock. We were able to generate a suitable Drosophila line using a Snap29 RNAi transgene located on a compatible chromosome. Upon testing key hits from our screen in this background, we found that autophagosomes redistributed similarly, supporting our conclusions. These new results have been included in the revised manuscript (see Figure 6)

Third, it would greatly strengthen the findings to monitor pre-fusion autophagosome localization without disrupting fusion. Such vesicles could be identified as Atg8a-positive Lamp-negative structures. The effects of dynein and rab depletion on the tracking of these structures in a post-induction time course would serve as an important validation of the authors' findings.  

Thank you for this helpful suggestion. As described above, we attempted time-lapse imaging of 3xmCherry-Atg8a and GFP-Lamp1-expressing fat body cells under various conditions to identify motile pre-fusion autophagosomes. However, we did not observe any vesicle movement, regardless of the starvation duration or experimental setup. As this likely reflects technical limitations of ex vivo fat body imaging, we were unable to achieve live tracking of autophagosome dynamics without introducing perturbations. This limitation is now discussed in the revised manuscript.

(2) The authors nicely show that depletion of Shot leads to relocalization of Atg8a to ectopic foci in Vps16A-depleted cells; they should confirm that this is a mislocalized ncMTOC by colabeling Atg8a with an MTOC component such as MSP300. The effect of Shot depletion on Atg8a localization should also be analyzed in the absence of Vps16A depletion.  

Thank you for this positive comment. We co-labeled Atg8a with the minus-end microtubule marker Khc-nod-LacZ in both shot single knockdown and shot; vps16A double knockdown cells. Ectopic Khc-nod-LacZ-positive MTOC foci were clearly visible in both conditions, and Atg8a-positive autophagosomes accumulated around these structures. These findings confirm that Shot depletion induces ectopic MTOC formation, which correlates with autophagosome relocalization. The new data have been incorporated into the revised manuscript (see Figure 1O-S).

(3) The authors report that depletion of Dynein subunits, either alone (Figure 6) or codepleted with Vps16A (Figure 2), leads to redistribution of mCherry-Atg8a punctae to the "cell periphery". However, only cell clones that contact an edge of the fat body tissue are shown in these figures. Furthermore, in these cells, mCherry-Atg8a punctae appear to localize only to contact-free regions of these cells, and not to internal regions of clones that share a border with adjacent cells. Thus, these vesicles would seem to be redistributed to the periphery of the fat body itself, not to the periphery of individual cells. Microtubules emanating from the perinuclear ncMTOC have been described as having a radial organization, and thus it is unclear that this redistribution of mCherry-Atg8a punctae to the fat body edge would reflect a kinesin-dependent process as suggested by the authors.  

Thank you for this detailed observation. We frequently observe autophagosomes accumulating in contact-free peripheral regions of dynein-depleted cells, resulting in an asymmetric distribution. While previous studies describe a radial microtubule organization in fat body cells, none of them directly label MT plus ends, the direction of kinesin-based transport.

To further explore this, we overexpressed a HA-tagged kinesin, Klp98A-3xHA, in both control and Vps16A RNAi backgrounds. Immunolabeling revealed that Klp98A localizes to the contact-free peripheral regions in both conditions, consistent with the distribution of autophagosomes in dynein knockdown cells. This supports our interpretation that kinesindependent transport drives autophagosome redistribution in the absence of dynein, and that fat body cells exhibit subtle asymmetries in MT polarity that influence this transport. These new results have been included in the revised manuscript (see Figure 3G, H).

(4) To validate whether the mCherry-Atg8a structures in Vps16A-depleted cells were of autophagic origin, the authors depleted Atg8a and observed a loss of mCherry- Atg8a signal from the mosaic cells (Figure S1D, J). A more rigorous experiment would be to deplete other Atg genes (not Atg8a) and examine whether these structures persist.  

Thank you for the suggestion to further validate our reporter. We depleted Atg1, a key kinase required for phagophore initiation, in the Vps16A RNAi background. This completely abolished the punctate mCherry-Atg8a distribution in knockdown cells (see Figure 1—figure supplement 1E, K), confirming that the labeled structures are indeed of autophagic origin.

(5) The authors found that only a subset of dynein, dynactin, rab, and rab effector depletions affected mCherry-Atg8a localization, leading to their suggestion that the most important factors involved in autophagosome motility have been identified here. However, this conclusion has the caveat that depletion efficiency was not examined in this study, and thus any conclusions about negative results should be more conservative.  

Thank you for this constructive feedback. We agree that negative results must be interpreted conservatively due to potential differences in knockdown efficiency. We have revised our conclusions accordingly, clarifying that the factors identified are key for autophagosome motility, while acknowledging the possibility of false negatives.

Reviewer #3 (Public review):  

Major concerns:

(1) The localization of EPG5 should be determined. The authors showed that EPG5 colocalizes with endogenous Rab7. Rab7 labels late endosomes and lysosomes. Previous studies in mammalian cells have shown that EPG5 is targeted to late endosomes/lysosomes by interacting with Rab7. EPG5 promotes the fusion of autophagosomes with late endosomes/lysosomes by directly recognizing LC3 on autophagosomes and also by facilitating the assembly of the SNARE complex for fusion. In Figure 5I, the EPG5/Rab7colocalized vesicles are large and they are likely to be lysosomes/autolysosomes.

Thank you for suggesting to improve our Epg5 localization data. We performed triple immunostaining for Atg8a, Lamp1-3xmCherry, and Epg5-9xHA in S2R+ cells. In addition to triple-positive structures—likely representing autolysosomes—we observed Atg8a and Epg59xHA double-positive vesicles that lacked Lamp1-3xmCherry signal, which likely correspond to pre-fusion autophagosomes. Based on these results, we propose that in addition to arriving via the endocytic route, Epg5 may also reach lysosomes through autophagosomes. These findings have been included in the revised manuscript (see Figure 5K).

(2) The experiments were performed in Vps16A RNAi KD cells. Vps16A knockdown blocks fusion of vesicles derived from the endolysosomal compartments such as fusion between lysosomes. The pleiotropic effect of Vps16A RNAi may complicate the interpretation. The authors need to verify their findings in Stx17 KO cells, as it has a relatively specific effect on the fusion of autophagosomes with late endosomes/lysosomes.  

Thank you for this valuable suggestion. We initially considered Syntaxin17 for validation; however, we recently found that loss of Syx17 leads to a HOPS-dependent tethering lock between autophagosomes and lysosomes, which would confound interpretation, as autophagosomes remain tethered to lysosomes (DOI: 10.1126/sciadv.adu9605). Therefore, Syntaxin17 loss is not suitable for our purpose. Among the remaining fusion SNAREs, one RNAi line targeting Snap29 was available on a compatible chromosome for generating Drosophila lines equivalent to those used in the screen. We established this Snap29 RNAicontaining tester line and crossed it with our top hits. We observed that autophagosome motility was comparable to that in the Vps16A RNAi background, further supporting our conclusions. These results have been incorporated into the revised manuscript (see Figure 6)

(3) Quantification should be performed in many places such as in Figure S4D for the number of FYVE-GFP labeled endosomes and in Figures S4H and S4I for the number and size of lysosomes.  

Thank you for pointing this out. We performed the suggested quantifications and statistical analyses for FYVE-GFP labeled endosomes, as well as for the number and size of lysosomes. The updated data are now presented in the revised Figure 5—figure supplement 1.

(4) In this study, the transport of autophagosomes is investigated in fly fat cells. In fat cells, a large number of large lipid droplets accumulate and the endomembrane systems are distinct from that in other cell types. The knowledge gained from this study may not apply to other cell types. This needs to be discussed.

Thank you for raising this important point. We agree that our findings may not be fully generalizable to all cell types. Given that the organization of the microtubule network depends on both cell function and developmental stage, it is plausible that the molecular machinery described here operates differently elsewhere. We now mention this limitation in the Discussion.

Minor concerns:  

(5) Data in some panels are of low quality. For example, the mCherry-Atg8a signal in Figure 5C is hard to see; the input bands of Dhc64c in Figure 5L are smeared.  

Thank you for pointing this out. We repeated the experiment shown in Figure 5C and replaced the panel with a clearer image. The smeared Dhc64C input bands in Figure 5L result from the unusually large size of this protein, which affects its electrophoretic migration. We mentioned this point in the corresponding figure legend.

(6) In this study, both 3xmCherry-Atg8a and mCherry-Atg8a were used. Different reporters make it difficult to compare the results presented in different figures.  

Thank you for this comment. Both 3xmCherry-Atg8a and mCherry-Atg8a are well-established reporters that behave similarly as autophagic markers. Nevertheless, to avoid confusion, we ensured that each figure uses only one type of reporter consistently, which is now clearly indicated in the revised manuscript.

(7) The small autophagosomes presented in Figures such as in Figure 1D and 1E are not clear. Enlarged images should be presented.  

Thank you for your suggestion. We repeated these experiments and replaced the relevant panels with higher-quality images, including enlarged insets to better visualize small autophagosomes. These updated figures are now included in the revised manuscript.

(8) The authors showed that Epg5-9xHA coprecipitates with the endogenous dynein motor Dhc64C. Is Rab7 required for the interaction?  

Thank you for this insightful question. We tested this by co-transfecting S2R+ cells with Epg5-9xHA and different forms of Rab7: wild-type, GTP-locked (constitutively active), and GDP-locked (dominant-negative). Our results indicate that the strength of Epg5-Dhc interaction does not change in the presence of either GTP-locked or GDP-locked Rab7. However, we believe that Epg5 and dynein are recruited to the vesicle membranes via Rab7 in vivo, so we did not include these results in the revised manuscript.

(9) The perinuclear lysosome localization in Epg5 KD cells has no indication that Epg5 is an autophagosome-specific adaptor.

Thank you for this important comment. Accordingly, we have toned down our statements about Epg5 functions throughout the revised manuscript.

Reviewer #1 (Recommendations for the authors):  

(1) Figure 6: What do "autolysosome maturation" and "small autolysosomes" mean? Do different numbers of lysosomes fuse to a single autophagosome?

Thank you for highlighting this point. We concluded that the formation of smaller autolysosomes—compared to controls—is likely due to a defect in autolysosome maturation, as is often the case. We had not explicitly considered whether a different number of lysosomes fuse with each autophagosome during this process. We clarified this issue in the revised manuscript.

(2) Figure 5A shows that the localization of endogenous Atg8 requires Epg5, but the data is not as clear as for mCherry-Atg8 (Figure 4B). Why the difference?  

Thank you for this question. The difference arises because the mCherry-Atg8a reporter strongly labels autolysosomes, as the mCherry fluorophore remains stable in acidic compartments. As a result, mCherry-Atg8a labels both autophagosomes and autolysosomes, but the strong autolysosomal signal originating from the surrounding GFP negative, nonRNAi cells can make accumulated autophagosomes appear fainter in fusion-defective cells (as in Figure 4). In contrast, endogenous Atg8a is degraded in lysosomes, and therefore labels only autophagosomes. This means that the appearance of these two experiments can be slightly different, but since in both cases autophagosomes no longer accumulate in the perinuclear region of Vps16A,Epg5 double RNAi cells we can conclude that Epg5 is required for autophagosome positioning. We explained this difference of the two methods in the revised manuscript where it first appears (Figure 1B and Figure 1—figure supplement 1A).

(3) Blue letters on the black micrographs are hard to see. Some of the other letters are also small and hard to read.  

Thank you for this suggestion. We improved the visibility and readability of the labels in the revised figures.

eLife Assessment

This is an important study that utilizes proteomic and genetic approaches to identify the glycoprotein quality control factor malectin as a pro-viral host protein involved in the replication of coronavirus. The evidence supporting this conclusion is convincing, although continued elucidation of the mechanistic basis of malectin-mediated viral replication would further strengthen these findings. This work will be of interest to cell biologists studying the molecular mechanisms of glycoprotein quality control and virologists studying the host-pathogen interactions.

Reviewer #1 (Public review):

In this manuscript, the authors employ a combined proteomic and genetic approach to identify the glycoprotein QC factor malectin as an important protein involved in promoting coronavirus infection. Using proteomic approaches, they show that the non-structural protein NSP2 and malectin interact in the absence of viral infection, but not in the presence of viral infection. However, both NSP2 and malectin engage the OST complex during viral infection, with malectin also showing reduced interactions with other glycoprotein QC proteins. Malectin KD reduce replication of coronaviruses, including SARS-COV2. Collectively, these results identify Malectin as a glycoprotein QC protein involved in regulating coronavirus replication that could potentially be targeted to mitigate coronavirus replication.

In the revised manuscript, the authors have addressed many of my comments from the previous submission. Notably, they've provided some additional mechanistic data, focused primarily on the activation of different stress signaling pathways, to help define malectin impacts viral replication, although this is mostly suggests that activation of these pathways may not be the main mechanism of malectin-dependent reductions in viral replication. Regardless, I'm sure this mechanism will be the focus of continued efforts on this project. They have also addressed other concerns related to interactions between OST and malectin, as well as the curious interactions between non-structural proteins with both ER and mitochondrial proteins. Overall, the authors have been responsive to my comments and comments from other reviewers, and the manuscript has been improved. It will be a good addition to eLife.

Reviewer #3 (Public review):

Summary:

In their revised manuscript, the authors addressed most of the reviewers' concerns. One concern was the emphasis on increased MLEC-OST interactions during infection, which the authors toned down in the revision. They clarified that MLEC interaction with OST is maintained-rather than increased-during infection, while its interaction with other QC factors decreases. They also added context and discussion of the co-localization of viral proteins with ER and mitochondrial proteins, noting that both nsp2 and MLEC localize to mitochondria-associated membranes (MAMs), providing a plausible explanation for these interactions.

Another concern involved the effects of MLEC KD on the cellular environment. To address this, the authors analyzed stress pathway activation and glycosylation of endogenous proteins in MLEC KD cells. They found only modest upregulation of the HSF1 pathway and no changes in the UPR or other stress responses, suggesting MLEC KD does not broadly disrupt ER proteostasis. Additionally, glycopeptide profiling showed only minor changes in host protein glycosylation, supporting a more direct role for MLEC in viral replication rather than general host glycoprotein disruption.

However, some weaknesses remain. Direct interaction between MLEC and nsp2 during infection was not detected, and the identified viral glycopeptides were limited to only five Spike sites. Furthermore, the mechanism by which MLEC promotes viral replication is still unclear.

In summary, the authors strengthened the manuscript by addressing reviewers' concerns through additional data, clarified language, and expanded discussion. While the overall support for MLEC's pro-viral role is solid, its precise mechanism of action remains speculative. Future work will be needed to directly link MLEC's activity to specific steps in viral protein biogenesis and replication.

Original summary: In this study, Davies and Plate set out to discover conserved host interactors of coronavirus non-structural proteins (Nsp). They used 293T cells to ectopically express flag-tagged Nsp2 and Nsp4 from five human and mouse coronaviruses, including SARS-CoV-1 and 2, and analyzed their interaction with host proteins by affinity purification mass-spectrometry (AP-MS). To confirm whether such interactors play a role in coronavirus infection, the authors measured the effects of individual knockdowns on replication of murine hepatitis virus (MHV) in mouse Delayed Brain Tumor cells. Using this approach, they identified a previously undescribed interactor of Nsp2, Malectin (Mlec), which is involved in glycoprotein processing and shows a potent pro-viral function in both MHV and SARS-CoV-2. Although the authors were unable to confirm this interaction in MHV-infected cells, they show that infection remodels many other Mlec interactions, recruiting it to the ER complex that catalyzes protein glycosylation (OST). Mlec knockdown reduced viral RNA and protein levels during MHV infection, although such effects were not limited to specific viral proteins. However, knockdown reduced the levels of five viral glycopeptides that map to Spike protein, suggesting it may be affected by Mlec.

Strengths:

This is an elegant study that uses a state-of-the-art quantitative proteomic approach to identify host proteins that play critical roles in viral infection. Instead of focusing on a single protein from a single virus, it compares the interactomes of two viral proteins from five related viruses, generating a high confidence dataset. The functional follow-ups using multiple live and reporter viruses, including MHV and CoV2 variants, convincingly depict a pro-viral role for Mlec, a protein not previously implicated in coronavirus biology.

Weaknesses:

Although a commonly used approach, AP-MS of ectopically expressed viral proteins may not accurately capture infection-related interactions. The authors observed Mlec-Nsp2 interactions in transfected 293T cells (1C) but were unable to reproduce those in mouse cells infected with MHV (3C). EIF4E2/GIGYF2, two bonafide interactors of CoV2 Nsp2 from previous studies, are listed as depleted compared to negative controls (S1D). Most other CoV2 Nsp2 interactors are also depleted by the same analysis (S1D). Previously reported MERS Nsp2 interactors, including ASCC1 and TCF25, are also not detected (S1D). Furthermore, although GIGYF2 was not identified as an interactor of MHV Nsp2/4 in human cells (S1D), its knockdown in mouse cells reduced MHV titers about 1000 fold (S4). The authors should attempt to explain these discrepancies.

More importantly, the authors were unable to establish a direct link between Mlec and the biogenesis of any viral or host proteins, by mass-spectrometry or otherwise. Although it is clear that Mlec promotes coronavirus infection, the mechanism remains unclear. Its knockdown does not affect the proteome composition of uninfected cells (S15B), suggesting it is not required for proteome maintenance under normal conditions. The only viral glycopeptides detected during MHV infection originated from Spike (5D), although other viral proteins are also known to be glycosylated. Cells depleted for Mlec produce ~4-fold less Spike protein (4E) but no more than 2-fold less glycosylated spike peptides (5D), compounding the interpretation of Mlec effects on viral protein biogenesis. Furthermore, Spike is not essential for the pro-viral role of Mlec, given that Mlec knockdown reduces replication of SARS-CoV-2 replicons that express all viral proteins except for Spike (6A/B).

Any of the observed effects on viral protein levels could be secondary to multiple other processes. Interventions that delay infection for any reason could lead to imbalance of viral protein levels, because Spike and other structural proteins are produced at a much higher rate than non-structural proteins due to the higher abundance of their cognate subgenomic RNAs. Similarly, the observation that Mlec depletion attenuates MHV-mediated changes to the host proteome (S15C/D) can also be attributed to indirect effects on viral replication, regardless of glycoprotein processing. In the discussion, the authors acknowledge that Mlec may indirectly affect infection through modulation of replication complex formation or ER stress, but do not offer any supporting evidence. Interestingly, plant homologs of Mlec are implicated in innate immunity, favoring a more global role for Mlec in mammalian coronavirus infections.

Finally, the observation that both Nsp2 (3C) and Mlec (3E/F) are recruited to the OST complex during MHV infection neither support nor refute any of these alternate hypotheses, given that Mlec is known to interact with OST in uninfected cells and that Nsp2 may interact with OST as part of the full length unprocessed Orf1a, as it co-translationally translocates into the ER.

Therefore, the main claims about the role of Mlec in coronavirus protein biogenesis are only partially supported.

Comments on revisions:

Figure 7B should be revised to show that MLEC maintains interactions with rather than recruited to the OST.

Author response:

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

Reviewer #1 (Public Review):

In this manuscript, the authors employ a combined proteomic and genetic approach to identify the glycoprotein QC factor malectin as an important protein involved in promoting coronavirus infection. Using proteomic approaches, they show that the non-structural protein NSP2 and malectin interact in the absence of viral infection, but not in the presence of viral infection. However, both NSP2 and malectin engage the OST complex during viral infection, with malectin also showing reduced interactions with other glycoprotein QC proteins. Malectin KD reduce replication of coronaviruses, including SARS-COV2. Collectively, these results identify Malectin as a glycoprotein QC protein involved in regulating coronavirus replication that could potentially be targeted to mitigate coronavirus replication.

Overall, the experiments described appear well performed and the interpretations generally reflect the results. Moreover, this work identifies Malectin as an important pro-viral protein whose activity could potentially be therapeutically targeted for the broad treatment of coronavirus infection. However, there are some weaknesses in the work that, if addressed, would improve the impact of the manuscript.

Notably, the mechanism by which malectin regulates viral replication is not well described. It is clear from the work that malectin is a pro-viral protein in the work presented, but the mechanistic basis of this activity is not pursued. Some potential mechanisms are proposed in the discussion, but the manuscript would be strengthened if additional insight was included. For example, does the UPR activated to higher levels in infected cells depleted of malectin? Do glycosylation patterns of viral (or non-viral) proteins change in malectindepleted cells? Additional insight into this specific question would significantly improve the manuscript.

We concur with the reviewer that the mechanism by which Malectin regulates viral replication is an important point to elucidate further. Our proteomics data were able to offer additional insight into the questions posed here. We examined the upregulation of protein markers of the UPR and other stress response pathways in cells depleted of MLEC (Fig. S15D). We find that the UPR pathways are moderately but insignificantly upregulated, while the Heat Shock Factor 1 (HSF1) pathway is moderately and significantly upregulated. The fold change increase of these marker proteins are relatively small, so while upregulation of this pathway may contribute to the suppression of CoV replication, it may not fully explain the phenotype.

In addition, to address the second question, we compared the glycosylation patterns of endogenous proteins in MLEC-KD cells (Fig. S15E-G). We found that there is a small increase in abundance of glycopeptides associated with LAMP2, SERPHINH1, RDX, RPL3/5, CADM4, and ITGB1, however these fold changes are small and tested to be insignificant. These results indicate there is relatively little modification of endogenous glycoproteins upon MLEC-depletion. These findings support a more direct role for MLEC in regulating viral replication.

We added the following section to the manuscript text to discuss these results:

“In uninfected cells, MLEC KD leads to relatively little proteome-wide changes, with MLEC being the only protein significantly downregulated and no other proteins significantly upregulated, supporting the specificity of MLEC KD in MHV suppression (Fig.  S15C). To determine whether MLEC KD alters general host proteostasis, we further examined the levels of protein markers of stress pathways based on previous gene pathway definitions(Davies et al., 2023; Grandjean et al., 2019; Shoulders et al., 2013) (Fig. S15D). We find that there are modest but significant increases in protein levels associated with the Heat Shock Factor 1 (HSF1) pathway, while the Unfolded Protein Response (UPR) pathways are largely unmodified. 

We also probed the effect of MLEC KD on endogenous protein glycosylation. We find that there is only a small increase in abundance of glycopeptides, including those associated with the ribosome (Rpl3, Rpl5), a cytoskeletal protein (Rdx), the integrin Itgb1, and the ER-resident chaperone Serphinh1 (Fig. S15E-G).”

“Our proteomics data reveals that there is only a modest increase in the Heat Shock Factor 1 (HSF1) pathway, while the Unfolded Protein Response is relatively unchanged (Fig. S15D). In addition, there are only minor increases in endogenous glycopeptide levels (Fig. S15E-G). Together, these results indicate that while MLEC KD leads to some alterations in ER proteostasis and host glycosylation, these changes are modest and may not be the primary mechanism by which MLEC KD hinders CoV replication.”

Further, the evidence for increased interactions between OST and malectin during viral infection is fairly weak, despite being a major talking point throughout the manuscript. The reduced interactions between malectin and other glycoproteostasis QC factors is evident, but the increased interactions with OST are not well supported. I'd recommend backing off on this point throughout the text, instead, continuing to highlight the reduced interactions.

We agree that the fold change increase of OST interactions with malectin are small compared to the fold change decrease of other glycoproteostasis factors We have modified the text to less emphasize this point and instead highlight the reduced interactions:

“Further, MHV infection retains the association of MLEC with the OST complex while titrating off other interactors, potentially leading to more efficient glycoprotein biogenesis.”

I was also curious as to why non-structural proteins, nsp2 and nsp4, showed robust interactions with host proteins localized to both the ER and mitochondria? Do these proteins localize to different organelles or do these interactions reflect some other type of dysregulation? It would be useful to provide a bit of speculation on this point.

We also find these ER and mitochondrial protein interactions curious, which we initially reported on (Davies, Almasy et al. 2020 ACS Infectious Diseases). In this prior report, we found that when expressed in HEK293T cells, SARS-CoV-2 nsp2 and nsp4 have partial localization to mitochondrial-associated ER membranes (MAMs), as determined by subcellular fractionation. Given that malectin has also been shown to have MAMs localization (Carreras-Sureda, et al. 2019 Nature Cell Biology), we have added additional text in the Discussion to speculate on this point:

“Additionally, MLEC has also been shown to localize to ER-mitochondria contact sites (MAMs)(Carreras-Sureda et al., 2019), which regulate mitochondrial bioenergetics. We have previously shown that SARS-CoV-2 nsp2 and nsp4 can partially localize to MAMs(Davies et al., 2020), so these viral proteins may also dysregulate MLEC and MAMs activity to promote infection.”

Again, the overall identification of malectin as a pro-viral protein involved in the replication of multiple different coronaviruses is interesting and important, but additional insights into the mechanism of this activity would strengthen the overall impact of this work.

Thank you for this endorsement. We hope the additional analyses and discussion points in the revised manuscript further homed in on a direct mechanistic function for MLEC in modulating viral replication.

Reviewer #2 (Public Review):

Summary:

A strong case is presented to establish that the endoplasmic reticulum carbohydrate binding protein malectin is an important factor for coronavirus propagation. Malectin was identified as a coronavirus nsp2 protein interactor using quantitative proteomics and its importance in the viral life cycle was supported by using a functional genetic screen and viral assays. Malectin binds diglucosylated proteins, an early glycoform thought to transiently exist on nascent chains shortly after translation and translocation; yet a role for malectin has previously been proposed in later quality control decisions and degradation targeting. These two observations have been difficult to reconcile temporally. In agreement with results from the Locher lab, the malectininteractome shown here includes a number of subunits of the oligosaccharyltransferase complex (OST). These results place malectin in close proximity to both the co-translational (STT3A or OST-A) and post-translational (STT3B or OST-B) complexes. It follows that malectin knockdown was associated with coronavirus Spike protein hypoglycosylation.

Strengths:

Strengths include using multiple viruses to identify interactors of nsp2 and quantitative proteomics along with multiple viral assays to monitor the viral life cycle.

Weaknesses:

Malectin knockdown was shown to be associated with Spike protein hypoglycosylation. This was further supported by malectin interactions with the OSTs. However, no specific role of malectin in glycosylation was discussed or proposed.

We have emphasized our hypotheses on this point in the discussion and added a summary figure to highlight the specific role of malectin.

Given the likelihood that malectin plays a role in the glycosylation of heavily glycosylated proteins like Spike, it is unfortunate that only 5 glycosites on Spike were identified using the MS deamidation assay when Spike has a large number of glycans (~22 sites). The mass spec data set would also include endogenous proteins. Were any heavily glycosylated endogenous proteins hypoglycosylated in the MS analysis in Fig 5D?

Thank you for this suggestion. We compared the glycosylation patterns of endogenous proteins in MLEC-KD cells (Fig. S15E-G). We found that there is a small increase in abundance of glycopeptides associated with LAMP2, SERPHINH1, RDX, RPL3/5, CADM4, and ITGB1, however these fold changes are small and tested insignificant. These results indicate there is relatively little modification of endogenous glycoproteins upon MLEC-depletion. We added the following sections:

“We also probed the effect of MLEC KD on endogenous protein glycosylation. We find that there is only a small increase in abundance of glycopeptides, including those associated with the ribosome (Rpl3, Rpl5), a cytoskeletal protein (Rdx), the integrin Itgb1, and the ER-resident chaperone Serphinh1 (Fig. S15E-G).”

“Our proteomics data reveals that there is only a modest increase in the Heat Shock Factor 1 (HSF1) pathway, while the Unfolded Protein Response is relatively unchanged (Fig. S15D). In addition, there are only minor increases in endogenous glycopeptide levels (Fig. S15E-G). Together, these results indicate that while MLEC KD leads to some alterations in ER proteostasis and host glycosylation, these changes are modest and may not be the primary mechanism by which MLEC KD hinders CoV replication.”

The inclusion of the nsp4 interactome and its partial characterization is a distraction from the storyline that focuses on malectin and nsp2.

We believe the nsp4 comparative interactome and functional genomics data offers a rich resource for further functional investigation by others, if made public. While we found the malectin and nsp2 storyline the most compelling to pursue, we believe the inclusion of the nsp4 data strengthens the overall approach, in agreement with Reviewer #3’s comments.

Reviewer #3 (Public Review):

Summary:

In this study, Davies and Plate set out to discover conserved host interactors of coronavirus non-structural proteins (Nsp). They used 293T cells to ectopically express flag-tagged Nsp2 and Nsp4 from five human and mouse coronaviruses, including SARS-CoV-1 and 2, and analyzed their interaction with host proteins by affinity purification mass-spectrometry (AP-MS). To confirm whether such interactors play a role in coronavirus infection, the authors measured the effects of individual knockdowns on replication of murine hepatitis virus (MHV) in mouse Delayed Brain Tumor cells. Using this approach, they identified a previously undescribed interactor of Nsp2, Malectin (Mlec), which is involved in glycoprotein processing and shows a potent pro-viral function in both MHV and SARS-CoV-2. Although the authors were unable to confirm this interaction in MHVinfected cells, they show that infection remodels many other Mlec interactions, recruiting it to the ER complex that catalyzes protein glycosylation (OST). Mlec knockdown reduced viral RNA and protein levels during MHV infection, although such effects were not limited to specific viral proteins. However, knockdown reduced the levels of five viral glycopeptides that map to Spike protein, suggesting it may be affected by Mlec.

Strengths:

This is an elegant study that uses a state-of-the-art quantitative proteomic approach to identify host proteins that play critical roles in viral infection. Instead of focusing on a single protein from a single virus, it compares the interactomes of two viral proteins from five related viruses, generating a high confidence dataset. The functional follow-ups using multiple live and reporter viruses, including MHV and CoV2 variants, convincingly depict a pro-viral role for Mlec, a protein not previously implicated in coronavirus biology.

Weaknesses:

Although a commonly used approach, AP-MS of ectopically expressed viral proteins may not accurately capture infection-related interactions. The authors observed Mlec-Nsp2 interactions in transfected 293T cells (1C) but were unable to reproduce those in mouse cells infected with MHV (3C). EIF4E2/GIGYF2, two bonafide interactors of CoV2 Nsp2 from previous studies, are listed as depleted compared to negative controls (S1D). Most other CoV2 Nsp2 interactors are also depleted by the same analysis (S1D). Previously reported MERS Nsp2 interactors, including ASCC1 and TCF25, are also not detected (S1D). Furthermore, although GIGYF2 was not identified as an interactor of MHV Nsp2/4 in human cells (S1D), its knockdown in mouse cells reduced MHV titers about 1000 fold (S4). The authors should attempt to explain these discrepancies.

We acknowledge these limitations in AP-MS from ectopically expressed viral proteins and have addressed these discrepancies with further elaboration in the text:

“A limitation of our study is the initial overexpression of individual proteins for AP-MS, in which we find some variation between our data with other AP-MS studies. We sought to overcome these variations by focusing on conserved interactors and testing interactions in a live infection context.”

“We also found GIGYF2-KD strongly suppressed MHV infection, despite GIGYF2 not interacting with MHV nsp2 (Fig. S1D), highlighting the importance of proteostasis factors in infection regardless of direct PPIs.”

More importantly, the authors were unable to establish a direct link between Mlec and the biogenesis of any viral or host proteins, by mass-spectrometry or otherwise. Although it is clear that Mlec promotes coronavirus infection, the mechanism remains unclear. Its knockdown does not affect the proteome composition of uninfected cells (S15B), suggesting it is not required for proteome maintenance under normal conditions. The only viral glycopeptides detected during MHV infection originated from Spike (5D), although other viral proteins are also known to be glycosylated. Cells depleted for Mlec produce ~4-fold less Spike protein (4E) but no more than 2-fold less glycosylated spike peptides (5D), compounding the interpretation of Mlec effects on viral protein biogenesis. Furthermore, Spike is not essential for the pro-viral role of Mlec, given that Mlec knockdown reduces replication of SARS-CoV-2 replicons that express all viral proteins except for Spike (6A/B).

Thank you, these are all important points. We have acknowledged these compounding factors in the Discussion:

“Concurrently, knockdown of MLEC leads to impediment of nsp production and aberrant glycosylation of other viral proteins like Spike, though it should be noted that the decrease in Spike glycopeptides is compounded by the overall decrease in Spike protein. Given that MLEC is pro-viral in a SARS-CoV-2 replicon model lacking Spike (Fig. 6), MLEC can promote CoV replication independent of Spike production.”

Any of the observed effects on viral protein levels could be secondary to multiple other processes.Interventions that delay infection for any reason could lead to an imbalance of viral protein levels because Spike and other structural proteins are produced at a much higher rate than non-structural proteins due to the higher abundance of their cognate subgenomic RNAs. Similarly, the observation that Mlec depletion attenuates MHV-mediated changes to the host proteome (S15C/D) can also be attributed to indirect effects on viral replication, regardless of glycoprotein processing. In the discussion, the authors acknowledge that Mlec may indirectly affect infection through modulation of replication complex formation or ER stress, but do not offer any supporting evidence. Interestingly, plant homologs of Mlec are implicated in innate immunity, favoring a more global role for Mlec in mammalian coronavirus infections.

We examined the upregulation of protein markers of the UPR and other stress response pathways in cells depleted of MLEC (Fig. S15D). We find that the UPR pathways are moderately but insignificantly upregulated, while the Heat Shock Factor 1 (HSF1) pathway is moderately and significantly upregulated. The fold change increase of these marker proteins are relatively small, so while upregulation of this pathway may contribute to the suppression of CoV replication, it may not fully explain the phenotype. Please all see similar points brought up by reviewer 1 (comment 1). We added the following section to the manuscript text to discuss these results:

“In uninfected cells, MLEC KD leads to relatively little proteome-wide changes, with MLEC being the only protein significantly downregulated and no other proteins significantly upregulated, supporting the specificity of MLEC KD in MHV suppression (Fig.  S15C). To determine whether MLEC KD alters general host proteostasis, we further examined the levels of protein markers of stress pathways based on previous gene pathway definitions(Davies et al., 2023; Grandjean et al., 2019; Shoulders et al., 2013) (Fig. S15D). We find that there are modest but significant increases in protein levels associated with the Heat Shock Factor 1 (HSF1) pathway, while the Unfolded Protein Response (UPR) pathways are largely unmodified. 

“Our proteomics data reveals that there is only a modest increase in the Heat Shock Factor 1 (HSF1) pathway, while the Unfolded Protein Response is relatively unchanged (Fig. S15D). […] Together, these results indicate that while MLEC KD leads to some alterations in ER proteostasis and host glycosylation, these changes are modest and may not be the primary mechanism by which MLEC KD hinders CoV replication.”

Finally, the observation that both Nsp2 (3C) and Mlec (3E/F) are recruited to the OST complex during MHV infection neither support nor refute any of these alternate hypotheses, given that Mlec is known to interact with OST in uninfected cells and that Nsp2 may interact with OST as part of the full length unprocessed Orf1a, as it co-translationally translocates into the ER. Therefore, the main claims about the role of Mlec in coronavirus protein biogenesis are only partially supported.

We have acknowledged this point in the Discussion. 

“We find that nsp2 interacts with several OST complex members, including DDOST, STT3A, and RPN1, though whether this is as part of the uncleaved Orf1a polyprotein during co-translational ER translocation or as an individual protein is unclear.”

Reviewer #2 (Recommendations For The Authors):

What is the proof that MLEC is a type I membrane protein? If it is strictly sequence analysis, this conclusion should be tapered in the text.

Our response: We have added appropriate evidence on the biochemical characterization of MLEC topology from Galli et al., 2011, and cryo-EM structural characterization by Ramírez et al., 2019.

“As it was surprising that nsp2, a non-glycosylated, cytoplasmic protein, would interact with MLEC, an integral ER membrane protein with a short two amino acid cytoplasmic tail(Galli et al., 2011; Ramírez et al., 2019), we assessed a broader genetic interaction between nsp2 and MLEC.”

Validation of some of the nsp2 and malectin interactome components by pulldowns should be included.

Our response: The interactions between nsp2 and Ddost, Stt3A, and Rpn1 passed a stringent confidence filter in our AP-MS experiment (Fig. 3C) based on several replication. For this reason, we do not believe additional validation by Western blotting will offer much useful information.

NGI-1 inhibition of glycosylation looks to be very weak in Fig. 5B and Fig. S14B.

Our response: It is important to note that the NGI-1 inhibition assay used a suboptimal NGI-1 concentration to prevent full suppression of MHV infection, which we have found previously. We have added this justification in the Methods section and associated figure legend (Fig. S14A).

“The 5 uM NGI-1 dosage was chosen as it resulted in partial inhibition of glycosylation while not completely blocking MHV infection.”

“This dosage and timing were chosen to partially inhibit the OST complex without fully ablating viral infection, as NGI-1 has been shown previously to be a potent positive-sense RNA virus inhibitor(Puschnik et al., 2017)  (Fig. S14)”

Summary model figure at the end would help to communicate the conclusions.

Our response: Thank you for this suggestion. We agree and have added a summary model figure at the end as suggested.

eLife Assessment

The paper is a fundamental study examining the role of CDK12 loss in prostate cancer. While previous studies have suggested that CDK12 loss confers HRD phenotypes, clinical trials using PARPi in CDK12 altered patients have not demonstrated significant benefit. This work investigates these mechanisms in depth and provides compelling evidence. A comprehensive genomic analysis serves an excellent resource to the field, showing that biallelic CDK12 alterations do not have genomic features of HRd. Moreover, the study explored both acute and chronic deletion of CDK12, with data suggestive of CDK12-altered cells being uniquely sensitive to CDK13 inhibition. While some minor weaknesses have been previously noted by the reviewers, the authors have adequately addressed these concerns with appropriate rigor.

Reviewer #1 (Public review):

Summary:

The authors were attempting to identify the molecular and cellular basis for why modulators of the HR pathway, specifically PARPi, are not effective in CDK12 deleted or mutant prostate cancers and they seek to identify new therapeutic agents to treat this subset of metastatic prostate cancer patients. Overall, this is an outstanding manuscript with a number of strengths and in my opinion represents a significant advance in the field of prostate cancer biology and experimental therapeutics.

Strengths:

The patient data cohort size and clinical annotation from Figure 1 are compelling and comprehensive in scope. The associations between tandem duplications and amplifications of oncogenes that have been well-credentialed to be drivers of cancer development and progression are fascinating and the authors identify that in those that have AR amplification for example, there is evidence for AR pathway activation. The association between CDK12 inactivation and various specific gene/pathway perturbations is fascinating and is consistent with previously published studies - it would be interesting to correlate these changes with cell line-based studies in which CDK12 is specifically deleted or inhibited with small molecules to see how many pathways/gene perturbations are shared between the clinical samples and cell and mouse models with CDK12 perturbation. The short-term inhibitor studies related to changes in HRD genes and protein expression with CDK12/13 inhibition are fascinating and suggest differential pathway effects between short inhibition of CDK12/13 and long-term loss of CDK12. The in vivo studies with the inhibitor of CDK12/13 are intriguing but not definitive

Weaknesses:

Given that there are different mutations identified at different CDK12 sites as illustrated in Figure 1B it would be nice to know which ones have been functionally classified as pathogenic and for which ones that the pathogenicity has not been determined. This would be especially interesting to perform in light of the differences in the LOH scores and WES data presented - specifically, are the pathogenic mutations vs the mutations for which true pathogenicity is unknown more likely to display LOH or TD? For the cell inhibition studies with the CDK12/13 inhibitor, more details characterizing the specificity of this molecule to these targets would be useful. Additionally, could the authors perform short-term depletion studies with a PROTAC to the target or short shRNA or non-selected pool CRISPR deletion studies of CDK12 in these same cell lines to complement their pharmacological studies with genetic depletion studies? Also perhaps performing these same inhibitor studies in CDK12/13 deleted cells to test the specificity of the molecule would be useful. Additionally, expanding these studies to additional prostate cancer cell lines or organdies models would strengthen the conclusions being made. More information should be provided about the dose and schedule chosen and the rationale for choosing those doses and schedules for the in vivo studies proposed should be presented and discussed. Was there evidence for maximal evidence of inhibition of the target CDK12/13 at the dose tested given the very modest tumor growth inhibition noted in these studies?

Reviewer #2 (Public review):

Summary:

The study explores the functional consequence of CDK12 loss in prostate cancer. While CDK12 loss has been shown to confer homologous recombination (HR) deficiency through premature intronic polyadenylation of HR genes, the response of PARPi monotherapy has failed. This study therefore performed an in-depth analysis of genomic sequencing data from mCRPC patient tumors, and showed that tumors with CDK12 loss lack pertinent HR signatures and scars. Furthermore, functional exploration in human prostate cancer cell lines showed that while the acute inhibition of CDK12 resulted in aberrant polyadenylation of HR genes like BRCA1/2, HR-specific effects were overall modest or absent in cell lines or xenografts adapted to chronic CDK12 loss. Instead, vulnerability to genetically targeting CDK13 resulted in a synthetic lethality in tumors with CDK12 loss, as shown in vivo with SR4825, a CDK12/13 inhibitor - thus serving as a potential therapeutic avenue.

The evidence supporting this study is based on in-depth genomic analyses of human patients, acute knockdown studies of CDK12 using a CDK12/13 inhibitors SR4835, adaptive knockout of CDK12 using LuCaP 189.4_CL and inducible re-expression of CDK12, CDK12 single clones in 22Rv1 (KO2 and KO5) and Skov3 (KO1), Tet-inducible knockdown of BRCA2 or CDK12 followed by ionizing radiation and measurement of RAD51 foci, lack of sensitivity generally to PARPi and platinum chemotherapy in cells adapted to CDK12 loss, loss of viability of CDK13 knockout in CDK12 knockout cells, and in vivo testing of SE4825 in LuCaP xenografts with intact and CDK12 loss.

Strengths:

Overall, this study is robust and of interest to the broader homologous recombination and CDK field. First, the topic is clinically relevant given the lack of PARPi response in CDK12 loss tumors. Second, the strength of the genomic analysis in CDK12 lost PCa tumors is robust with clear delineation that BRCA1/2 genes and maintenance of most genes regulating HR are intact. Specifically, the authors find that there is no mutational signature or genomic features suggestive of HR, such as those found in BRCA1/2 tumors. Lastly, novel lines are generated in this study, including de novo LuCaP 189.4_CL with CDK12 loss that can be profound for potential synthetic lethalities.

Weakness:

One caveat that continues to be unclear as presented, is the uncoupling of cell cycle/essentiality of CDK12/13 from HR-directed mechanisms. Is this purely a cell cycle arrest phenotype acutely with associated down-regulation of many genes?

While the RAD51 loading ssRNA experiments are informative, the Tet-inducible knockdown of BRCA2 and CDK12 is confusing as presented in Figure 5, shBRCA2 + and -dox are clearly shown. However, were the CDK12_K02 and K05 also knocked down using inducible shRNA or a stable knockout? The importance of this statement is the difference between acute and chronic deletion of CDK12. Previously, the authors showed that acute knockdown of CDK12 led to an HR phenotype, but here it is unclear whether CDK12-K02/05 are acute knockdowns of CDK12 or have been chronically adapted after single cell cloning from CRISPR-knockout.

Given the multitude of lines, including some single-cell clones with growth inhibitory phenotypes and ex-vivo derived xenografts, the variability of effects with SR4835, ATM, ATR, and WEE1 inhibitors in different models can be confusing to follow. Overall, the authors suggest that the cell lines differ in therapeutic susceptibility as they may have alternate and diverse susceptibilities. It may be possible that the team could present this more succinctly and move extraneous data to the supplement.

The in-vitro data suggests that SR4835 causes growth inhibition acutely in parental lines such as 22RV1. However, in vivo, tumor attenuation appears to be observed in both CDK12 intact and deficient xenografts, LuCAP136 and LuCaP 189.4 (albeit the latter is only nominally significant). Is there an effect of PARPi inhibition specifically in either model? What about the the 22RV1-K02/05? Do these engraft? Given the role of CDK12/13 in RNAP II, these data might suggest that the window of susceptibility in CDK12 tumors may not be that different from CDK12 intact tumors (or intact tissue) when using dual CDK12/13 inhibitors but rather represent more general canonical essential functions of CDK12 and CDK13 in transcription. From a therapeutic development strategy, the authors may want to comment in the discussion on the ability to target CDK13 specifically.

Reviewer #3 (Public review):

Significance:

About 5% of metastatic castration-resistant prostate cancers (mCRPC) display genomic alterations in the transcriptional kinase CDK12. The mechanisms by which CDK12 alterations drive tumorigenesis in this molecularly-defined subset of mCRPC have remained elusive. In particular, some studies have suggested that CDK12 loss confers a homologous recombination deficiency (HRd) phenotype, However, clinical studies have not borne out the benefit to PARP inhibitors in patients with CDK12 alterations, despite the fact that these agents are typically active against tumors with HRd.

In this study, Frank et al. reconcile these findings by showing that: (1) tumors with biallelic CDK12 alterations do not have genomic features of HRd; (2) in vitro, HR gene downregulation occurs with acute depletion of CDK12 but is far less pronounced with chronic CDK12 loss; (3) CDK12-altered cells are uniquely sensitive to genetic or pharmacologic inhibition of CDK13.

Strengths:

Overall, this is an important study that reconciles disparate experimental and clinical observations. The genomic analyses are comprehensive and conducted with a high degree of rigor and represent an important resource to the community regarding the features of this molecular subtype of mCRPC.

Weaknesses:

(1) It is generally assumed that CDK12 alterations are inactivating, but it is noteworthy that homozygous deletions are comparatively uncommon (Figure 1a). Instead many tumors show missense mutations on either one or both alleles, and many of these mutations are outside of the kinase domain (Figure 1b). It remains possible that the CDK12 alterations that occur in some tumors may retain residual CDK12 function, or may confer some other neomorphic function, and therefore may not be accurately modeled by CDK12 knockout or knockdown in vitro. This would also reconcile the observation that knockout of CDK12 is cell-essential while the human genetic data suggest that CDK12 functions as a tumor suppressor gene.

(2) It is not entirely clear whether CDK12 altered tumors may require a co-occurring mutation to prevent loss of fitness, either in vitro or in vivo (e.g. perhaps one or more of the alterations that occur as a result of the TDP may mitigate against the essentiality of CDK12 loss).

Author response:

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

Reviewer #1 (Public Review):

Given that there are different mutations identified at different CDK12 sites as illustrated in Figure 1B it would be nice to know which ones have been functionally classified as pathogenic and for which ones that the pathogenicity has not been determined. This would be especially interesting to perform in light of the differences in the LOH scores and WES data presented - specifically, are the pathogenic mutations vs the mutations for which true pathogenicity is unknown more likely to display LOH or TD?

Alterations were classified as pathogenic when resulting in frameshift, nonsense, or cause an aminoacid change likely to alter function (according to ANNOVAR).  Four patients were called CDK12^BAL but were negative for TDP signatures. Three of these had CDK12 mutations downstream of the kinase domain, which may be less likely to ablate protein activity. Most functionally validated pathogenic mutations include disruption of the kinase domain (PMID: 25712099). We added a sentence to the Results section (under “Identification of genomic characteristics that associate with CDK12 loss in prostate cancer”) to highlight this caveat on pathogenic mutation calls.

For the cell inhibition studies with the CDK12/13 inhibitor, more details characterizing the specificity of this molecule to these targets would be useful. Additionally, could the authors perform short-term depletion studies with a PROTAC to the target or short shRNA or non-selected pool CRISPR deletion studies of CDK12 in these same cell lines to complement their pharmacological studies with genetic depletion studies? Also perhaps performing these same inhibitor studies in CDK12/13 deleted cells to test the specificity of the molecule would be useful.

We are not aware of a CDK12-specific PROTAC, and generate such as reagent is beyond the scope of the present study. Regarding the specificity of the CDK12/13 inhibitor molecules, additional information on the specificity and in vivo dose selection were added to the Results section (under “CDK13 is synthetic lethal in cells with biallelic CDK12 loss”). Cells with CDK12-KO did not tolerate CDK13-KO, so we were unable to generate double knockouts to test for CDK12/13 inhibitor non-specific effects. 

Additionally, expanding these studies to additional prostate cancer cell lines or organdies models would strengthen the conclusions being made. More information should be provided about the dose and schedule chosen and the rationale for choosing those doses and schedules for the in vivo studies proposed should be presented and discussed. Was there evidence for maximal evidence of inhibition of the target CDK12/13 at the dose tested given the very modest tumor growth inhibition noted in these studies.

With respect to additional acute CDK12 loss models, our Tet-inducible shCDK12 models show only minor growth slowdown and do not appear to phenocopy the strong arrest or apoptosis seen with CDK12 KO or inhibition, respectively. Future work is ongoing to generate CDK12-degron regulated cell lines. We added a new immunoblot panel showing that acute CRISPR/sgRNA targeting of CDK12 does indeed lead to BRCA2 and ATM protein decrease (Fig. S4g), providing some orthogonal genomic targeting evidence of the acute HR gene effect.  We are continuing efforts to collect and generate additional CDK12^BAL cell models, in both 2D and 3D culture systems, but none are presently available. We added a 3D culture drug dose curve with LuCaP189.4 exposed to THZ531 (Fig. S7m), which confirms heightened sensitivity vs two CDK12-intact lines. 

Regarding assessments of CDK12 targets; as we are not aware of any unique CDK12 substrates, it is fair to ask but difficult to measure precise CDK12 inhibition by the compounds in tumors. We dosed mice using the same protocol as detailed in the original report testing SR4835 in mice (PMID: 31668947). We performed immunoblots on lysates from 3 and 28 day treated PDX tumors and did not see any consistent decreases in pRBP1(Ser2) or ATM or increases in γH2A.X (data not shown). However, we did see increases in APA usage and downregulation of DNA repair transcripts with three day treatment (Fig. 6k-l), as would be expected from on target acute effects.

Reviewer #2 (Public review)

One caveat that continues to be unclear as presented, is the uncoupling of cell cycle/essentiality of CDK12/13 from HR-directed mechanisms. Is this purely a cell cycle arrest phenotype acutely with associated down-regulation of many genes?

In regard to untangling the effects of cell arrest on HR gene expression, this is a difficult question given that many HR genes, including BRCA2, are S/G2 linked. We attempted to account for those effects in the acute CDK12 inhibition experiment by including a palbociclib (CDK4/6i) control, which caused cell arrest and decreased BRCA1/2 RNA expression with no apparent 5/3’ transcript imbalance determined by qPCR (Fig. 4e,g). Though overall BRCA1 and BRCA2 mRNA levels are lower in the stable 22Rv1-CDK12-KO2 and KO5 lines, they do not show selective 3’ loss (Fig. 5c), suggesting the downregulation in these lines is mostly due to their slower growth (Fig. S4k) and not intronic polyA usage.

While the RAD51 loading ssRNA experiments are informative, the Tet-inducible knockdown of BRCA2 and CDK12 is confusing as presented in Figure 5, shBRCA2 + and -dox are clearly shown. However, were the CDK12_K02 and K05 also knocked down using inducible shRNA or a stable knockout? The importance of this statement is the difference between acute and chronic deletion of CDK12. Previously, the authors showed that acute knockdown of CDK12 led to an HR phenotype, but here it is unclear whether CDK12K02/05 are acute knockdowns of CDK12 or have been chronically adapted after single cell cloning from CRISPR-knockout. 

As a clarification, the 22Rv1-CDK12-KO2 and 22Rv1-CDK12-KO5 are stable CRISPR knockout clonal lines that were expanded from single cells. We added a new figure to include more validation of these lines (Fig. S5). We tried multiple times to reproduce the HRd phenotype and PARPi sensitivity with siRNA and inducible shRNA lines but were unable to see clear sensitivity differences, despite seeing the expected shifts with shBRCA2 controls (data not shown). It is possible the degree of knockdown (~80%), timing (8 days), or specific cell lines used in our experiments were not sufficient to expose the acute phenotype by this method.

However, we were able to see acute HR gene decreases by inhibitor treatment (Fig. 4) or acute CRISPR (Fig. S4g).

Given the multitude of lines, including some single-cell clones with growth inhibitory phenotypes and ex-vivo derived xenografts, the variability of effects with SR4835, ATM, ATR, and WEE1 inhibitors in different models can be confusing to follow. Overall, the authors suggest that the cell lines differ in therapeutic susceptibility as they may have alternate and diverse susceptibilities. It may be possible that the team could present this more succinctly and move extraneous data to the supplement.  

We appreciate the complexity of the data and attempted to use multiple models to report consistency and variability. We are not able to ascertain what data would be extraneous, and elected to present data we view as relevant in the main figures while moving supporting data in the supplement.

The in-vitro data suggests that SR4835 causes growth inhibition acutely in parental lines such as 22RV1. However, in vivo, tumor attenuation appears to be observed in both CDK12 intact and deficient xenografts, LuCAP136 and LuCaP 189.4 (albeit the latter is only nominally significant). Is there an effect of PARPi inhibition specifically in either model? What about the 22RV1-K02/05? Do these engraft? Given the role of CDK12/13 in RNAP II, these data might suggest that the window of susceptibility in CDK12 (mutant) tumors may not be that different from CDK12 intact tumors (or intact tissue) when using dual CDK12/13 inhibitors but rather represent more general canonical essential functions of CDK12 and CDK13 in transcription. From a therapeutic development strategy, the authors may want to comment in the discussion on the ability to target CDK13 specifically.

Though the response of the CDK12^BAL models to some compounds is variable, we believe those mixed results are important and future studies may be able to better explain why some show shifts in sensitivity while others do not. We hope future studies with additional models will help determine which sensitivities are more consistently true, and perhaps provide explanations for differences between models.

Regarding SR4835, we find, and others have reported, a toxic (i.e. apoptotic) effect for in vitro treatment with dual CDK12/13 inhibitors (Fig. 4f, S4e,f); in fact, that may be why previous studies have used short timepoints in cell culture assays with these dual inhibitors. In mice, SR4835 was tolerated well but only LuCaP 189.4 showed statistically significant decreases in tumor volume and weight (Fig. 6j). We did not test PARPi responses in the PDX models, nor did we attempt engrafting the 22Rv1-CDK12-KO cell lines, but both would be worthwhile experiments in the future. Beyond CDK12^BAL tumors, we agree that CDK12/13 inhibitors could be effective in cancer therapies more generally (e.g. triggering acute HRd, loss of RNAP2 phosphorylation). We added a line to the discussion section about ongoing efforts to combine PARPi and CDK12/13i, which we expect to be synergistic in CDK12-intact tumors due to the acute loss phenotype. We certainly agree that development of a specific CDK13 inhibitor would be the ideal therapeutic option for CDK12^BAL tumors. However, CDK12 and CDK13 are 43% conserved at the protein level (PMID: 26748711), with 92% conservation in the active site (PMID: 30319007), and there are no available pharmacologic inhibitors that discriminate between CDK12 and CDK13.

Reviewer #3 (Public review):

It is generally assumed that CDK12 alterations are inactivating, but it is noteworthy that homozygous deletions are comparatively uncommon (Figure 1a). Instead many tumors show missense mutations on either one or both alleles, and many of these mutations are outside of the kinase domain (Figure 1b). It remains possible that the CDK12 alterations that occur in some tumors may retain residual CDK12 function, or may confer some other neomorphic function, and therefore may not be accurately modeled by CDK12 knockout or knockdown in vitro. This would also reconcile the observation that knockout of CDK12 is cell-essential while the human genetic data suggest that CDK12 functions as a tumor suppressor gene.

Thank you for the feedback. It is a keen observation that homozygous deletions of CDK12 are not typical, though many mutations are upstream frameshifts that are expected to lead to loss of functional protein and mRNA via nonsense mediated decay. LuCaP189.4, our only natural mutant model, has two upstream frameshifts leading to complete protein loss (Fig 5b, S4h-i). We also added a caveat previously mentioned (in response to Reviewer 1) that mutations downstream of the kinase domain may be less likely to be fully pathogenic. For upstream missense mutations, the possibility of neuromorphic function remains an intriguing possibility that cannot be ruled out and would not be captured in our current models. Hopefully additional models can be developed, both natural and engineered, to help dissect that question in future studies.  

It is not entirely clear whether CDK12 altered tumors may require a co-occurring mutation to prevent loss of fitness, either in vitro or in vivo (e.g. perhaps one or more of the alterations that occur as a result of the TDP may mitigate against the essentiality of CDK12 loss).

We concur. Another caveat with the CRISPR models, beyond reliance on upstream frameshift mutations, is the simultaneous loss of alleles. In human tumors, there may be a period of single copy loss before the second hit that may provide a window for adaptation. It is possible that sequential loss is far easier for a cell to tolerate than acute bi-allelic inactivation. We agree that the question of what (if any) cooperating genetic alterations are required to tolerate CDK12 loss is an important one that we plant to further explore in future work.

Recommendations for Authors:

Reviewer #1 (Recommendations for Authors):

The authors have thoroughly addressed all issues of data availability, reagents, in vivo protocols, and animal approvals associated with the studies presented in this manuscript. Specific comments and experimental suggestions that in my opinion would strengthen the conclusions of this interesting and compelling manuscript are included above

Reviewer #2 (Recommendations for the authors):

The authors were thorough in their studies. As a general note, switching between the cell lines is often overwhelming in interpreting the data given cell-to-cell variability in response. If possible, consolidating the text/conclusions in results would improve the readability of the manuscript.

The variety of cell lines and models is perhaps expansive at times, but we hope the inclusion of these different models helps support the conclusions. 

Is it possible to knockout CDK12 acutely using a degron-based approach, instead of utilizing an inhibitor that targets both CDK12/13?

There is a HeLa cell line made with analog-sensitive CDK12 (Bartkowiak, Yan, and Greenleaf 2016) but we were unaware of any such prostate lines at the time of this work. We are attempting to develop engineered prostate lines with specific CDK12 degradation but do not yet have them available.

How do the authors address a lower BRCA1/2 level in for example 22RV1-K05, does this cell line have increased sensitivity to PARPi over its parental 22RV1 line? Could this be added to Figure 5h/i?

The lower BRCA2 levels in 22Rv1CDK12-KO5 is likely due to the slower growth rate (Fig. S4k), as BRCA2 expression is S/G2 linked. While the mRNA level of BRCA2 overall is lower in the KO5 line, we do not observe the 5’/3’ transcript imbalance (Fig. 5c). The 22Rv1-CDK12-KO lines did not show increased sensitivity to carboplatin, while inducible shBRCA2 did (Fig. S7a), so we do not believe this lower BRCA2 confers functional HRd. We did test the KO lines with olaparib (Fig. S7d) and saw a modest increased sensitivity compared to parental 22Rv1, but not to the extent measured in the BRCA1 mutant line UWB1.289.

What is the clonality of the LuCAP 189.4 lines upon derivation? Is biallelic CDK12 loss seen in all cells?

We do not know the exact clonality of the LuCAP 189.4 PDX or CL model, but we do see highly uniform CDK12 protein loss in these cells (quantified by IHC staining, data not shown).

The authors state that 22RV1-K02/05 has an increased growth arrest to CDK13 inhibition. However, in Figure 6h, it appears the viability is not significantly different compared to the parental 22RV1 line. Similar aspects noted in 189.4-vec/CDK12?

We found that 22Rv1 KO2/KO5 have growth arrest with sgCDK13 and cell death with CDK12/13 inhibitor. We did notice that SR4835 did not show the differential effects we anticipated (Fig. 6h), as was seen with THZ531 (Fig. 6i). SR4835 is a non-covalent inhibitor, while THZ531 is a covalent binder, so there are some functional differences between these compounds that might explain the lack of differential effects in the isogenic lines in a 4 day in vitro assay. We included the SR4835 in vitro data because it was used for the in vivo experiment. THZ531 is not suited for animal use.

Could the authors comment on SR4835 response in vivo as a function of tumor growth rate?

The in vivo SR4835 treated LuCaP189.4 did show signs of reduced proliferation with decreased Cell Cycle and DNA Replication in the RNA-seq signatures, but a more detailed investigation into cell cycle arrest vs apoptotic response has yet to be fully explored. We plan to conduct additional PDX experiments if we can obtain a selective CDK13 inhibitor. 

Do the authors explore TDPs in their isogenic cell lines?

We performed low coverage WGS on the 22Rv1 KO clones and added that to the paper (Fig. S5c). We did not see any obvious signs of TDP. We suspect the phenotype takes longer to accumulate and is not apparent within the ~20 passages our clones underwent in culture. This would be consistent with the tumor analysis (Fig. 2b) showing increase in TDs from primary to metastatic tumors, suggesting TDs accumulate over time.

A future study may allow for screening synthetic lethals in the context of CDK12 loss in the presence or absence of SR4835 inhibition.

We are actively pursuing experiments to identify new synthetic lethal targets by CRISPR and drug screens in CDK12 loss models and hope to report those in a future study.

Reviewer #3 (Recommendations for the authors):

As discussed above, the authors may wish to adjust their terminology to "CDK12-altered" rather than "CDK12 lost" or "CDK12-inactivated" to leave open the possibility that some tumors may retain residual CDK12 function or adopt neomorphic functions.

Thank you for the additional comments and feedback. The possibility of neomorphic CDK12 allele function is important. As our models were all complete protein loss mutations, we decided to retain “biallelic loss” as our preferred nomenclature, but the note is well taken.

The plots in Figures 1f-h are interesting and suggest that certain cancer genes (especially oncogenes) are recurrently gained in CDK12-altered tumors. It may be interesting to look at this on the individual level rather than the cohort level to see whether any groups of oncogenes tend to be gained together in an individual patient - this could inform whether certain combinations of cancer drivers cooperate with CDK12 alteration to drive oncogenesis.

Thank you for the idea of looking at the patient-level for TDP-enriched oncogenes. A preliminary assessment did not identify recurrent co-gained oncogenes. We will continue these analyses as additional patient tumors with CDK12 alterations are identified. 

The finding that AR gene or enhancer are recurrently gained with TDP is interesting and I am curious whether the authors have thoughts on whether these alterations can also be seen in the 1-2% of CDK12altered primary prostate cancers that are treatment naïve, and where AR pathway alterations are not as frequently seen.

We did not focus on CDK12 altered primary prostate cancers, but we did check if there is AR amplification enrichment in the 6 CDK12^BAL cases of the TCGA-PRAD dataset and did not identify enrichment. However, with such small numbers we would hesitate to draw any hard conclusions. 

It could be interesting to more comprehensively characterize some of the CDK12 KO-adapted lines in Figure 5 (e.g. by WES or WGS) to determine whether they exhibit the TDP and/or whether they have acquired any secondary mutations that allow them to adapt to CDK12 loss.

We are planning to do further genomics characterization of the CDK12-KO lines and will hopefully include that in a future study. Genomic analyses of the 22Rv1 clones (see copy number plots in Fig. S5c) did not identify a TDP. We plan to repeat the genomic assessments over additional cell passages and we have planned additional experiments designed to understand why some cells tolerate CDK12 loss and others do not.

eLife Assessment

This useful study informs the transcriptional mechanisms that promote stem cell differentiation and prevent degeneration in the adult eye. Through inducible mouse mutagenesis, the authors uncover a dual role for a transcription factor (Sox9) in stem cell differentiation and prevention of retinal degeneration. The data at hand convincingly support to the main conclusions. The study will be of general interest to the fields of neuronal development and neurodegeneration.

Reviewer #1 (Public review):

Summary:

Hurtado et al. show that Sox9 is essential for retinal integrity, and its null mutation causes the loss of the outer nuclear layer (ONL). The authors then show that this absence of the ONL is due to apoptosis of photoreceptors and a reduction in the numbers of other retinal cell types such as ganglion cells, amacrine cells and horizontal cells. They also describe that Müller Glia undergoes reactive gliosis by upregulating the Glial Fibrillary Acidic Protein. The authors then show that Sox9+ progenitors proliferate and differentiate to generate the corneal cells through Sox9 lineage-tracing experiments. They validate Sox9 expression and characterize its dynamics in limbal stem cells using an existing single-cell RNA sequencing dataset. Finally, the authors show that Sox9 deletion causes progenitor cells to lose their clonogenic capacity by comparing the sizes of control and Sox9-null clones. Overall, Hurtado et al. underline the importance of Sox9 function in retinal cells.

Strengths:

The authors have characterized a myriad of striking phenotypes due to Sox9 deletion in the retina and limbal stem cells which will serve as a basis for future studies.

Weaknesses:

Hurtado et al. highlight the importance of Sox9 in the retina and limbal stem cells by describing several affects of Sox9 depletion in the adult eye. However, it is unclear how or where Sox9 precisely acts as a mechanistic investigation of the transcription factor's role in this tissue is lacking.

Reviewer #2 (Public review):

Summary:

Sox9 is a transcription factor crucial for development and tissue homeostasis, and its expression continues in various adult eye cell types, including retinal pigmented epithelium cells, Müller glial cells, and limbal and corneal basal epithelia. To investigate its functional roles in the adult eye, this study employed inducible mouse mutagenesis. Adult-specific Sox9 depletion led to severe retinal degeneration, including the loss of Müller glial cells and photoreceptors. Further, lineage tracing revealed that Sox9 is expressed in a basal limbal stem cell population that supports stem cell maintenance and homeostasis. Mosaic analysis confirmed that Sox9 is essential for the differentiation of limbal stem cells. Overall, the study highlights that Sox9 is critical for both retinal integrity and the differentiation of limbal stem cells in the adult mouse eye.

Strengths:

In general, inducible genetic approaches in the adult mouse nervous system are rare and difficult to carry out. Here, the authors employ tamoxifen-inducible mouse mutagenesis to uncover the functional roles of Sox9 in the adult mouse eye.

Careful analysis suggests that two degeneration phenotypes (mild and severe) are detected in the adult mouse eye upon tamoxifen-dependent Sox9 depletion. Phenotype severity nicely correlates with the efficiency of Cre-mediated Sox9 depletion.

Molecular marker analysis provides strong evidence of Mueller cell loss and photoreceptor degeneration.

A clever genetic tracing strategy uncovers a critical role for Sox9 in limbal stem cell differentiation.

Comments on revised submission:

The revised manuscript is very much improved and has addressed all my concerns.

Author response:

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

Reviewer #1 (Public review):

Hurtado et al. show that Sox9 is essential for retinal integrity, and its null mutation causes the loss of the outer nuclear layer (ONL). The authors then show that this absence of the ONL is due to apoptosis of photoreceptors and a reduction in the numbers of other retinal cell types such as ganglion cells, amacrine cells, and horizontal cells. They also describe that Müller Glia undergoes reactive gliosis by upregulating the Glial Fibrillary Acidic Protein. The authors then show that Sox9+ progenitors proliferate and differentiate to generate the corneal cells through Sox9 lineage-tracing experiments. They validate Sox9 expression and characterize its dynamics in limbal stem cells using an existing single-cell RNA sequencing dataset. Finally, the authors argue that Sox9 deletion causes progenitor cells to lose their clonogenic capacity by comparing the sizes of control and Sox9-null clones. Overall, Hurtado et al. underline the importance of Sox9 function in retinal and corneal cells.

Strengths:

The authors have characterized a myriad of striking phenotypes due to Sox9 deletion in the retina and limbal stem cells which will serve as a basis for future studies.

Weaknesses:

Hurtado et al. investigate the importance of Sox9 in the retina and limbal stem cells. However, the overall experimental narrative appears dispersed.

(1) The authors begin by characterizing the phenotype of Sox9 deletion in the retina and show that the absence of the ON layer is due to photoreceptor apoptosis and a reduction in other retinal cell types. The authors also note that Müller glia undergoes gliosis in the Sox9 deletion condition. These striking observations are never investigated further, and instead, the authors switch to lineage-tracing experiments in the limbus that seem disconnected from the first three figures of the paper. Another example of this disconnect is the comparison of Sox9 high and Sox9 low populations using an existing scRNA-seq dataset and the subsequent GO term analysis, which does not directly tie in with the lineage-tracing data of the succeeding Sox9∆/∆ experiments.

We thank the reviewer for their thoughtful observations. We would like to clarify the rationale behind the structure of our study and how the different parts are conceptually connected.

Our central aim was to investigate the role of Sox9 in the adult eye. Given that Sox9 has been extensively studied during embryonic development, we specifically chose to use an inducible conditional knockout strategy (CAG-CreERTM) in order to assess its function postnatally, in the adult eye. This approach revealed a severe retinal phenotype, whereas the cornea showed no overt phenotype. A major strength of our experimental design is that it allowed us to examine the role of Sox9 specifically in the adult eye, avoiding confounding effects from embryonic development. Nevertheless, this approach entails an inherent limitation: the mosaic nature of the CAG-CreERTM system leads to substantial variability in both the extent and distribution of Sox9 inactivation among individual animals. We invested considerable effort over extended periods to obtain reliable and biologically meaningful data despite this variability. We did not proceed further because this mosaicism poses a significant limitation when attempting to dissect downstream mechanisms in a consistent and reproducible manner, making it extremely challenging to perform in-depth mechanistic studies.

Regarding the cornea, given the absence of a clear phenotype upon Sox9 deletion, we expanded our investigation by adding lineage-tracing and transcriptomic analyses to better understand Sox9’s potential role in adult limbal epithelial stem cells. These additional experiments provided valuable insight into Sox9 function in the adult cornea, even in the absence of gross morphological changes. Thus, while the retinal and corneal data stem from different experimental approaches, they are unified by a shared goal: understanding the celltype-specific and tissue-specific functions of Sox9 in the adult eye.

To ensure that other readers do not perceive this apparent disconnect, and overstate our conclusions, we have modified the manuscript.  In the Introduction section, we have included the main findings from studies conducted to date on the role of Sox9 in the cornea and retina, and we have removed the corresponding section from the Discussion. We believe it is now clear that our study focuses on the role of Sox9 in the adult eye, in contrast to previous studies, which focused on the developing eye.

In the Discussion section, we have added a new paragraph at the beginning and end that explicitly addresses the relationship between the retinal and limbal findings, illustrating how a single transcription factor can play distinct roles in different tissues within the same organ.

Regarding the reviewer’s comment that the scRNA-seq analyses appear disconnected from the lineage-tracing data, we respectfully disagree. These analyses provide independent transcriptional confirmation that Sox9 is a marker of limbal stem cells, reinforcing the conclusions drawn from our in vivo experiments. These approaches are complementary and they converge on the same biological insight: Sox9 marks a population with stem-like properties in the adult limbus. Nevertheless, we acknowledge the reviewer’s concern and have moderated the tone of our statements in the revised version of the manuscript to better reflect the supporting nature of the scRNA-seq data, without overstating its functional implications.

(2) A major concern is that a single Sox9∆/∆ limbal clone has a sufficiently large size, comparable to wild-type clones, as seen in Figure 6D. This singular result is contrary to their conclusion, which states that Sox9-deficient stem cells minimally contribute to the maintenance of the cornea.

We thank the reviewer for this important observation.

Ligand-independent activity of Cre-ER fusion proteins has been repeatedly reported in various mouse models (Vooijs et al., 2001; Kemp et al., 2004; Haldar et al., 2009). This basal recombinase activity is thought to arise from inappropriate nuclear translocation or proteolysis of the Cre-ER fusion protein, leading to low-level recombination even in the absence of tamoxifen. Consistent with this, prior studies using the same CAGG-CreERTM; R26R-LacZ system for clonal analysis in the cornea have observed sparse reporter expression before tamoxifen administration (Dorà et al., 2015).

In line with these findings, we also detected minimal background LacZ staining in Sox9Δ/ΔLacZ corneas (mean surface area: 0.85%; n = 8 eyes). This low-level staining likely reflects recombination events in transient amplifying or more differentiated cells, which are not expected to generate long-lived clones. However, in the rare instance of a large clone, as shown in Figure 6D, we believe that a spontaneous recombination event may have occurred in a bona fide limbal stem cell, giving rise to a sustained contribution. To rigorously address this potential artefact and assess the true contribution of Sox9-deficient stem cells, we conducted a comparative analysis of 8 control (Sox9Δ/+-LacZ) and 5 mutant (Sox9Δ/ΔLacZ) corneas. This analysis revealed a highly significant 8-fold reduction in the LacZpositive surface area in mutant samples (Sox9Δ/+-LacZ: 6.65 ± 1.77%; Sox9Δ/Δ-LacZ: 0.85 ± 0.85%; paired t-test, p = 0.00017; Figs. 6E and F; Table S12).

We chose to include the image of the large clone in the main figure precisely because it does not align with our working hypothesis. We believe that showing such exceptions transparently is scientifically important and may be valuable for other researchers using similar inducible systems. Nonetheless, based on previous literature, the number of samples analyzed, and the statistically significant reduction in clonal contribution, we maintain that the observed phenotype reflects a true biological effect of Sox9 loss, supporting our conclusion that Sox9-deficient stem cells contribute minimally to corneal maintenance. To make that point clearer, we have introduced the following sentence in lines 462-464 of the revised version of the manuscript.

“A possible explanation for this clone may be that spontaneous ligand-independent activity of Cre-ER fusion may have occurred in a bona fide limbal stem cell, as previously reported (Vooijs et al., 2001; Kemp et al., 2004; Haldar et al., 2009, Dorà et al., 2015).”

Reviewer #2(Public revciew):

Sox9 is a transcription factor crucial for development and tissue homeostasis, and its expression continues in various adult eye cell types, including retinal pigmented epithelium cells, Müller glial cells, and limbal and corneal basal epithelia. To investigate its functional roles in the adult eye, this study employed inducible mouse mutagenesis. Adult-specific Sox9 depletion led to severe retinal degeneration, including the loss of Müller glial cells and photoreceptors. Further, lineage tracing revealed that Sox9 is expressed in a basal limbal stem cell population that supports stem cell maintenance and homeostasis. Mosaic analysis confirmed that Sox9 is essential for the differentiation of limbal stem cells. Overall, the study highlights that Sox9 is critical for both retinal integrity and the differentiation of limbal stem cells in the adult mouse eye.

Strengths:

In general, inducible genetic approaches in the adult mouse nervous system are rare and difficult to carry out. Here, the authors employ tamoxifen-inducible mouse mutagenesis to uncover the functional roles of Sox9 in the adult mouse eye.

Careful analysis suggests that two degeneration phenotypes (mild and severe) are detected in the adult mouse eye upon tamoxifen-dependent Sox9 depletion. Phenotype severity nicely correlates with the efficiency of Cre-mediated Sox9 depletion.

Molecular marker analysis provides strong evidence of Mueller cell loss and photoreceptor degeneration.

A clever genetic tracing strategy uncovers a critical role for Sox9 in limbal stem cell differentiation.

Weaknesses:

(1) The Introduction can be improved by explaining clearly what was previously known about Sox9 in the eye. A lot of this info is mentioned in a single, 3-page long paragraph in the Discussion. However, the current study's significance and novelty would become clearer if the authors articulated in more detail in the Introduction what was already known about Sox9 in retina cell types (in vitro and in vivo).

We appreciate this insightful comment. Following the reviewer`s suggestion, we have reorganized the manuscript to provide a clearer scientific context in the Introduction. Specifically, we have moved the relevant background information on Sox9 in different retinal cell types—previously included in a single, extended paragraph in the Discussion—into the Introduction. This helps to better frame our study within the context of existing knowledge.

Additionally, we have emphasized more explicitly that our work does not focus on embryonic development, as most previous studies on Sox9 have done, but instead investigates its role in the adult mouse retina and limbus/cornea. We believe this represents an important and novel aspect of our study, as the mechanisms of retinal maintenance and limbal stem cell differentiation in the adult have been less extensively studied.

(2) Because a ubiquitous tamoxifen-inducible CreER line is employed, non-cell autonomous mechanisms possibly contribute to the observed retina degeneration. There is precedence for this in the literature. For example, RPE-specific ablation of Otx2 results in photoreceptor degeneration (PMID: 23761884). Have the authors considered the possibility of non-cell autonomous effects upon ubiquitous Sox9 deletion?

Given the similar phenotypes between animals lacking Otx2 and Sox9 in specific cell types of the eye, the authors are encouraged to evaluate Otx2 expression in the tamoxifen-induced Sox9 adult retina.

We appreciate the insightful comment of the reviewer regarding the potential contribution of non-cell autonomous mechanisms to the retinal degeneration observed upon ubiquitous Sox9 deletion. We agree that this is an important consideration, particularly in the context of findings showing that RPE-specific deletion of Otx2 results in secondary photoreceptor degeneration.

However, we would like to emphasize that RPE-specific deletion of Sox9 does not lead to photoreceptor loss or retinal degeneration, as previously shown (Masuda et al., 2014; Goto et al., 2018; Cohen-Tayar et al., 2018) [PMID: 24634209; PMID: 29609731; PMID: 29986868]. In addition, it was shown that Sox9 deletion in the RPE caused downregulation of visual cycle genes but did not compromise photoreceptor integrity or survival. Interestingly, Otx2 expression was found to be upregulated in the absence of Sox9, further supporting the view that Sox9 is not a simple upstream regulator of Otx2 in the adult RPE (Matsuda, 2014). These findings suggest that RPE dysfunction alone cannot account for the severe retinal phenotype we observe in our model.

In our study, we observed that photoreceptor degeneration correlates strongly with the depletion of Sox9 Müller glial cells. Given the well-established supportive and neuroprotective roles of Müller glia, we interpret the retinal degeneration in our model to be primarily a consequence of Müller cell dysfunction (confirmed by the loss of Müller glia markers, such as SOX8 and S100). This interpretation is further supported by previous studies showing that selective ablation of Müller glia can lead to photoreceptor degeneration through cell-autonomous mechanisms (Shen et al., 2012) [PMID: 23136411].

Nevertheless, we agree that this possibility deserves further investigation, and we have acknowledged it in the following paragraph that has been added to the Discussion section (lines 511-523 of the revised ms):

“An important consideration in our model is the potential contribution of non-cell autonomous mechanisms to photoreceptor degeneration. Sox9 is expressed in both MG and RPE cells, and both cell types are known to support photoreceptor viability (Poché et al., 2008; Masuda et al., 2014). Notably, Sox9 and Otx2 cooperate to regulate visual cycle gene expression in the RPE (Masuda et al., 2014), and loss of Otx2 specifically in the adult RPE leads to secondary photoreceptor degeneration through non-cell autonomous mechanisms (Housset et al., 2013). However, RPE-specific deletion of Sox9 does not induce retinal degeneration and in fact results in Otx2 upregulation (Masuda et al., 2014; Goto et al., 2018; Cohen-Tayar et al., 2018), suggesting that Sox9 is not an upstream regulator of Otx2 in this context. Further investigation into the molecular and cellular interactions between MG, RPE, and photoreceptors may help to clarify the indirect pathways contributing to degeneration in the absence of Sox9.”

Consistent with the above, a new citation has been included:

Housset M, Samuel A, Ettaiche M, Bemelmans A, Béby F, Billon N, Lamonerie T. 2013. Loss of Otx2 in the adult retina disrupts retinal pigment epithelium function, causing photoreceptor degeneration. J Neurosci 33:9890–904. doi:10.1523/JNEUROSCI.1099-13.2013.

(3) The most parsimonious explanation for the dual role of Sox9 in retinal cell types and limbal stem cells is that the cell context is different. For example, Sox9 may cooperate with TF1 in photoreceptors, TF2, in Mueller cells, and TF3 in limbal stem cells, and such cell typespecific cooperation may result in different outcomes (retinal integrity, stem cell differentiation). The authors are encouraged to add a paragraph to the discussion and share their thoughts on the dual role of Sox9.

We thank the reviewer for this thoughtful and constructive suggestion. In , we have added a paragraph at the end of the Discussion addressing the potential dual role of Sox9 in the cornea and retina. In this new section, we discuss how Sox9 might exert distinct functions depending on the cellular context, possibly through interactions with different transcriptional partners in specific cell types. This may help explain the contrasting roles of Sox9 in maintaining retinal integrity versus regulating stem cell differentiation in the limbal epithelium.

(4) One more molecular marker for Mueller glial cells would strengthen the conclusion that these cells are lost upon Sox9 deletion.

We thank the reviewer for this constructive suggestion. To reinforce our conclusion that most Müller glial cells are lost following Sox9 deletion, we analysed the expression of S100, a well-established cytoplasmic marker of Müller glia. As S100 is primarily localized to the innermost Müller cell processes and not restricted to cell bodies, direct cell counting was not feasible. Instead, we quantified the S100+ signal intensity across defined retinal surface areas. This analysis revealed a statistically significant reduction in S100 signal in Sox9^Δ/Δ retinas compared to controls. These new data, included in the revised Figure 1 (panels F and G), support and extend our previous observations using SOX8, further confirming the loss of Müller glial cells in Sox9-deficient retinas.

We have also modified the manuscript based on this new evidences as follows:

In the Results section, lines 168-177 of the revised ms, we have added the following paragraph: “To independently validate the loss of MG cells in Sox9-deficient retinas, we examined the expression of S100, a cytoplasmic marker that labels the processes of adult Müller cells. In control retinas, strong S100 immunoreactivity was observed across the inner retina, outlining the typical radial projections of Müller glia (Fig. 1F). In contrast, Sox9Δ/Δ retinas with an extreme phenotype exhibited a marked reduction in S100 signal (Fig. 1G). Given the diffuse cytoplasmic localization of S100, we quantified its expression by measuring the fluorescence signal within a defined surface area of the retina. This analysis revealed a statistically significant reduction in S100 signal intensity in mutant samples (including both mild and extreme phenotypes) compared to controls (Fig. 1G; Table S4), further supporting the loss of MG cells upon Sox9 deletion.”

In Methods, line 684 of the revised ms, the anti-S100 antibody reference and its working dilution have been added.

(5) Using opsins as markers, the authors conclude that the photoreceptors are lost upon Sox9 deletion. However, an alternate possibility is that the photoreceptors are still present and that Sox9 is required for the transcription of opsin genes. In that case, Sox9 (like Otx2) may act as a terminal selector in photoreceptor cells. This point is particularly important because vertebrate terminal selectors (e.g., Nurr1, Otx2, Brn3a) initially affect neuron type identity and eventually lead to cell loss.

We perfectly understand the reviewer’s point. However, we believe that the possibility that Sox9 regulates opsin gene expression without affecting photoreceptor survival is very unlikely in our model. The primary evidence comes from the histological analysis shown in Figure 1B, where hematoxylin and eosin staining clearly demonstrates the complete loss of the ONL in Sox9^Δ/Δ retinas exhibiting the extreme phenotype. Similarly, DAPI counterstain also evidences the lack of the ONL in many of our immunofluorescence images of these samples.  This morphological disappearance of the ONL strongly supports the conclusion that photoreceptor cells are not merely transcriptionally silent but are physically absent.

Furthermore, TUNEL assays in two retinas with a mild phenotype revealed extensive apoptosis within the ONL, suggesting a progressive degeneration process rather than a selective transcriptional effect. While we acknowledge that transcriptional regulation of opsin genes by Sox9 cannot be entirely ruled out, the observed phenotype is more consistent with a structural loss of photoreceptors rather than a change in their molecular identity alone. Therefore, our data support the interpretation that Sox9 is required for photoreceptor survival, likely through non-cell autonomous mechanisms related to Müller glia dysfunction, rather than acting as a terminal selector within photoreceptor cells themselves.

(6) Quantification is needed for the TUNEL and GFAP analysis in Figure 3.

We have quantified the GFAP immunofluorescence signal across defined surface areas of the retina and found a statistically significant increase in GFAP expression in Sox9^Δ/Δ mutants compared to controls (Mann-Whitney U test, P = 0.0240; n = 4 controls, 10 mutants). These quantification data are now included in the revised Figure 3.

Regarding the TUNEL assay, although extensive apoptosis was clearly observed in two Sox9<^Δ/Δ retinas with a mild phenotype (as shown in Figure 3A), this pattern was not consistent across the full study mouse cohort. Out of 15 mutant samples analyzed (5 of them previously analyzed and 10 additional ones that have been newly analyzed), only two exhibited this pronounced apoptotic pattern. However, in the remaining 13 mutants, we did observe a small but statistically significant increase in the number of TUNEL+ cells compared to controls (zero-inflated Poisson test, P = 0.028, n = 5 controls, 13 mutants). These results are now included in Figure 3 and in Tables S7 and S8.

This pattern likely reflects the transient nature of apoptosis in the degenerative process, which may occur rapidly and thus be difficult to capture consistently at a single time point. Nevertheless, the quantification supports our conclusion that Sox9 loss is associated with increased photoreceptor cell death.

Based on the above, we have included the following paragraphs in the Results section of the manuscript:

In lines 224-252 of the revised ms, the final version of the paragraph is as follows: “Since photoreceptors are absent in severely affected Sox9-mutant retinas, we conducted TUNEL assays to study the role of cell death in the process of retinal degeneration. In control samples (n=5), almost no TUNEL signal was observed in the retina. In contrast, Sox9^Δ/Δ mice (n=15) showed numerous TUNEL+ cells, mainly located in the persisting ONL, indicating that photoreceptor cells were dying (Fig. 3A). Although extensive TUNEL staining in the ONL was clearly observed in two Sox9^Δ/Δ retinas with mild phenotypes, this pattern was not consistently present across the full cohort. In the remaining 13 mutant retinas, we observed a modest but noticeable increase in the number of apoptotic cells compared to controls (Fig. 3B; Table S7). Despite a high frequency of zero counts (particularly among controls), the difference between groups reached statistical significance when analyzed using a zeroinflated Poisson model (P = 0.028; n = 5 controls, 13 mutants). These findings suggest that photoreceptor apoptosis following Sox9 deletion may occur acutely and within a narrow temporal window, making it challenging to capture the full degenerative process at a single time point”.

Lines 263-269 of the revised ms: “To support these observations quantitatively, we measured GFAP fluorescence intensity across defined retinal surface areas in control and Sox9^Δ/Δ mice (Fig. 3D; Table S8). This analysis revealed a statistically significant increase in GFAP signal in mutant retinas compared to controls (Mann-Whitney U test, P = 0.0240; n = 4 controls, 10 mutants). These results are consistent with a progressive gliotic  following Sox9 deletion and provide further evidence that MG cells become reactive in the absence of Sox9”.

Similarly, the section “Estimation of the percentage of tamoxifen-induced, Cre-mediated recombination” has been expanded as follows:

Lines 660-665 of the revised ms: “In parallel, to quantify GFAP expression as a measure of MG reactivity, we analyzed GFAP immunofluorescence intensity across defined retinal surface areas. Given the cytoplasmic distribution of GFAP within glial processes, direct cell counting was not feasible. Instead, fluorescence intensity was measured using ImageJ, within full-thickness retinal regions in 20x microphotographs of a retinal sections stained for GAFP. The total GFAP signal was normalized to the measured area for each section”.

(7) Line 269-320: The authors examined available scRNA-Seq data on adult retina. This data provides evidence for Sox9 expression in distinct cell types. However, the dataset does not inform about the functional role of Sox9 because Sox9 mutant cells were not analyzed with RNA-Seq. Hence, all the data that claim that this experiment provides insights into possible Sox9 functional roles must be removed. This includes panels F, G, and H in Figure 5. In general, this section of the paper (Lines 269-320) needs a major revision. Similarly, lines 442-454 in the Discussion should be removed.

We thank the reviewer for this important observation. We agree that the scRNA-Seq dataset used in this section does not include Sox9 mutant cells and therefore does not allow us to assess the consequences of Sox9 loss-of-function. However, we believe that this analysis still provides valuable complementary information. Specifically, it confirms that Sox9 is expressed in a distinct population of limbal stem cells, and that its expression dynamically changes along differentiation trajectories. Although we do not infer causality or phenotypic consequences, the ability to observe how gene expression programs shift as Sox9 is downregulated offers insights into potential transcriptional programs associated with Sox9 activity.

We have carefully revised Lines 269–320 to remove any overinterpretations, and eliminated the corresponding lines in the Discussion (Lines 442–454). However, we have retained Panels G, and H in Figure 5 with updated text that reflect the descriptive nature of these findings, specifically to illustrate that the Sox9-positive cell signature is consistent with a stem cell genetic program, and that when Sox9 is downregulated some gene pathways involved in stem cell differentiation are upregulated.

Reviewer #1 (Recommendations for the authors):

Major points

(1) Figure 1C shows the proportions of Sox9+cells that express Sox8 in control, mild and extreme phenotypes. However, as no quantitative classification of mild and extreme phenotypes is reported along with Figure 1A, the large standard deviation for Sox9∆/∆ mild retina might be due to a misclassification of the sample. Therefore, the authors must ascribe each sample to "mild" or "extreme" based on a quantitative metric.

We appreciate the reviewer’s suggestion to clarify the classification criteria used to distinguish “mild” and “extreme” phenotypes in Sox9^Δ/Δ retinas. As noted, our classification was based on a qualitative, phenotypic assessment of retinal morphology in hematoxylin/eosin-stained sections. Specifically, retinas were classified as “extreme” when the outer nuclear layer (ONL) was completely absent, and as “mild” when the ONL was present, although often reduced in thickness. This classification reflects the observable structural depletion of the ONL and aligns well with the extent of Sox9 loss in Müller glial cells, as shown in Figure 1. We acknowledge that some variability exists within the “mild” group, likely due to differences in recombination efficiency and the mosaic nature of tamoxifen-induced deletion.

The phenotypic classification of each individual sample is explicitly provided in Supplementary Table S1. We have also added a statement in the Results section clarifying that this classification was based on qualitative histological criteria rather than a numerical threshold.

Lines 104-113 of the revised ms: “We categorized Sox9^Δ/Δ retinas into “mild” and “extreme” phenotypes in order to facilitate interpretation of our data. Clasification was based on a qualitative assessment of ONL integrity in histological sections. Specifically, samples were classified as “extreme” when the ONL was completely depleted, and as “mild” when the ONL persisted, albeit variably reduced in thickness. This phenotypic classification reflects observable structural differences rather than a fixed quantitative threshold. Some variability exists within the “mild” group, likely due to differences in recombination efficiency and the mosaic nature of tamoxifen-induced Cre-mediated Sox9 deletion”

(2) The authors infer Sox9 high and Sox9 low groups of limbal stem cells using an existing scRNA-seq dataset. However, an immunohistology-based validation of this difference is missing. Given that limbal stem cells express Sox9, the authors must examine the heterogeneity in Sox9 levels within the Sox8+ population to demonstrate their claim: "...Sox9 expression decreases as transiently amplifying progenitors undergo progressive differentiation from limbal to peripheral corneal cells." in Line 292. Ideally, this must be further validated using differentiation markers corresponding to CB and ILB populations that show lower Sox9 expression according to the pseudotime graph.

To validate the Sox9 expression results obtained with scRNA-seq, we performed double immunofluorescence for Sox9 and P63, the latter expressed by the basal cells of the limbal epithelium, but not by transient amplifying cells covering the corneal surface (Pellegrini et al., 2001, https://www.pnas.org/doi/abs/10.1073/ pnas.061032098). These results can be observed in the new panel 5F. Accordingly we have included a new paragraph in lines 369-396 of the revised version of the ms:

“To validate these results, we decided to closely examine Sox9 expression in the limbus using immunofluorescence. Previous analyses revealed that the outer limbus is approximately 100 μm wide, while the inner limbus is wider, around 240 μm (Altshuler 2021). We observed that in the region corresponding to the OLB, most cells showed strong Sox9 expression. In the area corresponding to the ILB, this immunoreactivity appeared weaker in the basal layer (corresponding to the ILB proper), and no expression was detected in the suprabasal layers (flattened cells; Fig 5F left). Double immunofluorescence for SOX9 and P63, which is expressed in basal cells of the limbal epithelium, but not by transient amplifying cells covering the corneal surface (Pellegrini et al., 2001) revealed that Sox9 expression was restricted to P63-positive cells (Fig 5F right). These observations confirm that Sox9 is expressed in a basal cell population within both the OLB and ILB, and that its expression decreases in differentiated transient amplifying cells. ”

We also have deleted  “This expression pattern is consistent with our immunofluorescence observations" from line 356 of the revised ms.

(3) The authors' claim of "...Sox9-null cells cannot survive or proliferate as well as their wildtype neighbors, and are hence outcompeted over time, leading to an essentially wild-type cornea" does not seem very convincing in the light of Fig.6D and S3B where Sox9 deletion can still allow for a large LacZ+ clone. Their claim of wild-type cornea due to out-competing neighbors must be validated by increasing the number of Sox9-null progenitors, which can be tested by administering tamoxifen for a significantly longer duration, leading to a majority Sox9 deficient progenitor population, and then examining limbal and corneal defects.

As previously discussed, we observed only one instance of a large LacZ+ clone across 8 Sox9^Δ/Δ-LacZ eyes. Based on prior reports of ligand-independent Cre activity (Vooijs et al., 2001; Kemp et al., 2004; Haldar et al., 2009; Dorà et al., 2015), we believe this rare event likely resulted from spontaneous recombination in a bona fide limbal stem cell, independent of tamoxifen administration. For this reason, we do not expect that increasing the dose or duration of tamoxifen would eliminate such rare events. Furthermore, due to the mosaic and highly variable recombination efficiency of the CAGG-CreERTM system in the adult eye (see McMahon et al., 2008), attempting to increase the TX dosage would likely lead to systemic toxicity or lethality, without guaranteeing full inactivation of the gene in the limbus. Thus, this system is not well-suited for generating a fully Sox9-deficient limbal epithelium. To overcome this limitation, we crossed our mice with the R26R-LacZ reporter line to track the clonal behavior of Sox9-deficient cells. In control animals (Sox9Δ/+-LacZ), LacZ+ stripes originating from limbal stem cells are readily observed. In contrast, in Sox9Δ/Δ-LacZ mutants, these clones are either absent or drastically reduced. This suggests that Sox9-null cells have a severely impaired ability to form and sustain clones. To rigorously quantify this effect, we compared 8 control and 5 mutant corneas, revealing a highly significant 8-fold reduction in LacZ-positive area in the mutants (6.65 ± 1.77% vs. 0.85 ± 0.85%; p = 0.00017; Fig. 6F; Table S12; Supp. Fig. X???), supporting our claim that Sox9null cells cannot survive or proliferate as well as their wild-type neighbors, and are hence outcompeted over time, leading to an essentially wild-type cornea.

Minor points

(1) Quantification for Figure 2C and 2D is missing.

We have now included quantification of BRN3A+ retinal ganglion cells (Figure 2E) across control and Sox9^Δ/Δ retinas. Cell counts were performed on matched retinal sections, and the difference between groups was found to be statistically significant through Mann–Whitney U test (Table S5).

Regarding PAX6/AP2a, we quantified inner retinal neurons by analyzing AP2α+ amacrine cells and PAX6+/AP2α- horizontal cells as distinct subpopulations, rather than simply comparing total PAX6 or AP2α immunoreactivity. This approach allowed us to better resolve specific neuronal subtype changes. Both populations showed a statistically significant reduction in Sox9-deficient retinas relative to controls. The quantification for these analyses has now been incorporated into the revised Figure 2F and G (Table S6).

Consequently with the above, the following paragraph of the Results section (line 210 of the revised ms:

“We also studied the status of other retinal cell types. The transcription factor BRN3A was used to identify ganglion cells (Nadal-Nicolás et al., 2009), which were shown to decrease in number in the mutant retinas, compared to control ones (Fig. 2C). Similarly, double immunodetection of the transcription factors PAX6 and AP2A was used to identify both amacrine and horizontal cells, as previously described (Marquardt et al., 2001; Barnstable et al., 1985; Edqvist and Hallböök, 2004), showing a similar reduction in both cell types in degenerated retinas (Fig. 2D).”

Has been modified as follows:

“We also studied the status of other retinal cell types. The transcription factor BRN3A was used to identify ganglion cells (Nadal-Nicolás et al., 2009), which were shown to decrease in number in the mutant retinas, compared to control ones (Figs. 2C and 2D and Table S5; n = 5 controls, n = 12 mutants; Mann-Whitney U test, P = 3 × 10^-4). Similarly, double immunodetection of the transcription factors PAX6 and AP2A was used to identify both amacrine and horizontal cells (Fig. 2E), as previously described (Marquardt et al., 2001; Barnstable et al., 1985; Edqvist and Hallböök, 2004), showing a similar reduction in both cell types in degenerated retinas (Figs. 2F and 2G and Table S6; AP2α+ amacrine cells: n = 3 controls, n = 8 mutants;  2-sample T-tests P = 0.029; PAX6+/AP2α− horizontal cells: n = 3 controls, n = 8 mutants; Mann-Whitney U test P = 0.021). These findings indicate that the loss of Sox9 in the adult retina ultimately leads to the degeneration of multiple inner retinal neuronal populations, beyond the previously described effects on photoreceptors and Müller glia.

(2) Figure 4G & H: The authors must mention that the dashed lines enclose the limbal area.

Done

(3) The authors infer from an existing scRNA-seq dataset that OLB cells have high Sox9 expression as compared to ILB and corneal populations. However, Figures 4A and B do not indicate the anatomical positions of these cell types. The authors must label these for the reader's reference as they state that "[Sox9] expression pattern is consistent with our immunofluorescence observations" in Line 280.

As previously indicated, we have generated a new panel 5F and a corresponding paragraph to illustrate Sox9 expression pattern in the limbus. Accordingly, we have removed the sentence from line 280.

(4) Quantification for Figures 6A and 6B is missing.

We have quantified the number of Sox9 and P63 positive cells in the limbus between mutant and control corneas and found no difference in the number of positive cells. We have included these data in new panel 6C and Table S11.

Reviewer #2 (Recommendations for the authors):

Line 24: "synapsis" should be "synapses".

Done

(1) Consider starting a new paragraph after line 30.

Done

(2) Lines 42-48: make clear that this paragraph provides information only for HUMAN SOX9.

We now distinguish which studies were conducted in humans and which in mice.

(3) Line 55: explain to the non-expert reader what the "visual cycle" is.

Done (lines 64-65 of the revised ms)

(4) Line 66: consider "inactivate" instead of "suppress".

We substituted “suppress” with “inactivate”

(5) Line 90-92: ONLY PCR for the cGMP will provide formal evidence that this is not present in the mouse line.

We agree with the reviewer that PCR genotyping is the most straightforward method to exclude the presence of the Pde6^brd1 allele. Although retinal degeneration was never observed in untreated or control animals in our study, we have now removed the term “formal possibility” from the text to better reflect this limitation.

We have modified the following paragraph (page 116 in the revised version of the manuscript): “Retinal degeneration was never observed in mice that had not been tamoxifen-treated, nor any other controls, eliminating the formal possibility that the retinal degeneration allele of photoreceptor cGMP phosphodiesterase 6b (Pde6brd1) was present in our mice (Bowes et al., 1990).”

As follows: “Retinal degeneration was never observed in mice that had not been tamoxifentreated, nor any other control groups, making the presence of the retinal degeneration allele of photoreceptor cGMP phosphodiesterase 6b (Pde6^brd1) unlikely in our mice (Bowes et al., 1990). However, we acknowledge that definitive exclusion of this possibility would require PCR-based genotyping.”

(6) Line 160-166: This paragraph needs a conclusion.

We agree with the reviewer and have added the following sentence at the end of the paragraph:

“These findings indicate that the loss of Sox9 in the adult retina ultimately leads to the degeneration of multiple inner retinal neuronal populations, beyond the previously described effects on photoreceptors and Müller glia”

(7) Line: 240-265: This paragraph ends without a conclusion.

We have include the following conclusion:

“Thus, Sox9 is expressed in a basal limbal stem cell population with the ability to form two types of long-lived cell clones involved in stem cell maintenance and homeostasis.”

(8) In Results, it needs to be specified when exactly in adulthood the tamoxifen treatment started. This information is only provided in the Methods.

We have specified the age of the mice at the onset of tamoxifen treatment (two months)  and included it in the schemes of Figs 1A, 4C, 4H, 6E.

(9) Line 250: Because live imaging is not conducted, the word "dynamics" is not suitable.

We substituted “dynamics” with “contribution”

(10) Panel C in Figure 6 is nice and helpful. Consider adding a similar panel in Figure 1.

Done.

(11) Line 420: is this the human Sox9 enhancer?

Yes. It is a human enhancer. We have indicated it in the text.

(12) Line 459: typo "detected tissue".

Corrected

(13) Line 448 and 468: citations are needed.

Line 448 is deleted in the revised version of the ms.

(14) 479: typo "clones clones'.

Corrected.

eLife Assessment

Shen et al. present a computational account of individual differences in mouse exploration when faced with a novel object in an open field from a previously published study (Akiti et al.) that relates subject-specific intrinsic exploration and caution about potential hazards to the spectrum of behaviors observed in this setting. Overall, this computational study is an important contribution that leverages a very general modeling framework (a Bayes Adaptive Markov Decision Process) to quantify and interrogate distinct drivers of exploratory behavior under potential threat. Given their assumptions, the modeling results are convincing: the authors are able to describe a substantial amount of the behavioral features and idiosyncracies in this dataset, and their model affords a normative interpretation related to inherent risk aversion and predation hazard "flexibility" of individual animals and should be of broad interest to researchers working to understand open-ended exploratory behaviors.

Reviewer #1 (Public review):

Summary:

This work computationally characterized the threat-reward learning behavior of mice in a recent study (Akiti et al.), which had prominent individual differences. The authors constructed a Bayes-adaptive Markov decision process model, and fitted the behavioral data by the model. The model assumed (i) hazard function staring from a prior (with free mean and SD parameters) and updated in a Bayesian manner through experience (actually no real threat or reward was given in the experiment), (ii) risk-sensitive evaluation of future outcomes (calculating lower 𝛼 quantile of outcomes with free 𝛼 parameter), and (iii) heuristic exploration bonus. The authors found that (i) brave animals had more widespread hazard priors than timid animals and thereby quickly learned that there was in fact little real threat, (ii) brave animals may also be less risk-aversive than timid animals in future outcome evaluation, and (iii) the exploration bonus could explain the observed behavioral features, including the transition of behavior from the peak to steady-state frequency of bout. Overall, this work is a novel interesting analysis of threat-reward learning, and provides useful insights for future experimental and theoretical work. However, there are several issues that I think need to be addressed.

Strengths:

- This work provides a normative Bayesian account for individual differences in braveness/timidity in reward-threat learning behavior, which complements the analysis by Akiti et al. based on model-free threat reinforcement learning.

- Specifically, the individual differences were characterized by (i) the difference in the variance of hazard prior and potentially also (ii) the difference in the risk-sensitivity in evaluation of future returns.

Weakness:

- Theoretically the effect of prior is diluted over experience whereas the effect of biased (risk-aversive) evaluation persists, but these two effects could not be teased apart in the fitting analysis of the current data.

- It is currently unclear how (whether) the proposed model corresponds to neurobiological (rather than behavioral) findings, different from the analysis by Akiti et al.

Comments on revisions:

The authors have adequately replied to all the concerns that I raised in my review of the original manuscript. I do not have any remaining concern, and I am now more convinced that this work provides novel important insights and stimulates future experimental and theoretical examinations.

Reviewer #3 (Public review):

Summary:

The manuscript presents computational modelling of the behaviour of mice during encounters with novel and familiar objects, originally reported in Akiti et al. (Neuron 110, 2022). Mice typically perform short bouts of approach followed by retreat to a safe distance, presumably to balance exploration to discover possible reward with the potential risk of predation. However, there is considerable heterogeneity in this exploratory behaviour, both across time as an individual subject becomes more confident in approaching the object, and across subjects; with some mice rapidly becoming confident to closely explore the object, while other timid mice never become fully confident that the object is safe. The current work aims to explain both the dynamics of adaptation of individual animals over time, and the quantitative and qualitative differences in behaviour between subjects, by modelling their behaviour as arising from model-based planning in a Bayes adaptive Markov Decision Process (BAMDP) framework, in which the subjects maintain and update probabilistic estimates of the uncertain hazard presented by the object, and rationally balance the potential reward from exploring the object with the potential risk of predation it presents.

In order to fit these complex models to the behaviour the authors necessarily make substantial simplifying assumptions, including coarse-graining the exploratory behaviour into phases quantified by a set of summary statistics related to the approach bouts of the animal. Inter-individual variation between subjects is modelled both by differences in their prior beliefs about the possible hazard presented by the object, and by differences in their risk preference, modelled using a conditional value at risk (CVaR) objective, which focuses the subject's evaluation on different quantiles of the expected distribution of outcomes. Interestingly, these two conceptually different possible sources of inter-subject variation in brave vs timid exploratory behaviour turn out not to be dissociable in the current dataset as they can largely compensate for each other in their effects on the measured behaviour. Nonetheless, the modelling captures a wide range of quantitative and qualitative differences between subjects in the dynamics of how they explore the object, essentially through differences in how subject's beliefs about the potential risk and reward presented by the object evolve over the course of exploration, and are combined to drive behaviour.

Exploration in the face of risk is a ubiquitous feature of the decision-making problem faced by organisms, with strong clinical relevance, yet remains poorly understood and under-studied, making this work a timely and welcome addition to the literature.

Strengths:

- Individual differences in exploratory behaviour are an interesting, important, and under-studied topic.

- Application of cutting-edge modelling methods to a rich behavioural dataset, successfully accounting for diverse qualitative and qualitative features of the data in a normative framework.

- Thoughtful discussion of the results in the context of prior literature.

Limitations:

- The model-fitting approach used of coarse-graining the behaviour into phases and fitting to their summary statistics may not be applicable to exploratory behaviours in more complex environments where coarse-graining is less straightforward.

Comments on revisions:

All recommendations to authors from the first review were addressed in the revised manuscript.

Author response:

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

Reviewer #1 (Public review):

This work computationally characterized the threat-reward learning behavior of mice in a  recent study (Akiti et al.), which had prominent individual differences. The authors  constructed a Bayes-adaptive Markov decision process model and fitted the behavioral data  by the model. The model assumed (i) hazard function starting from a prior (with free mean  and SD parameters) and updated in a Bayesian manner through experience (actually no real  threat or reward was given in the experiment), (ii) risk-sensitive evaluation of future  outcomes (calculating lower 𝛼 quantile of outcomes with free 𝛼 parameter), and (iii) heuristic  exploration bonus. The authors found that (i) brave animals had more widespread hazard  priors than timid animals and thereby quickly learned that there was in fact little real threat,  (ii) brave animals may also be less risk-aversive than timid animals in future outcome  evaluation, and (iii) the exploration bonus could explain the observed behavioral features,  including the transition of behavior from the peak to steady-state frequency of bout. Overall,  this work is a novel interesting analysis of threat-reward learning, and provides useful  insights for future experimental and theoretical work. However, there are several issues that I  think need to be addressed.

Strengths:

(1) This work provides a normative Bayesian account for individual differences in  braveness/timidity in reward-threat learning behavior, which complements the analysis by  Akiti et al. based on model-free threat reinforcement learning.

(2) Specifically, the individual differences were characterized by (i) the difference in the  variance of hazard prior and potentially also (ii) the difference in the risk-sensitivity in the  evaluation of future returns.

Weakness:

(1) Theoretically the effect of prior is diluted over experience whereas the effect of biased  (risk-aversive) evaluation persists, but these two effects could not be teased apart in the  fitting analysis of the current data.

(2) It is currently unclear how (whether) the proposed model corresponds to neurobiological ( rather than behavioral) findings, different from the analysis by Akiti et al.

We thank reviewer #1 for their useful feedback which we’ve used to improve the discussion,  formatting and clarity of the paper, and for highlighting important questions for future  extensions of our work.

Major points:

(1) Line 219

It was assumed that the exploration bonus was replenished at a steady rate when the animal  was at the nest. An alternative way would be assuming that the exploration bonus slowly  degraded over time or experience, and if doing so, there appears to be a possibility that the  transition of the bout rate from peak to steady-state could be at least partially explained by  such a decrease in the exploration bonus.

Section 2.2.3 explains the mechanism of the exploration bonus which motivates approach.  We think that the mechanism suggested by the reviewer is, in essence, what is happening in  the model. The exploration pool is indeed depleted over time or bouts of experience at the  object. In the peak confident phase for brave animals and the peak cautious phase for timid  animals, the rate of depletion exceeds the rate of regeneration, since the agent spends only  a single turn at the nest between bouts. In the steady-state phase, the exploration pool has  depleted so much previously that the agent must wait multiple turns at the nest for the pool  to regenerate to a sufficiently high value to justify approaching the object again.

We have updated section 2.2.3 to explain that agents spend one turn at the nest during peak  phase but multiple turns during steady-state phase. Hopefully, this makes our mechanism  clear:

“In simulations, when 𝐺(𝑡) is high, the agent has a high motivation to explore the object,  spending only a single turn in the nest state between bouts. In other words, the depletion  from 𝐺0 substantially influences the time point at which approach makes a transition from  peak to steady-state; the steady-state time then depends on the dynamics of depletion  (when at the object) and replenishment (when at the nest). In particular, in the steady-state  phases, the agent must wait multiple turns at the nest for 𝐺(𝑡)  to regenerate so that  informational reward once again exceeds the potential cost of hazard.“

(2) Line 237- (Section 2.2.6, 2.2.7, Figures 7, 9)

I was confused by the descriptions about nCVaR. I looked at the cited original literature  Gagne & Dayan 2022, and understood that nCVaR is a risk-sensitive version of expected  future returns (equation 4) with parameter α (α-bar) (ranging from 0 to 1) representing risk  preference. Line 269-271 and Section 4.2 of the present manuscript described (in my  understanding) that α was a parameter of the model. Then, isn't it more natural to report  estimated values of α, rather than nCVaR, for individual animals in Section 2.2.6, 2.2.7,  Figures 7, 9 (even though nCVaR monotonically depends on α)? In Figures 7 and 9, nCVaR  appears to be upper-bounded to 1. The upper limit of α is 1 by definition, but I have no idea why nCVaR was also bounded by 1. So I would like to ask the authors to add more detailed  explanations on nCVaR. Currently, CVaR is explained in Lines 237-243, but actually, there is  no explanation about nCVaR rather than its formal name 'nested conditional value at risk' in  Line 237.

Thank you for pointing out this error. We have corrected the paper to use nCVaR to refer to  the objective and nCVaR's α, or sometimes just α, to refer to the risk sensitivity parameter  and thus the degree of risk sensitivity.

(3) Line 333 (and Abstract)

Given that animals' behaviors could be equally well fitted by the model having both nCVaR ( free α) and hazard prior and the alternative model having only hazard prior (with α = 1), may  it be difficult to confidently claim that brave (/timid) animals had risk-neutral (/risk-aversive)  preference in addition to widespread (/low-variance) hazard prior? Then, it might be good to  somewhat weaken the corresponding expression in the Abstract (e.g., add 'potentially also'  to the result for risk sensitivity) or mention the inseparability of risk sensitivity and prior belief  pessimism (e.g., "... although risk sensitivity and prior belief pessimism could not be teased  apart").

Thank you for this suggestion, we have duly weakened the wording in the Abstract to say  “potentially more risk neutral”:

“Some animals begin with cautious exploration, and quickly transition to confident approach  to maximize exploration for reward; we classify them as potentially more risk neutral, and  enjoying a flexible hazard prior. By contrast, other animals only ever approach in a cautious  manner and display a form of  self-censoring; they are characterized by potential risk  aversion and high and inflexible hazard priors.”

Reviewer #2 (Public Review):

Shen and Dayan build a Bayes adaptive Markov decision process model with three key  components: an adaptive hazard function capturing potential predation, an intrinsic reward  function providing the urge to explore, and a conditional value at risk (CvaR, closely related  to probability distortion explanations of risk traits). The model itself is very interesting and  has many strengths including considering different sources of risk preference in generating  behavior under uncertainty. I think this model will be useful to consider for those studying  approach/avoid behaviors in dynamic contexts.

The authors argue that the model explains behavior in a very simple and unconstrained  behavioral task in which animals are shown novel objects and retreat from them in various  manners (different body postures and patterns of motor chunks/syllables). The model itself  does capture lots of the key mouse behavioral variability (at least on average on a  mouse-by-mouse basis) which is interesting and potentially useful. However, the variables in  the model - and the internal states it implies the mice have during the behavior - are  relatively unconstrained given the wide range of explanations one can offer for the mouse  behavior in the original study (Akiti et al). This reviewer commends the authors on an original  and innovative expansion of existing models of animal behaviour, but recommends that the  authors  revise their study to reflect the obvious  challenges . I would also recommend a  reduction in claiming that this exercise gives a normative-like or at least quantitative account  of mental disorders.

We thank reviewer #2 for highlighting some of the strengths of our paper as well as pointing  out important limitations of Akiti et al’s original study which we’ve inherited as well as some  limitations of our own method. We address their concerns below.

We have added a paragraph to the discussion discussing the limitations of the state  representation we adopted from Akiti’s study.

(Reviewer #1 had the same concern, see above) “Motivated by tail-behind versus  tail-exposed in Akiti et al. (2022), we model approach using a dichotomy between cautious  and confident approach states [...]”

We have reduced the suggestion that our model provides an account of mental disorders in  the abstract.

Before:

“On the other hand, “timid” animals, characterized by risk aversion and high and inflexible  hazard priors, display self-censoring that leads to the sort of asymptotic maladaptive  behavior that is often associated with psychiatric illnesses such as anxiety and depression.”

After:

“By contrast, other animals only ever approach in a cautious manner and display a form of  self-censoring; they are characterized by potential risk aversion and high and inflexible  hazard priors. “

My main comment is that this paper is a very nice model creation that can characterize the  heterogeneity rodent behavior in a very simple approach/avoid context (Akiti et al; when a  novel object is placed in an arena) that itself can be interpreted in a multitude of ways. The  use of terms like "exploration", "brave", etc in this context is tricky because the task does not  allow the original authors (Akiti et al) to quantify these "internal states" or "traits" with the  appropriate level of quantitative detail to say whether this model is correct or not in capturing  the internal states that result in the rodent behavior. That said, the original behavioral setup  is so simple that one could imagine capturing the behavioral variability in multiple ways ( potentially without evoking complex computations that the original authors never showed  the mouse brain performs). I would recommend reframing the paper as a new model that  proposes a set of internal states that could give rise to the behavioral heterogeneity  observed in Akiti et al, but nonetheless is at this time only a hypothesis. Furthermore, an  explanation of what would be really required to test this would be appreciated to make the  point clearer.

We thought very hard about using terms that might be considered to be anthropomorphic  such as ‘timid’ and ‘brave’. We are, of course, aware, of the concerns articulated by  investigators such as LeDoux about this. However, we think that, provided that we are clear  on the first appearance (using ‘scare’ quotes) that we are using them as indeed labels for  latent characteristics that capture correlations in various aspects of behaviour, they are more  helpful than harmful in making our descriptions understandable.

Reviewer #3 (Public Review):

Summary:

The manuscript presents computational modelling of the behaviour of mice during  encounters with novel and familiar objects, originally reported by Akiti et al. (Neuron 110, 2022)          . Mice typically perform short bouts of approach followed by a retreat to a safe  distance, presumably to balance exploration to discover possible rewards with the potential  risk of predation. However, there is considerable heterogeneity in this exploratory behaviour,  both across time as an individual subject becomes more confident in approaching the object,  and across subjects; with some mice rapidly becoming confident to closely explore the  object, while other timid mice never become fully confident that the object is safe. The  current work aims to explain both the dynamics of adaptation of individual animals over time,  and the quantitative and qualitative differences in behaviour between subjects, by modelling  their behaviour as arising from model-based planning in a Bayes adaptive Markov Decision  Process (BAMDP) framework, in which the subjects maintain and update probabilistic  estimates of the uncertain hazard presented by the object, and rationally balance the  potential reward from exploring the object with the potential risk of predation it presents.

In order to fit these complex models to the behaviour the authors necessarily make  substantial simplifying assumptions, including coarse-graining the exploratory behaviour into  phases quantified by a set of summary statistics related to the approach bouts of the animal.  Inter-individual variation between subjects is modelled both by differences in their prior  beliefs about the possible hazard presented by the object and by differences in their risk  preference, modelled using a conditional value at risk (CVaR) objective, which focuses the  subject's evaluation on different quantiles of the expected distribution of outcomes.  Interestingly these two conceptually different possible sources of inter-subject variation in  brave vs timid exploratory behaviour turn out not to be dissociable in the current dataset as  they can largely compensate for each other in their effects on the measured behaviour.  Nonetheless, the modelling captures a wide range of quantitative and qualitative differences  between subjects in the dynamics of how they explore the object, essentially through  differences in how subject's beliefs about the potential risk and reward presented by the  object evolve over the course of exploration, and are combined to drive behaviour.

Exploration in the face of risk is a ubiquitous feature of the decision-making problem faced  by organisms, with strong clinical relevance, yet remains poorly understood and  under-studied, making this work a timely and welcome addition to the literature.

Strengths:

(1) Individual differences in exploratory behaviour are an interesting, important, and  under-studied topic.

(2) Application of cutting-edge modelling methods to a rich behavioural dataset, successfully  accounting for diverse qualitative and qualitative features of the data in a normative  framework.

(3) Thoughtful discussion of the results in the context of prior literature.

Limitations:

(1) The model-fitting approach used of coarse-graining the behaviour into phases and fitting  to their summary statistics may not be applicable to exploratory behaviours in more complex  environments where coarse-graining is less straightforward.

(2) Some aspects of the work could be more usefully clarified within the manuscript.

We thank reviewer #3 for their positive feedback and helping us to improve the clarity of our  paper. We have added discussion they thought was missing.

Reviewer #1 (Recommendations for the authors):

(1) Line 25-28

This part of the Abstract might give an impression that timidity (but not braveness) is  potentially associated with psychiatric illness and even that timidity is thus inferior to  braveness. However, even though extreme timidity might indeed be associated with anxiety  or depression, extreme braveness could also be associated with other psychiatric or  behavioral problems. Moreover, as a population, the existence of both timid and brave  individuals could be advantageous, and it could be a reason why both types of individuals  evolutionarily survived in the case of wild animals (although Akiti et al. used mice, which may  have no or very limited genetic varieties, and so things may be different). So I would like to  encourage the authors to elaborate on the expression of this part of the Abstract and/or  enrich the related discussion in the Discussion.

This is an important point. We note on line 38 that excessive novelty seeking (potentially  caused by excessive braveness) could also be maladaptive.

Additionally, we have added a paragraph to the discussion discussing heterogeneity in risk  sensitivity within a population.

“Our data show that there is substantial variation in the degrees of risk sensitivity across the  mice.  Previous works have reported substantial interpopulation and intrapopulation  differences in risk-sensitivity in humans which depend on gender, age, socioeconomic  status, personality characteristics, wealth and culture (Rieger et al., 2015; Frey et al., 2017).  Despite the normative appeal of 𝛼 = 1, it is possible that a population may benefit from  including individuals with $\alpha$ different from 1.0 or highly negative priors. For example,  more cautious individuals could learn from merely observing the risky behavior of less  cautious individuals. Furthermore, we have only considered risk-sensitivity under epistemic  uncertainty in our work. Risk averse individuals, for instance with 𝛼 < 1 may be more  successful than risk-neutral agents in environments where there are unexpected dangers ( unknown unknowns). Risk-aversion is thus a temperament of ecological and evolutionary  significance (Réale et al., 2007).”

(2) Line 149

Section 2.2 consists of eight subsections. I think this organization may not be very  appealing, because there are a bit too many subsections, and their relations are not  immediately clear to readers. So I would like to encourage the authors to make an  elaboration. For example, since 2.2.1 - 2.2.5 describes a summary of model construction  and model fitting whereas 2.2.6-2.2.8 shows the results, it could be good to divide these into  separate sections (2.2.1 - 2.2.5 and 2.3.1 - 2.3.3).

Thank you for pointing this out. We’ve renumbered the sections as you’ve suggested.

(3) Line 347-8

Theoretically, the effect of prior is diluted over experience whereas the effect of biased  (risk-aversive) evaluation persists, as the authors mentioned in Lines 393-394. Then isn't it  possible to consider environments/conditions in which the two effects can be separated?

We appreciate this suggestion. Indeed, our original thought in modeling this experiment was  that this would be exactly the case here - with epistemic uncertainty reducing as the object  became more familiar. However, proving to an animal that a single environment is  completely stationary/fixed is hard - reflected in our conclusion here that the exploration  bonus pool replenishes. Thus, we argued in the discussion that a series of environments  would be necessary to separate risk sensitivity from priors.

(4) Line 407

It would be nice to add a brief phrase explaining how (in what sense) this model's  assumption was consistent with the reported behavior. Also, should the assumption of  having two discrete approach states (cautious and confident) itself be regarded as a  limitation of the model? If the tail-behind and tail-exposure approaches were not merely  operationally categorized but were indicated to be two qualitatively distinct behaviors in the  experiment by Akiti et al., it is reasonable to model them as two discrete states, but  otherwise, the assumption of two discrete states would need to be mentioned as a  simplification/limitation.

We have now removed line 407, and now have an additional  paragraph in the discussion  discussing the limitations of the tail-behind and tail-exposure state representation: “Motivated by tail-behind versus tail-exposed in Akiti et al. (2022), we model approach using  a dichotomy between cautious and confident approach states. This is likely a crude  approximation to the continuous and multifaceted nature of animal approach behavior. For  example, during approach animals likely adjust their levels of vigilance continuously (or  discretely; Lloyd and Dayan (2018)) to  monitor threat, and choose different velocities for  movement, and different attentional strategies for inspecting the novel object. We hope  future works will model these additional behavioral complexities, perhaps with additional  internal states, and corroborate these states with neurobiological data.”

(5) Line 418

The authors contrasted their model-based analyses with the model-free analyses of Akiti et  al. Another aspect of differences between the authors' model and the model of Akiti et al. is  whether it is normative or mechanistic: while how the model of Akiti et al. can be biologically  implemented appears to be clear (TS dopamine represents threat TD error, and TS  dopamine-dependent cortico-striatal plasticity implements TD error-based update of  model-free threat prediction), biological implementation of the authors' model seems more  elusive. Given this, it might be a fruitful direction to explore how these two models can be  integrated in the future.

We enthusiastically agree that it would be most interesting in the future to explore the  integration of the two models - and, in the discussion ( Lines 537-548, 454-461) , point to  some first steps that might be fruitful along these lines. There are two separate  considerations here: one is that our account is mostly computational and algorithmic,  whereas Akiti’s model is mostly algorithmic and implementational; the second is, as noted by  the reviewer, that our account is model-based, whereas Akiti’s model is model-free (in the  sense of reinforcement learning; RL). These are related - thanks in no small part to the work  from the group including Akiti, we know a lot more about the implementation of model-free  than model-based RL. However, our model-based account does reach additional features of  behavior not captured in Akiti et al.’s model such as bout duration, frequency, and approach  type. Thus, the temptation of unification.

(6) Line 426

Related to the previous point, it would be nice to more specifically describe what variable TS  dopamine can represent in the authors' model if possible.

In the discussion  (Lines 454-461) , we speculate that  TS dopamine could still respond to the  physical salience of the novel object and affect choices by determining the potential cost of  the encountered threat or the prior on the hazard function. For example, perhaps ablating TS  dopamine reduces the hazard priors which leads to faster transition from cautious to  confident approach and longer bout durations, consistent with the optogenetics behavioral  data reported in Akiti et al.

Reviewer #2 (Recommendations for the authors):

My guess is simpler versions of the model would not fit the data well. But this does not mean  for example that the mice have probability distortions (CvaR) or that even probabilistic  reasoning and the internal models necessary to support them are acting in the behavioral  context studied by Akiti. So related to the above, I would ask what other models would fit and  would not fit the data? And what does this mean?

These are good points. Our model provides an approximately normative account of the  animals’ behavior  in terms of what it achieves relative to a utility function. In practice, the  animals could deploy a precompiled model-free policy (which does not rely on probabilistic  computations) that is exactly equivalent to our model-based policy. With the current  experiment, we cannot conclude whether or not the animals are performing the prospective  calculations in an online manner. Of course, the extent to which animals or humans are  performing probabilistic computations online and have internal models are on-going  questions of study.

Model comparison is difficult because currently we do not know of any other risk-sensitive  exploration models. We cannot directly compare to the model in Akiti et al. since our model  explains additional features of behavior: bout duration, frequency, and approach type.  Indeed, our model is as simple as it can be in the sense with the exception of nCVaR,  removing any of the other parameters makes it difficult to fit some animals in our dataset. In the future, our model could be used to fit other datasets of risk-sensitive exploration and,  ideally,  be compared to other models.

Explaining why animals avoid the novel object in what the offers call benign environment is a  very tricky issue. In Akiti et al, the readers are not yet convinced that the mice know that this  environment is benign. Being placed in an arena with a novel object presents mice with a  great uncertainty and we do not know whether they treat this as benign. Therefore, the  alternative explanations in this study need to be carefully discussed in lieu of the limitations  of the initial study.

It is certainly true that it is unclear if the arena is  completely  benign to the animals. However,  the amount of time the animal spends in the center of the arena decreases significantly from  habituation to novelty days. This suggests that the animals avoid the novel object largely  because of the object itself, rather than the potential danger associated with the arena.  Furthermore, the animals are not reported as exhibiting more extreme behaviours such as  freezing. In any case, our account is relative in the sense that we are comparing the time the  animal spends at the object versus elsewhere in the environment, driven by the relative  novelty and relative risk of the environment versus the object. Trying to get more absolute  measures of these quantities would require a richer experimental set-up, for instance with  different degree of habituation or experience of the occurrence of (other) novel objects, in  general.

We added a short note to the discussion to explain this:

“Fourth, we modeled the relative amount of time the animal spends at the object versus  elsewhere in the environment which depends on the differential risk in the two states.  However, it is likely the animals avoid the novel object largely because of the object itself,  rather than the potential danger associated with the arena since they spend much less time  at the center of the arena during novelty than habituation days.”

Figure 2 - how confident are the authors that each mouse differs from y=1? Related to this,  the behavior in Akiti is very noisy and changes across time. I am not sure if the authors fully  describe at what levels their model captures the behavior vs not in a detailed enough  fashion.

We have performed a random permutation test on the minute-to-minute data. We have  updated Figure 2 so that brave animals that pass the Benjamini–Hochberg procedure y>1 at  level q=0.05 are represented with solid green dots and animals that don’t pass are  represented with hollow dots. 8 out of 11 brave animals passed Benjamini–Hochberg.

Reviewer #3 (Recommendations for the authors):

(1) I could not find information in the preprint about code availability. Please consider making  the code public to help others apply these modelling methods.

We have released code and included the url in the paper in the Methods section.

(2) Though the manuscript was generally clearly written, there were a number of places  where some additional information or clarification would be useful:

a) Please define and explain the terms 'tail-behind' and 'tail-exposed' (used to describe  approach bout types) when first used.

We have added definitions when we first mention these terms:

“[...] 'tail-behind' (bouts where the animal's nose was closer to the object than the tail for the  entire bout) and 'tail-exposed' (bouts where the animal's tail is closer to the object than the  nose at some point during the bout), associated respectively with cautious risk-assessment  and engagement”

b) At lines 57-58 when contrasting the 'model-free' account of Akiti et al with the 'model-based' account of the current work, it would be worth clarifying that these terms are  being used in the RL sense rather than e.g. a model-based analysis of the data.  

We have updated the relevant lines to say “model-free/based reinforcement learning”.

c) Line 61, the phrase 'the significant long-run approach of timid animals despite having  reached the "avoid" state' is unclear as the 'avoid' state has not been defined.

We updated the terminology to “avoidance behavior” to be consistent with Akiti et al.  Avoidance refers to the animal routinely avoiding the object and therefore being unable to  learn whether it is safe.

d) It was not completely clear to me how the coarse-graining of the behaviour was  implemented. Specifically, how were animals assigned to the brave, intermediate, or timid  group, and how were the parameters of the resulting behavioural phases fit?

Sorry that this was not clear. Section 2.1 explains how the minute-to-minute behavioral data  was coarse-grained and how animal groups were assigned. We have added further  explanation of Figure 2 to the main text:

“Fig 2 summarizes our categorization of the animals into the three groups: brave,  intermediate, and timid based on the phases identified in the animal's exploratory  trajectories. Timid animals spend no time in confident approach and are plotted in orange at  the origin of Fig 2. Brave animals differ from intermediate animals in that their approach time  during the first ten minutes of the confident phase is greater than the last ten minutes ( steady-state phase). Brave animals are plotted in green above and intermediate animals  are plotted in black below the y=1 line in Fig 2.”

We also added extra information to outline the goal, and methodology of coarse-graining and  animal grouping:

“We sought to capture  these qualitative differences (cautious versus confident) as well as  aspects of the quantitative changes in bout durations and frequencies as the animal learns  about their environment. To make this readily possible, we abstracted the data in two ways:

averaging  bout statistics over time, and clustering the animals into three groups with  operationally distinct behaviors.”

e) What purpose does the 'retreat' state serve in the BAMDP model (as opposed to  transitioning directly from 'object' to 'nest' states), and why do subjects not pass through it  following 'detect' states?

Thank you for pointing this out. We have updated Figure 3 to note that the two “detected  states” also point to the “retreat” state. The reviewer is correct that there could be alternative  versions of the state diagram, and the ‘retreat’ state could indeed have been eliminated.  However, we thought that it was helpful to structure the animal’s progress through state  space.

f) Why was the hazard function parameterised via the mean and SD at each time step rather  than with a parametric form of the mean and SD as a function of time?

Since the agent can only spend 2, 3, or 4 turns at the object states, we didn’t see a need to  parameterize the mean and SD as a function of time. Doing so is a good solution to scaling  up the hazard function to more time-steps.

(3) There were also a couple of points that could potentially be usefully touched on in the  discussion:

a) What, if any, is the relationship between the CVaR objective and distributional RL? They  seem potentially related due to both focussing on quantiles of the outcome distribution.

We have added a paragraph to the discussion discussing the connection between  distributional RL and CVaR:

“CVaR is known to come in different flavors in the case of temporally-extended behavior.  Gagne and Dayan (2021) introduces two alternative time-consistent formulations of CVaR:  nested CVaR (nCVaR) and precommitted CVaR (pCVaR). nCVaR and pCVaR both enjoy  Bellman equations which make it possible to compute approximately optimal policies without  directly computing whole distributions of the outcomes. We use nCVaR in this study for its  computational efficiency. There is, of course, great current interest in distributional  reinforcement learning (Bellemare et al., 2023b) which does acquire such whole  distributions, not the least because of prominent observations linking non-linearities in the  response functions of dopamine neurons to methods for learning distributions of outcomes ( Dabney et al., 2020; Masset et al., 2023; Sousa et al., 2023). One functional motivation for  considering entire outcome distributions is the possibility of using them to determine  risk-sensitive policies (Gagne and Dayan, 2021).

While it is possible to compute CVaR directly from return distributions, Gagne and Dayan  (2021) showed that this can lead to temporally inconsistent policies where the agent  deviates from its original plans (the authors called this the fixed CVaR or fCVaR measure).

Rather further removed from our model-based methods is work from Antonov and Dayan  (2023), who consider a model-free exploration strategy which exploits full return distributions  to compute the value of perfect information which is used as a heuristic for trying actions  with uncertain consequences. Future works can examine risk-sensitive versions of Antonov  and Dayan (2023)'s computationally efficient model-free algorithm as one solution to the  burdensome computations in our model-based method.”

b) Why normatively might subjects have non-neutral risk preference as captured by the  CvaR?

We also added a paragraph to the discussion discussing the advantage of heterogeneity in  risk sensitivity within a population:

(Reviewer #1 had the same question, see above) “Our data show that there is substantial  variation in the degrees of risk sensitivity across the mice.  Previous works have reported  substantial interpopulation and intrapopulation differences in risk-sensitivity in humans which  depend on gender, age, socioeconomic status, personality characteristics, wealth and culture [...]”

c) Relevance of the current modelling work to clinical conditions characterised by  dysregulation of risk assesment (e.g. anxiety or PTSD).

We’ve added a paragraph to the discussion:

“Inter-individual differences in risk sensitivity are also of critical importance in psychiatry,  reflected in a panoply of anxiety disorders (Butler and Mathews, 1983; Giorgetta et al., 2012;  Maner et al., 2007; Charpentier et al., 2017), along with worry and rumination (Gagne and  Dayan, 2022). Understanding the spectrum of   extreme priors and extreme values of 𝛼  could have therapeutic implications, adding significance to the search for tasks that can  more cleanly separate them.”

d) Is it surprising to see differences in risk preference (nCVaR) between the familiar object  and novel object condition, given that risk preference might be conceptualised as a trait  rather than a state variable?

Thank you for raising this point. You are right that we expected risk sensitivity (nCVaR alpha)  to be the same between FONC and UONC animals on average. It is difficult to know if alpha  is higher for FONC than UONC animals due to the non-identifiability between alpha and  hazard priors. We have added this discussion to the paper:

“This is surprising if we interpret 𝛼 as a trait that is stable through time. Unfortunately, due to  the non-identifiability between 𝛼 and hazard priors, we cannot verify whether 𝛼 is actually  higher for FONC animals than UONC animals.”

eLife Assessment

The manuscript by Li and coworkers analyzed astrocytic differentiation of midbrain floor plate-patterned neural cells originating from human iPS cells, with a LMX1A reporter. This valuable work identifies transcriptomic differences at the single-cell level, between astrocytes generated from LMX1A reporter positive or negative cells, as well as non-patterned astrocytes and neurons. The evidence is solid, but the paper can be strengthened by further analyses of the transcriptomic data, and astrocytic morphology; also, searching for some of the differentially expressed genes by immunohistochemistry in different regions of the mammalian brain, or in human specimens, would be very informative.

Editorial note: To ensure a thorough evaluation of the revised manuscript, we invited a third reviewer to assess whether the authors had sufficiently addressed the concerns raised in the initial round of peer review. This additional reviewer confirmed that the authors responded partially to the original reviewers requests. While he/she also provided a set of new comments, these do not alter the original assessment or editorial decision regarding the manuscript. For transparency and completeness, the additional comments are included below.

Reviewer #3 (Public Review):

Summary:

In this manuscript, Li and coworkers present experiments generated with human induced pluripotent stem cells (iPSCs) differentiated to astrocytes through a three-step protocol consisting of neural induction/midbrain patterning, switch to expansion of astrocytic progenitors, and terminal differentiation to astroglial cells. They used lineage tracing with a LMX1A-Cre/AAVS1-BFP iPSCs line, where the initial expression of LMX1A and Cre allows the long-lasting expression of BFP, yielding BFP+ and BFP- populations, that were sorted when in the astrocytic progenitor expansion. BFP+ showed significantly higher number of cells positive to NFIA and SOX9 than BFP- cells, at 45 and 98 DIV. However, no significant differences in other markers such as AQP4, EAAT2, GFAP (which show a proportion of less than 10% in all cases) and S100B were found between BFP-positive or -negative, at these differentiation times. Intriguingly, non-patterned astrocytes produced higher proportions of GFAP positive cells than the midbrain-induced and then sorted populations. BFP+ cells have enhanced calcium responses after ATP addition, compared to BFP- cells. Single-cell RNA-seq of early and late cells from BFP- and BFP+ populations were compared to non-patterned astrocytes and neurons differentiated from iPSCs. Bioinformatic analyses of the transcriptomes resulted in 9 astrocyte clusters, 2 precursor clusters and one neuronal cluster. DEG analysis between BFP+ and BFP- populations showed some genes enriched in each population, which were subject to GO analysis, resulting in biological processes that are different for BFP+ or BFP- cells.

Strengths:

The manuscript tries to tackle an important aspect in Neuroscience, namely the importance of patterning in astrocytes. Regionalization is crucial for neuronal differentiation and the presented experiments constitute a trackable system to analyze both transcriptional identities and functionality on astrocytes.

Weaknesses:

The presented results have several fundamental issues, to be resolved, as listed in the following major points:

(1) It is very intriguing that GFAP is not expressed in late BFP- nor in BFP+ cultures, when authors designated them as mature astrocytes.<br /> (2) In Fig. 2D, authors need to change the designation "% of positive nuclei".<br /> (3) In Fig. 2E, the text describes a decrease caused by 2APB on the rise elicited by ATP, but the graph shows an increase with ATP+2APB. However, in Fig. 2F, the peak amplitude for BFP+ cells is higher in ATP than in ATP+2APD, which is mentioned in the text, but this is inconsistent with the graph in 2E.<br /> (4) The description of Results in the single-cell section is confusing, particularly in the sorted CD49 and unsorted cultures. Where do these cells come from? Are they BFP-, BFP+, unsorted for BFP, or non-patterned? Which are the "all three astrocyte populations"? A more complete description of the "iPSC-derived neurons" is required in this section to allow the reader to understand the type and maturation stage of neurons, and if they are patterned or not.<br /> (5) A puzzling fact is that both BFP- and BFP- cells have similar levels of LMX1A, as shown in Fig. S6F. How do authors explain this observation?<br /> (6) In Fig. 3B, the non-patterned cells cluster away from the BFP+ and BFP-; on the other hand, early and late BFP- are close and the same is true for early and late BFP+. A possible interpretation of these results is that patterned astrocytes have different paths for differentiation, compared to non-patterned cells. If that can be implied from these data, authors should discuss the alternative ways for astrocytes to differentiate.<br /> (7) Fig. 3D shows that cluster 9 is the only one with detectable and coincident expression of both S100B and GFAP expression. Please discuss why these widely-accepted astrocyte transcripts are not found in the other astrocytes clusters. Also, Sox9 is expressed in neurons, astrocyte precursors and astrocytes. Why is that?<br /> (8) Line 337, Why authors selected a log2 change of 0.25? Typically, 1 or a higher number is used to ensure at least a 2-fold increase, or a 50% decrease. A volcano plot generated by the comparison of BFP+ with BFP- cells would be appropriate. The validation of differences by immunocytochemistry, between BFP+ and BFP-, is inconclusive. The staining is blur in the images presented in Fig. S8C. Quantification of the positive cells, without significant background signal, in both populations is required.<br /> (9) Lines 349-351: BFP+ cells did not show higher levels of transcripts for LMX1A nor FOXA2. This fact jeopardizes the claim that these cells are still patterned. In the same line, there are not significant differences with cortical astrocytes, indicating a wider repertoire of the initially patterned cells, that seems to lose the midbrain phenotype. Furthermore, common DGE shared by BFP- and BFP+ cells when compared to non-patterned cells indicate that after culture, the pre-pattern in BFP+ cells is somehow lost, and coincides with the progression of BFP- cells.<br /> (10) For the GO analyses, How did authors select 1153 genes? The previous section mentioned 287 genes unique for BFP+ cells. The Results section should include a rationale for performing a wider search for the enriched processes.<br /> (11) For Fig. 4C and 4D, both p values and the number of genes should be indicated in the graph. I would advise to select the 10 or 15 most significant categories, these panels are very difficult to read. Whereas the listed processes for BFP+ have a relation to Parkinson disease, the ones detected for BFP- cells are related to extracellular matrix and tissue development. Does it mean that BFP+ cells have impaired formation of this matrix, or defective tissue development? This is in contradiction of enhanced calcium responses of BFP+ cells compared to BFP- cells.<br /> (12) Both the comparison between midbrain and cortical astrocytes in Fig. S8A, and the volcano plot in S8B do not show consistent changes. For example, RCAN2 in Fig. S8A has the same intensity for cortical and midbrain cells, but is marked as an enriched gene in midbrain in the p vs log2FC graph in Fig. S8B.

Author response:

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

Response to Reviewer #3:

We thank reviewer 3 for spending their valuable time on commenting on our revised paper.

We would like to reiterate the central conclusion of this work, which appears to have been missed by Reviewer 3. Using a BFP-expressing lineage tracer hPSC line for tracking LMX1A+ midbrain-patterned neural progenitors and their differentiated progeny, we discovered a loss of the LMX1A lineage during pluripotent stem cell differentiation into astrocytes, despite BFP+ neural progenitors were the dominant population at the onset of astrocyte induction.

Hence, the take-home message of this study is, as summarized in the abstract, ‘ the lineage composition of iPSC-derived astrocytes may not accurately recapitulate the founder progenitor population’ and that one should not take for granted that in vitro/stem cell-derived astrocytes are the descendants of the dominant starting neural progenitors (which is a general assumption in PSC publications as described in the paper and our response to reviewers).

Please find below our point-by-point response to reviewer comments. We have re-ordered the points according to their relative importance to our main conclusions.

‘ the lineage composition of iPSC-derived astrocytes may not accurately recapitulate the founder progenitor population’ and that one should not take for granted that in vitro/stem cell derived astrocytes are the descendants of the dominant starting neural progenitors (which is a general assumption in PSC publications as described in the paper and our response to reviewers).

Please find below our point-by-point response to their comments. We have re-ordered the points according to their relative importance to our main conclusions.

…. They used lineage tracing with a LMX1A-Cre/AAVS1-BFP iPSCs line, where the initial expression of LMX1A and Cre allows the long-lasting expression of BFP, yielding BFP+ and BFP- populations, that were sorted when in the astrocytic progenitor expansion. BFP+ showed significantly higher number of cells positive to NFIA and SOX9 than BFP- cells …

This is a misunderstanding by reviewer 3. As indicated in the first sentence of the second section, BFP- populations used for functional and transcriptomic analysis was not sorted BFP^- cells, but those derived from unsorted, BFP⁺ enriched populations. Our scRNAseq analysis indicated that they were transcriptomically aligned to human midbrain astrocytes. This finding is consistent with the fact that they are derived from midbrain-patterned neural progenitors, presumably minority LMX1A- progenitors.

Reviewer 3’s comments indicate that they misunderstood the primary aims of our study as a mere functional and transcriptomic comparison of the two astrocyte populations.

(9) BFP+ cells did not show higher levels of transcripts for LMX1A nor FOXA2. This fact jeopardizes the claim that these cells are still patterned. In the same line, there are not significant differences with cortical astrocytes, indicating a wider repertoire of the initially patterned cells, that seems to lose the midbrain phenotype. Furthermore, common DGE shared by BFP- and BFP+ cells when compared to non-patterned cells indicate that after culture, the pre-pattern in BFP+ cells is somehow lost, and coincides with the progression of BFP- cells.

The reviewer seems to assume that astrocytes derived from LMX1A+ ventral midbrain progenitors must retain LMX1A expression. We do not take this view and do not claim this in this study. Moreover, we have discussed in the paper that due to a lack of transcriptomic studies of in vivo track regional progenitors (such as LMX1A), it remains unknown whether and to what extent patterning gene expression is maintained in astrocytes of different brain regions.

Our findings on the lack of LMX1A and FOXA2 in BFP+ astrocytes are supported by several published single-cell transcriptomic studies of human midbrain astrocytes (La Manno et al. 2016; Agarwal et al. 2020; Kamath et al. 2022). We have a paragraph of discussion on this topic in both the original and updated versions of the paper with the relevant publications cited.

Other points raised by reviewer 3

(1) It is very intriguing that GFAP is not expressed in late BFP- nor in BFP+ cultures, when authors designated them as mature astrocytes.

We did not designate our cells as ‘mature’ astrocytes but ‘astrocytes’ based on their global gene expression with the human fetal and adult brain astrocytes as references.

Moreover, ‘mature’ only appeared once in the paper indicating that our cells lie in between the fetal and adult astrocytes in maturity.

(2) In Fig. 2D, authors need to change the designation "% of positive nuclei".

To be corrected in the version of record.

(3) In Fig. 2E, the text describes a decrease caused by 2APB on the rise elicited by ATP, but the graph shows an increase with ATP+2APB. However, in Fig. 2F, the peak amplitude for BFP+ cells is higher in ATP than in ATP+2APD, which is mentioned in the text, but this is inconsistent with the graph in 2E.

To be corrected in the version of record.

(4) The description of Results in the single-cell section is confusing, particularly in the sorted CD49 and unsorted cultures. Where do these cells come from? Are they BFP-, BFP+, unsorted for BFP, or non-patterned? Which are the "all three astrocyte populations"? A more complete description of the "iPSC-derived neurons" is required in this section to allow the reader to understand the type and maturation stage of neurons, and if they are patterned or not.

As previously reported in the reference cited, CD49 is a novel human astrocyte marker. This is independent of BFP expression. For all three astrocyte populations studied here (BFP+, BFP-, and non-patterned astrocytes), we included both CD49f+ sorted and unsorted samples to account for selection bias caused by FACS. iPSC-derived neurons were included in the sequencing study to provide a reference for cell-type annotation. They were generated following a GABAergic neuron differentiation protocol. However, their maturation stages and/or regional characteristics are not relevant to astrocytes.

(5) A puzzling fact is that both BFP- and BFP- cells have similar levels of LMX1A, as shown in Fig. S6F. How do authors explain this observation?

This figure panel shows that LMX1A, LMX1B and FOXA2 are essentially NOT expressed in these astrocytes.

(6) In Fig. 3B, the non-patterned cells cluster away from the BFP+ and BFP-; on the other hand, early and late BFP- are close and the same is true for early and late BFP+. A possible interpretation of these results is that patterned astrocytes have different paths for differentiation, compared to non-patterned cells. If that can be implied from these data, authors should discuss the alternative ways for astrocytes to differentiate.

Both BFP+ and BFP- astrocyte are from ventral midbrain patterned neural progenitors, while non-patterned neural progenitors are more akin to that of forebrain. Figure 3B is expected and confirms the patterning effect.

(7) Fig. 3D shows that cluster 9 is the only one with detectable and coincident expression of both S100B and GFAP expression. Please discuss why these widely-accepted astrocyte transcripts are not found in the other astrocytes clusters. Also, Sox9 is expressed in neurons, astrocyte precursors and astrocytes. Why is that?

S100B and GFAP are classic astrocyte markers in certain states. We are not relying only on two markers but the genome-wide expression profile as the criteria for astrocytes. As shown in the unbiased reference mapping to multiple human brain astrocyte scRNA-seq datasets, all our astrocyte clusters were mapped with high confidence to human astrocytes.

SOX9 is an important regulator for astrogenesis, so its expression is expected in precursors (doi.org/10.1016/j.neuron.2012.01.024). In addition, recent studies have uncovered that SOX9 expression is also reported in foetal striatal projection neurons and early postnatal cortical neurons, where SOX9 regulates neuronal synaptogenesis and morphogenesis (dois:10.1016/j.fmre.2024.02.019; 10.1016/j.neuron.2018.10.008). Therefore, the expression of SOX9 in multiple cell types was expected. Instead of using a few selected markers for cell-type annotation, we employed a genomic approach relying on an unbiased reference mapping approach and a combination of various markers to ascertain our annotation results.

(8) Line 337, Why authors selected a log2 change of 0.25? Typically, 1 or a higher number is used to ensure at least a 2-fold increase, or a 50% decrease. A volcano plot generated by the comparison of BFP+ with BFP- cells would be appropriate. The validation of differences by immunocytochemistry, between BFP+ and BFP-, is inconclusive. The staining is blur in the images presented in Fig. S8C. Quantification of the positive cells, without significant background signal, in both populations is required.

We used a lenient threshold owing to the following considerations: 1) High FC does not necessarily mean biological relevance, as gene expression does not necessarily translate to protein expression. Therefore, a smaller FC value could also be biologically meaningful. 2) Balance between noise and biological differences. Any threshold was chosen arbitrarily. 3) We are identifying a trend rather than pinpointing a specific set of

The quality was unfortunately reduced due to restrictions on file size upon submission. A high resolution Fig. S8C is available.

(10) For the GO analyses, How did authors select 1153 genes? The previous section mentioned 287 genes unique for BFP+ cells. The Results section should include a rationale for performing a wider search for the enriched processes.

GO enrichment using unique DEGS may not capture the wider landscape of the transcriptomic characteristics of BFP⁺ astrocytes. The 287 unique genes were only differentially expressed in BFP⁺ astrocytes. However, apart from these 287 genes, other genes among the 1187 DEGs were differentially expressed in BFP⁺ astrocytes and in one other population.

(11) For Fig. 4C and 4D, both p values and the number of genes should be indicated in the graph. I would advise to select the 10 or 15 most significant categories, these panels are very difficult to read. Whereas the listed processes for BFP+ have a relation to Parkinson disease, the ones detected for BFP- cells are related to extracellular matrix and tissue development. Does it mean that BFP+ cells have impaired formation of this matrix, or defective tissue development? This is in contradiction of enhanced calcium responses of BFP+ cells compared to BFP- cells.

Information on all DEGs, including p values and numbers, is provided in Supplementary data 1-5.

BFP+ astrocytes do have enrichment for GO terms related to extracellular matrix and tissue development, although not as obvious as BFP- astrocytes. Previous work have shown that both in vitro and in vivo derived astrocytes are functionally heterogeneous, containing functionally distinct subtypes exhibiting different GO enrichment profiles (doi: 10.1016/j.ygeno.2021.01.008; 10.1038/s41598-024-74732-7).

(12) Both the comparison between midbrain and cortical astrocytes in Fig. S8A, and the volcano plot in S8B do not show consistent changes. For example, RCAN2 in Fig. S8A has the same intensity for cortical and midbrain cells, but is marked as an enriched gene in midbrain in the p vs log2FC graph in Fig. S8B.

These are integrated analyses of published human datasets. S8A and S8B show the same data in different formats. The differences are better shown in the volcano plot/easier detected by the human eye.

These are integrated analysis of published human datasets. S8A and S8B are the same data shown in different format. Differences are better shown in volcano plot /easier detected by the human eye. RCAN2 had a higher average expression in the midbrain than in the telencephalon, albeit small, and the difference was statistically significant (as shown in the volcano plot).

The following is the authors’ response to the original reviews

Reviewer 1:

In vitro nature of this work being the fundamental weakness of this paper

We disagree with this statement. As explained in the provisional response, the aim of this study was to test the validity of a general concept applied in pluripotent stem cell research that pluripotent stem cell-derived astrocytes faithfully represent the lineage heterogeneity of their ancestral neural progenitors and hence preserve the regionality of such progenitors. Our genetic lineage study is justified for addressing this in vitro-driven question. However, we have highlighted the rationale where appropriate in the revised paper.

If regional identity is not maintained, so what? Don't we already know that this can happen? The authors acknowledge that this is known in the discussion.

Importance of regional identity: Growing evidence demonstrates the functional heterogeneity of brain astrocytes in health and disease. Therefore, for in vitro disease modeling, it is believed that one should use astrocytes represent the anatomy of disease pathology; for example, midbrain astrocytes for studying dopamine neurodegeneration and Parkinson’s disease. Understanding the dynamics of stem cell-derived astrocytes and identifying astrocyte subtypes is important for their biomedical applications.

Regional identity change/Discussion: It seems that the reviewer misunderstood the context in which the ‘identity change’ was discussed. The literature referred to (in the Discussion) concerns shifts in regional gene expression in bulk-cultured cells. In the days of pre-single-cell analysis/lineage tracking, one cannot distinguish whether this was due to a change in the transcriptomic landscape in progenies of the same lineage or alterations in lineage heterogeneity, but to interpret at face value as regional identity was not maintained. In the revised paper, we have made an effort to indicate that ‘regional identity’ is used broadly to refer to lineage relationships and/or traits rather than static gene expressioin.

validation of the markers/additional work

The scNAseq analysis performed in this study compared the profiles of astrocytes derived from LMX1A+ and LMX1A- ventral midbrain-patterned neural progenitors. Since it is not possible to perform genetic lineage tracking in humans and an analogous mouse lineage tracer line is not available, in vivo validation of these markers with respect to their lineage relationship is not currently feasible. However, we took advantage of abundant single-cell human astrocyte transcriptomic datasets and validated our genes in silico. We also validated the differential expression of selected markers in late BFP+ and BFP- astrocytes using immunocytochemistry, where reliable antibodies are available. The results of the additional analyses are presented in Figure S8 and Supplemental Data 5.

Knowledge gaps concerning astrocyte development

Reviewer 1 pointed out a number of knowledge gaps concerning astrocyte development, such as the transcriptomic landscape trajectories of midbrain floor plate cells as they progress towards astrocytes. Indeed, the limited knowledge on regional astrocyte molecule heterogeneity restricts the objective validation of in vitro-derived astrocyte subtypes and the development of novel approaches for their generation in vitro. We agree with the need for in-depth in vivo studies using model organisms, although these are beyond the scope of the current work.

Reviewer 2:

(1) The authors argue that the depletion of BFP seen in the unsorted population immediately after the onset of astrogenic induction is due to the growth advantage of the derivatives of the residual LMX1A- population. However, no objective data supporting this idea is provided, and one could also hypothesize that the residual LMX1A- cells could affect the overall LMX1A expression in the culture through negative paracrine regulation.

We acknowledge the lack of evidence-based explanation for the depletion of BFP+ cells in mixed cultures. We were unable to perform additional experiments because of resource limitations. The design of the LMX1A-Cre/AAVS1-BFP lineage tracer line determines that BFP is expressed irreversibly in LMX1A-expressing cells or their derivatives regardless of their LMX1A expression status. Therefore, the potential negative paracrine regulation of LMX1A by residual LMX1A- cells should not affect cells that have already turned on BFP. We have highlighted the working principles of the LMX1A tracer line in the revised manuscript.

(2) Furthermore, on line 124 it is stated that: "Interestingly, the sorted BFP+ cells exhibited similar population growth rate to that of unsorted cultures...". In the face of the suggested growth disadvantage of those cells, this statement needs clarification.

To avoid confusion, we have removed the statement.

(3) Regarding the fidelity of the model system, it is not clear to me how the TagBFP expression was detected in the BFP+ population supposedly in d87 and d136 pooled astrocytes (Fig S6C) while no LMX1A expression was observed in the same cells (Fig S6F).

The TagBFP tracer is expressed in the progenies of LMX1A+ cells, regardless of their LMX1A expression status. We have gone through the MS text to ensure that this information has been provided.

(4) The generated single-cell RNASeq dataset is extremely valuable. However, given the number of conditions included in this study (i.e. early vs late astrocytes, BFP+ vs BFP-, sorted vs unsorted, plus non-patterned and neuronal samples) the resulting analysis lacks detail. For instance, from a developmental perspective and to better grasp the functional significance of astrocytic heterogeneity, it would be interesting to map the identified clusters to early vs late populations and to the BFP status.

We performed additional bioinformatics analysis, which provided independent support for the relative developmental maturity suggested by functional assays. The additional data are now provided in the revised Figure 3B, C, E.

Moreover, although comprehensive, Figure S7 is complex to understand given that citations rather than the reference populations are depicted.

The information provided in the revised Figure S7.

(5) Do the authors have any consideration regarding the morphology of the astrocytes obtained in this study? None of the late astrocyte images depict a prototypical stellate morphology, which is reported in many other studies involving the generation of iPSC-derived astrocytes and which is associated with the maturity status of the cell.

The morphology of our astrocytes was not unique to the present study. Many factors may influence the morphology of astrocytes, such as the culture media and supplements used, and maturity status. Based on the functional assays and limited GFAP expression, our astrocytes were relatively immature.

eLife Assessment

This manuscript reports valuable results on the role of MDC1 and Treacle in DSB repair in rDNA repeats. It has been previously established that MDC1 is replaced by Treacle as the main adaptor in the nucleolar DNA damage response. This work provides convincing evidence that MDC1 is required for the recruitment of RAD51 and BRCA1 to DSBs in rDNA. The work involves multiple MDC1 knockout models and establishes that RFN8-RNF168 act downstream of MDC1 in the recruitment of the HR machinery to nucleolar DSBs.

Reviewer #1 (Public review):

This study elucidates the molecular linkage between the mobilization of damaged rDNA from the nucleolus to its periphery and the subsequent repair process by HDR. The authors demonstrate that the nucleolar adaptor protein Treacle mediates rDNA mobilization, and the MDC1-RNF8-RNF168 pathway coordinates the recruitment of the BRCA1-PALB2-BRCA2 complex and RAD51 loading. This stepwise regulation appears to prevent aberrant recombination events between rDNA repeats. This work provides compelling evidence for the recruitment of the Treacle-TOPBP1-NBS1 complex to rDNA DSBs and demonstrates the critical role of MDC1 in the rDNA damage response. There are some issues with the over-interpretation of results as described subsequently. Some aspects could be strengthened, for example, a potential role of the RAP80-Abraxas axis, the origin of the repair synthesis (HDR vs. NHEJ)

Reviewer #2 (Public review):

Summary:

DNA double-strand breaks (DSB) in repeated DNA pose a challenge for repair by homologous recombination (HR) due to the potential of generating chromosomal aberrations, especially involving repeats on different chromosomes. This conceptual caveat led to a long-held notion that HR is not active in repeated DNA, which was disproven in groundbreaking work by Chiolo showing in Drosophila that DSBs in pericentromeric repeats are mobilized to the nuclear periphery for repair by HR. A similar mechanism operates in mouse cells, as shown by the Gautier laboratory, but the mobilization goes to the nucleolar periphery, called nucleolar caps. In this manuscript, the authors reexamine the role of MDC1 in the mobilization of DSBs in rDNA in human cells. Previous work has shown that MDC1 is replaced by Treacle, the gene associated with Treacher Collins syndrome 1, in its role as the main adaptor of the DNA damage response, and these results are confirmed here. The novelty of this contribution lies in the discovery that MDC1 is required downstream in the recruitment of BRCA1 and RAD51 to nucleolar DSBs that were mobilized to the nucleolar cap. Using multiple MCD knockout models and DSBs induced by the nuclease PpoI, which cleaves at nuclear sites as well as in the 28S rDNA, convincingly documents this role of MDC1 and shows that it acts upstream of the RNF8-RNF168 ubiquitylation axis. Using a proxy assay of co-localization of EdU incorporation at DSBs (gammaH2AX), evidence is provided that MDC1 is required for HR in rDNA. MDC1 was not required for RAD51 recruitment to IR-induced foci, but it is unclear whether this is related to the different DSB chemistry (enzymatic versus IR) or to the localization of the DSB (rDNA versus unique sequence genome).

Strengths:

(1) The manuscript is well-written, and the experimental evidence is nicely presented.

(2) Multiple MDC1 knockout models are used to validate the results.

(3) Convincing back-complementation data clarify the relationship between MDC1 and RNF8.

Weaknesses:

(1) The recruitment of BRCA2 was not directly demonstrated. This caveat could be recognized, as IF for BRCA2 is challenging.

(2) PpoI also induces DSBs in the non-rDNA genome. These DSBs would be an ideal control to establish nucleolar specificity of the events described and clarify whether the difference between IR and PpoI is the chemical structure of the DSB or the location of the DSB.

eLife Assessment

In this important work, the authors present a new transformer-based neural network designed to isolate and quantify higher-order epistasis in protein sequences. They provide solid evidence that higher-order epistasis can play key roles in protein function. This work will be of interest to the communities interested in modeling biological sequence data and understanding mutational effects.

Reviewer #1 (Public review):

The authors present an approach that uses the transformer architecture to model epistasis in deep mutational scanning datasets. This is an original and very interesting idea. Applying the approach to 10 datasets, they quantify the contribution of higher-order epistasis, showing that it varies quite extensively.

Suggestions:

(1) The approach taken is very interesting, but it is not particularly well placed in the context of recent related work. MAVE-NN, LANTERN, and MoCHI are all approaches that different labs have developed for inferring and fitting global epistasis functions to DMS datasets. MoCHI can also be used to infer multi-dimensional global epistasis (for example, folding and binding energies) and also pairwise (and higher order) specific interaction terms (see 10.1186/s13059-024-03444-y and 10.1371/journal.pcbi.1012132). It doesn't distract from the current work to better introduce these recent approaches in the introduction. A comparison of the different capabilities of the methods may also be helpful. It may also be interesting to compare the contributions to variance of 1st, 2nd, and higher-order interaction terms estimated by the Epistatic transformer and MoCHI.

(2) https://doi.org/10.1371/journal.pcbi.1004771 is another useful reference that relates different metrics of epistasis, including the useful distinction between biochemical/background-relative and background-averaged epistasis.

(3) Which higher-order interactions are more important? Are there any mechanistic/structural insights?

Reviewer #2 (Public review):

Summary:

This paper presents a novel transformer-based neural network model, termed the epistatic transformer, designed to isolate and quantify higher-order epistasis in protein sequence-function relationships. By modifying the multi-head attention architecture, the authors claim they can precisely control the order of specific epistatic interactions captured by the model. The approach is applied to both simulated data and ten diverse experimental deep mutational scanning (DMS) datasets, including full-length proteins. The authors argue that higher-order epistasis, although often modest in global contribution, plays critical roles in extrapolation and capturing distant genotypic effects, especially in multi-peak fitness landscapes.

Strengths:

(1) The study tackles a long-standing question in molecular evolution and protein engineering: "how significant are epistatic interactions beyond pairwise effects?" The question is relevant given the growing availability of large-scale DMS datasets and increasing reliance on machine learning in protein design.

(2) The manuscript includes both simulation and real-data experiments, as well as extrapolation tasks (e.g., predicting distant genotypes, cross-ortholog transfer). These well-rounded evaluations demonstrate robustness and applicability.

(3) The code is made available for reproducibility.

Weaknesses:

(1) The paper mainly compares its transformer models to additive models and occasionally to linear pairwise interaction models. However, other strong baselines exist. For example, the authors should compare baseline methods such as "DANGO: Predicting higher-order genetic interactions". There are many works related to pairwise interaction detection, such as: "Detecting statistical interactions from neural network weights", "shapiq: Shapley interactions for machine learning", and "Error-controlled non-additive interaction discovery in machine learning models".

(2) While the transformer architecture is cleverly adapted, the claim that it allows for "explicit control" and "interpretability" over interaction order may be overstated. Although the 2^M scaling with MHA layers is shown empirically, the actual biological interactions captured by the attention mechanism remain opaque. A deeper analysis of learned attention maps or embedding similarities (e.g., visualizations, site-specific interaction clusters) could substantiate claims about interpretability.

(3) The distinction between nonspecific (global) and specific epistasis is central to the modeling framework, yet it remains conceptually underdeveloped. While a sigmoid function is used to model global effects, it's unclear to what extent this functional form suffices. The authors should justify this choice more rigorously or at least acknowledge its limitations and potential implications.

(4) The manuscript refers to "pairwise", "3-4-way", and ">4-way" interactions without always clearly defining the boundaries of these groupings or how exactly the order is inferred from transformer layer depth. This can be confusing to readers unfamiliar with the architecture or with statistical definitions of interaction order. The authors should clarify terminology consistently. Including a visual mapping or table linking a number of layers to the maximum modeled interaction order could be helpful.

Reviewer #3 (Public review):

Summary:

Sethi and Zou present a new neural network to study the importance of epistatic interactions in pairs and groups of amino acids to the function of proteins. Their new model is validated on a small simulated data set and then applied to 10 empirical data sets. Results show that epistatic interactions in groups of amino acids can be important to predict the function of a protein, especially for sequences that are not very similar to the training data.

Strengths:

The manuscript relies on a novel neural network architecture that makes it easy to study specifically the contribution of interactions between 2, 3, 4, or more amino acids. The study of 10 different protein families shows that there is variation among protein families.

Weaknesses:

The manuscript is good overall, but could have gone a bit deeper by comparing the new architecture to standard transformers, and by investigating whether differences between protein families explain some of the differences in the importance of interactions between amino acids. Finally, the GitHub repository needs some more information to be usable.

eLife Assessment

This manuscript uses simulations to describe the dynamics of the Pseudomonas-derived cephalosporinase PDC-3 β-lactamase and its mutants to better understand antibiotic resistance. The finding that clinically observed mutations alter the flexibility of the Ω- and R2-loops, reshaping the cavity of the active site, is useful to the field. However, the evidence is considered incomplete; there is a lack of description of methods, and there is a need for additional analysis to demonstrate statistical significance, visualisation of the Markov states, analysis to explain changes due to the different mutations, and possible simulations in the presence of substrates to shed direct light on modulation mechanisms.

Reviewer #1 (Public review):

Summary:

This manuscript uses adaptive sampling simulations to understand the impact of mutations on the specificity of the enzyme PDC-3 β-lactamase. The authors argue that mutations in the Ω-loop can expand the active site to accommodate larger substrates.

Strengths:

The authors simulate an array of variants and perform numerous analyses to support their conclusions.

The use of constant pH simulations to connect structural differences with likely functional outcomes is a strength.

Weaknesses:

I would like to have seen more error bars on quantities reported (e.g., % populations reported in the text and Table 1).

Reviewer #1 (Public review):

Summary:

This manuscript uses adaptive sampling simulations to understand the impact of mutations on the specificity of the enzyme PDC-3 β-lactamase. The authors argue that mutations in the Ω-loop can expand the active site to accommodate larger substrates.

Strengths:

The authors simulate an array of variants and perform numerous analyses to support their conclusions.

The use of constant pH simulations to connect structural differences with likely functional outcomes is a strength.

Weaknesses:

I would like to have seen more error bars on quantities reported (e.g., % populations reported in the text and Table 1).

eLife Assessment

This manuscript employs cryo-EM, mutational analysis, and biochemical assays to explore the molecular basis by which glutamine promotes filamentation and regulates the activity of human glutamine synthetase (hGS) by stabilizing interactions between hGS decamers. The studies supporting this mechanism are solid, but could be improved by providing more clarity and by addressing methodological issues in the cryoEM data processing workflow. This work will be of particular interest and useful to groups interested in understanding the molecular basis of nutrient sensing, cellular metabolism, and structural regulation of enzyme activity.

Reviewer #1 (Public review):

Summary:

The study is methodologically solid and introduces a compelling regulatory model. However, several mechanistic aspects and interpretations require clarification or additional experimental support to strengthen the conclusions.

Strengths:

(1) The manuscript presents a compelling structural and biochemical analysis of human glutamine synthetase, offering novel insights into product-induced filamentation.

(2) The combination of cryo-EM, mutational analysis, and molecular dynamics provides a multifaceted view of filament assembly and enzyme regulation.

(3) The contrast between human and E. coli GS filamentation mechanisms highlights a potentially unique mode of metabolic feedback in higher organisms.

Weaknesses:

(1) The mechanism underlying spontaneous di-decamer formation in the absence of glutamine is insufficiently explored and lacks quantitative biophysical validation.

(2) Claims of decamer-only behavior in mutants rely solely on negative-stain EM and are not supported by orthogonal solution-based methods.

Reviewer #2 (Public review):

The authors set out to resolve the high-resolution structure of a glutamine synthetase (GS) decamer using cryo-EM, investigate glutamine binding at the decamer interface, and validate structural observations through biochemical assays of ATP hydrolysis linked to enzyme activity. Their work sits at the intersection of structural and functional biology, aiming to bridge atomic-level details with biological mechanisms - a goal with clear relevance to researchers studying enzyme catalysis and metabolic regulation.

Strengths and weaknesses of methods and results:

A key strength of the study lies in its use of cryo-EM, a technique well-suited for resolving large, dynamic macromolecular complexes like the GS decamer. The reported resolutions (down to 2.15 Å) initially suggest the potential for detailed structural insights, such as side-chain interactions and ligand density. However, several methodological limitations significantly undermine the reliability of the results:

(1) Cryo-EM data processing: The absence of critical details about B-factor sharpening - a standard step to enhance map interpretability - is a major concern. For high-resolution maps (<3 Å), sharpening is typically applied to resolve side-chain features, yet the submitted maps (e.g., those in Figures 1D, 2D, and supplementary figures) appear unprocessed, with density quality inconsistent with the claimed resolutions. This makes it difficult to evaluate whether observed features (e.g., glutamine binding) are genuine or artifacts of unsharpened data.

(2) Modeling and density consistency: The structural models, particularly for glutamine binding at the decamer interface, do not align with the reported resolution. The maps shown in Figure 2D and Supplementary Figure S7 lack sufficient density to confidently place glutamine or even surrounding residues, conflicting with claims of 2.15 Å resolution. Additionally, fitting a non-symmetric ligand (glutamine) into a symmetry-refined map requires justification, as symmetry constraints may distort ligand placement.

(3) Biochemical assay controls: While the enzyme activity assays aim to link structure to function, they lack essential controls (e.g., blank reactions without GS or substrates, substrate omission tests) to confirm that ATP hydrolysis is GS-dependent. The use of TCEP, a reducing agent, is also not paired with experiments to rule out unintended effects on the PK/LDH system, further limiting confidence in activity measurements.

Achievement of aims and support for conclusions:

The study falls short of convincingly achieving its goals. The claimed high-resolution structural details (e.g., side-chain densities, ligand binding) are not supported by the provided maps, which lack sharpening and show inconsistencies in density quality. Similarly, the biochemical data do not robustly validate the structural claims due to missing controls. As a result, the evidence is insufficient to confirm glutamine binding at the decamer interface or the functional relevance of the observed structural features.

Likely impact and utility:

If these methodological gaps are addressed, the work could make a meaningful contribution to the field. A well-resolved GS decamer structure would advance understanding of enzyme assembly and ligand recognition, while validated biochemical assays would strengthen the link between structure and function. Improved data processing and clearer reporting of validation steps would also make the structural data more reliable for the community, providing a resource for future studies on GS or related enzymes.

Additional context:

Cryo-EM has transformed structural biology by enabling high-resolution analysis of large complexes, but its success hinges on rigorous data processing and validation steps that are critical to ensuring reproducibility. The challenges highlighted here are not unique to this study; they reflect broader issues in the field where incomplete reporting of methods can obscure the reliability of results. By addressing these points, the authors would not only strengthen their current work but also set a positive example for transparent and rigorous structural biology research.

Reviewer #3 (Public review):

In this manuscript, the authors propose a product-dependent negative-feedback mechanism of human glutamine synthetase, whereby the product glutamine facilitates filament formation, leading to reduced catalytic specificity for ammonia. Using time-resolved cryo-EM, the authors demonstrate filament formation under product-rich conditions. Multiple high-quality structures, including decameric and di-decameric assemblies, were resolved under different biochemical states and combined with MD simulations, revealing that the conformational space of the active site loop is critical for the GS catalysis. The study also includes extensive steady-state kinetic assays, supporting the view that glutamine regulates GS assembly and its catalytic activity. Overall, this is a detailed and comprehensive study. However, I would advise that a few points be addressed and clarified.

(1) In Figure 2D and Supplementary Figure 7, the extra density observed between the two decamers does not appear to have the defining features of a glutamine. A less defined density may be expected given the nature of the complex, but even though mutagenesis assays were performed to support this assignment, none of these results constitutes direct and conclusive evidence for glutamine binding at this site. I would thus suggest showing the density maps at multiple contour thresholds to allow readers to also better evaluate the various small molecules under turnover conditions that cannot be well fitted based on this density map, helping to provide a more balanced interpretation of the results.

(2) On the same point regarding the density for the enzyme under turnover conditions, more details should be provided about the symmetry expansion and classification performed, and also show the approximate ratio of reconstructions that include this density. Did you try symmetry expansion followed by focused classification, especially on the interface region?

(3) The interface between the two decamers of the model needs to be double-checked and reassigned, especially for the residues surrounding the fitted glutamine. For example, the side chain of the Lys residue shown in the attached figure is most likely modeled incorrectly.

Author response:

Reviewer #1 (Public review):

Summary:

The study is methodologically solid and introduces a compelling regulatory model. However, several mechanistic aspects and interpretations require clarification or additional experimental support to strengthen the conclusions.

Strengths:

(1) The manuscript presents a compelling structural and biochemical analysis of human glutamine synthetase, offering novel insights into product-induced filamentation.

(2) The combination of cryo-EM, mutational analysis, and molecular dynamics provides a multifaceted view of filament assembly and enzyme regulation.

(3) The contrast between human and E. coli GS filamentation mechanisms highlights a potentially unique mode of metabolic feedback in higher organisms.

Weaknesses:

(1) The mechanism underlying spontaneous di-decamer formation in the absence of glutamine is insufficiently explored and lacks quantitative biophysical validation.

(2) Claims of decamer-only behavior in mutants rely solely on negative-stain EM and are not supported by orthogonal solution-based methods.

We thank the reviewer for the summary and noting of the strengths. We agree that the evolutionary divergence of metabolic feedback in GS homologs is a fruitful avenue for future studies. With regard to the weaknesses, the di-decamer in the absence of glutamine only forms under high (higher than physiological) concentrations of enzyme. Our primary evidence for the mutant behavior was the lack of crosslinking (Figure 1E), with supplementary support from the negative stain. In the revised version we will soften the language to say “reduced” rather than “did not support” filament formation.

Reviewer #2 (Public review):

The authors set out to resolve the high-resolution structure of a glutamine synthetase (GS) decamer using cryo-EM, investigate glutamine binding at the decamer interface, and validate structural observations through biochemical assays of ATP hydrolysis linked to enzyme activity. Their work sits at the intersection of structural and functional biology, aiming to bridge atomic-level details with biological mechanisms - a goal with clear relevance to researchers studying enzyme catalysis and metabolic regulation.

Strengths and weaknesses of methods and results:

A key strength of the study lies in its use of cryo-EM, a technique well-suited for resolving large, dynamic macromolecular complexes like the GS decamer. The reported resolutions (down to 2.15 Å) initially suggest the potential for detailed structural insights, such as side-chain interactions and ligand density. However, several methodological limitations significantly undermine the reliability of the results:

(1) Cryo-EM data processing: The absence of critical details about B-factor sharpening - a standard step to enhance map interpretability - is a major concern. For high-resolution maps (<3 Å), sharpening is typically applied to resolve side-chain features, yet the submitted maps (e.g., those in Figures 1D, 2D, and supplementary figures) appear unprocessed, with density quality inconsistent with the claimed resolutions. This makes it difficult to evaluate whether observed features (e.g., glutamine binding) are genuine or artifacts of unsharpened data.

(2) Modeling and density consistency: The structural models, particularly for glutamine binding at the decamer interface, do not align with the reported resolution. The maps shown in Figure 2D and Supplementary Figure S7 lack sufficient density to confidently place glutamine or even surrounding residues, conflicting with claims of 2.15 Å resolution. Additionally, fitting a non-symmetric ligand (glutamine) into a symmetry-refined map requires justification, as symmetry constraints may distort ligand placement.

(3) Biochemical assay controls: While the enzyme activity assays aim to link structure to function, they lack essential controls (e.g., blank reactions without GS or substrates, substrate omission tests) to confirm that ATP hydrolysis is GS-dependent. The use of TCEP, a reducing agent, is also not paired with experiments to rule out unintended effects on the PK/LDH system, further limiting confidence in activity measurements.

Achievement of aims and support for conclusions:

The study falls short of convincingly achieving its goals. The claimed high-resolution structural details (e.g., side-chain densities, ligand binding) are not supported by the provided maps, which lack sharpening and show inconsistencies in density quality. Similarly, the biochemical data do not robustly validate the structural claims due to missing controls. As a result, the evidence is insufficient to confirm glutamine binding at the decamer interface or the functional relevance of the observed structural features.

Likely impact and utility:

If these methodological gaps are addressed, the work could make a meaningful contribution to the field. A well-resolved GS decamer structure would advance understanding of enzyme assembly and ligand recognition, while validated biochemical assays would strengthen the link between structure and function. Improved data processing and clearer reporting of validation steps would also make the structural data more reliable for the community, providing a resource for future studies on GS or related enzymes.

We disagree with the reviewer’s overall assessment.

With regard to sharpening and resolution: we examined sharpened maps and in a revised version will present additional supplementary figures showing these maps side by side. We note that the resolutions reported are global and that the most interesting features are, of course, in the periphery and subject to conformational and compositional heterogeneity. We will include supplementary figures of core side chain densities that are more like what are expected by the reviewer in the revision. 

With regard to modeling: the apo filament and turnover filament datasets were handled nearly identically. The additional density is therefore likely not artefactual to the symmetry operator - however, the lower resolution in this region noted by the reviewer is worthy of further exploration. The maps are public and we think this is the most plausible interpretation of the density, which we based primarily on the biochemical data and will include more speculation in the version.

With regard to the biochemical controls: we point the reviewer to Figure S1, which shows that omission of ammonia or glutamate in the wild-type (tagless) system removes any coupling of the reactions. We will perform the additional controls to publication quality in the revised version along with the TCEP control. We note that the reducing agent is present across all experiments, ruling out an effect on any specific result. The inclusion of TCEP is also very standard in other published uses of the Coupled ATPase assay (e.g. PMID: 31778111 and PMID: 32483380 by our first author)

Additional context:

Cryo-EM has transformed structural biology by enabling high-resolution analysis of large complexes, but its success hinges on rigorous data processing and validation steps that are critical to ensuring reproducibility. The challenges highlighted here are not unique to this study; they reflect broader issues in the field where incomplete reporting of methods can obscure the reliability of results. By addressing these points, the authors would not only strengthen their current work but also set a positive example for transparent and rigorous structural biology research.

All the data is public and the reviewer or anyone is free to reinterpret the maps and models - and we encourage that rather than just an interpretation of our static figures. In addition, we will upload the raw micrograph data for the apo filament and turnover filament datasets to EMPIAR prior to submitting the revision.

Reviewer #3 (Public review):

In this manuscript, the authors propose a product-dependent negative-feedback mechanism of human glutamine synthetase, whereby the product glutamine facilitates filament formation, leading to reduced catalytic specificity for ammonia. Using time-resolved cryo-EM, the authors demonstrate filament formation under product-rich conditions. Multiple high-quality structures, including decameric and di-decameric assemblies, were resolved under different biochemical states and combined with MD simulations, revealing that the conformational space of the active site loop is critical for the GS catalysis. The study also includes extensive steady-state kinetic assays, supporting the view that glutamine regulates GS assembly and its catalytic activity. Overall, this is a detailed and comprehensive study. However, I would advise that a few points be addressed and clarified.

(1) In Figure 2D and Supplementary Figure 7, the extra density observed between the two decamers does not appear to have the defining features of a glutamine. A less defined density may be expected given the nature of the complex, but even though mutagenesis assays were performed to support this assignment, none of these results constitutes direct and conclusive evidence for glutamine binding at this site. I would thus suggest showing the density maps at multiple contour thresholds to allow readers to also better evaluate the various small molecules under turnover conditions that cannot be well fitted based on this density map, helping to provide a more balanced interpretation of the results.

(2) On the same point regarding the density for the enzyme under turnover conditions, more details should be provided about the symmetry expansion and classification performed, and also show the approximate ratio of reconstructions that include this density. Did you try symmetry expansion followed by focused classification, especially on the interface region?

(3) The interface between the two decamers of the model needs to be double-checked and reassigned, especially for the residues surrounding the fitted glutamine. For example, the side chain of the Lys residue shown in the attached figure is most likely modeled incorrectly.

We thank the reviewer for the feedback. As noted above, we will include supplemental figures that show maps at multiple thresholds and sharpening schemes. We noted in the manuscript and above that our interpretation here is based on integrating biochemical evidence alongside the density and will make that even more clear in the revised manuscript. The filaments +/- the putative glutamine density were processed nearly identically, but we will attempt various schemes of focused classification/symmetry expansion in the revision as well. However, we point out that there is extensive averaging there that makes modeling a bit trickier than expected given the global resolution.

eLife Assessment

This valuable work explores the timely idea that aperiodic activity in human electrophysiology recordings is dynamically modulated in response to task events in a manner that may be relevant for behavioral performance. Moreover, the authors present solid evidence that, in some circumstances, these aperiodic changes might be misinterpreted as oscillatory changes.

Reviewer #1 (Public review):

Summary:

Frelih et al. investigated both periodic and aperiodic activity in EEG during working memory tasks. In terms of periodic activity, they found post-stimulus decreases in alpha and beta activity, while in terms of aperiodic activity, they found a bi-phasic post-stimulus steepening of the power spectrum, which was weakly predictive of performance. They conclude that it is crucial to properly distinguish between aperiodic and periodic activity in event-related designs as the former could confound the latter. They also add to the growing body of research highlighting the functional relevance of aperiodic activity in the brain.

Strengths:

This is a well-written, timely paper that could be of interest to the field of cognitive neuroscience, especially to researchers investigating the functional role of aperiodic activity. The authors describe a well-designed study that looked at both the oscillatory and non-oscillatory aspects of brain activity during a working memory task. The analytic approach is appropriate, as a state-of-the-art toolbox is used to separate these two types of activity. The results support the basic claim of the paper that it is crucial to properly distinguish between aperiodic and periodic activity in event-related designs as the former could confound the latter. They also add to the growing body of research highlighting the functional relevance of aperiodic activity in the brain. Commendably, the authors include replications of their key findings on multiple independent data sets.

Comments on the previous version:

The authors have addressed several of the weaknesses I noted in my original review, specifically, they softened their claims regarding the theta findings, while simultaneously strengthening these findings with additional analyses (using simulations as well as a new measure of rhythmicity, the phase autocorrelation function, pACF). Most of the other suggested control analyses were also implemented. While I believe the fact that the participants in the main sample were not young adults could be made even more explicit, and the potential interaction between age and aperiodic changes could be unpacked a little in the discussion, the age of the sample is definitely addressed upfront.

Reviewer #2 (Public review):

Summary:

In this manuscript, Frelih et al, investigate the relationship between aperiodic neural activity, as measured by EEG, and working memory performance, and compares this to the more commonly analyzed periodic, and in particular theta, measures that are often associated with such tasks. To do so, they analyze a primary dataset of 57 participants engaging in an n-back task, as well as a replication dataset, and use spectral parameterization to measure periodic and aperiodic features of the data, across time. In the revision, the authors have clarified some key points, and added a series of additional analyses and controls, including the use of an additional method, that helps to complement the original analyses and further corroborates their claims. In doing so, they find both periodic and aperiodic features that relate to the task dynamics, but importantly, the aperiodic component appears to explain away what otherwise looks like theta activity in a more traditional analysis. This study therefore helps to establish that aperiodic activity is a task-relevant dynamic feature in working memory tasks and may be the underlying change in many other studies that reported 'theta' changes, but did not use methods that could differentiate periodic and aperiodic features.

Strengths:

Key strengths of this paper include that it addresses an important question - that of properly adjudicating which features of EEG recordings relate to working memory tasks - and in doing so provides a compelling answer, with important implications for considering prior work and contributing to understanding the neural underpinnings of working memory. The revision is improved by showing this using an additional analysis method. I do not find any significant faults or error with the design, analysis, and main interpretations as presented by this paper, and as such, find the approach taken to be a valid and well-enacted. The use of multiple variants of the working memory task, as well as a replication dataset significantly strengthens this manuscript, by demonstrating a degree of replicability and generalizability. This manuscript is also an important contribution to motivating best practices for analyzing neuro-electrophysiological data, including in relation to using baselining procedures. I think the updates in the revision have helped to clarify the findings and impact of this study.

Weaknesses:

Overall, I do not find any obvious weaknesses with this manuscript and it's analyses that challenge the key results and conclusions. Updates through the revision have addressed my previous points about adding some additional notes on the methods and conclusions.

Reviewer #3 (Public review):

Summary:

Using a specparam (1/f) analysis of task-evoked activity, the authors propose that "substantial changes traditionally attributed to theta oscillations in working memory tasks are, in fact, due to shifts in the spectral slope of aperiodic activity." This is a very bold and ambitious statement, and the field of event-related EEG would benefit from more critical assessments of the role of aperiodic changes during task events. Unfortunately, the data shown here does not support the main conclusion advanced by the authors.

Strengths:

The field of event-related EEG would benefit from more critical assessments of the role of aperiodic changes during task events. The authors perform a number of additional control analyses, including different types of baseline correction, ERP subtraction, as well as replication of the experiment with two additional datasets.

Comments on previous revisions:

The authors have completed a substantial revision based on the comments from all of the reviewers. Overall, the major claims of the initial report have been profoundly tempered.

[Editors' note: We determined that this revised version appropriately tempers some of the prior claims and addresses the concerns raised by the reviewers through two rounds of review.]

eLife Assessment

To evaluate phenotypic correlations between complex traits, this study aimed to measure the genetic overlap of traits by evaluating GWAS signals assisted by eQTL signals. They suggested an improved version of the previous Sherlock to integrate SNP-level signals into gene-level signals. Then they compared 59 human traits to identify known and novel genetic distance relationships. This work is valuable to the field, but still needs substantial improvement because many parts of the paper are incomplete.

Reviewer #1 (Public review):

The authors tried to quantify the difference between human complex traits by calculating genetic overlap scores between a pair of traits. Sherlock-II was devised to integrate GWAS with eQTL signals. The authors claim that Sherlock-II is superior to the previous version (robustness, accuracy, etc). It appears that their framework provides a reasonable solution to this important question, although the study needs further clarification and improvements.

(1) Sherlock-II incorporates GWAS and eQTL signals to better quantify genetic signals for a given complex trait. However, this approach is based on the hypothesis that "all GWAS signals confer association to complex trait via eQTL", which is not true (PMID: 37857933). This should be acknowledged (through mentioning in the text) and incorporated into the current setup (through differential analysis - for example, with or without eQTL signals, or with strong colocalization only).

(2) When incorporating eQTL, why did the authors use the top p-value tissues for eQTL? This approach seems simpler and probably more robust. But many eQTLs are tissue-specific. Therefore, it would also be important to know if eQTLS from appropriate tissues were incorporated instead.

(3) One of the main examples is the novel association between Alzheimer's disease and breast cancer. Although the authors provided a molecular clue underlying the association, it is still hard to comprehend the association easily, as the two diseases are generally known to be exclusive to each other. This is probably because breast cancer GWAS is performed for germline variants and does not consider the contribution of somatic variants.

(4) It would help readers understand the story better if a summary figure of the entire process were provided. The current Figure 1 does not fulfil that role.

(5) Figure 2 is not very informative. The readers would want to know more quantitative information rather than a heatmap-style display. Is there directionality to the relationship, or is it always unidirectional?

(6) In Figure 3, readers may want to know more specific information. For example, what gene signals are really driving the hypoxia signal in Alzheimer's disease vs breast cancer? And what SNP signals are driving these gene-level signals?

Reviewer #2 (Public review):

Summary:

The authors introduce a gene-level framework to detect shared genetic architecture between complex traits by integrating GWAS summary statistics with eQTL data via a new algorithm, Sherlock-II, which aggregates signals from multiple (cis/trans) eSNPs to produce gene-phenotype p-values. Shared pathways are identified with Partial-Pearson-Correlation Analysis (PPCA).

Strengths:

The authors show the gene-based approach is complementary and often more sensitive than SNP-level methods, and discuss limitations (in terms of no directionality, dependence on eQTL coverage).

Weaknesses:

(1) How do the authors explain data where missing tissues or sparse eQTL mapping are available? Would that bias as to which genes/traits can be linked and may produce false negatives or tissue-specific false positives?

(2) Aggregating SNP-level signals into gene scores can be confounded by LD; for example, a nearby causal variant for a different gene or non-expression mechanism may drive a gene's score, producing spurious gene-trait links. How do the authors prevent this?

(3) How the SNPs are assigned to genes would affect results, this is because different choices can change which genes appear shared between traits. The authors can expand on these.

(4) Many reported novel trait links remain speculative without functional or orthogonal validation (e.g., colocalization, perturbation data). Thus, the manuscript's claims are inconclusive and speculative.

(5) It would be best to run LD-aware colocalization and power-matched simulations to check for robustness.

Author response:

Reviewer #1 (Public review):

The authors tried to quantify the difference between human complex traits by calculating genetic overlap scores between a pair of traits. Sherlock-II was devised to integrate GWAS with eQTL signals. The authors claim that Sherlock-II is superior to the previous version (robustness, accuracy, etc). It appears that their framework provides a reasonable solution to this important question, although the study needs further clarification and improvements.

(1) Sherlock-II incorporates GWAS and eQTL signals to better quantify genetic signals for a given complex trait. However, this approach is based on the hypothesis that "all GWAS signals confer association to complex trait via eQTL", which is not true (PMID: 37857933). This should be acknowledged (through mentioning in the text) and incorporated into the current setup (through differential analysis - for example, with or without eQTL signals, or with strong colocalization only). 

The reviewer is correct that in this version of the tool, we focused on SNPs with effect on gene expression, as the majority of the SNPs identified by GWASs are non-coding SNPs. In the future improvement, we should also include coding SNPs that change the amino acid sequence of genes. We will discuss this point more in the revised manuscript.

(2) When incorporating eQTL, why did the authors use the top p-value tissues for eQTL? This approach seems simpler and probably more robust. But many eQTLs are tissue-specific. Therefore, it would also be important to know if eQTLS from appropriate tissues were incorporated instead. 

This is a simple scheme to incorporate eQTL data from multiple tissues, assuming that the tissue that gives the strongest association is most relevant, or mainly mediates the effect from the SNP to the phenotype. This is a reasonable approach given that the tissues of origin for most of the phenotypes are unknown. In the future improvement, we should incorporate eQTL data from the appropriate tissue(s) if that is known.

(3) One of the main examples is the novel association between Alzheimer's disease and breast cancer. Although the authors provided a molecular clue underlying the association, it is still hard to comprehend the association easily, as the two diseases are generally known to be exclusive to each other. This is probably because breast cancer GWAS is performed for germline variants and does not consider the contribution of somatic variants. 

This is due to one of the limitations of the current algorithm: no direction of association is predicted explicitly. It could be that increasing the expression of a gene reduced the risk of one disease but increase the risk of another. Currently we have to analyze the details of the SNPs to infer direction once overlapping genes are found. This needs improvement in the future.  

(4) It would help readers understand the story better if a summary figure of the entire process were provided. The current Figure 1 does not fulfil that role. 

We plan to incorporate reviewer's suggestion in the revised manuscript.

(5) Figure 2 is not very informative. The readers would want to know more quantitative information rather than a heatmap-style display. Is there directionality to the relationship, or is it always unidirectional? 

We will consider a different presentation in the revised manuscript.

(6) In Figure 3, readers may want to know more specific information. For example, what gene signals are really driving the hypoxia signal in Alzheimer's disease vs breast cancer? And what SNP signals are driving these gene-level signals? 

We will add these information in the revised manuscript.

Reviewer #2 (Public review):

Summary:

The authors introduce a gene-level framework to detect shared genetic architecture between complex traits by integrating GWAS summary statistics with eQTL data via a new algorithm, Sherlock-II, which aggregates signals from multiple (cis/trans) eSNPs to produce gene-phenotype p-values. Shared pathways are identified with Partial-Pearson-Correlation Analysis (PPCA).

Strengths:

The authors show the gene-based approach is complementary and often more sensitive than SNP-level methods, and discuss limitations (in terms of no directionality, dependence on eQTL coverage).

Weaknesses:

(1) How do the authors explain data where missing tissues or sparse eQTL mapping are available? Would that bias as to which genes/traits can be linked and may produce false negatives or tissue-specific false positives?

Missing tissues or sparse eQTL certainly can produce false negatives as the signals linking the two phenotypes are simply not captured in the data. It is less likely to produce false positives as long as the statistical test is well controlled.   

(2) Aggregating SNP-level signals into gene scores can be confounded by LD; for example, a nearby causal variant for a different gene or non-expression mechanism may drive a gene's score, producing spurious gene-trait links. How do the authors prevent this? 

When there are multiple SNPs in LD with multiple genes nearby, it is generally difficult to map the causal SNP and the causal gene it affected, and thus there will be spurious gene-trait links. When we calculate the global similarity based on the gene-trait association profiles,  we tried to control this by simulating with random GWASs that have the same power as the real GWAS and preserve the LD structure, as the spurious links will also be present in the simulated data (but may appear in different loci) that are used to calibrate the statistical significance. 

(3) How the SNPs are assigned to genes would affect results, this is because different choices can change which genes appear shared between traits. The authors can expand on these. 

We assign SNPs to genes based on their strongest eQTL association from the available data. Improvement can be made if the relevant tissues for a trait are known (see response to Reviewer 1 above).

(4) Many reported novel trait links remain speculative without functional or orthogonal validation (e.g., colocalization, perturbation data). Thus, the manuscript's claims are inconclusive and speculative. 

We agree with the reviewer that the reported trait links are speculative, and they should be treated as hypotheses generated from the computational analyses. To truly validate some of these proposed relationships, deeper functional analyses and experimental tests are needed.

(5) It would be best to run LD-aware colocalization and power-matched simulations to check for robustness. 

We agree more control on LD and power-matched simulations will be important for testing the robustness of the predictions.

Author response:

Public Reviews:

Reviewer #1 (Public review):

Summary:

In this review, the author covered several aspects of the inflammation response, mainly focusing on the mechanisms controlling leukocyte extravasation and inflammation resolution.

Strengths:

This review is based on an impressive number of sources, trying to comprehensively present a very broad and complex topic.

Weaknesses:

(1) This reviewer feels that, despite the title, this review is quite broad and not centred on the role of the extracellular matrix.

(2) The review will benefit from a stronger focus on the specific roles of matrix components and dynamics, with more informative subheadings.

(3) The macrophage phenotype section doesn't seem well integrated with the rest of the review (and is not linked to the ECM).

(4) Table 1 is difficult to follow. It could be reformatted to facilitate reading and understanding

(5) Figure 2 appears very complex and broad.

(6) Spelling and grammar should be thoroughly checked to improve the readability.

This review focuses on the whole extravasation journey of leukocyte and highlights involvement of extracellular matrix (ECM) in multiple phases of the process. ECM may exert their roles either as a collective structure or as individual components. In the revision, for those functions involving specific matrix components, we will emphasize the matrix components and incorporate this information to subheadings as suggested. The parts of macrophage phenotype (Section 10-11) are included for its pivotal roles on deciding the tissue fate following inflammation (ie. to resolve / to regenerate damages incurred or to sustain inflammation), which is an important aspect of this review. ECM could modify macrophage phenotypes either directly (section 10) or indirectly via modulations of tissue stiffness or other cell types like fibroblasts (section 9). However, as pointed out by other reviewers as well, we acknowledge that Section 11 does not integrate well enough to the rest of the review. We plan to reorganize this part and to emphasize its link to ECM during the revision for better integration. We will reformat Table 1 for easier comprehension. We will consider restructuring Figure 2, which outlines various events influencing tissue decision of resolution/inflammation, perhaps by breaking up into two separate figures, to better focus the message. We will also check the language to improve readability.

Reviewer #2 (Public review):

Summary:

The manuscript is a timely and comprehensive review of how the extracellular matrix (ECM), particularly the vascular basement membrane, regulates leukocyte extravasation, migration, and downstream immune function. It integrates molecular, mechanical, and spatial aspects of ECM biology in the context of inflammation, drawing from recent advances. The framing of ECM as an active instructor of immune cell fate is a conceptual strength.

Strengths:

(1) Comprehensive synthesis of ECM functions across leukocyte extravasation and post-transmigration activity.

(2) Incorporation of recent high-impact findings alongside classical literature.

(3) Conceptually novel framing of ECM as an active regulator of immune function.

(4) Effective integration of molecular, mechanical, and spatial perspectives.

Weaknesses:

(1) Insufficient narrative linkage between the vascular phase (Sections 2-6) and the in-tissue phase (Sections 7-10).

(2) Underrepresentation of lymphocyte biology despite mention in early sections.

(3) The MIKA macrophage identity framework is only loosely tied to ECM mechanisms.

(4) Limited discussion of translational implications and therapeutic strategies.

(5) Overly dense figure insets and underdeveloped links between ECM carryover and downstream immune phenotypes.

(6) Acronyms and some mechanistic details may limit accessibility for a broader readership.

We will add a transition paragraph between Section 6 and Section 7 to provide a narrative that the extravasation processes affect downstream leukocyte functions. While lymphocytes follow a similar extravasation principle, their in-tissue activities differ from innate leukocytes. We will thus include discussion of lymphocyte-ECM crosstalk to Section 8 and/or 9 in the revision. We will restructure Section 11 and Figure 3 to better integrate to the rest of the review: In the current manuscript, we merely describe the capability of the MIKA framework to describe identity of any tissue macrophages and thus the framework could serve as a roadmap to facilitate identity normalization of pathological macrophages. We plan, in the revision, by employing the MIKA framework, to discuss and demonstrate linkage between macrophage identities and expression/production of modulators to functional ECM effectors described in Section 8-9. Regarding the comment of limited discussion of translational implications / therapeutic strategies, we will try to enrich this aspect throughout the manuscript where appropriate, in addition to the existing ones (eg. line 293-297; 388-391; 460-463; 512-517) We will also revise figure structure in general to avoid too dense information and to improve clarity. We will consider to provide a glossary explaining specialized terms to expand readership accessibility.

Reviewer #3 (Public review):

Summary & Strengths:

This review by Yu-Tung Li sheds new light on the processes involved in leukocyte extravasation, with a focus on the interaction between leukocytes and the extracellular matrix. In doing so, it presents a fresh perspective on the topic of leukocyte extravasation, which has been extensively covered in numerous excellent reviews. Notably, the role of the extracellular matrix in leukocyte extravasation has received relatively little attention until recently, with a few exceptions, such as a study focusing on the central nervous system (J Inflamm 21, 53 (2024) doi.org/10.1186/s12950-024-00426-6) and another on transmigration hotspots (J Cell Sci (2025) 138 (11): jcs263862 doi.org/10.1242/jcs.263862). This review synthesizes the substantial knowledge accumulated over the past two decades in a novel and compelling manner.

The author dedicates two sections to discussing the relevant barriers, namely, endothelial cell-cell junctions and the basement membrane. The following three paragraphs address how leukocytes interact with and transmigrate through endothelial junctions, the mechanisms supporting extravasation, and how minimal plasma leakage is achieved during this process. The subsequent question of whether the extravasation process affects leukocyte differentiation and properties is original and thought-provoking, having received limited consideration thus far. The consequences of the interaction between leukocytes and the extracellular matrix, particularly regarding efferocytosis, macrophage polarization, and the outcome of inflammation, are explored in the subsequent three chapters. The review concludes by examining tissue-specific states of macrophage identity.

Weaknesses:

Firstly, the first ten sections provide a comprehensive overview of the topic, presenting logical and well-formulated arguments that are easily accessible to a general audience. In stark contrast, the final section (Chapter 11) fails to connect coherently with the preceding review and is nearly incomprehensible without prior knowledge of the author's recent publication in Cell. Mol. Life Sci. CMLS 772 82, 14 (2024). This chapter requires significantly more background information for the general reader, including an introduction to the Macrophage Identity Kinetics Archive (MIKA), which is not even introduced in this review, its basis (meta-analysis of published scRNA-seq data), its significance (identification of major populations), and the reasons behind the revision of the proposed macrophage states and their further development. Secondly, while the attempt to integrate a vast amount of information into fewer figures is commendable, it results in figures that resemble a complex puzzle. The author may consider increasing the number of figures and providing additional, larger "zoom-in" panels, particularly for the topics of clot formation at transmigration hotspots and the interaction between ECM/ECM fragments and integrins. Specifically, the color coding (purple for leukocyte α6-integrins, blue for interacting laminins, also blue for EC α6 integrins, and red for interacting 5-1-1 laminins) is confusing, and the structures are small and difficult to recognize.

We agree with and appreciate the specific and helpful suggestions by the reviewer. During the revision, we will provide the requested background description of MIKA to enhance accessibility of general readership. As pointed out by other reviewers, since this part (Section 11) is less well-integrated to the rest of the review, we will restructure this part by linking tissue macrophage identities under MIKA framework to modulation of functional ECM effectors described in previous sections (Section 8-9). We acknowledge the current figure organization might be overly information-dense and will consider breaking down the contents to multiple figures. The size and color-coding issues will also be addressed.

Author response:

Public Reviews:

Reviewer #1 (Public review):

Summary:

This work aims to elucidate the molecular mechanisms affected in hypoxic conditions, causing reduced cortical interneuron migration. They use human assembloids as a migratory assay of subpallial interneurons into cortical organoids and show substantially reduced migration upon 24 hours of hypoxia. Bulk and scRNA-seq show adrenomedullin (ADM) up-regulation, as well as its receptor RAMP2, confirmed atthe protein level. Adding ADM to the culture medium after hypoxic conditions rescues the migration deficits, even though the subtype of interneurons affected is not examined. However, the authors demonstrate very clearly that ineffective ADM does not rescue the phenotype, and blocking RAMP2 also interferes with the rescue. The authors are also applauded for using 4 different cell lines and using human fetal cortex slices as an independent method to explore the DLXi1/2GFP-labelled iPSC-derived interneuron migration in this substrate with and without ADM addition (after confirming that also in this system ADM is up-regulated). Finally, the authors demonstrate PKA-CREB signalling mediating the effect of ADM addition, which also leads to up-regulation of GABAreceptors. Taken together, this is a very carefully done study on an important subject - how hypoxia affects cortical interneuron migration. In my view, the study is of great interest.

Strengths:

The strengths of the study are the novelty and the thorough work using several culture methods and 4 independent lines.

Weaknesses:

The main weakness is that other genes regulated upon hypoxia are not confirmed, such that readers will not know until which fold change/stats cut-off data are reliable.

Reviewer #2 (Public review):

Summary

The manuscript by Puno and colleagues investigates the impact of hypoxia on cortical interneuron migration and downstream signaling pathways. They establish two models to test hypoxia, cortical forebrain assembloids, and primary human fetal brain tissue. Both of these models provide a robust assay for interneuron migration. In addition, they find that ADM signaling mediates the migration deficits and rescue using exogenous ADM.

Strengths:

The findings are novel and very interesting to the neurodevelopmental field, revealing new insights into how cortical interneurons migrate and as well, establishing exciting models for future studies. The authors use sufficient iPSC lines including both XX and XY, so the analysis is robust. In addition, the RNAseq data with re-oxygenation is a nice control to see what genes are changed specifically due to hypoxia. Further, the overall level of validation of the sequencing data and involvement of ADM signaling is convincing, including the validation of ADM at the protein level. Overall, this is a very nice manuscript.

Weaknesses:

I have a few comments and suggestions for the authors. See below.

Reviewer #3 (Public review):

Summary:

The authors aimed to test whether hypoxia disrupts the migration of human cortical interneurons, a process long suspected to underlie brain injury in preterm infants but previously inaccessible for direct study. Using human forebrain assembloids and ex vivo developing brain tissue, they visualized and quantified interneuron migration under hypoxic conditions, identified molecular components of the response, and explored the effect of pharmacological intervention (specifically ADM) on restoring the migration deficits.

Strengths:

The major strength of this study lies in its use of human forebrain assembloids and ex vivo prenatal brain tissue, which provide a direct system to study interneuron migration under hypoxic conditions. The authors combine multiple approaches: long-term live imaging to directly visualize interneuron migration, bulk and single-cell transcriptomics to identify hypoxia-induced molecular responses, pharmacological rescue experiments with ADM to establish therapeutic potential, and mechanistic assays implicating the cAMP/PKA/pCREB pathway and GABA receptor expression in mediating the effect. Together, this rigorous and multifaceted strategy convincingly demonstrates that hypoxia disrupts interneuron migration and that ADM can restore this defect through defined molecular mechanisms.

Overall, the authors achieve their stated aims, and the results strongly support their  conclusions. The work has a significant impact by providing the first direct evidence of hypoxia-induced interneuron migration deficits in the human context, while also nominating a candidate therapeutic avenue. Beyond the specific findings, the methodological platform - particularly the combination of assembloids and live imaging - will be broadly useful to the community for probing neurodevelopmental processes in health and disease.

Weaknesses:

The main weakness of the study lies in the extent to which forebrain assembloids

recapitulate in vivo conditions, as the migration of interneurons from hSO to hCO does not fully reflect the native environment or migratory context of these cells. Nevertheless, this limitation is tempered by the fact that the work provides the first direct observation of human interneuron migration under hypoxia, representing a major advance for the field. In addition, while the transcriptomic analyses are valuable and highlight promising candidates, more in-depth exploration will be needed to fully elucidate the molecular mechanisms governing neuronal migration and maturation under hypoxic conditions.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) The authors should examine if all cortical interneurons are affected by ADM or only subtypes (Parvalbumin/Somatostatin).

We thank the reviewer for raising this important question. In our study, we utilized the Dlx1/2b::eGFP reporter to broadly label cortical interneurons; however, this system does not distinguish specific interneuron subtypes. To address this, in the revised version of the manuscript we will use the single-cell RNA sequencing data and immunostainings to provide this information. Based on previous analyses from Birey et al (Cell Stem Cell, 2022), we expect interneurons within assembloids to express mostly calbindin (CALB2) and somatostatin (SST) at this in vitro stage of development; parvalbumin subtype appears later based on data from Birey et al (Nature, 2017) and more recently from Varela et al, (bioRxiv, 2025).

In parallel, we will analyze available scRNA-seq data from developing human primary brain tissue a similar age as the one used in the manuscript, and check whether these subtypes of interneurons are similar to the ones within assembloids.

(2) The authors should test more candidates from their bulk RNA-seq data with different fold changes for regulation after hypoxia, to allow the reader to judge at which cut-off the DEGs may be reproducible. This would make this database much more valuable for the field of hypoxia research.

We appreciate the reviewers’ thoughtful suggestion. In addition to the bulk RNA-seq analysis, we did validate several upregulated hypoxia-responsive genes with varying fold changes by qPCR; these include PDK1, PFKP, VEGFA (Figure S1). 

We go agree that in-depth investigation of specific cut-offs would be interesting, however, this could be the focus of a different manuscript.

Reviewer #2 (Recommendations for the authors):

(1) Can the authors comment on the possibility of inflammatory response pathways being activated by hypoxia? Has this been shown before? While not the focus of the manuscript, it could be discussed in the Discussion as an interesting finding and potential involvement of other cells in the Hypoxic response.

We thank the reviewer this important comment about inflammation. Indeed, hypoxia has been shown to activate the inflammatory response pathways. In various studies, it was found that HIF-1a can interact with NF-κB signaling, leading to the upregulation of pro-inflammatory cytokines such as IL-1β, IL-6, and TNF-α (Rius et al., Cell, 2008; Hagberg et al., Nat Rev Neurol, 2015).

In our transcriptomics data (Figure 2D), and to the reviewers’ point, we identified enrichment of inflammatory signaling response following the hypoxic exposure. Since hSO at the time of analyses do contain astrocytes, we think these glia contribute to the observed pro-inflammatory changes. Based on these results and because ADM is known to have strong anti-inflammatory properties, the effects of ADM on hypoxic astrocytes should be investigated in future studies focused on hypoxia-induced inflammation. In the revision, we will address this comment in the discussion section and cite the appropriate papers.

(2) Could the authors comment on the mechanism at play here with respect to ADM and binding to RAMP2 receptors - is this a potential autocrine loop, or is the source of ADM from other cell types besides inhibitory neurons? Given the scRNA-seq data, what cell-to-cell mechanisms can be at play? Since different cells express ADM, there could be different mechanisms in place in ventral vs dorsal areas.

Based on our scRNA-seq data in hSOs showing significant upregulation of ADM expression in astrocytes and progenitors, we speculate that the primary mechanism is likely to involve paracrine interactions. However, we cannot exclude autocrine mechanisms with the included experiments. Dissecting these interactions in a cell-type specific manner could be an important focus for future ADM-related studies.

To address the question about the possible different mechanisms in ventral versus dorsal areas, in the revision we will plot and include in the figures the data about the cell-type expression of ADM and its receptors in hCOs.

(3) For data from Figure 6 - while the ELISA assays are informative to determine which pathways (PKA, AKT, ERK) are active, there is no positive control to indicate these assays are "working" - therefore, if possible, western blot analysis from assembloid tissue could be used (perhaps using the same lysates from Figure 3) as an alternative to validate changes at the protein level (however, this might prove difficult); further to this, is P-CREB activated at the protein level using WB?

We thank the reviewer for this comment and the observation. Although we did not include a traditional positive control in these ELISA assays, several lines of evidence indicate that the measurements are reliable. First, the standard curves behaved as expected, and all sample values fell within the assay’s dynamic range. Second, technical replicates showed low variability, and the observed changes across experimental conditions (e.g., hypoxia vs. control) were consistent with the expected biological responses based on previous literature. We agree that including western blot validation would strengthen the findings, and we will note this for our future studies focused on CREB and ADM.

(4) Could the authors comment further on the mechanism and what biological pathways and potential events are downstream of ADM binding to RAMP2 in inhibitory neurons? What functional impact would this have linked to the CREB pathway proposed? While the link to GABA receptors is proposed, CREB has many targets beyond this.

We appreciate the reviewers’ insightful question. Currently, not much is known about the molecular pathways and downstream cellular events triggered by ADM binding to RAMP2 in inhibitory neurons, and in general in brain cells. The data from our study brings the first information about the cell-type specific expression of ADM in baseline and hypoxic conditions and is one of the key novelties of our study.

While the signaling landscape of ADM in interneurons is largely unexplored, several studies in other (non-brain) cell types have demonstrated that ADM binding to RAMP2 can activate downstream cascades such as the cAMP/PKA/CREB pathway, PI3K/AKT, and ERK/MAPK, all of which are also known to be critical regulators of neuronal development and survival. These previously published data along with our CREB-targeted findings in hypoxic interneurons, suggest ADM–RAMP2 signaling could influence multiple aspects of interneuron biology, but these remain to be evaluated in future studies.

We agree with the reviewer that CREB has a wide range of transcriptional targets. We decided to focus on GABA as a target of CREB for two main reasons, including: (i) GABA signaling has been previously shown to play an important role in the migration of cortical interneurons, and (ii) a previous study by Birey et al. (Cell Stem Cell, 2022) demonstrated that CREB pathway activity is essential for regulating interneuron migration in assembloid models of Timothy Syndrom, thus further providing evidence that dysregulation of CREB activity disrupts migration dynamics.

While our study provides a first step toward uncovering the mechanisms of interneuron migration protection by ADM, we fully acknowledge that future work will be needed to delineate the full spectrum of ADM–RAMP2 downstream signaling events in inhibitory neurons and other brain cells.

(5) Does hypoxia cause any changes to inhibitory neurogenesis (earlier stages than migration?) - this might always be known, but was not discussed.

We appreciate this question from the reviewer; however, this was not something that we focused on in this manuscript due to the already large amount of data included. A separate study focusing on neurogenesis defects and the molecular mechanisms of injury for that specific developmental process would be an important next step.

(6) In the Discussion section, it might be worth detailing to the readers what the functional impact of delayed/reduced migration of inhibitory neurons into the cortex might result in, in terms of functional consequences for neural circuit development.

We thank the Reviewer for the suggestion of detailing the functional impact of reduced inhibitory neuron migration. We will revise the manuscript by incorporating a paragraph about this in the Discussion section.

Reviewer #3 (Recommendations for the authors):

Most of the evidence presented is convincing in supporting the conclusions, and I have only minor suggestions for improvement:

(1) The bulk RNA-seq was performed in hSOs only, which may not fully capture the phenotypes of migrating or migrated interneurons. It would be valuable, if feasible, to sort migrated cells from hSO-hCO assembloids and specifically examine their molecular mediators.

We thank the reviewer for this suggestion. While it is likely that the cellular environment will have some influence on a subset of the molecular changes, based on all the data from the manuscript and our specific target, the RNA-sequencing on hCOs was sufficient to capture essential changes like ADM upregulation. The in-depth exploration on differential responses of migrated versus non-migrated interneurons to hypoxia could be the focus of a different project.

(2) In Figure 3, it is striking that cell-type heterogeneity dominates over hypoxia vs. control conditions. A joint embedding of hSO and hCO cells could provide further insight into molecular differences between migrated and non-migrated interneurons.

We thank the reviewer for this observation and opportunity to clarify. Since we manually separated the assembloids before the analyses, we processed these samples separately. That is why they separate like this. In the revision, we will add data about ADM expression and its receptors’ expression in the hCOs.

(3) It would be helpful to expand the discussion on how closely the migration observed in hSO-hCO assembloids reflects in vivo conditions, and what environmental aspects are absent from this model. This would better frame the interpretation and translational relevance of the findings.

We thank the Reviewer for bringing up this important point. Although the assembloid model offers the unique advantage of allowing the direct investigation of migration patterns of hypoxic interneurons, we fully agree it does not fully recapitulate the in vivo environment. While there are multiple aspects that cannot be recapitulated in vitro at this time (e.g. cellular complexity, vasculature, immune response, etc), we are encouraged by the validation of our main findings in ex vivo developing human brain tissue, which strongly supports the validity of our findings for in vivo conditions.

We will expand our discussion to include more details and the need to validate these findings using in vivo models, while also acknowledging that different species (e.g. rodents versus non-human primates versus humans) might have different responses to hypoxia.

(4) The authors suggest that hypoxia is also associated with delayed interneuron maturation, yet the bulk RNA-seq data primarily reveal stress and hypoxia-related genes. A more detailed discussion of why genes linked to interneuron maturation and function were not strongly affected would clarify this point.

We thank the Reviewer for the opportunity to clarify.

The RNAseq data was performed during the acute stages of hypoxia/reoxygenation and we think a maturation phenotype might be difficult to capture at this point and would require analysis at later in vitro assembloid maturation stages.

Our speculation about a possible maturation defect is based on data from previous studies from developmental biology that showed failure of interneurons to reach their final cortical location within a specified developmental window will impair their integration within the neuronal network, and thus lead to maturation defects and possible elimination by apoptosis.

Since preterm infants suffer from countless hypoxic events over multiple months, we suggest these repetitive events are likely to induce cumulative delays in migration, inability of interneurons to reach their target in time, followed by abnormal integration within the excitatory network, and eventual elimination of some of these interneurons through apoptosis. However, the direct demonstration of this effect following a hypoxic insult would require prolonged in vivo experiments in rodents to follow the migration, network integration and apoptosis of interneurons; to our knowledge this experimental design is not technically feasible at this time.

(5) Relatedly, while the focus on interneuron migration is well justified, acknowledging how hypoxia might also impact other aspects of cortical development (e.g., progenitor proliferation, neuronal maturation, or circuit integration) would place the findings in a broader developmental framework and strengthen their relevance.

We appreciate the Reviewer’s suggestion to discuss the role of hypoxia on other processes during cortical development. In the revised manuscript, we will include citations about the effects of hypoxia on interneuron proliferation, maturation and circuit integration as available, and also expand to other cell types known to be affected.

(6) Very minor: in Figure S3C and D, it was not stated what the colors mean (grey: control, yellow: hypoxia)

Thank you for pointing out this error and we will correct it in our revision.

eLife Assessment

In this manuscript, the authors investigate the migration of human cortical interneurons under hypoxic conditions using forebrain assembloids and developing human brain tissue, and probe the underlying mechanisms. The study provides the first direct evidence that hypoxia delays interneuron migration and identifies adrenomedullin (ADM) as a potential therapeutic intervention. The findings are important, and the conclusions are convincingly supported by experimental evidence.

Reviewer #1 (Public review):

Summary:

This work aims to elucidate the molecular mechanisms affected in hypoxic conditions, causing reduced cortical interneuron migration. They use human assembloids as a migratory assay of subpallial interneurons into cortical organoids and show substantially reduced migration upon 24 hours of hypoxia. Bulk and scRNA-seq show adrenomedullin (ADM) up-regulation, as well as its receptor RAMP2, confirmed atthe protein level. Adding ADM to the culture medium after hypoxic conditions rescues the migration deficits, even though the subtype of interneurons affected is not examined. However, the authors demonstrate very clearly that ineffective ADM does not rescue the phenotype, and blocking RAMP2 also interferes with the rescue. The authors are also applauded for using 4 different cell lines and using human fetal cortex slices as an independent method to explore the DLXi1/2GFP-labelled iPSC-derived interneuron migration in this substrate with and without ADM addition (after confirming that also in this system ADM is up-regulated). Finally, the authors demonstrate PKA-CREB signalling mediating the effect of ADM addition, which also leads to up-regulation of GABAreceptors. Taken together, this is a very carefully done study on an important subject - how hypoxia affects cortical interneuron migration. In my view, the study is of great interest.

Strengths:

The strengths of the study are the novelty and the thorough work using several culture methods and 4 independent lines.

Weaknesses:

The main weakness is that other genes regulated upon hypoxia are not confirmed, such that readers will not know until which fold change/stats cut-off data are reliable.

Reviewer #2 (Public review):

Summary

The manuscript by Puno and colleagues investigates the impact of hypoxia on cortical interneuron migration and downstream signaling pathways. They establish two models to test hypoxia, cortical forebrain assembloids, and primary human fetal brain tissue. Both of these models provide a robust assay for interneuron migration. In addition, they find that ADM signaling mediates the migration deficits and rescue using exogenous ADM. The findings are novel and very interesting to the neurodevelopmental field, revealing new insights into how cortical interneurons migrate and as well, establishing exciting models for future studies. The authors use sufficient iPSC line,s including both XX and XY, so the analysis is robust. In addition, the RNAseq data with re-oxygenation is a nice control to see what genes are changed specifically due to hypoxia. Further, the overall level of validation of the sequencing data and involvement of ADM signaling is convincing, including the validation of ADM at the protein level. Overall, this is a very nice manuscript. I have a few comments and suggestions for the authors.

Strengths and Weaknesses:

(1) Can the authors comment on the possibility of inflammatory response pathways being activated by hypoxia? Has this been shown before? While not the focus of the manuscript, it could be discussed in the Discussion as an interesting finding and potential involvement of other cells in the Hypoxic response.

(2) Could the authors comment on the mechanism at play here with respect to ADM and binding to RAMP2 receptors - is this a potential autocrine loop, or is the source of ADM from other cell types besides inhibitory neurons? Given the scRNA-seq data, what cell-to-cell mechanisms can be at play? Since different cells express ADM, there could be different mechanisms in place in ventral vs dorsal areas.

(3) For data from Figure 6 - while the ELISA assays are informative to determine which pathways (PKA, AKT, ERK) are active, there is no positive control to indicate these assays are "working" - therefore, if possible, western blot analysis from assembloid tissue could be used (perhaps using the same lysates from Figure 3) as an alternative to validate changes at the protein level (however, this might prove difficult); further to this, is P-CREB activated at the protein level using WB?

(4) Could the authors comment further on the mechanism and what biological pathways and potential events are downstream of ADM binding to RAMP2 in inhibitory neurons? What functional impact would this have linked to the CREB pathway proposed? While the link to GABA receptors is proposed, CREB has many targets beyond this.

(5) Does hypoxia cause any changes to inhibitory neurogenesis (earlier stages than migration?) - this might always be known, but was not discussed.

(6) In the Discussion section, it might be worth detailing to the readers what the functional impact of delayed/reduced migration of inhibitory neurons into the cortex might result in, in terms of functional consequences for neural circuit development.

Reviewer #3 (Public review):

Summary:

The authors aimed to test whether hypoxia disrupts the migration of human cortical interneurons, a process long suspected to underlie brain injury in preterm infants but previously inaccessible for direct study. Using human forebrain assembloids and ex vivo developing brain tissue, they visualized and quantified interneuron migration under hypoxic conditions, identified molecular components of the response, and explored the effect of pharmacological intervention (specifically ADM) on restoring the migration deficits.

Strengths:

The major strength of this study lies in its use of human forebrain assembloids and ex vivo prenatal brain tissue, which provide a direct system to study interneuron migration under hypoxic conditions. The authors combine multiple approaches: long-term live imaging to directly visualize interneuron migration, bulk and single-cell transcriptomics to identify hypoxia-induced molecular responses, pharmacological rescue experiments with ADM to establish therapeutic potential, and mechanistic assays implicating the cAMP/PKA/pCREB pathway and GABA receptor expression in mediating the effect. Together, this rigorous and multifaceted strategy convincingly demonstrates that hypoxia disrupts interneuron migration and that ADM can restore this defect through defined molecular mechanisms.

Overall, the authors achieve their stated aims, and the results strongly support their conclusions. The work has a significant impact by providing the first direct evidence of hypoxia-induced interneuron migration deficits in the human context, while also nominating a candidate therapeutic avenue. Beyond the specific findings, the methodological platform - particularly the combination of assembloids and live imaging - will be broadly useful to the community for probing neurodevelopmental processes in health and disease.

Weaknesses:

The main weakness of the study lies in the extent to which forebrain assembloids recapitulate in vivo conditions, as the migration of interneurons from hSO to hCO does not fully reflect the native environment or migratory context of these cells. Nevertheless, this limitation is tempered by the fact that the work provides the first direct observation of human interneuron migration under hypoxia, representing a major advance for the field. In addition, while the transcriptomic analyses are valuable and highlight promising candidates, more in-depth exploration will be needed to fully elucidate the molecular mechanisms governing neuronal migration and maturation under hypoxic conditions.

eLife Assessment

This study provides novel and fundamental insights into the long-term use of DREADDs to modulate neuronal activity in nonhuman primates. The exceptional evidence demonstrates the peak dynamics and the subsequent stability of chemogenetic effects for 1.5 years, informing the experimental designs and the interpretation of highly impactful chemogenetic studies in macaques. The protocols, data, and outcomes can serve as guidelines for future experiments. Therefore, the findings will be of significant interest to the field of chemogenetics and may also be of broader interest to researchers and clinicians who seek to utilize viral vectors and/or related genetic technologies.

Reviewer #1 (Public review):

Summary:

Inhibitory hM4Di and excitatory hM3Dq DREADDs are currently the most commonly utilized chemogenetic tools in the field of nonhuman primate research, but there is a lack of available information regarding the temporal aspects of virally-mediated DREADD expression and function. Nagai et al. investigated the longitudinal expression and efficacy of DREADDs to modulate neuronal activity in the macaque model. The authors demonstrate that both hM4Di and hM3Dq DREADDs reach peak expression levels after approximately 60 days and that stable expression was maintained for up to two years for hM4Di and at least one year for hM3Dq DREADDs. During this period, DREADDs effectively modulated neuronal activity, as evidenced by a variety of measures, including behavioural testing, functional imaging, and/or electrophysiological recording. Notably, some of the data suggest that DREADD expression may decline after two-three years. This is a novel finding and has important implications for the utilization of this technology for long-term studies, as well as its potential therapeutic applications. Lastly, the authors highlight that peak DREADD expression may be significantly influenced by the presence of fused or co-expressed protein tags, emphasizing the importance of careful design and selection of viral constructs for neuroscientific research. This study represents a critical step in the field of chemogenetics, setting the scene for future development and optimization of this technology.

Strengths:

The longitudinal approach of this study provides important preliminary insights into the long-term utility of chemogenetics, which has not yet been thoroughly explored.

The data presented are novel and inclusive, relying on well-established in vivo imaging methods as well as behavioral and immunohistochemical techniques. The conclusions made by the authors are generally supported by a combination of these techniques. In particular, the utilization of in vivo imaging as a non-invasive method is translationally relevant and likely to make an impact in the field of chemogenetics, such that other researchers may adopt this method of longitudinal assessment in their own experiments. Rigorous standards have been applied to the datasets, and the appropriate controls have been included where possible.

The number of macaque subjects (20) from which data was available is also notable. Behavioral testing was performed in 11 subjects, FDG-PET in 5, electrophysiology in 1, and [11C]DCZ-PET in 15. This is an impressive accumulation of work that will surely be appreciated by the growing community of researchers using chemogenetics in nonhuman primates.

The implication that chemogenetic effects can be maintained for up to 1.5-2 years, followed by a gradual decline beyond this period, is an important development in knowledge. The limited duration of DREADD expression may present an obstacle in the translation of chemogenetic technology as a potential therapeutic tool, and it will be of interest for researchers to explore whether this limitation can be overcome. This study therefore represents a key starting point upon which future research can build.

Weaknesses:

None.

Reviewer #2 (Public review):

Summary:

This paper reports histological, PET imaging, functional and behavioural data evaluating the longevity of AAV2 infection in multiple brain areas of macaques in the context of DREADD experiments. The central aim is to provide unprecedented information about how long the expression of HM4di or HM3dq receptors are expressed and efficient in modulating brain functions after vector injections. The data show peak expression after 40 to 60 days of vector injection, and stable expressions for up to 1.5 years for hM4di, and that hM3dq remained mostly at 75% of peak after a year, declining to 50% after 2 years. DREADDs effectively modulated neuronal activity and behaviour for approximately two years, evaluated with behavioural testings, neural recordings or FDG-PET. A statistical evaluation revealed that vector titers, DREADD type and tags contribute to the measured peak level of DREADD expression.

The article present a thorough discussion of the limitations and specificities of chemogenetic approaches in monkeys.

Strength:

These are unique data, in non-human primate (NHP), an animal model that not only features physiological and immunological characteristics similar to humans, but also contributes to neurobiological functional studies over long timescales with experiments spanning months or years. This evaluation of long-term efficacy of DREADDs will be very important for all laboratories using chemogenetics in NHP but also for future use of such approach in experimental therapies. The longevity estimates are based on multiple approaches including behavioural and neurophysiological, thus providing information on functional efficacy of DREADD expression.

Performing such evaluation requires specific tools like PET imaging that very few monkey labs have access to. This study was done by the laboratory that has developed the radiotracer c11-DCZ, used here, a radiotracer binding selectively to DREADDs and providing, using PET, quantitative in vivo measures of DREADD expression. This study and its data should thus be a reference in the field, providing estimates to plan future chemogenetic experiments.

Publishing databases of experimental outcomes in NHP DREADD experiments is crucial for the community because such experiments are rare, expensive and long. It contributes to refining experiments and reducing the number of animals overall used in the domain.

Weaknesses:

This study is a meta-analysis of several experiments performed in one lab. The good side is that it combined a large amount of data that might not have been published individually; the down side is that all things where not planned and equated, creating a lot of unexplained variances in the data. However, this was judiciously used by the authors to provide very relevant information. One might think that organized multi-centric experiments planned using the knowledge acquired here, will provide help testing more parameters, including some related to inter-individual variability, and particular genetic constructs.

Reviewer #3 (Public review):

Summary

This manuscript, from the developers of the novel DREADD-selective agonist DCZ (Nagai et al., 2020), utilizes a unique dataset where multiple PET scans in a large number of monkeys, including baseline scans before AAV injection, 30-120 days post-injection, and then periodically over the course of the prolonged experiments, were performed to access short- and long-term dynamics of DREADD expression in vivo, and to associate DREADD expression with the efficacy of manipulating the neuronal activity or behavior. The goal was to provide critical insights into practicality and design of multi-year studies using chemogenetics, and to elucidate factors affecting expression stability.

Strengths are systematic quantitative assessment of the effects of both excitatory and inhibitory DREADDs, quantification of both the short-term and longer-term dynamics, a wide range of functional assessment approaches (behavior, electrophysiology, imaging), and assessment of factors affecting DREADD expression levels, such as serotype, promoter, titer (concentration), tag, and DREADD type.

These finding will undoubtedly have a very significant impact on the rapidly growing, but still highly challenging field of primate chemogenetic manipulations. As such, the work represents an invaluable resource for the community.

eLife Assessment

This study presents an important finding on the role of GATA4 in aging- and OA-associated cartilage pathology. The conclusions are well supported by compelling in vitro and in vivo evidence. This work will be of broad interest to both cell biologists and orthopedic/skeletal health clinicians.

Reviewer #1 (Public review):

Summary:

This manuscript assesses the differences between young and aged chondrocytes. Through transcriptomic analysis and further assessments in chondrocytes, GATA4 was found to be increased in aged chondrocyte donors compared to young. Subsequent mechanistic analysis with lentiviral vectors, siRNAs, and a small molecule were used to study the role of GATA4 in young and old chondrocytes. Lastly, an in vivo study was used to assess the effect of GATA4 expression on osteoarthritis progression in a DMM mouse model.

Strengths:

This work linked the over expression of GATA4 to NF-kB signaling pathway activation, alterations to the TGF-b signaling pathway, and found that GATA4 increased the progression of OA compared to the DMM control group. Indicating that GATA4 contributes to the onset and progression of OA in aged individuals.

Comments on revised version:

Great work! All my concerns have been well addressed.

Reviewer #2 (Public review):

Summary:

This study elucidated the impact of GATA4 on aging- and injury-induced cartilage degradation and osteoarthritis (OA) progression, based on the team's finding that GATA expression is positively correlated with aging in human chondrocytes. By integrating cell culture of human chondrocytes, gene manipulation tools (siRNA, lentivirus), biological/biochemical analyses and murine models of post-traumatic OA, the team found that increasing GATA4 levels reduced anabolism and increased catabolism of chondrocytes from young donors, likely through upregulation of the BMP pathway, and that this impact is not correlated with TGF-β stimulation. Conversely, silencing GATA4 by siRNA attenuated catabolism and elevated aggrecan/collagen II biosynthesis of chondrocytes from old donors. The physiological relevance of GATA4 was further validated by the accelerated OA progression observed in lentivirus-infected mice in the DMM model.

Strengths:

This is a highly significant and innovative study that provides new molecular insights into cartilage homeostasis and pathology in the context of aging and disease. The experiments were performed in a comprehensive and rigorous manner. The data were interpreted thoroughly in the context of the current literature.

Weaknesses:

The only aspect that would benefit from further clarification is a more detailed discussion of aging-associated ECM changes in the context of prior literature.

Reviewer #3 (Public review):

Summary:

This is an exciting, comprehensive paper that demonstrates the role of GATA4 on OA-like changes in chondrocytes. The authors present elegant reverse translational experiments that justify this mechanism and demonstrate the sufficiency of GATA4 in a mouse model of osteoarthritis (DMM), where GATA4 drove cartilage degeneration and pain in a manner that was significantly worse than DMM alone. This could pave the way for new therapies for OA that account for both structural changes and pain.

Strengths:

(1) GATA4 was identified from human chondrocytes.

(2) IHC and sequencing confirmed GATA4 presence.

(3) Activation of SMADs is clearly shown in vitro with GATA4 overexpression.

(4) The role of GATA4 was functionally assessed in vivo using the mouse DMM model, where the authors uncovered that GATA4 worsens OA structure and hyperalgesia in male mice.

(5) It is interesting that GATA4 is largely known to be found in cardiac cells and to have a role in cardiac repair, metabolism, and inflammation, among other things listed by the authors in the discussion (in liver, lung, pancreas). What could this new knowledge of GATA4 mean for OA as a potentially systemically mediated disease, where cardiac disease and metabolic syndrome are often co-morbid?

Weaknesses:

I do not have further comments. Thank you for addressing the previously mentioned concerns.

Author response:

The following is the authors’ response to the previous reviews

Reviewer #2 (Public review):

The only aspect that would benefit from further clarification is a more detailed discussion of aging-associated ECM changes in the context of prior literature. 

Thank you. Please refer to the new section (Lines 604-617)

Reviewer #3 (Public review):

(1) It would be useful to explain why GATA4 was chosen over HIF1a, which was the most differentially expressed. 

Thank you. Please refer to Lines 530-537.  

“Of note, Hypoxia-Inducible Factor 1α (HIF1 α) was the most differentially expressed gene predicted to regulate chondrocyte aging. The connection between HIF1 α and aging has been previously reported.[32] Furthermore, additional studies have investigated HIF1 in association with OA and assessed its use as a therapeutic target.[33,34] Therefore, we decided to focus on GATA4, which was less studied in chondrocytes but highly associated with cellular senescence, an aging hallmark. However, our selection did not dampen the importance of HIF1α and other molecules listed in Figure 1D in chondrocyte aging. They can be further studied in the future using the same strategy employed in the current work.”

(2) In Figure 5, it would be useful to demonstrate the non-surgical or naive limbs to help contextualize OARSI scores and knee hyperalgesia changes. 

In the current study, we focused on the DMM control and DMM Gata4 virus groups so we did not include a sham control group. We recognized this was a limitation of this study.  

(3) While there appear to be GATA4 small-molecule inhibitors in various stages of development that could be used to assess the effects in age-related OA, those experiments are out of scope for the current study.  

We agree with this comment that the results are still preliminary, which was the reason that we put it in the supplementary materials. However, we felt like the result is informative, which will support the potential of GATA4 as a therapeutic target and inspire the development of more specific inhibitors. Therefore, we would still keep the results in the current study.

eLife Assessment

This is a useful tool for code-less analysis of patterns in cell migratory behaviours in vivo using intravital microscopy data and allows correlation with spatial features of the tumour microenvironment. There is a clear need for these tools to make quantitative analysis, comparison and interpretation of complex cell tracking data more accessible and solid evidence is provided of its applicability to tracks generated by both proprietary and open tracking software.

Reviewer #1 (Public review):

In this work, Rios-Jimenez and Zomer et al have developed a 'zero-code' accessible computational framework (BEHAV3D-Tumour Profiler) designed to facilitate unbiased analysis of Intravital imaging (IVM) data to investigate tumour cell dynamics (via the tool's central 'heterogeneity module' ) and their interactions with the tumour microenvironment (via the 'large-scale phenotyping' and 'small-scale phenotyping' modules). A key strength is that it is designed as an open-source modular Jupyter Notebook with a user-friendly graphical user interface and can be implemented with Google Colab, facilitating efficient, cloud-based computational analysis at no cost. In addition, demo datasets are available on the authors GitHub repository to aid user training and enhance the usability of the developed pipeline.

To demonstrate the utility of BEHAV3D-TP, they apply the pipeline to timelapse IVM imaging datasets to investigate the in vivo migratory behaviour of fluorescently labelled DMG cells in tumour bearing mice. Using the tool's 'heterogeneity module' they were able to identify distinct single-cell behavioural patterns (based on multiple parameters such as directionality, speed, displacement, distance from tumour edge) which was used to group cells into distinct categories (e.g. retreating, invasive, static, erratic). They next applied the framework's 'large-scale phenotyping' and 'small-scale phenotyping' modules to investigate whether the tumour microenvironment (TME) may influence the distinct migratory behaviours identified. To achieve this, they combine TME visualisation in vivo during IVM (using fluorescent probes to label distinct TME components) or ex vivo after IVM (by large-scale imaging of harvested, immunostained tumours) to correlate different tumour behavioural patterns with the composition of the TME. They conclude that this tool has helped reveal links between TME composition (e.g. degree of vascularisation, presence of tumour-associated macrophages) and the invasiveness and directionality of tumour cells, which would have been challenging to identify when analysing single kinetic parameters in isolation.<br /> While the analysis provides only preliminary evidence in support of the authors conclusions on DMG cell migratory behaviours and their relationship with components of the tumour microenvironment, conclusions are appropriately tempered in the absence of additional experiments and controls.

The authors also evaluated the BEHAV3D TP heterogeneity module using available IVM datasets of distinct breast cancer cell lines transplanted in vivo, as well as healthy mammary epithelial cells to test its usability in non-tumour contexts where the migratory phenotypes of cells may be more subtle. This generated data is consistent with that produced during the original studies, as well as providing some additional (albeit preliminary) insights above that previously reported. Collectively, this provides some confidence in BEHAV3D TP's ability to uncover complex, multi-parametric cellular behaviours that may be missed using traditional approaches.

While the tool does not facilitate the extraction of quantitative kinetic cellular parameters (e.g. speed, directionality, persistence and displacement) from intravital images, the authors have developed their tool to facilitate the integration of other data formats generated by open-source Fiji plugins (e.g. TrackMate, MTrackJ, ManualTracking) which will help ensure its accessibility to a broader range of researchers. Overall, this computational framework appears to represent a useful and comparatively user-friendly tool to analyse dynamic multi-parametric data to help identify patterns in cell migratory behaviours, and to assess whether these behaviours might be influenced by neighbouring cells and structures in their microenvironment.

When combined with other methods, it therefore has the potential to be a valuable addition to a researcher's IVM analysis 'tool-box'.

Reviewer #2 (Public review):

Summary:

The authors produce a new tool, BEHAV3D to analyse tracking data and to integrate these analyses with large and small scale architectural features of the tissue. This is similar to several other published methods to analyse spatio-temporal data, however, the connection to tissue features is a nice addition, as is the lack of requirement for coding. The tool is then used to analyse tracking data of tumour cells in diffuse midline glioma. They suggest 7 clusters exist within these tracks and that they differ spatially. They ultimately suggest that these behaviours occur in distinct spatial areas as determined by CytoMAP.

Strengths:

The tool appears relatively user-friendly and is open source. The combination with CytoMAP represents a nice option for researchers.

The identification of associations between cell track phenotype and spatial features is exciting and the diffuse midline glioma data nicely demonstrates how this could be used.

Reviewer #3 (Public review):

The manuscript by Rios-Jimenez developed a software tool, BEHAV3D Tumor Profiler, to analyze 3D intravital imaging data and identify distinctive tumor cell migratory phenotypes based on the quantified 3D image data. Moreover, the heterogeneity module in this software tool can correlate the different cell migration phenotypes with variable features of the tumor microenvironment. Overall, this is a useful tool for intravital imaging data analysis and its open-source nature makes it accessible to all interested users.

Strengths:

An open-source software tool that can quantify cell migratory dynamics from intravital imaging data and identify distinctive migratory phenotypes that correlate with variable features of the tumor microenvironment.

Weaknesses:

Motility is the main tumor cell feature analyzed in the study together with some other tumor-intrinsic features, such as morphology. However, these features are insufficient to characterize and identify the heterogeneity of the tumor cell population that impacts their behaviors in the complex tumor microenvironment (TME). For instance, there are important non-tumor cell types in the TME, and the interaction dynamics of tumor cells with other cell types, e.g., fibroblasts and distinct immune cells, play a crucial role in regulating tumor behaviors. BEHAV3D-TP focuses on analysis of tumor-alone features, and cannot be applied to analyze important cell-cell interaction dynamics in 3D.

eLife Assessment

This study uses steered molecular dynamics simulations to interrogate force transmission in the mechanosensitive NOMPC channel, which plays roles including soft-touch perception, auditory function, and locomotion. The valuable finding that the ankyrin spring transmits force through torsional rather than compression forces may help understand the entire TRP channel family. The evidence is considered to be solid, although full opening of the channel is not seen, and it has been noted that experimental validation of reduced mechanosensitivity through mutagenesis of proposed ankyrin/TRP domain coupling interactions would help substantiate the findings.

Reviewer #1 (Public review):

Summary:

This manuscript uses molecular dynamics simulations to understand how forces felt by the intracellular domain are coupled to opening of the mechanosensitive ion channel NOMPC. The concept is interesting - as the only clearly defined example of an ion channel that opens due to forces on a tethered domain, the mechanism by which this occur are yet to be fully elucidated. The main finding is that twisting of the transmembrane portion of the protein - specifically via the TRP domain that is conserved within the broad family of channels- is required to open the pore. That this could be a common mechanism utilised by a wide range of channels in the family, not just mechanically gated ones, makes the result significant. It is intriguing to consider how different activating stimuli can produce a similar activating motion within this family. While the authors do not see full opening of the channel, only an initial dilation, this motion is consistent with partial opening of structurally characterized members of this family.

Strengths:

Demonstrating that rotation of the TRP domain is the essential requirement for channel opening would have significant implcaitions for other members of this channel family.

Weaknesses:

The manuscript centres around 3 main computational experiments. In the first, a compression force is applied on a truncated intracellular domain and it is shown that this creates both a membrane normal (compression) and membrane parallel (twisting) force on the TRP domain. This is a point that was demonstrated in the authors prior eLife paper - so the point here is to quantify these forces for the second experiment.

The second experiment is the most important in the manuscript. In this, forces are applied directly to two residues on the TRP domain with either a membrane normal (compression) or membrane parallel (twisting) direction, with the magnitude and directions chosen to match that found in the first experiment. Only the twisting force is seen to widen the pore in the triplicate simulations, suggesting that twisting, but not compression can open the pore. This result is intriguing and there appears to be a significant difference between the dilation of pore with the two force directions. When the forces are made of similar magnitude, twisting still has a larger effect than forces along the membrane normal.

The second important consideration is that the study never sees full pore opening, rather a widening that is less than that seen in open state structures of other TRP channels and insufficient for rapid ion currents. This is something the authors acknowledge in their prior manuscript Twist may be the key to get this dilation, but we don't know if it is the key to full pore opening. Structural comparison to open state TRP channels supports that this represents partial opening along the expected pathway of channel gating.

Experiment three considers the intracellular domain and determines the link between compression and twisting of the intracellular AR domain. In this case, the end of the domain is twisted and it is shown that the domain compresses, the converse to the similar study previously done by the authors in which compression of the domain was shown to generate torque.

Reviewer #2 (Public review):

This study uses all atom MD simulation to explore the mechanics of channel opening for the NOMPC mechanosensitive channel. Previously the authors used MD to show that external forces directed along the long-axis of the protein (normal to the membrane) results in AR domain compression and channel opening. This force causes two changes to the key TRP domains adjacent to the channel gate: 1) a compressive force pushes the TRP domain along the membrane normal, while 2) a twisting torque induces a clock-wise rotation on the TRP domain helix when viewing the bottom of the channel from the cytoplasm. Here, the authors wanted to understand which of those two changes are responsible for increasing the inner pore radius, and they show that it is the torque. The simulations in Figure 2 probe this question with different forces, and we can see the pore open with parallel forces in the membrane, but not with the membrane-normal forces. I believe this result as it is reproducible, the timescales are reaching 1 microsecond, and the gate is clearly increasing diameter to about 4 Å. This seems to be the most important finding in the paper, but the impact is limited since the authors already shows how forces lead to channel opening, and this is further teasing apart the forces and motions that are actually the ones that cause the opening.

Reviewer #3 (Public review):

Summary:

This manuscript by Duan and Song interrogates the gating mechanisms and specifically force transmission in mechanosensitive NOMPC channels using steered molecular dynamics simulations. They propose that the ankyrin spring can transmit force to the gate through torsional forces adding molecular detail to the force transduction pathways in this channel.

Strengths:

Detailed, rigorous simulations coupled with a novel model for force transduction.

Weaknesses:

Experimental validation of reduced mechanosensitivity through mutagenesis of proposed ankyrin/TRP domain coupling interactions would greatly enhance the manuscript.

eLife Assessment

This important study examined the complexity of emergent dynamics of large-scale neural network models after perturbation (perturbational complexity index, PCI) and used it as a measurement of consciousness to account for previous recordings of humans at various anesthetized levels. The evidence supporting the conclusion is convincing and constitutes a unified framework for different observations related to consciousness. There are many fields that would be interested in this study, including cognitive neuroscience, psychology, complex systems, neural networks, and neural dynamics.

Reviewer #1 (Public review):

Summary:

This paper attempts to measure the complex changes of consciousness in the human brain as a whole. Inspired by the perturbational complexity index (PCI) from classic research, authors introduce simulation PCI (𝑠𝑃𝐶𝐼) of a time series of brain activity as a measure of consciousness. They first use large-scale brain network modeling to explore its relationship with the network coupling and input noise. Then the authors verify the measure with empirical data collected in previous research.

Strengths:

The conceptual idea of the work is novel. The authors measure the complexity of brain activity from the perspective of dynamical systems. They provide a comparison of the proposed measure with four other indexes. The text of this paper is very concise, supported by experimental data and theoretical model analysis.

Comments on revisions:

The manuscript is in good shape after revision. I would suggest that the author open-source the code and data in this study.

Reviewer #2 (Public review):

Summary:

Breyton and colleagues analysed the emergent dynamics from a neural mass model, characterised the resultant complexity of the dynamics, and then related these signatures of complexity to datasets in which individuals had been anaesthetised with different pharmacological agents. The results provide a coherent explanation for observations associated with different time series metrics, and further help to reinforce the importance of modelling when integrating across scientific studies.

Strengths:

* The modelling approach was clear, well-reasoned and explicit, allowing for direct comparison to other work and potential elaboration in future studies through the augmentation with richer neurobiological detail.

* The results serve to provide a potential mechanistic basis for the observation that Perturbational Complexity Index changes as a function of consciousness state.

Weaknesses:

* Coactivation cascades were visually identified, rather than observed through an algorithmic lens. Given that there are numerous tools for quantifying the presence/absence of cascades from neuroimaging data, the authors may benefit from formalising this notion.

* It was difficult to tell, graphically, where the model's operating regime lay. Visual clarity here will greatly benefit the reader.

Comments on revisions:

The authors have addressed my concerns.

eLife Assessment

This useful study examines the contribution of synaptotagmin 1 and synaptotagmin 7 to metabolite antigen presentation to mucosal-associated invariant T (MAIT) cells; it begins to address a critical gap in our understanding of the antigen presentation mechanisms to these cells. Strengths of the study include the use of Mtb to study the dynamics of antigen presentation to MAIT cells instead of a synthetic antigen. However, the strength of the evidence to support the conclusion is currently incomplete. The conclusions could be enhanced by additional dissection of some of the cell biological events that lead to antigen presentation by MR1.

Reviewer #1 (Public review):

Summary:

The manuscript "Synaptotagmin 1 and Synaptotagmin 7 promote MR1-mediated presentation of Mycobacterium tuberculosis antigens", authored by Kim et al., showed that the calcium-sensing trafficking proteins Synaptotagmin (Syt) 1 and Syt7 specifically promote (are critical for) MAIT cell activation in response to Mtb-infected bronchial epithelial cell line BEAS-2B (Fig. 1) and monocyte-like cell line THP-1 (Figure 3) . This work also showed co-localization of Syt1 and Syt7 with Rab7a and Lamp1, but not with Rab5a (Figure 5). Loss of Syt1 and Syt7 resulted in a larger area of MR1 vesicles (Figure 6f) and an increased number of MR1 vesicles in close proximity to an Auxotrophic Mtb-containing vacuoles during infection (Figure 7ab). Moreover, flow organellometry was used to separate phagosomes from other subcellular fractions and identify enrichment of auxotrophic Mtb-containing vacuoles in fractions 42-50, which were enriched with Lamp1+ vacuoles or phagosomes (Figures 7e-f).